Mathematical Statistician and Engineering Applications

Comparison study of Machine Learning Algorithm and Data Science based Machine Learning Algorithm Malware Detection

Sunita Choudhary, Anand Sharma — 2022-07-19

With quick development and advancement of the web, malware is one of major advanced perils these days. Hence, malware discovery is a significant component in the security of PC frameworks. These days, assailants by and large plan polymeric malware, it is typically a kind of malware that ceaselessly changes its unmistakable component to trick recognition strategies that utilizes run of the mill signature-based techniques. For that reason, the requirement for Machine Learning based identification emerges. In this work, we will acquire standard of conduct that might be accomplished through static or dynamic examination, a while later we can apply unique ML strategies to recognize regardless of whether it's malware. Conduct based Detection techniques will be talked about to take advantage from ML calculations in order to approach social-based malware acknowledgment, furthermore, grouping model. In this paper, study related between two major components. First one is machine learning algorithm apply on data set directly. Second is same Machine learning algorithm apply with Data science pre-processing steps.

Modeling of Three-Phase Written-Pole Motor and Performance Predetermination using Numerical Methods

Manjunath B. Ranadev, V. R. Sheelavant, R. L. Chakrasali — 2022-07-19

Modeling has attracted increasing attention from researchers. A physical system transformation to a mathematical base is important to analyse the behaviour of the system. The solution of various engineering problems depends on appropriate mathematical models. Differential equations are the basic tool for the mathematical modeling of electric machines. A model that describes the machine performance is used to analyse the dynamic behaviour of a 3-phase written-pole motor.

Mathematical modeling of a 3-phase written-pole motor will help us to do the numerical analysis. It provides insight and useful guidance for originating the applications. A method for studying the behaviour of a 3-phase written-pole motor under steady state condition is described. The numerical tools such as Gauss-Seidel method and Successive Over-Relaxation method are used. This study forms the foundation for further research and development.

Information and Analytical Support of Business Models and Management Systems of Enterprises in China and Russia in the Digital Economy

Piao Yihua, Nina V. Shashlo, Deng Xiaoyu, Alexey A. Kuzubov — 2022-07-19

This work is devoted to the deepening of theoretical provisions, substantiation of the theoretical and methodological foundations for the formation of enterprise information processes and the development of practical recommendations for their use in the management of industrial enterprises in the context of the development of global economic relations.

The article has formed a system of indicators for assessing the effectiveness of the information management process in order to determine the possibility and feasibility of its use in the management of an industrial enterprise.

Indicators, the calculation of which is based on internal sources of information of an industrial enterprise according to management and financial accounting data, form the basis of the information process for managing the activities of an industrial enterprise.

For the analysis, the data were combined with the subject area into indicators of labor, production and financial spheres.

In order to improve the process of managing an industrial enterprise, partial indicators of assessing the effectiveness of the information process of managing an industrial enterprise were calculated on the basis of the synthesis of expert assessment methods: by scores and the method of analyzing hierarchies.

Based on the results of the study, priority indicators have been identified for calculating the integral indicator of ensuring the effectiveness of the information process of managing an industrial enterprise with ensuring the reliability, content, completeness and relevance of the information process.

The factors that ensure the efficiency of the information process of enterprise management are identified, thanks to which it becomes possible to design in advance several approaches to organizing the future activities of an industrial enterprise.

Methodical Approach to Bankruptcy Prediction Model Development

2022-07-19

There is a likelihood of a crisis in any organization, which is determined by objective factors such as the fluctuations in market conditions, periodic modernization of technology, the changes in the organization of production, the change of personnel or external conditions, and often political circumstances. Bankruptcy is a critical variant of crisis phenomena at an enterprise.

Of course, bankruptcy is an important element of the market system structure, and its purpose is to protect social-economic processes from the results of ineffective activities of their members and the failure to fulfill their obligations.

Development of Modern Approaches to Exchange-Traded Financial Asset Evaluation

Andrey V. Koren, Tatiana E. Danilovskikh, Tatiana V. Ershova — 2022-07-19

Estimation of exchange-traded financial asset fair value is one of the most important tasks in modern financial science. However, the importance of methods for prediction of future prices is constantly changing. The study provides a comparative analysis of fundamental and technical analysis methods, reveals their advantages and disadvantages. Such characteristics of fundamental analysis as profit, income, profitability and the level of debt burden are considered in detail, the degree of their influence on the price of securities is analyzed. After the study, conclusions were drawn about the increasingly narrow use of technical analysis during short-term and investment problem solution. A prediction is given for a further decrease of fundamental analysis importance as the main method of assessment. To obtain these conclusions, we used statistical data reflecting the dynamics of exchange-traded assets when companies entered IPOs. The data obtained indicate a clear correlation between the innovation component of a business, market share and its price. At the same time, the company profit has finally ceased to be the main characteristic of the business during its assessment. Additionally, they studied the relationship between the fairness of exchange-traded asset evaluation and quantitative easing (QE) programs. Conclusions are made about the need to increase the forecast prices in the case of a soft monetary policy application.

Minimal Prime Ideals in Generalized Almost Distributive Fuzzy Lattices

T. Sangeetha, Dr. S. Senthamilselvi — 2022-07-19

The Minimal Prime Ideals (MPIs) in Generalized Almost Distributive Fuzzy Lattices are presented in this article (GADFL). In GADFL, we have also deduced certain properties and characteristic theorems of MPIs. Further, we have also derived the following theorems:let and be two ideals of GADFL . Then is a MPI belonging to both and and finally, every ideal of GADFL is the union of all MPIs containing it.

Elliptic Curve Cryptography Based Homomorphic End-to-End Encryption Security in Cloud Computing

Dr. G. N. Kodanda Ramaiah, Savita A. Harkude — 2022-07-19

Cloud computing is turning into the principal computing model in the future because of its benefits, for example, high asset use rate and save significant expense of execution. The existing algorithms for security issues in cloud computing are basically advanced version of cryptography. Mainly cloud computing algorithm are concerning about data security and privacy preserving of user. To protect the privacy of data owner and data user the cryptographic data are shared and stored in cloud storage by applying HE-homomorphic encryption. According to environment of HE, this paper proposed a new hybrid algorithm by combining the two algorithms of cryptography i.e., AES and ECC with the concept of splitting algorithm. By the implementation of this hybrid algorithm, we will split any file into binary version and then after processing the cryptography technique. This hybrid algorithm provided higher protection against attacks over cloud storage and data sharing.

Crypto Hash Signature Adopted Blockchain to Detect Spiteful Node to Enhance the Throughput in WSN

Ambika Bhuvaneswari C., E. D. Kanmani Ruby — 2022-07-19

Secured routing is the definite maneuvers in a wireless sensor network (WSN) for distributing data packets to the base station. However, attacker node outbreaks will occur throughout the routing process, exacerbating the wireless sensor network's functioning. As a result, a secure routing protocol is essential to ensure route fortification and the effectiveness of wireless sensor networks. For this reason, many studies have shown an interest in improving routing protocol security by adopting cryptographic structures and trust regulation methods. However, most secured routing protocols are dynamically awful in real-time scenarios, making it difficult to distinguish between unsecured routing node performances. In the same way, solutions for detecting and mitigating node attacks are still challenging. In this, Crypto Hash signature (CHS) token are generated for the flow accesses with a secret key belonging to each routing sensor node. The assessment metrics corresponding to average energy consumption, and throughput of token transactions is measured. Then the simulation performance of the proposed SDOR-CHS-SD method provide 18.46%, 8.37% and 26.77% higher token transactions throughput, when compared with existing method like SDOR-SSOA, SDOR-DSM and SDOR-RIL respectively.

Prediction of Fluctuation, Expansion Ratios and Gas Hold Up in a Three Stage Gas-solid Fluidized Bed – A Statistical Approach

M. Krishna Prasad, Jyoti Kaparapu, P. Satya Sagar — 2022-07-19

Multistage fluidized bed has shown greater performance than the single stage in terms adsorption efficiency, recovery efficiency etc. Thus, experiments have been carried out in a three stage gas-solid fluidized bed. The hydrodynamic characteristicssuch as fluctuation ratio, expansion ratio and gas holdups have been studied. The dependency of these quantities with the system variables like particle size, initial static bed height and superficial gas velocity in each stage of a three-stage fluidized bed have been analyzed. It has been found that both fluctuation and expansion ratios are inverse functions of particle size and static bed height whereas a direct function of superficial gas velocity. However the gas holdup is a direct function of all the three operating parameter.It has also been observed that bed fluctuation and expansion goes on decreasing from lower bed to the upper bed. Further correlations have been developed for all the three stages for fluctuation ratio, expansion ratio and gas holdup using statistical analysis. It has been found that the experimental values of the responses agree well with the values predicted from the models.

Statistical Methods for Instability in Production of Major Crops in National Capital Region (NCR) of Haryana

Madhu Ahlawat, Pooja Kumari — 2022-07-19

The instability in agriculture production of major crops of NCR-districts of Haryana state is studied using Cuddy Della Valle Index method (CDVI). The study includes 13 National Capital Region (NCR) district like Bhiwani, Mahendergarh, Jind, Rewari, Gurugram, Faridabad, Jhajjar, Rohtak, Sonipat, Panipat, Karnal, Mewat and Palwal except the newly established district Charkhi Dadari. For the study a period of last fifteen years, that is, from 2004-05 to 2018-19 is taken for the analysiswhich is divided in to three equal sub-intervals: period-I (2004-05 to 2008-09), period-II (2009-10 to 2013-14) and period-III (2014-15 to 2018-19). The secondary data is collected from the various issues of the Economical Survey of Haryana and the Statistical Abstract of Haryana for the period of 15 years, that is, from 2004-05 to 2018-19.

Design And Implementation of a Low-Cost Smart Surveillance Robot for Secured Monitoring System

2022-07-19

Modern surveillance requires a lot of human effort. The international border monitoring also requires human involvement. By using automated robot, distant location monitoring can be easily achieved. An idea is proposed to design a robot for location tracking and for security purpose. It is primarily used for location where human surveillance is not possible. The robot is commanded to reach the destined location then using the wireless camera it transmits the scene. The robot comprises of three steps namely receiving the data, going to the location, and transmitting the surrounding data through the camera. The transmitter section consists of four buttons. When the first button is pressed, the DC motor attached with the robot runs and the robot reaches the destined location. The signal comes from the transmitter reaches the receiver section and the Arduino gives the command to rotate the DC motor. After reaching the location, it starts to transmit the surrounding scene through the wireless camera placed in the robot. Based on the input command the robot reaches the destined location. The wireless transmitting camera can be used for surveillance purpose. A firearm is attached to the robot. Whenever any intruder passes the restricted place, the firearm can be used to stun the intruder. An extra button is provided in the transmitter section for firing the weapon manually. An LCD display is used to see the current location of the robot. All these components are interfaced using Arduino board.

Neutrosophic Beta Omega Closed Sets in Neutrosophic Topological Spaces

S. Pious Missier, A. Anusuya, A. Nagarajan — 2022-07-19

We introduce and examine several applications of neutrosophic beta omega closed sets namely , , , , in this study. We also introduce the concept of neutrosophic betaomega continuous mapping in neutrosophic topology. Furthermore, we study the relation between the neutrosophic betaomega continuous mapping with the already existing neutrosophic continuous mapping. In addition, we discuss the properties of neutrosophic betaomega continuous mapping.

Design and Implementation of Virtual Machine Migration Method in Cloud Computing

Shubhashish Goswami, Digvijay Singh, Sachin Sharma — 2022-07-19

The cloud is a communication architecture that includes virtual machines, data centers, hosts, and brokers. For cloudlet execution, the broker looks for the most reliable virtual machine. Uncertainty may arise in the network as to which system is overloaded. A work technique is proposed in this study to improve the system's fault tolerance. The proposed enhancement is based on the ACO algorithm, which can determine the best virtual machine to migrate a cloudlet to. The proposed algorithm's performance is being evaluated on Cloudsim in terms of execution time and energy consumption. When compared to TESA, the simulation results showed that ACO (Ant Colony Optimization) takes the least amount of time and energy to run (Three Step Encryption Algorithm). The proposed algorithm can be used in cloud computing for load balancing.

A Research Paper on Lung Field Segmentation Techniques using Digital Image Processing

Gunjan Bhatnagar, Ashish Gupta, Yogesh Kumar — 2022-07-19

In this review study, we investigate various strategies for lung field segmentation by utilizing digital image processing. In chest radiographs (CXRs), lung field dissection segmentation is and will continue to be a crucial phase in the process of automatically evaluating pictures of this kind. We describe a method for the segmentation of the lung field that is based on a boundary map of high quality that was detected using a structured edge detector, which is a contemporary border detector (SED). A SED has previously been trained to recognize lung limits by using manually delineated lung sections in CXRs as training data. Following this step, the masked and tagged boundary map is converted into the active contour map (ACM). In conclusion, the lung contours that are created by following filter phases that are based on Gaussian and dilate features are the contours that have the highest rate of trust in the ACM. Our method is evaluated using aberrant lung pictures obtained from chest x-rays, and it is demonstrated to be superior, in terms of the amount of processing time required, to segmentation utilizing a universal contour map.

Research Paper on Security Threats from Temperature Changes Occurrence by using Artificial Intelligence (AI) and Machine Learning (ML)

Ankit Mathani, Gunjan Bhatnagar, Ashish Gupta — 2022-07-19

In the past, climate change has been a significant problem all over the world; nonetheless, it has been challenging to mobilize collective action in response to the problem. In spite of the knowledge that climate change is a threat to our species as well as the life that already exists on our planet, this continues to be the case. The researchers who have looked into this phenomenon have come to the conclusion that effective communication regarding climate change is predicated on messages that are both emotionally charged and personally meaningful, and that pictures, in particular, are essential in order to generate awareness and concern about the subject. In spite of the widespread belief that this is the case, the majority of the conventional modes of communication that professionals employ when interacting with members of the general public are frequently founded on discoveries made in the scientific community. These studies, on the other hand, typically fail to convey either the urgency or the relevance of this huge occurrence. Utilizing Artificial Intelligence (AI) and Machine Learning Is the Purpose of Our Report So That We Can Help Bridge This Gap And Build A Tool To Promote Awareness About The Effects Of Climate Change The purpose of our report is to use AI and ML so that we can help bridge this gap and build a tool to promote (ML).

Use of Hierarchical Temporal Memory to Assess Reactive and Proactive Dissonance for Anomaly Signal Management

Dr. Nidhi Mishra, Dr. F Rahman, Mr. Om Hari Naryan Kushwaha — 2022-07-19

A compelling group the board framework offers prompt receptive or proactive treatment of possible problem areas, including packed circumstances and dubious developments, which relieve or evades serious episodes and fatalities. The group the board space creates spatial and transient goal that requests different modern components to quantify, concentrates, and interact with the information to deliver a significant reflection. Swarm the board incorporates demonstrating the developments of a group to project compelling systems that help fast emersion from a risky and deadly circumstance. Web of Things (IoT) advancements, AI procedures, and specialized techniques can be utilized to detect the group trademark/thickness and proposition early recognition of such occasions or far superior expectation of likely mishaps to illuminate the administration specialists. Different AI strategies have been applied for swarm the board; in any case, the quick progression in profound various leveled models that gains from a nonstop stream of information has not been completely explored in this specific situation. For instance, Hierarchical Temporal Memory (HTM) has shown strong capacities for application areas that require internet learning and demonstrating transient data. This paper proposes another HTM-based structure for peculiarity identification in a group the board framework. The proposed system offers two capabilities: (1) responsive discovery of group oddities and (2) proactive location of peculiarities by foreseeing expected irregularities before occurring. The exact assessment demonstrates that HTM accomplished 94.22%, which outflanks k-Nearest Neighbor Global Anomaly Score (kNN-GAS) by 18.12%, Independent Component Analysis-Local Outlier Probability (ICA-LoOP) by 18.17%, and Singular Value Decomposition Influence Outlier (SVD-IO) by 18.12%, in swarm different irregularity location. Besides, it shows the capacity of the proposed alarming system in anticipating potential group irregularities. For this reason, a mimicked swarm dataset was made utilizing the MassMotion swarm recreation device.

Control of Attacks by Neural Networks to Make Batch Amplitude Disturbances and Manhattan-Distance Constraints Counterproductive

Dr. Dev Ras Pandey, Dr. Atul Bhardwaj, Dr. Nidhi Mishra — 2022-07-19

As of late, with the advancement of profound learning innovation, brain networks assume an undeniably significant part in an ever increasing number of fields. Notwithstanding, research shows that brain networks are helpless against the assault of ill-disposed models. The reason for this paper is to concentrate on the standard of ill-disposed models age and propose another technique for creating antagonistic models. Contrasted and existed strategies, our technique accomplishes better misdirection rate and bothers less pixels of pictures. During an age in clump aspect emphasis, different pixels are irritated while Manhattan-Distance imperatives are added to them. Our calculation performs well in tests. Contrasted and Carlini-Wagner technique, just 60 additional aspects are bothered, which demonstrates that the calculation cost of our calculation is totally OK. Plus, contrasted and FGSM calculation, the duplicity rate increments by 12% while the age seasons of them are practically same.

Maximum Co-Location Patterns of Mining Based on Count-Ordered Citations-Analysis of Spatial Databases

Dr. Nidhi Mishra, Dr. Kumar Shwetabh — 2022-07-19

Searching for the spatial co-area that shows up as often as possible in neighboring space is generally utilized in numerous areas, including cell phone administrations and traffic the board. To accomplish this objective, the SGCT calculation further develops different calculations which use tables to find applicant sets. It utilizes an undirected chart to mine up-and-comers of the maximal co-area designs first, then, at that point, utilizes a dense tree design to store example clubs of competitors. In any case, as how much information develops, the SGCT calculation might store enormous number of hubs during the time spent producing the tree. In this paper, we propose another system which will think about the quantity of occasions of every occasion. We propose a Count-Ordered Instances-tree to record competitors of connection sets. From our trial results, we show that our methodology needs more limited time and costs less extra room than the SGCT calculation.

Intrusion Detection Using Combination of GA Based Feature Selection and Random Forest Machine Learning Supervised Approach

Sachin Sharma, Shubhashish Goswami, Gesu Thakur — 2022-07-19

As of late, the fast advancement of web innovation brings numerous serious organization security issues connected to vindictive interruptions. Interruption Detection System is viewed as one of the huge procedures to defend the organization from both outer and inward assaults. In any case, with the quick development of the IoT organization, cyberattacks are additionally evolving rapidly, and numerous obscure sorts are appearing in the contemporary organization climate. Thusly, the productivity of conventional mark based and oddity based Intrusion Detection System is inadequate. We propose a clever Intrusion Detection System, which utilizes a developmental strategy based include choice methodology and a Random Forest-based classifier. The development based include selector utilizes an imaginative Fitness Function to choose the significant elements and decreases aspects of the information, which raise the Ture Positive Rate and lessen the False Positive Rate simultaneously. With extraordinary high precision in multi-order errands and remarkable abilities of taking care of commotion in gigantic information situations, the Random Forest strategy is broadly utilized in peculiarity identification. This examination proposes a structure that can choose all the more consistent highlights and further develop the order results as contrasted and different innovations. The proposed structure is tried and investigated UNSW-NB15 datasets and NSL-KDD datasets. Different measurable outcomes and itemized correlation with different strategies are introduced inside this article.

A Deep Learning Approach for Detecting Diabetic Macular Edema through Analyzing Retinal Images

Dr. Nidhi Mishra, Dr. Apoorva Singh, Dr. Akanksha — 2022-07-19

Clinical imaging developed quickly to assume an imperative part in the conclusion and treatment of an illness. Robotized examination of clinical picture examination has expanded successfully using profound learning procedures to get much speedier groupings once prepared and learn significant highlights for explicit assignments, demonstrated to be assessable in clinical practice and an important device to help dynamic in the clinical field. Inside Opthalmology, Optical Coherence Tomography (OCT) is a volumetric imaging methodology that purposes the conclusion, observing, and estimating reaction to treatment in the eyes. Early discovery of eyes sicknesses including Diabetic Macular Edema (DME) is crucial interaction to keep away from confusion like visual impairment. This work utilized a profound convolutional brain organization (CNN) based technique for the DME order tasks. To exhibit the effect of convolutional, five models with various Convolutional layers were assembled then the best one chose given assessment measurements. The exactness of the model improved while expanding the quantity of Convolutional Layers and accomplished 82% by 5-Convolutional Layer, Precision and Recall of the CNN model per DME class were 87%% and 74%, individually. These outcomes featured the capability of profound learning in helping dynamics in patients with DME.

An Autonomous Navigation System Using the Passing Motion Planner Approach

Mrs. Smita Premanand, Mr. Kapil Kelkar, Mr. Sourabh Kumar Shrivastav — 2022-07-19

This study proposes a genuine robot accommodating passing movement organizer to be utilized in swarms. The proposed technique figures out how to pass people on foot with smooth speed increase and deceleration by utilizing passing movement learning. A critical element of the proposed strategy is that it is prepared on a basic group reenactment with both dynamic and fixed people on foot. The mastered passing way of behaving can be utilized straightforwardly in an independent route. Assessments utilizing the group recreations demonstrate that the proposed technique beats the current ones as far as progress rate, appearance time, and staying away from the walkers. The proposed route structure is carried out on a versatile robot and exhibited its effective route between walkers in a science gallery.

Apply Machine Learning with Smartphones to Detect and Recommend the Right Treatment for Plant Diseases

Dr. Nidhi Mishra, Dr. F Rahman, Dr. PritiKumari — 2022-07-19

Plant sicknesses cause significant harvest creation misfortunes around the world, and a great deal of huge exploration exertion has been coordinated toward making plant infection distinguishing proof and therapy methods more viable. It would be of extraordinary advantage to ranchers to have the option to use the ongoing innovation to use the difficulties confronting horticultural creation and thus further develop crop creation and activity productivity. In this work, we planned and executed an easy-to-understand cell phone-based plant illness discovery and treatment suggestion framework utilizing AI (ML) methods. CNN was utilized for include extraction while the ANN and KNN were utilized to order the plant infections; a substance-based sifting proposal calculation was utilized to recommend significant medicines for the recognized plant illnesses after grouping. The aftereffect of the execution shows that the framework accurately identified and suggested treatment for plant illnesses.

Efficient Application of Competitive Multiagent Learning by Neuroevolution

Dr. Munshi Lal Patel, Mrs. Smita Premanand, Mr. Kapil Kelkar — 2022-07-19

Multiagent frameworks give an ideal climate to the assessment and examination of true issues utilizing support learning calculations. Most customary ways to deal with multiagent learning are impacted by lengthy preparation periods as well as high computational intricacy. Flawless (NeuroEvolution of Augmenting Topologies) is a well-known developmental methodology used to get the best performing brain network design frequently used to handle streamlining issues in the field of man-made reasoning. This paper uses the NEAT calculation to accomplish serious multiagent learning on a changed pong game climate in a productive way. The contending specialists keep various guidelines while having comparable perception space boundaries. The proposed calculation uses this property of the climate to characterize a particular neuro transformative system that gets the ideal strategy for every one of the specialists. The ordered outcomes show that the proposed execution accomplishes ideal conduct in an exceptionally short preparation period when contrasted with existing multiagent support learning models.

Evaluation of Correlations and Cluster Analysis of Genetic Variants, Heritability, and Genetic Advances Using Mutagens Driven Ten Variants of Brassica Campestris

Mr. Om Hari Naryan Kushwaha, Mr. Dindayal Singh, Dr. Utkarsh Anand — 2022-07-19

The review was taken up to appraise genotypic and phenotypic changeability, heritability with hereditary development, relationship, and bunch examination on development and yield boundaries in 10 variations of assortment T-9 of Brassica campestris (toria). These variations were acquired through mutagenesis. Examination of change uncovers that there are profoundly massive contrasts among every one of the characteristics considered. Completely, phenotypic coefficients of fluctuation were higher than their separate genotypic coefficients of change for every one of the characteristics. High GCV was noticed for cases plant-1, snout length, and seed yield plant-1 while most noteworthy PCV was displayed for auxiliary branches plant-1. Expansive sense heritability went from most reduced 16.41 for auxiliary branches per plant to most noteworthy 72.35 for plant level. Units plant-1, plant level, mouth length, case length, and seed yield plant-1 showed high hereditary development over mean percent. The consequences of connection uncovered that seed yield plant-1 showed an exceptionally huge positive relationship with stem border, optional branches, cases on a fundamental raceme, unit plant-1, and 100 seed weight, albeit critical negative connection with bill length. The attributes which showed a positive connection with seed yield were the fundamental supporter of high seed yield plant-1. All genotypes were assembled into two groups at 3% linkage. Bunch I contains variations with a most extreme mean incentive for plant level, raceme length, and case length though group II genotypes showed high mean incentive for unit plant-1, 100 seed weight, and seed yield plant-1. The consequence of hereditary uniqueness in variations shows that it is useful in the choice of prevalent yield credits in the rearing system of oilseed Brassicas.

Structural Analysis of Environmental Approaches towards Creating Optimizations of Organic Media for Calluses and Cell Suspensions

Dr. Apoorva Singh, Dr. Vinod Kumar Yadav, Dr. Pradeep Kumar Kesharwani — 2022-07-19

Convention for callus enlistment utilizing the leaf explant of Rauvolfia serpentine was normalized utilizing planned natural vermicompost separate and coelomic liquid (removed from the night crawlers Eudriluseugeniae) of different mixes utilized, 30% vermicompost + 4% coelomic liquid was viewed as the best for callus acceptance. Bountiful, sparkly white callus was seen following fourteen days; further smooth white separable callus came about after the 6th seven-day stretch of culture. This on examination with Murashige and Skoog medium enhanced with various blends of BAP (1 mg L-1) + IBA (0.125 mg L-1) and BAP (1.0 mg L-1) + 2, 4-D (0.125 mg L-1) was made individually. Of the various proportions attempted, a 3:1 proportion of vermicompost extricate: coelomic liquid was viewed as best for starting cell suspension societies. Phytochemical examination detailed 34.83±0.14 mg/g of absolute phenols and 0.063±0.002 mg/g of all out flavonoids from the extricated calli and cell suspension tests. Reserpine is recognized as the significant alkaloid in the callus too in cell suspension culture (15.151 maintenance times in HPLC examination). These phytochemicals delivered by in vitro societies can be essentially utilized for drug reasons. The present review showed huge callus advancement on natural vermicompostextricate (30%) media and its monetary worth.

A Narrowing Analysis on Sound Loss during Transmission on Perforated Natural Fiber Panels

Dr. Dolamani Sahu, Dr. Anil Kumari, Dr. Akanksha — 2022-07-19

Sound transmission misfortune (STL) is a learn about the sound energy that is kept from communicating through a wall or a parcel, it is fundamental, particularly for clamor protection applications. The principal point of this study is to explore and dissect the STL ability of punctured normal fiber sandwich boards, which went about as sound protection material. The target of this study is to decide the impact of hole width of the punctured board on STL, as well as to decide the ideal hybridization blend sandwich board of punctured board with normal fiber that conveys great STL. In this review, STL estimation was done by utilizing a two-load impedance tube strategy combined with LMS Test Lab programming and LMS SCADAS Mobile DAQ framework. Normal filaments utilized in this study are coconut fiber, oil palm fiber, and pineapple leaf fiber. The normal filaments were ready in round and hollow shapes with three unique thicknesses of 1 cm, 2 cm, and 3 cm to squeeze into the example holder of the impedance tube. Every regular fiber will be tried after it was hybridized with a punctured board of various hole breadth sizes and the STL estimation results are gotten and investigated. The estimation results show all examples had reached their most noteworthy STL at the recurrence range 3000 Hz to 4000 Hz. Furthermore, pineapple leaf fiber hybridized with a punctured board of 3 mm's whole breadth is viewed as the ideal blend where it accomplished the most noteworthy STL of 71.80 dB among all the test tests.

Modeling and Analysis of Low Velocity Effect (LVI) on Mixed Lamination Fibers

Dr. Priti Kumari, Dr. Anushruti, Dr. Apoorva Singh — 2022-07-19

Composites experience corruption of primary solidness because of different kinds of effect stacking bringing about the harm which is challenging to see from the outer layer of the design. The paper manages the limited component model (FEM) to concentrate on the conceivable displaying methodology in low-speed influence (LVI) and disappointment system of carbon fiber built up polymer (CFRP) composite cover of CCF300/epoxy and its primary reactions. In limited component estimation, a proposed three-layered moderate harm model is utilized to decide the intralaminar harm, while the firm contact definition is utilized to break down the interlaminar harm. The disappointment model exhibitions are approved and checked given various limit conditions while keeping up with the effective energy. Through reenactment, the variety in limit conditions fundamentally changes the underlying reactions and energy retention of the covers. It is trusted this study will be an extraordinary device in deciding the different composite.

Assessment of the Impact on Steel Walls and Bracing Systems in the Analysis of Seismic Waves during a Building Moment Resistance Frame

Yogesh Kumar, Tejraj, Digvijay Singh — 2022-07-19

The designs in seismic regions are for the most part vulnerable to serious harm. Alongside the gravity, loads structures need to endure the horizontal loads too which tends to foster high burdens. By and large shear walls and bracings are introduced in the design to improve their horizontal firmness and flexibility and limit their parallel relocations to give well-being to the designs. The basic issues in the seismic plans are principally story floats and sidelong relocations. Four distinct kinds of edge building models are created and assessed with the assistance of ETABS. In the present work, G+21 multi-story, second opposing edge building models are viewed as utilizing steel shear walls and two sorts of bracings. The arrangement considered for all models is 20mX20m and the strategy used for examination is the reaction range investigation technique. All individuals were planned according to IS456:2000, IS800:2007, and the load mix for seismic power was considered according to IS1893 (part-1):2016. Correlation between each of the four models was performed based on the following boundaries for example Uprooting, Stiffness, and Natural period. The outcome is communicated as charts, and figures and correlations are finished according to IS1893 (part-1):2016. The primary focal point of this study is to see the enhanced model.

A Novel Algorithm for Foreground Moving Object Detection

V. Naga Bushanam, M. Rama Krishna Murthy — 2022-07-19

Recognition and tracking of moving object recognition from the video sequences is an important study subject because it may be utilized in a variety of applications. Tracking tries to find and predict specific movements of observed objects throughout a time period, whereas recognition allows for the return of object forms identified in the picture. As a result, detection may have a significant influence on the tracking process as a whole. The topic of detection is the emphasis of this article. The Optical flow technique, Frame difference (FD) technique and Background subtraction (BS) technique are most used detection methods right now. We provide a detection technique depending on the BS and FD methods because it is especially well suited to quick actual operations; nevertheless, optical flow has a greater computational expense due to the high density estimations. It is possible to accomplish sparse detection quickly by combining the BS and FD methods with the Edge detectors and Laplace filters. One of the major contributions offered as a result of this, is the development of an algorithm for the moving object recognition based on a quite extended combination of fundamental real-time surveillance processes. For typical benchmark datasets, experimental findings demonstrate that the proposed technique has superior noise suppression and detection accuracy than existing methods.

Anime Face Generation using Generative Adversial Networks in Deep Learning

Anjana M S, Dr. Dhanya N M — 2022-07-25

Anime characters are used not only in books, but also in entertainment, awareness shows, video games, etc. In the recent times, there are many systems built for anime face generations. There are also various kinds of Artificial Intelligence approaches used to solve this, but the most famous ones are Generative Adversial Networks. The main reason is that the efficiency and performance of Generative Adversial Networks are found to be increasing as days go by. This paper is written to compare the quality of images, performance and efficiency and performance of two types of Generative Adversial Networks; namely Deep Convolutional Generative Adversial Network, and Style Generative Adversial Net-work2. Fréchet Inception Distance (FID) is taken as the evaluation metric for the systems implemented. For Deep Convolutional Generative Adversial Network, an FID score of 624.04 is found; whereas for Style Generative Adversial Net-work2, an FID score of 30.6 is found. From the resultant images and the Fréchet Inception Distance score, it is evident that Style Generative Adversial Network2 is the best model for anime face generation

Child Safety Wearable and Visually Impaired Assistive Device with Location Tracking System Using IoT

Dr.A.Vasantharaj, T.Thanusri, N.M.Yamuna, K.Elakkiya — 2022-07-25

The idea of a smart wearable system for children and visually impaired is discussed in this paper. The key advantage of this wearable above others is that it can be used with any cell phone and does not required an expensive smart phone are a very tech-savy individual to operate. The GPS, GSM, Wi-Fi and Arduino technologies were used to create a smart wearable gadget for kid safety and to assist visually impaired people with a location tracking system. The prototype system uses a heartbeat and temperature sensor to monitor the health of children. The child will be safe from threads as a result of this. There are many wearable’s available now a days that crack children’s everyday habits and activities and also assist in locating them utilizing Wi-Fi and Bluetooth services. However, it appears that both the parent and the child are communicating in securely. As a result, the goal is to create a text and Email enabled link in between child’s wearable and the relevant parent via SMS. Global System for Mobile Communication is the primary concept behind this. For issuing an emergency alarm, the proposed solution is wearable gear with cellular network and Wi-Fi connectivity. When the push button is touched, an alarm is sent in variety of ways, including SMS and Email, in the event of emergency.

Finger Vein Recognition Using Deep Learning

Hrishikesh Krishnan, Sangita Khare — 2022-07-25

Recently, finger vein recognition technology has gained wide acceptance in both research as well as in commercial uses like access control and authentication. Finger vein recognition is a novel biometric technology which is challenging to spoof and has a wide array of potential applications. Many deep learning based finger vein recognition system has been proposed so far. The vein images are always prone to quality degradations due to the noise, blurring and illumination variations introduced while capturing the images using near infrared technique. However, most of the existing deep learning methods for finger vein recognition are based on ideal vein images (images with minimum image quality). Since the network is trained based on the vein images which are having a minimum image quality, the performance of recognition may get affected when the vein image quality is poor. We propose a transfer learning based model which is trained using vein images with varying image quality. From a unique vein image, four different quality images (original image quality, blurred image, noisy image1, and noisy image2) will be generated and used for training the model. To the best of our knowledge, this is the first work based on transfer learning model that relies upon varying qualities of vein images in order to improve the overall recognition performance. We have utilized SDUMLA vein image dataset for experiments. The experimental results shows that the proposed approach can perform better than the existing deep learning based methods.

Numerical Analysis Of Machine Foundations on Infilled Geosynthetic Reinforced Soil Bed

A.Aejaz Ahamed, Surya Muthukumar, G. Santhoshkumar — 2022-07-25

Machine foundations require careful analysis and design as it involves severe dynamic loads in addition to the standard design loads of gravity. Furthermore, the magnitude and nature of the operating loads are mainly dependent on the type of machine. The foundation must aid the smooth functioning of machines during usual operation and also ensure structural integrity under unusual loading circumstances, especially during resonance. Such severe conditions may be avoided by varying the stiffness and the mass of the structure which alters the natural frequency of the system and demands revisit to the design of foundations. To expedite the process, a detailed 3D finite element analysis is carried out in the present study using a finite element software (ANSYS v 2021). Higher-rated machines now have greater tolerances and regulated behaviour thanks to advancements in manufacturing technology. To achieve greater efficiency in the machine performance, this study emphasizes the necessity of more vital collaboration between foundation designers and machine manufacturers. The paper presents the modal analysis of machine foundations resting on different ground conditions namely, Unreinforced and Reinforced with geocells . The findings are expressed in the form of vibration characteristics (i.e. natural frequency and mode shapes), which demonstrate how different elements of the structure respond under different dynamic loading situations. Furthermore, the influence of sloping ground near the machine foundations is highlighted in the study.

Estimation of Daily Groundwater Table Using Backpropagation Neural Network Model by Assessing Training Algorithm

Varna Vishakar V, Ayush Jain, Zohaib Ahmed Khan, Ayush Kumar — 2022-07-25

The Onça stream is a Brazilian waterway, a canal on the left bank of the Rio das Velhas and a tributary of the São Francisco River. Although it plays an important role in the management of groundwater levels in the Ribeirao Preto region, the final groundwater capacity is declining in many parts of Brazil in response to abstraction. Predicting and forecasting stream water level using simple but effective methods can provide a reliable tool for future management of groundwater. In the present study, the daily water level of a particular well from the year 2004 to 2014 was considered as the dataset. The adopted database was segregated as 70% for training the above-mentioned models and the remaining 30% data was utilized for model testing. One of the soft computing methods, Backpropagation Neural Network was utilized for the prediction of daily groundwater table. In this, BPNN model, various training algorithm were utilized and compared based on the value of coefficient of correlation (R)and the time taken. The model also assessed by various statistical parameters like root means square error (RMSE), mean total error (MAE), determination coefficient (R²), Normalized mean biased error (NMBE). The results depicted that the developed model have good forecasting ability.

Leaf Disease Classification Using Machine Learning Techniques

Jesifica Cinthamani.C, Dr.Aisha Banu.W — 2022-07-25

Machine learning techniques are widely used in many fields, in this paper machine learning classification algorithms are used in an aquaponic system for plant disease classification. Aquaponics is an efficient and smart method of growing crops in water. In an aquaponic system, freshwater fishes known to benefit leafy crops the most. But the plants get affected due to certain disease in the leaves. In this paper, four different supervised machine learning algorithms are used to detect and classify the tomato leaf diseases. The process involved in the classification of the tomato leaf disease are leaf Image acquisition, Image preprocessing, augmentation, feature extraction and classification.The classifier algorithms used for classifying the leaf disease are Support Vector Machine (SVM), Knearest neighbour(KNN), random forest and decision trees. Feature extraction is done with vgg16 convolution neural network architecture. By applying the four classification algorithm over the features extracted images, Support Vector Machine (SVM) performed well among the four classification algorithms by classifying the tomato leaf diseases with an accuracy of 99%.

An Investigational Study on Ensemble Learning Approaches to Solve Object Detection Problems in Computer Vision

Sree Roja Rani Allaparthi, Jeyakumar G — 2022-07-25

Object Detection is a challenging task in computer vision, which is used to identify all or required objects in the given images or videos. The object detection tasks are widely used in many real-world image classifications and face recognition applications like self-driving cars and autonomous robots etc. Considering the challenges in object detection, this paper proposes to present a study on ensemble learning-based approaches to solving object detection problems. The proposed study uses the YOLO algorithmic model (You Only Look Once) to formulate the ensemble learning model with multiple YOLOV3 variants (YOLOV3-320-weights, YOLOV3-SPP and YOLOV3-Tiny). This ensemble learning model, formulated in this study (named YOLOV3-ensembled) is a combination of these algorithmic models. This study, initially, predicts the objects using the YOLO variants individually. Then the variants are combined to detect the objects. The experimental setup included the evaluation metrics IoU (Intersection over Union) and mAP (mean Average Precision). The comparative performance analysis of the ensemble model with other individual models is presented in this paper. It is observed from the results that the YOLOV3-320-weight model could predict the objects more accurately with good IoU scores and mAP scores.

Identification of Homogeneous Zones in Kerala Through K-Means Clustering

Zeta Paul, Keerthana A, Archana Nair — 2022-07-25

Rainfall is a major source of water to flora and fauna. The variations in rainfall greatly affect agriculture and the daily life of human beings. Surface temperature is another factor that influences and brings changes in the climatic conditions, which in turn affects the environment as a whole. Over the past few years, the global mean surface temperature follows a steady increasing trend, while the trend of Kerala monsoon and post monsoon rainfall remains unpredictable. These variables has been analyzed and studied to determine the changes and future threats. The temperature and monsoon rainfall data has been extracted for a total of 58 grid points all over Kerala for the years 1951 to 2019 (69 years). k- means clustering method is applied to form clusters or groups with similar traits. Clustering assisted to regionalize Kerala State into places of homogeneous characteristics with respect to rainfall, temperature and its combinations. The optimal cluster number is obtained by Elbow’s method, which is found to be 4. The cluster divisions is 10-22-8-18 for rainfall and 9-16-18-15 for temperature. The cluster results for the combination of rainfall and temperature concluded to be same as that of rainfall alone, implying the greater importance of rainfall in delineating the zones.

Identification of Tomato Disease Detection and Classification with Infected Areas Using Deep Neural Networks

R. Ramya, P. Kumar, M. Senthil kumar, V. Sounthar Raj, M. Mohamed Faizal — 2022-07-25

Agriculture is indisputably the backbone of our nation. India is the second-largest producer of agricultural products globally. In terms of people to feed, Indian agriculture lags well behind other countries in terms of per hectare production in practically all crops. Plant infections cause major economic and production losses and curtail agricultural production quantity and quality. Producers need to monitor their plants regularly and observe any primary symptoms to prevent plant sickness at a low cost and save a significant part of the production. In recent days, technology has played a vital role in all research fields. So, the help of technology is used to detect plant infection. In India, technology-based modern agriculture is the most required to make more profit in every part of agriculture. Thus, the application of technology in agriculture, such as precision agriculture, may assist enhance productivity, improve the condition of Indian farmers, and preserve their products. Thus, the overall progress of production is obtained. Detection of infections in crop management is peremptory for agriculture to be sustainable. However, because to the cluttered background in today's agricultural economy, automated crop infection diagnosis and prediction is a major difficulty. The Internet of Things (IoT) and deep learning have played a significant role in the recent decade, gathering a tremendous amount of contextual data to recognise agricultural infections. This paper describes a real-time technique for detecting tomato leaf infection based on deep convolutional neural networks such as mobileNet and ResNet CNN models. Tuning the hyper-parameters and altering the pooling combinations on a system enhances deep neural network performance. A neural compute stick comprised of dedicated CNN hardware blocks is used to deploy the pre-trained deep CNN model onto a PIC microcontroller.TTL is used to connect the output from the MATLAB software in the personal computer and PIC microcontroller. The PIC microcontroller is programmed with an embedded C programme, and the ESP8266 IoT module is used to transfer data from the microcontroller to the cloud server, where it is displayed on a web page. The deep learning model obtains more accuracy in the detection of leaf infections, indicating the practicality of this technology and the treatment of infected leaf infections.

Covid-19- Findings From A Survey of Online Consumers of Different States in India

SREEDEVI SANKAR C, APARNA SAJEEV, P SANGEETHA — 2022-07-25

Online sales have increased significantly during lockdown as a result of the recent Covid-19 pandemic. A survey utilising Google Forms was used to collect the data. Both qualitative and quantitative data are used to support this study. Therefore, the goal of this project is to examine how internet commerce has changed both before and after COVID-19.

Optimization of SHA 256 with Finetune Pipeline and Parallel Processing with Split Techniques

2022-07-25

The designBy facilitating the effective transfer of sensitive information across all parties and the system's decentralization, blockchain-based technology could address various security issues in the IT sector, we examine the many strategies for optimizing SHA-256 block hash solving that are available, as even a little increase in block hash solving may have a significant impact on the bitcoin mining industry and decentralization, and authors explain some of the relevant efforts in this paper. The Secure Hash Algorithm-256 (SHA-256) is a cryptographic algorithm found in anything from the Internet of Things microdevices to supercomputers. This paper investigates many SHA-256 implementations and cryptographic hash function. Also, authors propose the finetune pipeline and parallel processing with Split- protocol.

Unified Measurement Cup for the Food Industry

Chirag Satapathy, Mihir Pitambare, Sanskriti Binani, Gauri Joshi — 2022-07-25

In the present scenario, there exists various instruments in the food industry to measure weight and volume of multiple ingredients used in certain specific sustenance that we consume. To fulfil a product recipe, serving size or quantity and quality requirements of the food served, these ingredients should be measured. These various ingredients in the food should be measured with extreme care and accuracy as it ensures that they are equally distributed in the food product, and the end flavor of all of them remains the same. At home, when mother’s bake cakes or desserts, they measure each ingredient properly to make the food taste appetizing. Multiple digitized instruments are out there in the market, but none perform multiple tasks together and also none measure temperature of the ingredients or food being prepared. Hence, in this research paper, we aim to design and develop an All-in-One measurement cup using an Arduino, force transducer and IR temperature sensors that not only saves money but also space by not investing on multiple kitchen instruments and keeping the efficiency and accuracy of the measurements precise

Study on Quantitative Estimation of Seawater Quality in Harbor Using Wind-Powered Seawater Exchange System

Changhwan Jang, Seongsoo Kim, Jinho Kim — 2022-07-25

The seawater exchange breakwater (SEB) constructed at Jumunjin Harbor on Korea’s eastern coast is composed of a semicircular overtopping seawall on the north side of the offshore breakwater and single outlet through the breakwater (CASE 0). Additional counter-measures, CASE 1 and CASE 2, work to improve water quality in the harbor are examined here. A wind-powered seawater exchange system (WSES) including a transit pipeline with 781.9 meters in length and 1.5 meters in diameter is linked to 3 multi-outlets set along the berthing areas of the harbor. The WSES pumps up offshore seawater by a hydraulic pump operated from wind power. The outflow at each outlet for each case is calculated and compared with the existing case, CASE 0. When the actual discharge is 71,000m3/day, the coefficient of correlation is measured Cq=0.11. The seawater exchange rates in the harbor concerned three cases were computed using coupled flow and diffusion numerical modeling system. The numerical model results demonstrate that CASE 2 for the WSES with transit pipeline and multi-outlet are more effective than CASE 0 for the SEB with single outlet in spite of reduced influx rate due to long transit pipeline, and consequent increment of head loss.

Click-through Rates and Consumer Satisfaction: A Pilot Study Analyzing the Preferences of South Korean Online News Consumers

Eunchan Kim, Kwangtek Na, Byungjoon Yoo, Yeunwoong Kyung — 2022-07-25

Consumers often respond negatively to news information owing to a lack of information in notification messages. This pilot study compares consumer satisfaction and click-through rates between an existing application and our newly-developed news notification application that shows emoticons and text summaries. Eighty South Korean adults with prior experience in using news notification applications were randomly divided into four groups of 20 for this study. One of the following conditions of receiving news notifications was assigned to each group: news without additional information, news with emoticons, news with text summary, and news with both emoticons and text summary. We found that the average click-through rate and average satisfaction score were significantly higher among participants who saw the additional text summary. Similar results were observed for consumer satisfaction and visualizing emoticons. Our findings indicate that information abundance increases consumer satisfaction. Mobile technology that provides information delivery methods that complement consumers’ needs may lead to higher consumer satisfaction levels.

AI Convergence Education Programs Combined with Humanities Elements

Yuri Hwang, Youngmi Choi, Eunsun Choi, Namje Park — 2022-07-25

Artificial intelligence (AI) is accelerating its development through the convergence of various fields and studies. Among them, the convergence of AI and humanities draws attention from academia. Therefore, this paper shows the results of developing AI Convergence education programs by combining humanities elements. The first of the educational programs proposed in this paper combines Unhaeduchangjipyo, an ancient document of Korean historical value, to teach the expert system, one of the applications of AI. The document’s concept is similar to the modern expert system because Unhaeduchangjipyo exemplified how to cope with infectious diseases. This program was developed for middle school students and deals with the topics of how to analyze problems with infectious diseases, designing medical-related expert systems, and developing expert systems through programming. The second educational program incorporates popular paintings of Monet’s, and a fairy tale, Goldilocks and the Three Bears. Students can easily understand supervised learning with the paintings. Moreover, Goldilocks, which has become a symbol of just the right state without any shortage or remaining, is an example of an appropriate dataset related with overfitting and underfitting. This program was developed for elementary school students and includes the types of machine learning, the understanding of overfitting and underfitting, and the game of the principles of overfitting and underfitting. We hope to provide implications for AI humanities and AI convergence education through this paper.

Digital Therapeutics from Data Analytics and Information Security Perspectives

Eunsun Choi, Namje Park — 2022-07-25

As chronic diseases increase due to aging, digital therapeutics have been created to address unmet demand due to increased social burdens such as health insurance finances and medical accessibility problems. The digital therapeutics are a sub-concept included in digital healthcare, an industrial area that manages personal health and diseases by incorporating ICT technology into the medical service field. Among digital healthcare technologies, digital therapeutics that can prevent, manage, and treat diseases, not just health care, are expected to play a vital role in future health care services. However, many people still have low conceptual awareness of digital treatments, so very few people use them. Therefore, this paper describes methods for increasing the conceptual awareness of digital treatments and spreading digital therapeutics regarding data analytics and information security. Accurate data analysis is an essential element in all processes of the design, development, and application of digital therapeutics. In addition, a strict response strategy to cyber threats is needed because personal data is used for analysis. Consequently, it is recommended to develop educational programs for students, teachers, and the public and provide materials necessary for education to make digital therapeutics known to many people. In addition, it is urgent to establish an environment where digital treatments can be used safely by activating the MyHealthway platform based on MyData and establishing a security model suitable for digital therapeutics. These methods will provide an opportunity for people needing digital treatments to access them.

A Study on the Performance Comparison of the Fundus Image Generation Model

Yong Suk Kim — 2022-07-25

Recently, in the medical field, research using medical data based on artificial intelligence image processing technology has been actively conducted. However, research in the medical field using deep learning has many difficulties in obtaining data because personal information and medical information exist. This causes a lot of time and economic losses in conducting research. Therefore, in this study, a medical image generation study was conducted to realize the characteristics of lesions based on abnormal medical images among medical data to activate deep learning research in the medical field. In this study, three lesions, including normal images, were screened using the 'Ocular Disease Intelligent Recognition' open dataset, and a total of 356 fundus images were used. The deep learning model used for generating fundus images in this study is Res U-Net, which adds Residual Block to the U-Net structure that generates existing fundus images, and produces an image similar to the actual fundus image. In addition, for the performance comparison between the existing U-Net model and the Res U-Net model in this study, the fundus images generated by each model were quantitatively compared and evaluated through three image similarity indicators and ophthalmologist verification. Comparative evaluation results showed that Res U-Net showed higher results in all image similarity indicators than conventional models, especially Fréchet inception distance (FID) showed 8 times better performance. As a result of the ophthalmologist's verification of the generated fundus image, the average area under the curve (AUC) of the four lesions was U-Net 0.7705, Res U-Net 0.7415, indicating that the image generated in this study was more similar to the original image. As a future study, the addition and removal of lesions and patient information generation models will be studied based on fundus images.

Development of Emotional Sympathetic Healing and Relief Services for Senior Care

Hee-Chul Kim — 2022-07-25

In this paper, we collect biometric information such as activity volume, location information, heart rate, etc. as a wearable device of personalized health care service for senior care based on PHR platform. Design and implement lifelog collection and storage functions in medical institution PHR data. The emotionally sensitive emotional processing engine monitors, classifies, and stores sensor data collected from senior care sensors through the design phase, and associates real-time analysis system development with AI algorithms with emergency call service systems. It separates call ratings inside and outside the nursing home, and monitors emotional states such as joy, sadness, anger, and fear. Ha robot cameras are installed to support everyday safety and are used to provide residential security, safety, and security notification services. Health and safety tracking and managing medical products on the Internet (; IoMT) to develop solutions for emotionally-balanced healing and peace of mind services for senior care combined with devices.

A Study on the Mechanical Properties According to the 3D Shape of the Internal Structure in Additive Manufacturing

Ah-In An, Joon-Seong Lee, Seong-Gyu Cho — 2022-07-25

The additive manufacturing (AM) can freely process the shape of the product compared to the conventional subtractive manufacturing(SM), and there is an advantage in reducing the weight of the product. The weight reduction of the product is mainly achieved by making a certain cavity inside the product. Therefore, in addition to the Bravais lattice structure that has been used as a three-dimensional cavity pattern, an additional new lattice structure is proposed, and the stress distribution in the lattice structure will be confirmed through simple compressive stress analysis. Based on this, we will check which structure shows the most universally excellent mechanical properties.

Determination of Optimal Channel Spacing Based on Spectrums for Spreading Codes in Split Channel BFSK

Yu-Ra Heo, Seong-Hyun Kim, Eui-Rim Jeong — 2022-07-25

This paper proposes optimal channel spacing for Split Channel BFSK (Binary Frequency Shift Keying) with direct sequence spread spectrum. To this end, first, the inter-channel interference is observed for various spreading gains and codes through computer simulation. The data rate is 1 MHz and two tone spacing of SC-BFSK is 4 MHz. The receiver filter bandwidth for each tone is 700 kHz. The spreading code is selected between random PN (Pseudo Noise) code or all 1. According to the results, it is confirmed that optimum inter-channel spacing depends on the spreading gains and codes. Specifically, for small spreading gains, all 1 code shows better out-of-band interference characteristics than the PN code while PN code is preferable for large spreading gains. Therefore, neighboring channel spacing should be decided as a function of spreading gain, and a preferable spreading code exists in terms of inter-channel interference mitigation.

Optimal Receiver Filter Design for Spreading Gains in Split Channel BFSK Environment

Mina Lee, Seong-Hyun Kim, Eui-Rim Jeong — 2022-07-25

In this paper, we design optimal receiving filters for spreading gains (SG) in Split Channel Binary Frequency Shift Keying (SC-BFSK) systems. For the filter design, the data transmission speed is 1 Mbps, the two tones spacing is 4 MHz, and the channel is an additive white Gaussian noise (AWGN). By changing filter passband bandwidth and transition band, the bit error ratio (BER) performance is observed. Among many combinations of passband and transition band, we select the filter that provides the best BER performance. According to the results, the filter specification is a function of spreading gain. According to the results of the simulation, the optimal receiver filter for SG = 1 is the one with a passband bandwidth of 600 kHz and a transition band of 500 kHz. The optimal receiver filter for SG = 4 is the filter with a passband bandwidth of 200 kHz and a transition band of 200 kHz. The optimal receiver filter for SG = 16 is the filter with a passband bandwidth of 100 kHz and a transition band of 100 kHz. Those results confirm the optimal receiver filter is a function of the SGs, and as the SG is larger, the passband bandwidth and the transition band become smaller.

Prediction of Signal to Noise Ratio Based on Deep Learning in Tactical Wireless Communication Systems

A-Min Jo, Jae-Woong Choi, Eui-Rim Jeong — 2022-07-25

In this paper, we propose a prediction technique for future signal to noise ratio based on deep learning in tactical wireless communication systems. The communication system considered in this paper receives with multiple antennas and transmits in the future using the same antenna. We propose a deep learning model that predicts SNR for each transmit antenna in future transmission situations based on SNR received from multiple receive antennas in the past. Received ratio or a recorded ratio of a received SNR is set to 10 to 100 . If there is no received record nor SNR record, the received SNR is set through linear interpolation of the previously received SNR and the subsequent received SNR. According to the simulation results, wideband signals (4MHz) show better predictive performance than the narrowband signals (25kHz). In case of wideband, the proposed method is about 0.37 to 0.98 superior to the conventional method when the moving speed is over 20 . For narrowband signals, the proposed method is about 0.29 to 0.88 better than the conventional method for the moving speed over 20 . Those result indicates that the proposed prediction technique can be applied to antenna selection problem that provides the best SNR.

Selection of Optimum Transmit Antenna Based on Convolutional Neural Networks in Mobile Fading Channels

Jeong-Eun Oh, Jae-Woong Choi, Eui-Rim Jeong — 2022-07-25

This paper proposes a new technique for selecting optimal transmit antennas using convolutional neural network (CNN) in mobile communication environments. The communication system considered in this paper has multiple antennas and time-division duplexing (TDD) mode. In a receiver mode, it utilizes all the antennas but uses only one antenna in a transmitter mode. The proposed method is an optimal antenna selection technique based on CNN for future transmission time. The input of the CNN the signal to ratios (SNRS) for the past received signals. Conventional method selects the optimal antenna based on the average of the past received SNRs or the most recently received SNR. We compare the proposed method with two conventional methods through computer simulation. According to the simulation results, by changing the mobile speed and the probability (or frequency) of receiving, the proposed CNN method has the highest accuracy in wideband signals while the convolutional method using the recent received SNR has the highest accuracy in narrowband signals.

Retracted

Retracted — 2022-07-25

Retracted

Landslide Detection Using Unmanned Aerial Vehicle (UAV)

2022-07-25

Natural disaster comes in many forms and landslides are one of the most common disasters in Malaysia. This event itself had caused chaos and ruin thousands of civilians. The effort in preventing this natural disaster has increased through time. As a result, a monitoring system can be useful in reducing the risk of hazard. Unmanned aerial vehicles (UAVs) or drones equipped with cameras were used in this study to acquire clear alerts of a landslide-prone location. It is also to develop a 3D map model in order to combine data and compare the slope condition of the research region. Drones, also known as unmanned aerial vehicles (UAVs), are used in many industries that have embraced UAVs. Model view and data of Bukit Antarabangsa and Jalan Hillview may be obtained using a double-grid mission plan on a UAV utilizing Pix4D Capture which is installed in an iPad mini 4th generation. Pix4D Cloud was used to create 3D Mapping Models, Mozaic Data, and Digital Surface Models (DSM). Slope condition or angle may be assessed using DSM data. The gradient of the same research area was measured at a different time period, the slope measurements of the same research region were compared. To examine the influence of gradient and slope conditions, the data will be compared with a second flight session at the same study area. The slope changes show that different day and time has an influence on slope instability, which can result in landslides.

Trends of Consumer Behavior Analysis in E-Commerce for Fake/Spam Opinion Detection

Maysara Mazin Badr Alsaad, Prof. Dr. Hiren Joshi — 2022-07-25

User-generated reviews can have a substantial impact on an organization's income in e-commerce. When making a purchase or deciding on a service or product, online customers depend on the opinions of others. Thus, e-commerce product reviews' trustworthiness is critical for organizations, and it can have a direct impact on their reputation and revenue. As a result, some online purchasing and selling companies employ fraudsters to create bogus or fake opinions or reviews on their websites. The objective of fake reviews is to mislead customers into making the wrong purchasing decision. In the last two decades, different methods of detecting fraudulent reviews have been intensively investigated. However, there is still a lack of the literature surveys that can really investigate and summaries the current methods and challenges facing fake opinions detection. In order to tackle this problem, this survey sums up the publicly available datasets and their gathering methods for the detection of fraudulent reviews. It examines the methods that are currently in use for feature engineering for fake review analysis, as well as deep learning and classical machine learning that have been implemented for fake review classification and finds any inconsistencies and limitations

Modeling and Simulation of Performance and Range of Electric Sweeper and Fuel Cell Sweeper

Anastasija Ignjatovska, Zlatko Petreski — 2022-07-25

In the last decade, major progress in alternative propulsion technologies for vehicles has been made. In these terms, batteries are widely used as an alternative source of energy. However, the concept of a fuel cell propulsion system has also been attracting greater attention. In this paper, electric sweeper and fuel cell sweeper are modeled in order to determine their performance, that is their longitudinal dynamics. Maximum speed and acceleration are determined, as well as required traction force to overcome all resistance forces and achieve the required speed. Furthermore, the change of power of the electric motor, its torque and angular speed are calculated for a given test method defined by EN 15429-2:2012 standard. These dynamic characteristics could be useful for a simple analysis of the characteristics of a vehicle with a new or modified design. Additionally, the model that defines the longitudinal dynamics has been expanded to determine the working range. A validation test of the mathematical model has been conducted

Challenges in Teaching Mathematics Concepts: Assessment, Teaching Strategies, and Students' Attitude

Dr. Evelyn A. Amor — 2022-07-25

This study determined the challenges in teaching selected Mathematics Concepts (MC) regarding assessment, teaching strategies, and students' attitudes. The respondents were all mathematics teachers of the NIPSC System. Four hundred randomly selected Mathematics newcomers. A descriptive-associative research design questionnaire undergone a reliability and validity test used. Appropriate statistical tools are used for data treatment. Results revealed a middle-aged, female, married female with long years of teaching experience mathematics teachers, while students, 17 years old or younger, predominantly female and taking up Education, Technology or Criminology programs. A quiz was the highly utilised assessment strategy, and the lecture method is highly utilised in teaching strategy. MC is found excellent and dependent on age and degree program.

Furthermore, assessment strategies were significantly associated with experience, age, education, experience and training, and strategies. Students' attitude is highly significant but differed in campuses but not courses pursued. Mathematics teachers were highly conventional in both assessment and teaching strategies.

Refrigeration System Innovation

Vecinte G. Aranga — 2022-07-25

Developmental research to design and fabricate the Refrigeration System Innovation, and to evaluate its efficiency on processing time, repair cost, operating cooling temperature, and operating current rating of the unit on removing, discharging, and refilling of lubricating oil as compared with adapting the Conventional Method (CM) and using the Original Refrigeration and Air-Conditioning (ORAC) Unit. The solenoid’s installation of the valve, access valves, and pressure switches interconnected functions with the original components of the system to ease the entire operation of discharging and refilling lubricating oils for maximum usage of the unit. Results revealed that the processing time of removing, discharging, and refilling the lubricating oil is six times faster; repair cost efficiency of 2 ¼ times lower; efficiency on operating cooling temperature was 10% improvement; efficiency in the operating current by 4% improvement. Adapting the RSI is a highly recommended HVAC-RT program to produce globally competitive RAC technicians

A Study of the World's Minimum Energy Performance Standard for Electric Motors and Drives

S.Manoharan, P.Palpandian, B.Mahalakshmi, N.Divya — 2022-07-25

It has been proven that industrial electric motors consume 30-40% of all electricity generated worldwide. The electric motor system consumes roughly 70% of the electrical power spent by industrial segment in which induction motors play a significant role. Nowadays, we prefer energy-efficient electric motors, which not only save money on power but also minimize greenhouse gas emissions. As a result, regulatory plans are in place in the EU, as well as the USA, China, and other countries, requiring new installations to comply with gradually increasing efficiency requirements. This study examines the IEC/EN 60034-30-1: 2014 Standard, which aims to standardize efficiency classes all around the world. This paper also discusses International norms and domestic legislation for the high efficiency electric motors and the systems that power them, as well as the universal trend of electrical motors meeting minimum energy performance standards (MEPS), with a focus on the new EU 2019/1781, amendment (EU) 2021/341 for electrical motors and drives with varying speeds.

Sentiment Analysis and Topic Modeling from Tweets about the Covid-19 Vaccine

Bhoomika Gupta, Sunita Daniel — 2022-07-25

Coronavirus disease 19 (COVID-19) was discovered near the end of 2019 in Wuhan, China, and has since spread rapidly throughout the world. The most effective technique for combating the COVID-19 pandemic is to rapidly and successfully develop COVID-19 vaccines. Most people's primary source of information on health and vaccination is now the Internet. Twitter is one such platform that allowed users to gather as well as disseminate information. This paper uses data mining techniques to extract data from Twitter and evaluates the sentiments associated with COVID-19 vaccines and vaccination drives being conducted in India. The purpose of the research was to gain information about the perceptions of the general public towards COVID-19 vaccines in India using Exploratory Data Analysis, Sentiment Analysis and Topic Modelling. Moreover, the research aimed to analyse the underlying factors that contributed to the respective attitudes among people. VADER Sentiment Analyzer was used to identify Positive, Negative and Neutral sentiments among people and the dominant sentiment came out to be Positive. Then, Exploratory Data Analysis was used to study different types of users with regard to the three sentiments and a time-based analysis was done to identify the events that triggered a particular reaction. To further analyse the public emotions, text2emotion was used and 5 different emotions (happy, sad, angry, fear and surprised) were identified. Using Latent Dirichlet Allocation various topics were identified related to the vaccines

Availability Analysis of Carded Sliver Yarn Production System Using Numerical Technique

Dr. Vikas Modgil, Dr. Parveen Kumar, Dr. Upender Kumar — 2022-07-25

This research work focuses on analysis of Long Term Availability (LTA) And Transient Availability (TA) of the complex system namely Carded Sliver yarn Production System of a Textile Industry with the application of Markov Method (MM). The mathematical model for the system concerned has been developed on the basis of actual working conditions. The model developed is being solved using numerical method namely Runge-Kutta (RK) to compute TA i.e. Time dependent. Further LTA has been computed by applying normalizing condition, and recursive method. On the basis of LTA and TA analysis the most critical subsystem has been identified. Here, component D i.e. Draw Frame is most critical component in the present case. Further, repair priorities have been proposed on the basis of impact of Repair Rate (RR) on LTA and TA of the system chosen. The proposed repair priority for component D, B and C are I, II and III respectively.

An Upper Bound on the Partition Dimension of Comb Product Graph Wheel and Path

Faisal, Rojali , Mohd Sham Bin Mohamad — 2022-07-25

Research with the title: An Upper Bound on The Partition Dimension of Comb Product Graph Wheel and Path. This research is the development of the results of calculations, research methods with a qualitative model approach to the partition dimensions of the comb product between the wheel and path graph investigated in this study. The main objective in this research is to determine the partition dimension of the upper bound of this graph. To achieve optimal results, the researcher expands the wheel graph completion partition to find the comb product graph completion partition. The results of this study provide an upper bound on the partition dimensions of the comb product graph between the wheel graph and the path as the main result, throughout this section, the graphs for is the wheel graph with n+1 vertices. The path graphs for m≥1 is a graph with and . The lower limit of the partition dimensions should be examined further in future research.

The Impact of Anger Expression Patterns and Social Support on the Job Stress of Clinical Nurses

Young Hwa Lee, Hyo Yeol Jang — 2022-07-25

This study attempted to provide the basic data to ultimately help mediate the stress related to the nurse's job by identifying the types of anger control, social support, and job stress for clinical nurses, as well as identifying factors affecting their job stress, based on the development of a program. This is a descriptive research study to determine the patterns of clinical nurses' anger expression, social support, job stress level, and related factors that affect their job stress. The participants in this study were 142 nurses working at a university hospital in Seoul who understood and consented to the study's purpose. Data were collected using self-reported questionnaires and analyzed using descriptive statistics, independent t-test, ANOVA, Pearson coefficient, and hierarchical multiple regression. As a result of the multiple regression analysis conducted to identify the factors influencing job stress, anger expression type and social support showed 12.3% of explanatory power for job stress, and among them, anger suppression (β=.284, p<.003) was the most important influencing factor. This study is significant because it recognizes the importance of job stress for nurses and identifies the factors that influence their stress, taking into account job characteristics as well as socio-psychological characteristics. It is hoped that this will serve as a foundation for developing an intervention program to manage job stress factors in the future.

Comparison of Efficiency of Shotcrete and FRP System for Strengthening of Masonry Structures by ABAQUS Software

2022-07-25

A high percentage of buildings made worldwide are buildings made of masonry materials, which are often built before the development of the latest seismic design criteria and have weak shear strength inside plane and bend outside. In this paper, by understanding the necessity of strengthening masonry structures against earthquakes and comparing the system of repair and seismic strengthening of buildings with masonry materials, the ability of both shotcrete and FRP system has been evaluated and simulated. The results of finite element analysis of a sample of brick buildings modeled in ABAQUS software have been compared with the results of tests performed on unreinforced and masonry buildings and reinforced brick buildings with reinforced concrete coating. Also, in order to investigate the effect of strengthening with FRP layers on the seismic performance of unreinforced masonry walls, several masonry walls reinforced with FRP layers were modeled at the same time and with the same behavior as damaged concrete with plastic properties in compressive and tensile loading. The effect of each of these characteristics has been investigated by comparing the hysteresis and energy-time curves. The results show that the shotcrete reinforcement method, improves the final strength and ductility of the masonry wall, has weaknesses such as imposing additional mass on the foundation and the need for manpower and special equipment, which leads to exorbitant costs. FRP plate due to features such as high strength and low weight, immunity against corrosion, and easy and fast installation; it has become a suitable alternative to traditional methods for seismic retrofitting.

An Analysis of Students Perception on Online Teaching-Learning Process Using Inferential Statistics: An Empirical Study with Special Reference to Indian Students

Dr.Manju Shree Raman, Dr.P.Praba Devi, Dr.M.Sudarsana Rao, Dr.N.Nithya — 2022-07-25

The outbreak of COVID- 19 facilitates a new arena in the field of education. Though online learning has been at practice in every institution, the lockdown due to COVID-19 has led to an extensive use of the capabilities of Blackboard, Moodle’s, Lecture Capture system, and other software that facilitate online learning. The purpose of the present study is to assess the e-learning perceptions of the students. A survey based research was carried out through a self-administered questionnaire, based on students perception with regard to the online teaching-learning process carried out by educational institutions during Covid-19 lockdown period and the data was collected between March 2020 - May 2020 with an objective of assessing the perception of e- learning process among the students, identifying the barriers in the process of e-learning, to measure the satisfaction of the students on e-Learning process and to identify the strength and weakness of the e-learning system from students perspective. The present study was done based on two important factors, perception on e-learning and barriers to e-learning and the data was collected from a total of 1089 students inclusive of both UG and PG programmes through google forms. The survey inferred that there is difference in the perception on e-learning processes with respect to gender of the students. The study also shows that the internet infrastructure is an important factor to enable the students learns virtually. Students perceive that internet connectivity, Internet usage limits and the occurrence of online technical issues to have association with the successful e learning process.

Thus, the present study enables to understand the opinion of students on the widespread practice of the online-teaching which happens in almost all institutions in today’s scenario.

Investigation of partial Discharge Suppression in Electronic Converters using Electrets based on Corona Charging

Hira Mehreen, Dr. Salman Amin, Yasir Butt, Muhammad Bin Zubaid Ramay — 2022-07-25

Partial Discharge (PD) is a limiting factor in electrical insulation and electrical wires especially in power electronics related setup/applications. Basically, it behaves as the conduction bridge between a conductor and an insulator where voltage travels from conductor to insulator crossing the insulation components and makes the components less efficient in high voltage operations. To overcome the limitations of partial discharge, certain methods are being designed such as geometry-based methods and material-based methods etc., just to mention a few. In the former solution, the geometry of the components is changed in such a way that partial discharge is reduced to much lower level, but these methods are very expensive and sophisticated at the same time, while the latter ones used specific kind of materials that are added to the power electronics components or systems so that the partial discharge can be suppressed. These methods include adding some substances that increase the viscosity, breaking point and potential of the components and comparatively less expensive in providing the efficient outcomes. Owing the on-board partial discharge limitations, in this research, a new method is contributed, in which the electronic components such as inverters, DC-DC converters and rectifiers are coated with ELECTRET with the help of Corona Charging, which helps reducing the partial discharge of the converters to an average of 5% in comparison with the conventional conformal coating and almost 20% as compared to the non-coated converters

Challenge for Sustainable Business Model Innovation: Consumers´ Perception of Sustainability

Michaela Krechovska, Alena Palacka — 2022-07-25

Sustainability has become one of the world trends and a challenge for companies in recent years. The ability to innovate existing business models or move to new sustainable business models represents an important element to improve companies' sustainability performance. However, many business model innovations are not successful. The authors consider consumers' perception of sustainability and sustainable business development in the context of their purchasing decisions as one of the key prerequisite for successful sustainable business models innovation. The aim of the paper is to examine consumers' perception of sustainability in the Czech Republic and to compare the research results with the results of other research and studies in order to identify differences or possible specifics in consumers' perception and behavior. Consumers' perception of sustainability and its consequences for purchasing decisions of Czech population were examined by means of quantitative research using an online questionnaire among Czech population over the age of 18. The research sample was presented by 1070 respondents. A quota selection of respondents according to gender, age, level of education, region and size of place of residence was carried out. Our research results confirmed that sustainability is an important topic for Czech population and it is also an important factor influencing their purchasing behavior. It can be concluded that the results of our research correspond to the results of other studies conducted in the Czech Republic, but also abroad

Factors Influencing Adoption of Agile Project Management in Construction Industry

Anoop Prakash, Dr. K Maddulety, Dr. Vanita Bhoola — 2022-07-25

Construction industry is a labor-intensive industry defined by its capability to deliver complex and unique projects with stringent quality requirements against constrained time and cost parameters. In civil construction projects, project uncertainties, strong customer demands, technological disruptions and challenging environments demand a more adaptive approach to civil construction. Agile approaches are a proven project management technique that has given rise to a more adaptive approach and successful outcomes to software development projects. Today, project teams use agile methodologies in several industries beyond software development to meet customer demands in an environment disrupted by advanced technology. Past literature suggests that even though extensive evidence exists about effective use and benefits of agile project management approaches in the software industry, there is a lack of empirical studies in the construction industry and projects. Using data from 530 respondents from different industries and geographic regions, we have tested the main factors influencing adoption of agile project management and further examined the moderating effects of other variables. Our findings suggests that people factors, agile practices and Construction 4.0 (technologies that digitize, automate and integrate the construction process) are the main drivers of agile project management in construction industry. Further, factors such as organization culture, business needs, project characteristics & close communication influences agile project management in construction industry.

Analyzing Factors that Effecting Tourism Demand of Zhejiang Tourist Festival, Based on 2019 Xitang Hanfu Cultural Festival Using SEM Method

Rui Wang, Chukiat Chaiboonsri, Kanchana Chokethaworn — 2022-07-25

A typical case of festival tourism in Zhejiang Province, the Xitang Hanfu Cultural Festival, was selected. A questionnaire survey was conducted in the field, and the sample data were subjected to factor analysis and reliability testing with the help of statistical analysis software SPSS and AMOS, and structural equation modeling was applied to verify the hypotheses of this paper. The data analysis of the empirical study shows that: 1. festival tourism tourists' perception has a significant effect on festival tourism satisfaction, while supporting facilities, information and consultation, shopping environment, external environment, price factor, and festival experience have a significant effect on festival tourism satisfaction; 2. supporting facilities, external environment, price factor, and festival experience have a significant effect on festival tourism loyalty, while information and consultation, and shopping environment have no significant effect on tourism There is no significant effect on tourism loyalty; 3. There is a significant positive relationship between tourism satisfaction and loyalty. The study of the relationship between tourist-centered perceived value and tourist satisfaction and loyalty has guiding significance for the development and development of festival tourism in Zhejiang Province.

Psychological Immunity, Attachment, and Technology: Exploring the Influence of Technology on Attachment and Psychological Immunity of Secondary School Students

Sampurna Guha, Dr. Nimisha Beri — 2022-07-25

Technology is influencing various domains of human life such as education. Employment, purchase behavior, psycho-social and socio-emotional engagement. Studies reveal that the use of social media has hampered and negatively influenced the social attachment among youth especially adolescents causing a sense of social isolation, dependence on virtual avatars, increased distraction, anxiety attacks, and relatively poor social connectedness. This has been found to adversely impact the social functioning and attachment of the individual which further adversely influences the Psychological Immunity.

Research has pointed out the adverse effects of technology use on the physical health and immunity of individuals by affecting vision, posture, mental health, and emotional well-being. The present study makes use of a survey research design to collect data from 300 senior secondary school students (class XI), studying in private and public schools of Delhi-NCR with the aim of exploring the adverse influences of technology on their social attachment relationships and Psychological Immunity. The participants responded on a self-developed, validated questionnaire modeled on a 5-point Likert scale.

The findings clearly indicate that students are dependent on technology often treading the thin line between optimal use and overuse. The study found the overuse of technological devices in form of tablets, smartphones, laptops, and desktops along with i-pods and smartwatches, among students owing largely to the sudden shift from offline to remote classes due to the Covid -19 pandemic. The students reported poor social connectedness and low levels of social attachment to parents and siblings among both private and government school students. Also, it was seen t influence and impact the Psychological Immunity of the respondents.

The study recommends the appropriate and monitored usage of technological devices among school-going adolescents, the need for training in mindfulness, and other positive behavioral management supports. It is imperative for parents and teachers to understand the emotional needs of the students and provide need-based psycho-social support and emotional care. There is also a need to connect adolescents with counseling services to facilitate the development of resilience and positive self-esteem.

The Sme Framework for Promoting the Strategic Competitiveness Advantages in Thailand

Sutana Boonlua, Arisaphat Suttidee — 2022-07-25

The main aim of this research is to empirically investigate the strategic competitiveness advantages of the SMEs in Thailand. There are two theories contributions: (1) the resource-based view (RBV) and (2) strategic competitiveness advantages. The data should be collected for at least 25 years, during 2000 to 2025 from various sources. This research contributes to knowledge that helps with the formation of strategic competitiveness advantages and balance scorecard theories through business firms. The research’s conceptual framework can be applied in other countries. The triple helix is internationally recognized as a network of two parties namely business (SMEs) and government. There are many researches from both developed and emerging countries documenting the success of the model. This helps to build systematic and sustainable collaborations of these two entities which helps strengthening the local and regional economies.

The Impact of Covid 19- Towards Insurance and its Benefits to the Public

Dr. Vidhya K., Dr. Bhupendra Kumar, Dr. Umamaheswari K., Dr. Neelam Maurya — 2022-07-25

The COVID-19 impact is catastrophic, beyond imagination and its repercussions on the livelihoods of millions of people are going to be devastating. Pandemic intensifies the awareness towards insurance and the financial risks that could lead to loss of income and increased expenses. The world has witnessed a lot of awareness developed during this crisis is overwhelming, as it testifies to the importance of health insurance as well as life insurance across different channels such as social media, television commercials, advertising billboards, etc. The increasing death toll due to the covid pandemic has led to an awareness of various insurance schemes. Primary Data was collected from the respondents of Coimbatore region by using convenience sampling methodology. A total of 120 questionnaires were distributed and gathered from the participants in the study. The Data was collected through questionnaire survey method. Secondary data was obtained from various sources such as journals, websites, publications etc., The Ranking method and Weighted Average method are used to analyze the collected data. Results indicate that people start valuing the importance of insurance policies and their benefits.

Fuzzification of Newton’s Forward Interpolation Method and Exploring its Application in Stock Market

Dr. Toralima Bora, Dr. Jutimala Bora — 2022-07-25

Numerical methods always give approximate results. On the other hand fuzzification of any number gives a result which falls within a range and it enhances the level of accuracy and therefore in this paper an attempt has made to fuzzify Newton’s Forward Interpolation Formula a0nd also developed computer programs for the Classical and the Fuzzified Newton’s Forward Interpolation Formula. The results of Fuzzified Newton’s Forward Interpolation Formula and Classical Newton’s Forward Interpolation Formula have been compared. Fifteen examples are taken under consideration for comparison. It has been seen that the methods provide more or less same results in classical and fuzzified form. The application of Interpolation method in the field of stock market is also discussed in this paper.

An investigation on the barriers in the Implementation of Sustainable Supply Chain Management in Indian Construction Industry

Aditya Mudigonda, M. Gopal, Naik T. Dhilip — 2022-07-25

In terms of supply chain adoption, present construction industry practises lag. While the use of sustainable supply chain management principles in manufacturing has yielded great success, construction firms are constrained by age-old processes. A study was conducted to determine the main barriers to implementing sustainable supply chain practises in the construction industry. The questionnaire survey was designed to synthesise and inherit previous findings and required replies from industry participants. This study, using a sample of 279 replies, determines the challenges to the implementation of sustainable supply chain practises in the construction sector. The critical impediments are assessed and ranked using the Relative Importance Index approach.

Soil Stabilization with Vertical Piles Bearing Capacity and Settlement Characteristics of Cohesive Soil Reinforced with Sand, M Sand, Quarry Dust Piles

Aarthi K., Poongothai S. P. — 2022-07-25

The cohesive soil deposits are unsuitable for construction activities because of their poor geotechnical properties such as less bearing capacity and high settlement. Modern urban development necessitate construction and development activities in available soft clay deposits. It is a usual practice to provide fine aggregate piles in soft clay deposits to improve its properties. Due to the unavailability of natural fine aggregate resources and critical environmental issues involved, the feasibility of using M sand and quarry dust as floating granular vertical pile material as a substitute to natural aggregate have been experimented in this work. For various footing sizes and various pile depths the California bearing ratio test was conducted. The settlement reduction factor and group pile efficiency are estimated by studying the load settlement characteristics. It is found that the M Sand reinforced piles increases the bearing capacity and reduces the settlement and recommended to be used for improving the bearing capacity of soft clay soils.

Shear Connector Slabs Longitudinal Shear Resistance of Sandwich Panels with Truss and Trapezoidal shear connectors

Aarthi.K, Pavithran.R — 2022-07-25

Sandwich panel are being extensively and increasingly used in building construction because of their reduced dead weight, energy efficiency and aesthetic appearance. These slabs can be easily handled and erected. This work presents the experimental results of effect of shear connectors on flexural behaviour of sandwich slab panels. Truss shaped and trapezoidal shaped shear connectors have been used to study the shear interaction between the top and bottom layer of concrete. Self-Compacting concrete forms the top and bottom layer of concrete with Expanded poly Styrene as the middle layer. Few recommendations on the suitability of shear connectors have been proposed. The performance of trapezoidal shear connectors is superior than the truss shaped shear connectors. The longitudinal shear transfer between top and bottom layers of concrete and middle layer of expanded poly Styrene is better in trapezoidal shear connectors. The performance was analytically studied using Ansys software which verifies the experimental results.

Comparison of the Memorability of Text and Visual (Background) Sounds During their Fade-In in Short Scenes With Subtitles

Tamin Rahi, Prof. Dr. Karsten Huffstadt, Selma Al — 2022-07-25

To test memory performance between nonverbal stimuli in the form of moving images (GIFs) and verbal stimuli in the form of written-down words, three hypotheses were investigated. Short scenes from the cult series The Simpsons were enriched with the above stimuli. The first hypothesis was based on the assumption that GIFs would score higher than words on free recall. This could not be proven. The same applies to the hypothesis that GIFs would be remembered better than words during serial recall. The last hypothesis to be investigated also proved not to be significant. This was based on the assumption, that words are inferior to moving images in terms of recognition. It is assumed that the superimposed scenes overwrote the GIFs during the memorization phase, since they were the same stimulus form. This meant that the attention of the subjects was distracted by the scenes. The high playback values of individual stimuli were also striking. It was assumed that these stimuli were associated with emotions and that the subjects therefore focused their attention on them, as a result of which they were remembered more strongly.

Efficient Contracting Theory and Sources of Efficient Contracting Demand for Financial Accounting Information in Pandemic Covid-19 Era

Viola Syukrina E. Janrosl, Argo Putra Prima, Iskandar Muda — 2022-07-29

The spread of Covid-19 affects the economic stability of a country. This economic stability also has an impact on corporate governance. To strengthen corporate governance, contracts must be made as efficient as possible. This is because efficient contracts are known to be very important in good corporate governance. This paper aims to discuss the theory of efficient contracts and the sources of demand for efficient contracts for companies in the Covid-19 Pandemic Era. The method used in this paper uses a qualitative approach whose data sources are obtained online. The results of this study are expected to provide information as a reference for companies, investors, and stakeholders who anticipate future pandemics.

Core Concepts of a Model of Audit Firm Culture (AFC) in Pandemic Situation

Henny Zurika Lubis, Ilham Ramadhan Nasution, Iskandar Muda — 2022-07-29

The purpose of research aim to know core concepts about the audit firm culture model (AFC) in a pandemic situation and impact to the financial statements in the organization. The qualitative methods uses with a literature review from various sources. Results shows in the current pandemic situation, forcing auditors to conduct a remote audit process that affects the completeness and reliability of audit evidence. Therefore, both the client company and the audit firm are recommended to have a deeper understanding of the use of carried out.

Asymptotic Behaviour and Boundedness of Fourth Order Difference Equations

G. Jayabarathy, J. Daphy Louis Lovenia, A. P. Lavanya, D. Darling Jemima — 2022-07-29

Asymptotic Behaviour and Boundedness of Fourth Order Difference Equations

On the Detour Based Indices

Gopika S., Lakshmi Anil, P. D. Aiswarya, Supriya Rajendran — 2022-07-30

Topological index is a real value associated with a graph that should be structurally invariant. Many topological indices were defined, and many among them have been used to model chemical and pharmacological properties, as well as many other features of molecules. We study two topological indices based on detour distance. Detour index for any graph that is connected is defined as maximum distances or detour distances between every unordered paired vertices of . Detour Harary index for any graph that is connected is defined as the sum of reciprocal of the maximum distances or detour distances between every unordered paired vertices of . Here, in this paper we establish formulae to calculate the Detour index and detour Harary index of a few graph structures and also corona product of two particular graphs. Also we compare the results for both these indices .

Energy-Efficient Hybrid Routing Protocol to Extend the Network Lifetime in IoT Applications

Jyoti Sharma, Surendra Kumar Patel, V. K. Patle — 2022-07-30

A Wireless Sensor Network (WSN) is made up of several inexpensive, low-power, compact sensor nodes that are densely placed across the monitoring area. Wireless connection creates a multi-hop wireless network system. WSN is particularly well suited for deployment in harsh environments and remote monitoring locations that are not suitable for personnel. It has significant advantages like a large coverage area and broad application prospects in the fields of military, environmental monitoring, industrial control, and urban transportation. IoT-enabled networks, however, suffer a variety of difficulties because of the enormous heterogeneous data generated by many sensing devices, including long communication delays, limited throughput, and short network lifetimes. In this study, a hybrid cluster-based routing protocol model is suggested that makes use of the advantages of both butterfly and particle swarm optimization methods. The suggested approach splits the network into many clusters and chooses the best node as the cluster leader to exacerbate the network's premature demise. The typical cluster-based routing protocols PSO and BOA are assessed using simulation results in terms of the quantity of alive nodes, throughput, and remaining energy of the nodes.

A Hybrid Contextual Probabilistic Graph Clustering and Link Prediction Model for Complex Social Networking Data

2022-07-30

Probabilistic based graph community detection plays a vital role in the complex social networking datasets. Since, most of the conventional approaches are difficult to predict the new type of link prediction using the standard graph community clustering measures. Also, traditional clustering measures use nearest neighbour measures instead of contextual similarity in order to predict the relationship among the different graph nodes. In order to optimize the contextual node clustering and link prediction, a hybrid dynamic scalable measure is proposed for the community clustering on complex networks. In this work, a hybrid graph clustering and link prediction approaches are proposed on the complex social networking dataset for better decision making patterns. Experimental results prove that the proposed contextual probabilistic graph clustering and link prediction approach has better efficiency than then conventional models on complex social networking datasets.

Improvement of 5G Network Coverage and Capacity by Using MIMO-A/B Antenna Planning in Prediction Software

Reshma Begum Shaik, Dr. G. Sasikala — 2022-07-30

Cellular and Mobile communication system has grown exponentially (rapidly). This improvement motivating the companies move towards to plan the new network continuously. It works from first generation to fourth generation. Now many cellular companies planned and started their scenarios toward fifth generation (5G). In every generation they are increasing the data rate transmission to achieve user’s requirements. This paper is an attempt to contribute in this field to give more details about these challenges. The 5G Radio network formation comprises coverage assessment and capability estimation. Planning method and scenario used in this 3GPP planning is in MIMO-A/B antenna and 5.8MHz frequency for good reporting, the Government allocated frequency for cellular operators. This network formation is replicated by utilizing Link planner software. In present project, we plan the network for Hyderabad city area.

Aspect-Based Analysis of Social Media Post and its Review

P. Rethina Sabapathi, Dr. K. P. Kaliyamurthie — 2022-07-30

The exponential growth of the social media provides the platform to users for sharing and generating information. Once the information is shared in the social media it will receive lots of commands, reviews and reply messages. In some cases the message received will not be associated with the post shared. Taking this problem into consideration, the author proposed an aspect based sentimental analysis method for analyzing the content posted and its received messages. The sentimental score output mostly used to identify the positive, negative and neutral sentences with respect to the exacting aspect. Then a total score formulae is used to determine the association between the content posted with its reply messages. Finally, an experiment is designed and processed in different way to visualize the data are provided.

Earth Quake Data Scheduling Using Starvation Free Scheduling Scheme

Dr. Priscilla Joy, Dr. S. Shirly, Dr. R. Venkatesan, Dr. K. Ramalakshmi — 2022-07-30

The emergence of sensor nodes leads to the need for scheduling of packets in real time. This will lead to efficient packet delivery with less delay and less power consumption. In the existing multilevel scheduling schemes, First Come First Serve Scheduling (FCFS) is commonly used to schedule the real-time packets. FCFS leads to starvation. Hence, Starvation Free Dynamic Multilevel Packet (SF-DMP) scheduling scheme is applied. Using this an earthquake data is scheduled which is taken from the dataset. Furthermore, the performance is high due to the emergent data from the earthquake dataset will be send fast to any system assigned there by an alert can be sent.

Exploration of Energy-Efficient Data Center Design for Diverse Cloud Environments

Sanju Rajan, Linda Joseph — 2022-07-30

Cloud computing enables you to access your information from anywhere at any time. The model enables on demand network of share pool of configured computing resources. With the expansion of computer and mobile users’ accessibility and storage has become the main concern. In dispensed systems, such as cloud computing, heterogeneity is becoming increasingly popular. Hardware is becoming more specialised and diverse due to requirements for high performing computers. Studies show thorough examinations of schedules executed in cloud computing settings. This also makes it imperative to optimize resources while maximizing performance of systems. The majority of this progressive analysis of energy potency has mostly focused on the process component improvement. The results of simulations for two-grade, three-grade, and three-grade high-speed data center designs demonstrate that the simulator is capable of managing power of computing and networking components by scaling of voltages or frequencies and shutting down systems. In order to illustrate an energy-aware cloud computing data centre simulation environment, this paper introduces a model environment dubbed GreenCloud Simulator for contemporary energy learning of cloud computing data centres in realistic configurations.

Conceptualization, Construction and Theoretical Relationship Between Neuro Science, Neuro Accounting and Neuro Economics in Pandemic Era

Esa Setiana, Irawan, Iskandar Muda — 2022-07-30

This paper aims to know and investigates of conceptualization, construction and theoretical relationship between neuro science, neuro accounting and neuro economics in pandemic era. The main objective of this research is to collect important developments and contributions of efficient market theory and rationality related to neuro science, neuro accounting and neuro economics. Analysis is conducted by using examining library materials or also called library research. Research findings that neuroscience, neuro science, neuro accounting and neuro economics can help investors by Indonesian publicly traded companies during a pandemic. The results suggest that the literature on accounting behavior is converging into a new field, neuro science, neuro accounting and neuro economics, does indeed take place at the investment decision stage. The results of a review of market data in Indonesia also show that the Composite Stock Price Index (JCI) in Indonesia is also very volatile even though it shows lower volatility during the pandemic compared to before the pandemic. Since the emergence of the Covid pandemic in Indonesia in 2020, investor psychology has been greatly influenced by their irrational behavior.

Gray Wolf Optimization Based Torque Ripple Reduction & Speed Control of SRM Drive for Industrial Applications

Patti Ranadheer, N. Prabakaran — 2022-07-30

In this manuscript, speed as well as current controlling technique for lowering the SRM torque ripple is proposed. The rigorous SRM conduct has been examined over the aspects like torque, speed as well as current. Depending on aspects, the speed of motor has been lowered and regulated the ripples of torque. Later, control-signal has been produced in order to control SRM speed. Therefore, we have projected an augmented speed & controlling the current with the model of PWM for reducing the ripples of torque. The current optimization model depending on torque and current control strategy has been projected in order to control the SRM speed. Moreover, GWO (Gray-wolf-optimization) algorithm relied controller has been used for examining the SRM torque & speed. Ultimately, the projected strategy has been applied in domain of Simulink/MATLAB. Proposed model performance analysis has been contrasted and demonstrated with current strategies like ANFIS & ANN algorithm strategies.

KDD ON CXR Images of COVID 19 by JPEG Coefficient Filter Technique

S. M. Manimegalai, Dr. T. Ramaprabha — 2022-07-30

Prevent the spread of virus utilizing computer-based analysis for the goal of fast and reliable identification of corona virus disease (COVID-19). Chest X-ray imaging has many advantages in image processing methods, such as low cost, portability, speed, and ease of use. The impact of many image enhancement techniques are investigated in this research. The impact of many image enhancement techniques are investigated in this research. The impact of many common picture enhancement techniques is investigated in this research. The Random Forest (RF) classifier produces the highest accuracy value, positive predictive value, true positive rate, Area under the receiver operating characteristic curve value, Area under the precision recall curve value which are 84.03% accuracy level, 0.86 of positive predictive value, 0.84 of true positive rate value level, 0.96 of area under the receiver operating characteristic curve value, and 0.91 of area under the precision recall curve value This research work finds that the Random Forest learning model is most recommended model with JPEG Coefficient Filtering technique for CXR image classifications of COVID -19 Image dataset.

Impact of Proposed Corporate Income Tax in UAE - Special Reference to FDI and GDP

Jaishu Antony, Zaheda Daruwala, Edmund Christopher — 2022-07-30

In this research paper, the authors provide an empirical approach to evaluate the expected impact of first-time corporate income tax implementation on an economy. The main objective is to investigate how taxes could affect foreign direct investment in the host country and its economic growth. The authors use a comparative study approach by evaluating the outcomes in countries with similar economic environments. Data collected from other GCC countries like Saudi Arabia, Oman, Qatar and Kuwait are used as samples from the time they imposed corporate income tax for the first time. A Mann- Whitney U test is employed to test the statistical significance of the variables in each of the four countries. It reveals a negative relationship of the corporate tax imposition on the sample countries' FDI growth and GDP growth. Using this result as a basis, policymakers can expect similar results in the UAE in short to medium term. However, if policy makers are made aware they could act pro-actively to implement policies that could reduce the adverse impact of tax implementation and may also manage to reverse the impact, projecting themselves as role models for others to pursue.

A Comparative Study of Clustering Routing Protocols in Underwater Wireless Sensor Networks

Poonam, Vikas Siwach, Harkesh Sehrawat — 2022-07-30

Underwater wireless sensor networks have acquired a lot of consideration of research. About two third part of our earth is occupied by water. Underwater wireless networks are used for exploration of resources, collection of data under the water, detection of flood, strategic surveillance systems, and unmanned underwater automobiles. In underwater wireless networks, nodes are there which are deployed under the water and on the surface. Limited energy of sensor nodes has an important consequence on underwater sensor network. Reliability and energy consumption are important issues in underwater sensor networks.

The main motive in underwater sensor networks is to activate the nodes having inadequate battery power for a long time. It has been a crucial issue to design an energy efficient technique for reduction of energy consumption of underwater sensor network. Clustering method is used to save energy in UWSN. In clustering technique whole network is divided into various subparts called clusters, where each cluster consists of one controller node called as cluster head node. LEACH (Low Energy Adaptive Clustering Hierarchy) is the basic hierarchical clustering routing protocol used for conservation of energy. LEACH protocol is self-organizing hierarchical clustering routing protocol that allots energy evenly on all sensor nodes in sensor network. Energy consumption is avoided using cluster head and data aggregation method.

A Novel Approach for Exoplanet Classification on Kepler Light Flux Data

Abhishek Jha, Aayush Bajaj, Lakshay Vashisth, Dr. V. K. Saini — 2022-07-30

NASA’s Kepler mission to search for extrasolar planets has collected data from hundreds of thousands of star systems and has discovered nearly 1000 confirmed exoplanets in addition to over 3000 unconfirmed candidates. The mission discovers exoplanets using transit photometry, which detects the transit of a planet in front of a star as transient drops in stellar intensity. We propose a novel machine learning based approach to detect these exoplanets from Kepler’s light flux dataset using robust preprocessing methods. We observed the dataset to be a highly imbalanced one having positive classes totalling less than 1% of the entire dataset and further, there was an inherent non-uniformity in the recorded data for positive samples. Our novel approach comprises applying Normalisation followed by Gaussian Smoothing and Fourier Transformation on this dataset before classifying it using Linear Support Vector Classifier. On such challenging data, our approach pushes the test F1 score to 1.0. While pursuing this objective of maximising test precision and recall, we also perform a comparative study of several data transformations in combination with various state-of-the-art modelling algorithms. We also compare our results with previous work and highlight the improvements obtained via our methodology.

Zooming Algorithm Based Edge Detection Techniques for Plant Leaf Disease Identification using Convolutional Neural Networks optimized with Battle Royale Optimization

Mrs. Greeshma O. S., Dr. Sasikala P., Dr. Balakrishnan S. G. — 2022-07-30

Agriculture is a very important sources for every living things but it is affected with various issues like plant diseases. To predict the plant leaf diseases in an early stage is very necessary in the agriculture field, which improve the productivity. Thus, to improve the effectiveness of the plant leaf disease identification process and classification, a Deep Learning (DL) based optimization approach was developed. In this manuscript, a Zooming Algorithm based edge detection model is used for identification and Convolutional Neural Networks optimized with Battle Royale Optimization (CNN-BRO) is proposed for classification. The implementation of this work is done by MATLAB and the parameters are calculated. Thus, the proposed CNN-BRO approach was attained 25.34% ,15.45% high accuracy, 28.5% , 11.46% high specificity, 27.97% ,10.25% high sensitivity, 30.22% ,13.95% high recall, 16.38% , 9.1%% high precision, 25.27%, 13.21% high F-score and 59.99%, 42.86% lower system error than other existing methods like Support Vector machine with exponential spider monkey optimization (SVM-SMO) and Bacterial Foraging optimization based Radial Basis Function Neural Network (BRBFNN-BFO).

Local or Micro or Minor Aerodynamic Modifications in Tall Buildings: A Critical Review

Harish, Awadhesh Kumar — 2022-07-30

Tall buildings are going taller and taller in height, because of innovation in structural systems by new construction techniques and advances in engineering methodologies. Traditionally, architectural studies, design quality, site location, etc. have been taken into account in place of aerodynamic factors when determining the geometry and alignment of tall buildings. The exterior configuration of the structure is one of the key factors that influence designed wind pressure and reactions. As a result, they’re bluff bodies associated with high motion induced by the wind structure. The most effective way to increase the safety and use of tall buildings in heavy winds is through aerodynamic modifications.Depending on the impact of modifying the outside geometry of the structure, the aerodynamic modifications are divided into two Varity, i.e. local or micro or minor (corner-cut, recession, etc.) and global or macro or major (twisting, setback, etc.) modifications. Also, this study comprehensively explores the recent/past local or micro or minor aerodynamic modifications on tall buildings. Local, micro, or minor modifications (corner-cutting, corner-rounding, chamfering, etc.) can reduce wind load by 30–60% because they make it easier for the shear layer to reconnect and reduce the trailing area on the building's windward side. This study presents a fast description of several local, micro, or minor aerodynamic improvements to reduce wind stress on tall buildings. Aerodynamic modifications considerably lessen the influence of dynamic loading on tall buildings.

Water Level Sensor at Parking Lot Using IoT Notification System

2022-07-30

The capital city of Malaysia experiencing flash floods more frequently and they do not always confine to the monsoon season. Kuala Lumpur had to face two kinds of flash flood around the year such as fluvial flash floods and drainage systems and it remains one of the most serious environmental problems. These phenomena have some unique characteristics such as they are sudden, unexpected and most of the time violent in nature. It shows that the roads and highways, houses, and vehicles are directly affected, damaged, and disrupted by flash floods. The problem is this situation really gives a lot of worries to netizens who live in the city. They need to always be alert about their cars leaving at the parking lot when the flood suddenly hits the parking lot and ruins their vehicles so this water level sensor at the parking lot is a really suitable warning system that will be provided to people when facing the flash flood. The method is the two major components consisting of the water level sensor and for the data, the transmission will use Bluetooth shield through the smartphone application. It will be integrated with a local webserver through which users can monitor the data from any device connected to the same Wi-Fi. Direct notification from smartphones can help the owner of the vehicles to always be alert when a flash flood occurs. This project is expected to solve problems faced by flash flood victims by implementing an early water level sensor mechanism at the parking lot so they will be able to save their vehicles. The objective of this research is to reduce the damages of the vehicles due to flooding at the parking lot and develop an early warning system for a flash flood at the parking lot using an IoT system.

Exploring the Effectiveness of SRGAN As a Video Upsampler

Lakshay Vashisth, Aayush Bajaj, Abhishek Jha, Prof. Ajay Kaushik — 2022-07-30

SRGAN, a generative adversarial network (GAN) has been one of the state-of-the-art techniques for image super-resolution. The proposed perceptual loss function, which is further composed of an adversarial loss and a content loss, is able to upscale images effectively by a factor of 4. While enough research and experiments have been carried out to evaluate the performance of SRGAN on images, its potential on videos is still obscure. In this paper, our main objective is to generate high-resolution counterparts of low-resolution videos via SRGAN and evaluate the network’s capabilities on the same. To quantify the model’s performance, we have used two image quality assessment metrics, Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM). While PSNR has been in use for a long time and is widely considered to be a tried and tested approach, SSIM is a newer metric that is designed based on three factors i.e. luminance, contrast, and structure, in order to relate it more to the mechanics of the human visual system. Through these two metrics, we aim to gauge the effectiveness of SRGAN as a video upsampler, from the perspective of reconstructed pixel quality as well as human perception.

Text Recognition from Images Using Artificial Intelligence

Dr. S. Shanmuga Priya, Mrs. A. Barveen, Suhayla Highum, Dr. L. Mary Gladence — 2022-07-30

Mortify group from incorporator violence occurrence. Some Gary is well known for the offense other vary from time to time depending upon kernel range of reasons that doubt upper old method investigation method that many organizations have that much intelligence and effective method to identify Non offensive group using text. The output experiments text analysis to provide a strong application.

The Role of IOT and Cyber Warfare in Developing the Health Care Devices

2022-07-30

The healthcare system of today is considered to be one of the most vulnerable systems to cyber-attacks. It is because the healthcare system handles sensitive data like payments, the history of a patient as well as private patient information, and the list goes on. When it comes to cyberattacks, healthcare systems cause significant harm to both organizations and patients. Due to the increased demands for high-quality healthcare and the increasing cost of care, ubiquitous healthcare is seen as a technology-based solution to tackle global health problems. Particularly, the recent advancements in the field of the Internet of Things have led to the creation of the Internet of Medical Things (IoMT). Although these inexpensive and widespread sensing devices could transform reactive healthcare into preventative the privacy and security concerns associated with these devices are often ignored. Since medical devices collect and process extremely sensitive personal health information these devices as well as their connected communications must be extremely secure to safeguard the privacy of the patient. However, the miniature IoMT devices are very limited in computing power and a few security options are available within these devices. Furthermore, with the wide usage of IoMT devices managing and ensuring security of IoMT devices is extremely challenging and constitute the primary problems that hinder the use IoMT IoMT to use in clinical settings. In this paper, security and privacy issues and threats, as well as the requirements and research directions for the future in the area of IoMT are examined, giving an overview of most recent methods.

Fair Value Disclosures Score Analysis of Indonesian Banking based on Pillar 3 of Basel II

Ita Mustika, Desyana Putri, Iskandar Muda — 2022-08-02

This research aims to know about the implementation of Fair Value Disclosures Score Analysis of Indonesian Banking based on Pillar 3 of Basel II to financial statements as regulated by PSAK 68. To minimize this risk, Basel II is implemented to increase standards for banks that go public to manage their risk management properly. The research method used is a quantitative descriptive approach by measuring the value of CAR, BOPO, NPL, and ROA in the Indonesian Banking Statistics report. The results of the analysis show that the implementation of fair value disclosure average already has an increased level of risk management.

The Prospect Theory Utility Function and Empirical Evidence for the Post of Pandemic Situation

Juninetenth Keeply F Sitanggang, Mardani, Iskandar Muda — 2022-08-02

This study examines the behavior of stock trading activities and JCI that cause noise after the Covid-19 pandemic. The research methodology carried out is qualitative research by conducting analysis sourced from library materials or also known as literature research. The sample data observed is stock trading data and JCI for the period December 2020 - 2021. The results of this research observation found during a pandemic. In addition, investors are completely correcting and overreactions that occur at trading intervals during the pandemic. Our analysis has adopted a new framework for determining stock market prices.

The Theory of Executive Compensation and Empirical Compensation Research Before and after in Pandemic Situation

Noviani, Junita Putri Rajana Harahap, Iskandar Muda — 2022-08-02

This paper aims to provide an easy-to-use executive compensation model and explained challenges and problems with empirical research. This study examines the theoretical and empirical literature on executive compensation. The research methodology was carried out using descriptive analysis related to data and literature review without conducting data testing. The explanation that can be put forward from the findings of this study is that first, there is an impression that institutional owners as the majority shareholder ask for Executive Compensation placed in the management ranks or even those who become managers to minimize earnings engineering, because if the institutional owner as the majority shareholder asks the manager to do engineering profits that benefit him, the minority shareholders and the stock market will discount the company's stock price which will actually harm the majority shareholders themselves. So Executive Compensation is synonymous with low earnings management. Second, the dominance of institutional owners causes managers to be unable to act opportunistically which tends to benefit themselves but may harm the owners. So that managers cannot freely manipulate the profit figures generated by the company. Third, institutional investors are far more forward-looking than individual investors who only focus on current profits.

A Literature Review with Relevant Measurement Approaches to Risk Reporting in Pandemic Situations

Rudianto Simamora, Ferry Laurensius, Iskandar Muda — 2022-08-02

The purpose of this study is to analyze the relevance of the financial reporting risks of companies going public during the pandemic. This methodology is carried out with a brief review of several previous studies, so that it can be concluded concisely and in detail without conducting data testing. The results of many studies show that the Corona Virus Disease Pandemic 2019 has changed the company's operational cycle, including reporting risks. This pandemic situation has forced the company to change its business plans and implementation. In a short time, the company can lose its income due to limited business activities. This condition is aggravated by the absence of anticipation of protection of the company's financial condition. Companies that have a high risk during the pandemic will be prodromal. In relation to reporting risks, establishing a good and comprehensive context in risk management will help recognize risks in the first stage, namely crisis/ prodromal symptoms. The right steps at this prodromal stage will be very decisive when the risk is already at an advanced stage. In addition, the establishment of a contingency plan at the risk mitigation stage can also help overcome the risks that actually occur. The formulation of a good contingency plan will be useful to reduce the impact of these risks. Reporting risks triggered by the strong impact of the pandemic will be addressed by conducting appropriate risk management. Proper risk management and supported by thorough planning, can anticipate unexpected risks, and also minimize the impact if risks really occur.

Contractual Incentives for Information Production and Market Failures in Pandemic COVID 19 Situation

Irawan, Esa Setiana, Iskandar Muda — 2022-08-02

This article aims to investigate contractual incentives for information production and market failures during the Covid 19 pandemic. The research methodology carried out is qualitative research by conducting analysis sourced from library materials or also known as literature research. The results of this analysis show that Contractual Incentives can cause inefficiencies in information production, even worse than market failures. We also point out that failure to account for the cost of Contractual Incentives when prescribing collaboration can result in a much lower-than-expected rate of return. This is related to moral hazard in the context of agency theory because there is an asymmetry of information between the principal and the agent. Information asymmetry is an imbalance of information that occurs because there are parties who can obtain and utilize information for their benefit whereas other parties cannot obtain the same information. The way to overcome the problem of Contractual Incentives and market failures can be done correctly and correctly. First the solution is viewed from the perspective of pure agency theory. The solution includes designing Contractual Incentives to control moral hazard, recruiting managers and providing salaries that can maximize their utility, conducting direct supervision, conducting indirect supervision, giving managers a portion of the company's results, conflict control between managers and shareholders, management compensation contracts and conflict resolution. Both solutions are carried out when the costs associated with Contractual Incentives are considered, there are circumstances in which collaboration—even in an arrangement that maximizes social utility—is not an ideal approach to achieving the fulfillment of most social goals. Therefore, it should carefully consider Information Production and Market Failures additional factors before Contractual Incentives engage in collaborative efforts.

Compliance Rate of Indonesian Taxpayers during the COVID-19 Pandemic

Andi Auliya Ramadhany, Debbi Chyntia Ovami, Iskandar Muda — 2022-08-02

This study aims to know about the compliance rate of Indonesian taxpayers during the COVID-19 Pandemic. This research is based on planned behavior theory. This research uses qualitative methods with literature studies. The results obtained from this study are that the level of compliance of Indonesian taxpayers during the pandemic is declining, the provision of tax incentives is considered effective but not optimal because this policy is underutilized by taxpayers. The government should review the effectiveness of the self-assessment system. The Indonesian government should think of a surefire strategy to improve taxpayer compliance.

Analysis of Major Depressive Disorder Using Ensemble Technique

Anjali Muralidharan, Dr. Kumaran.U. — 2022-08-02

Depression is a medical condition that exists on continuum of severity ranging from mild, momentary states of low mood to severe, long term symptoms that have a major impact on an individual’s life. It is one of the primary causes of disability worldwide and contributes significantly to the global illness burden. The complexity for analyzing the major depressive disorder can be implemented using mental health dataset acquired from the World Health Organization. Ensemble machine learning aims to supervise on the strength and weakness of commonly used machine learning models, in order to minimize the risk in decision making. The experimental outcomes reveal that our ensemble technique used to analyze the depression brought out high acceptability in term of prediction and an accuracy of 95% was achieved.

Hybrid Cryptographic solution using RSA, Blowfish and MD5 for Information Security in Cloud Computing

Anjana, Dr. Ajit Singh — 2022-08-02

Cloud computing is a cutting-edge computing model in which computational resources are made available through the Internet as services. Cloud storage services must come equipped with privacy and security features. And they have become an issue in cloud computing as a result of the data owner's lack of control and reliance on the cloud computing provider. Simultaneously, as cloud services transmit large amounts of data, the likelihood of thieves obtaining data increases. The current approach is being offered to address the challenges associated with delivering a secure cloud storage service. It enables users to securely transport data within a cloud system by using RSA, MD5 and Blowfish encryption techniques. Cloud computing is a well-established, scalable, and profitable technique of providing commercial services. The basic purpose of cloud applications is to provide organisations with flexible access to the computer systems in order to improve their performance. Cloud data security, a performance research to determine how to improve the encryption technology's security, and an assessment of cloud computing and its potential repercussions Data is encrypted with RSA Partial before being transmitted to the cloud server. Following upload, the hash value is determined using the MD5 hashing algorithm. All of these approaches are subjected to the following processes. Encryption/Decryption, Uploading data to the cloud, Hashing.

Comparative Analysis between Cooperative Caching and Non- Cooperative Caching in VANETs

Dr. Meenu Dave, Ms. Nisha Wadhawan, — 2022-08-02

ITS (Intelligent Transportation System) has become an important research field in recent years. One of the important components of Intelligent Transportation System is Vehicular networking, promising numerous, new applications for efficient road traffic, safety, and, infotainment. The dissemination of updated and consistent data in VANETs is very crucial as moving vehicles change their positions frequently. Cache inconsistency and outdated data can increase communication cost, and can result in higher query latency. This study's goal is to provide creative proactive caching strategies to lower communication latency in VANETs. We have considered the paradigm of cooperative caching, in which Roadside Units (RSUs) cooperate with remote servers to increase the rate of transfer of information and reduce overall network latency.

Segmenting and Classification of Covid-19 in Lung CT Scan Images Using Various Transfer Learning Algorithms and Performance Enhancement by Ensemble Based Approaches

Jemima P., Swapna T. R. — 2022-08-02

Covid-19 Pandemic has affected India’s economy and way of life. Early detection is the key to avoid further spread. For accurate detection of Covid-19 on different imaging modalities, many Deep Learning based image processing algorithms have been proposed. This paper proposes a methodology to detect Covid through CT scans of the Lungs. They are segmented to remove the noise and then classified using various transfer learning-based approaches. U-Net is used for segmenting the CT scans. The ground truths for segmented CT scans required for U-Net were created using Otsu’s global thresholding algorithm which produced higher PSNR value of 2.3898 db and lower MSE value of 37800.5621 when compared to other algorithms such as K-Means, Watershed, Fuzzy C Means and local thresholding. U-Net produced 97.50% accuracy. IOU for Covid and Non Covid images were found to be 94.22% and 94.87% respectively. Dice Coefficients for Covid and Non Covid images were found to be 97.02% and 97.37% respectively. These segmented images were given as input to the classification algorithms like Convolution Neural Network (CNN) and transfer learning-based algorithms like VGG-16, VGG -19, DenseNet-169 and DenseNet-201.The accuracies for these models were found to be 85.45%, 88.05%, 88.48%, 94.74%, 98.78% respectively. The DenseNet-201 outperformed all the algorithms. CNN, VGG16, VGG19 showed average performance. In order to make these average learners into strong learners the ensemble models like model averaging, weighted average ensemble, majority voting were considered. The respective accuracies were found to be 91.11%, 91.71% and 89.89%. Weighted Average ensemble performed better than other ensemble-based approaches.

Coronary Artery Disease Prediction and Analysis using Machine Learning Techniques

Jasmine Jinitha A, Dr. S. Mangayarkarasi — 2022-08-02

Coronary Artery Disease (CAD) contains a huge variety of heart-associated illnesses are one of the leading causes of death globally in recent decades. Cardiovascular diseases account for 31 percent of all fatalities worldwide. The scientific affiliation generates a huge quantity of scientific information associated with cardiovascular disease, which need to be well tested to forecast cardiovascular disease. In current days, Machine learning (ML) has emerge as the number one method for the evolution of predictive models within the health-care industry, and it become determined to check how accurate their prediction scores are based on the data collected. The contemporary dataset from the UCI heart repository database is utilized in this proposal, which employs machine learning approaches. To look at the coronary illness, these strategies use 13 clinical parameters from the patient. As a result, supporting human beings in identifying whether or not or now no longer they are at threat for coronary heart illness is tremendously desirable. Gradient Boosting, Decision Tree, Random Forest, SVM, KNN, and Logistic Regression are some of the Supervised ML classifiers employed in this study to deploy a model for heart disease prediction. A 10-fold cross-validation testing option became used to assess the algorithms performance. Also researcher used tuning of the hyper parameter, number of nearest neighbors, namely k, the instance-based (KNN) classifier. Result indicates that compared to unique ML strategies, Gradient Boosting Classifier and Ada Boost Classifier algorithms gives 86.88%, Random Forest Classifier gives 88.15% and K Neighbors Classifier and SVM producing 90% accuracy in lots much less time for the prediction. This model (KNN) or SVM can be useful to the medical practitioners at their medical institution as Decision Making Support System.

Importance of Supply Chain Management for Enhancing the Quality of Product

Dr. Rakesh Gupta, Dr. Pranav Mishra — 2022-08-02

In this study, the importance of strategies that aim to deal with supply chain management has been discussed. The main purpose of this study is to improve the quality of the products by enhancing the supply chain of the organizations. However, certain problems that have been associated with the management of the supply chain have been analysed by focusing on separate ways that can be implemented in order to solve these problems to ensure smooth operations. In the introduction section, the background of this supply chain management has been discussed followed by the problems faced in this area. The objectives of this study have given light to numerous factors that can enhance the quality of the products to improve the brand image of an organization. In the literature review section, the importance of enhancing the quality of the products has been discussed with an emphasis on the role of the supply chain employed by an organization. It has been analysed that the supply chain of an organization plays a significant role in satisfying the needs of the internal as well as external stakeholders of an organization. In the methodology section, secondary data collection has been conducted in the form of thematic analysis. These themes have helped to analyse the ways in which the operation of the supply chain can be improved by an organization. In addition, a range of factors that influence the better operation of the supply chain to improve the quality of the products have also been given light in this section of this research work. Furthermore, a few recommendations have also been discussed that can be implemented to improve the future performance of an organization.

Virtual Fencing Using Yolo Framework in Agriculture Field

Mr. Surendranath H, Mrs. Manasa K. Chigateri, Mrs. Kavyashree S. N. — 2022-08-02

The Internet of Things (IoT) is a physical thing with an ecological connection that is reachable online. IoT is used in many different ways, including smart agriculture, smart healthcare, smart retail, smart homes, cities, energy commitment, poultry, farming, smart water management, and other contemporary purposes. Human-animal conflict poses a serious problem in the agricultural sector, as it can lead to the loss of major resources and the threat to human life. The farmer loses their crops, livestock, property, and perhaps their lives as a result. In order to prevent the presence of wild animals, this area must be regularly examined. This research offered a framework for monitoring the field in relation to this problem. This entails first locating the intruder in the area by using a sensor, following which a camera will take a photo of the intruder and classify it using machine learning and image processing, after which the raspberry pi will support the right action based on type of intruder. Finally, the notification is received. Keywords— IOT, Raspberry-pi, IR sensor, LCD, Web Camera, Speakers.

Design of Raspberry Pi Web Based Energy Monitoring System for Residential Electricity Consumption

Mr. Khaja Moinuddin, Kumuda P. — 2022-08-02

Electric power is the basic requirement of human beings, which is used for domestic, industrial and agricultural purposes. In the process of energizing, the electric current must flow through electronic circuits of the equipment. This flow of electric current can be controlled and monitored by a Smart Meter, which has an electronic chip for monitoring the energy consumed and a wireless module for data communication.

Iot and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control

Mrs. Suvarna S. Patil, Ms. Nidhi Patil — 2022-08-02

We propose a low-cost enabled COVID-19 standard operating procedure (SOP) compliance system that counts the number of people entering and leaving vicinity, ensures physical distancing, detection of face mask managers of violations. The system comprises of multiple sensor nodes communicating with a centralized server. The data stored on the server can be used for compliance auditing, real-time monitoring, and planning purposes. The system does not record the personal information of attendees nor provide contact tracing information

An Analytical Study of the Efficacy, Reliability, and Acceptability of the Covid –19 Vaccine in the State of Kerala, India

Gouri Priya S. B, Anisha T. D, Ambili S. Nair — 2022-08-02

We study the problem of people testing positive for COVID-19 even after vaccination. In a practical scenario, vaccination is a crucial step to tackle the pandemic along with other non-pharmaceutical measures. However, people are testing COVID-19 positive even after taking one or both the vaccine doses. This paper examines whether vaccination is effective for the pandemic, how reliable and acceptable the vaccines are by focusing on the case of Kerala, India. Kerala is a state with a population of 3.46 crores and almost 2.8 crore people are administered with the first or second dose of vaccine. According to a report published on August 11th, Kerala had over 40,000 vaccinated people testing positive for COVID-19. Here we analyse the data collected from across the state in the month of September 2021. In addition, we examine vaccine acceptability in the state by comparing the data collected in September 2021 and March 2022.

Development of Mobile Application Media to Grow Students' Interest in Learning Print Graphic Design at the Vocational High School Level

2022-08-02

Vocational High School (VHS) or SMK aims to produce graduates who are ready to work in accordance with their competencies. However, field facts show that there are many problems in the learning process, especially in printing graphic design (PGD) subjects so that conceptual understanding and student interest in learning PGD material have not been achieved. The purpose of this research is to create a mobile learning that can foster students' interest in learning and conceptual understanding of PGD material using a contextual teaching and learning model. The object of this research is vocational school students who have taken printing graphic design subjects. The research model uses the Sugiyono R&D model with the development product in the form of a mobile application with apk extension which is developed using Android Studio. The results of the use trial validation get an average percentage of 87.88%. While the average result of the test of student interest in learning gets a percentage of 82.8% and indicates that CTL-loaded mobile learning is very feasible to use and has a very high influence in growing vocational students' learning interest in PGD material.

A New Approach for Turning Handwritten Text into Digitalized Text Using DNN in Real Complex Scenarios

Dr. R. Josphineleela R., Yogashree G. S., Monisha G. S. — 2022-08-02

Human handwritten character recognition plays a significant role in many public and private sectors, including banking, medicine, and education. Many studies have been conducted on hand-written text recognition to ensure that stroke edges of each character can be accurately recognized. Even though a number of technologies are available to recognize characters, none provide effective recognition of stroke edges. To overcome this problem, Deep Neural Network (DNN) is used as a methodology to identify the edges of the character, so as to convert the handwritten character into a human-readable form. The major challenge is to recognize the edges of each character with a high degree of accuracy. The recognition process is extremely accurate and efficient in identifying the stroke of the handwritten character. According to the overall performance of the proposed approach, it proves to be most efficient in giving a benchmark as well as flexible in conversion with much less time constraint.

Factors Afecting Gree Fmcg Attitude and Purchase Intention in Vietnam

Nguyen Ngoc Quang, — 2022-08-02

This research aims to examine the impact of the consumer social orientation and environmental concern on attitudes towards green FMCG (Fast Moving Consumer Goods) and the influence of these attitudes on the purchase intention. A quantitative study was conducted based on a questionnaire sent to 872 respondents in three Vietnamese cities. Exploratory factor analysis (EFA) has been used to assess the reliability of scales and validity of factors; then, confirmatory factor analysis (CFA) was used to assess the qualification of the model, expressed hypotheses and the direct, indirect relationships. The results demonstrated the role of the consumer orientation and environmental concern in the FMCG green consumer behavior. Otherwise, this research does not only reveal that the consumption of green FMCG is guided by the social valorization but also that the environmental factors are considered in the purchasing decisions of these products. This study can be taken as a model on which firms will base themselves to build a green FMCG market in Vietnam. FMCG marketer can play on the social determining factor of consumer orientation to change their attitude and intention to acquire green products.

Efficiency Capital Market and Financial Reporting in Era COVID-19

Debbi Chyntia Ovami, Andi Auliya Ramadhany, Iskandar Muda — 2022-08-02

The COVID-19 influence on global financial markets. The capital market is said to be efficient, one of which is if the stock price reflects the overall information available in the market. The implementation of IFRS is comparability and quality of financial reports between companies. The study used a literature review approach to the relationship between IFRS and Capital Market Efficiency. The methodology is carried out with a brief review of several previous studies, so that it can be concluded briefly and in detail without testing the data by collecting data and articles from various sources which are summarized into a qualitative analysis review. The results of the study found capital market efficiency and company financial reporting in the covid-19 era causing high stock price fluctuations before and after the announcement of the confirmation of the COVID-19 case in Indonesia and the market reacting negatively to the announcement causing information content. In the announcement has been used as a basis for investors to buy and sell shares. Indonesia, which has adopted IFRS through the circular SP 28/DHMS/OJK/IV/2020, the Financial Services Authority (OJK) regarding guidelines for accounting treatment, especially in the application of PSAK 71-Financial Instruments and PSAK 68-Fair Value Measurement related to the impact of the Covid-19 pandemic. With this provision, it is hoped that it can help companies/entities in Indonesia in dealing with uncertainties in financial reporting to make several provisions as a step that determines the company's future, not only by relying on data obtained from the previous period.

Analysis of Financial Statements in Credit Analysis at Rural Bank

2022-08-02

This paper purpose to give information to the reader about the analysis of financial statement for credit analysis at rural bank. The research methodology used in this research is library research. The finding are: Before disbursing credit to debtors, credit analysis is required through the analysis of financial statements. Measurement of company performance through financial ratios can provide an overview of how the company is managed and its ability to pay its obligations. The results of this study can explain about (1) the importance of financial position for banks in approve credit applications, (2) analysis procedures of the prospective debtor's financial statements by bank, (3) analysis of the 5C principles of prospective debtors who carried out by banks, (4) effectiveness of supervision credit, and (5) minimize or prevent the occurrence of bad credit by debtors that can harm the bank as a creditor.

Motivation for Profit Management and Bonus Goals During the Covid 19 Pandemic

Rebecca Evadine, Januardi Mesakh, Iskandar Muda — 2022-08-02

This study contribute to the study of financial accounting, specifically profit management methods and their relationship with executive compensation during the Covid19 pandemic. The descriptive qualitative method was used to explore the motivation of profit and compensation management by comparing it with previous literature showing similar characteristics. Sequence, in particular past events that have had an effect on volatile financial conditions. In this uncertain situation, companies can use accounting techniques in financial statements to mitigate the negative impact of the pandemic on the operations of companies such as BigBath Accounting, Bailout Funds and Stimulus Packages; Large Profit Flattening and Loss Avoidance Accounting; Loose Accounting Rules During the Pandemic; and fiscal assistance packages. Accounting important role in reducing the impact of the pandemic on a company's operations. While accounting cannot be blamed for the negative effects of the pandemic on business, as a social science it can be blamed for not helping to mitigate of the pandemic on financial performance.

Car Side Mirrors Angle Prediction Based on Pupil Position

M. V. S. Sai Hiranmayee, Kandra Ksheeraj — 2022-08-02

The car side mirrors angle prediction can be done based on the driver’s pupil position using machine learning. Using a camera that is placed in front of the driver, the distance between the camera and pupils, and the angle between the camera and pupils are detected by computer vision. This paper is briefed about our experimental setup and the idea of automating the car side mirrors adjustment with the car side mirrors optimum angle prediction based on the pupil position.

Physical and Mechanical Properties of Calcinated Materials under Various Curing Conditions

Kiran Kumar Poloju, Kota Srinivasu — 2022-08-02

This article aims to demonstrate that cement may be substituted with calcined materials and to determine the rheological properties of calcined materials utilized in developing a geopolymer mortar, such as normal consistency, setting time, and compressive strength. The new technology may determine conventionally or geopolymer cement's physical and mechanical characteristics. First, however, studies on the total replacement of fly ash with GGBS (0–100%) with various concentrations of sodium hydroxide (8M and 10M) with a Sodium Silicate/Sodium Hydroxide (SS/SH) ratio of 2.5. This research would also serve as a model for future researchers in this sector. As a result, the features of geopolymer paste and mortar to cement were examined in this study under various curing techniques. The results of the tests showed that adding additional GGBS to geopolymer paste reduces the setting time of the paste and raises the standard consistency value for intermediate mixes when compared to fly ash mixtures. The study's key conclusion was that increasing the sodium hydroxide concentration improved the properties of the mortar when compared to ordinary cement mortar. As a result, the maximal strength for 10M under oven and outdoor curing is greater than that of any other mix.

Telework Affecting Job Stress and Job Performance of Telecommunication Business Employees

Rati Maneengam, Manop Chunin — 2022-08-02

This research is a quantitative research. Samples were 396 telecommunication business. Research instruments were questionnaire. Data were analyzed using frequency, percentage, mean, standard deviation, and multivariate analysis of variance (MANOVA). The results showed that: (1) Employees with different frequency of working outside an office were not different in job stress (F=1.77 df=1 p=0.183) and job performance (F=0.09 df=1 p=0.757). (2) Employees with different time spent teleworking were not different in job stress (F=1.70 df=2 p=0.182), while employees with different time spent teleworking were different in job performance (F=5.25, df=2, p=0.006) at a statistical significance level of 0.01. (3) Employees with different alternative worksite were different in job stress (F=3.11 df=2 p=0.045) at a statistical significance level of 0.05, and employees with different alternative worksite were different in job performance (F=11.71 df=2 p=0.000) at a statistical significance level of 0.01. (4) Employees with different telecommunications technology were different in job stress (F=7.95 df=2 p=0.000) and job performance (F=5.34 df=2 p=0.000) at a statistical significance level of 0.01. (5) Employees with different types of employment were different in job stress (F=8.33 df=1 p=0.000) at a statistical significance level of 0.01 and job performance (F=6.01 df=1 p=0.015) at a statistical significance level of 0.05. (6) Employees with different preference towards telework were different in job stress (F=13.73 df=4 p=0.000) at a statistical significance level of 0.01, while employees with different preference towards telework were not different in job performance (F=1.36 df=4 p=0.245).

An Exhaustive Review of Blockchain Based Approaches for Enhancing Security in Internet of Things Environment

Anil Verma, Prof A.Seetharaman — 2022-08-02

Bridging the currently available trust domain remains as the biggest challenge in internet of things. Block chains have newly fascinated the attention of shareholders through an extensive duration of industries: from finance to several utilities. It is a hopeful expertise for instituting trust in IoT networks, where network nodes do not inevitably trust each other. Recent explosion of the internet around block chains would remain as a stable fit for security in internet of things sector. The block chain technology could provide synchronized as well as secure transactions over multiple users and it offer perfect shared time stamped records which can't be modified by anyone. It permits distributed peer-to-peer network in which non-trusting members could communicate with one other without the need for trusted intermediary, in an authenticated manner. This current research work presents a comprehensive review of the background of Internet of Things (IoT), the scope of block chain based IoT security systems and the research problem prevailing in this field.

Image Deblurring based on Weiner Deconvolution Method using Markov Basis

K. Maheswari, P. Vedhakalaivani, N. M. Gokila, S. Dharani — 2022-08-02

The purpose of this paper is to enhance our understanding of the mathematics underneath image blurring and deblurring. Numerous references and materials such as books have been raised in order to gain a better understanding of underlying processes involved in deblurring. Tests were carried out to determine the efficacy of the fundamental methods. Through this research, we were capable of improving our knowledge of the sensitivity of the error observed in a few of the naive disturbing solutions that were investigated. Furthermore, we gained a greater understanding of spectral filtering techniques' potential to reduce the impact of noise on deblurring strategies. In this report, we discuss a way of deblurring digital images using HB filters that generate from Average filter by using HB Markov basis. Using these filters, we have deburred the motion-blurred images. The results indicate that HB filters perform better in terms of peak signal-to-noise and Root Mean Square Error.

Under Publication

Under Publication — 2022-07-19

Under Publication

Under Publication — 2022-07-19

Under Publication

A Study on Student’s View towards Online Learning Platforms in Higher Educational Institutions (Hei”S)

2022-08-17

Online education is electronically supported learning system . It is established with a proper Internet support and helpful for taking classes. Both the teacher and the students play a vital role in the success of this platform. It is very supportive in pandemic to both the students and faculties where the student can attend their regular classes in various platform anywhere provided they have internet access. These digital platforms have given a great solution to continue learning as HEI’s can reach their students in this pandemic .The study objective is to understand the perception of students towards online learning in Higher Educational Institutions. The researcher has conducted this study in Chennai, Tamilnadu with a well designed questionnaire .A sample of 285 students from various educational institutions from different years and different courses were included as sample for the study. The study objective was to understand the perception of students towards online education. The major research findings help to understand student’s attitude towards online education.

Survey on Forest Misfortune Mitigation Systems: A Review

Theresa Jose, A. Jesudoss — 2022-08-17

One-third of the Earth's surface is covered by forests, which play a vital role in preserving biodiversity, regulating climate, reducing air pollution, providing habitat for various species, and supporting a livelihood for billions of people. Yet, despite our dependence on forests, we are still allowing them to disappear. This paper describes the various works in the field of forest misfortune mitigation systems such as prediction forest fire, animal poaching, and deforestation. The merits and demerits of various works has been reviewed and explored in this paper.

Digital Image Reconstruction of Generative Adversarial Network (GAN) with Edge Connect (EC) Inpainting Algorithm

Shraddha Jain Sharma, Ratnalata Gupta — 2022-08-17

Prevailing real changes and hazy images impact picture inpainting techniques based on neural network models. Consistencies on visible connectivity. Overfitting and overlearning spectacles can simply appear in the image inpainting dealing out the process. This paper aims to fill missing parts of a scratched or damaged image by a generative adversarial network (GAN). In image inpainting, this model is crucial. For inpainting, we suggest a two-stage paradigm that separates the task into two stages: structure prediction and image achievement. This model initially predicts the image structure of the missing area in the form of edge maps. A GAN aims to estimate whether the repair area is accurate. We present a generative adversarial network (GAN) system to complete images with an Edge connected inpainting algorithm.

The edge producer consumes images edges of each ordinary and abnormal picture has a missing portion. As a first step, the picture completion network fills in the vacant areas with hallucinated borders. Here proposed an algorithm for eliminating target objects from digital images. This technique presented correctly building with the clean consistency of a midway blocked a building external from an evolution of images. Our proposed model can handle large-scale missing pixels while still delivering correct results.

Road Edge Detection and Illumination via Driving Video Mining in All Weather Conditions

A.Yovan Felix, Deepika S., Gayathri S., S. Gowri, D. Devi — 2022-08-17

A Road edge distinguishing proof calculation is created in the proposed system. The new thought of this strategy is to utilize characteristic road edge, as well as the white strip for road data securing. The characteristic road edge does not be effectively dirtied as the white path creator does, so it shows better flexibility to the open-air condition. In the calculation, we utilize both the pixel highlight and the edge highlight to recognize the road edge, which is alluded to as the entire road model. Since a few roads obliges are utilized to guarantee the road edge discovery, the calculation is invulnerable to the impact of the image's unsettling influence. The calculation of the road edge distinguishing proof incorporates two phases: introduction identification and following location. The introduction organize identifies the road edge from the entire road image. The following calculation utilizes the region of interest to restrict the distinguishing zone, which can spare a lot of time. To give a proportion of the unwavering quality of the road distinguishing result, this paper introduces a road edge ID estimation work, which can assess the dependability of the road edge

Fekete -Szego Results for Some Classes of Analytic Functions using Q-Difference Error Function Related to Shell Shaped Region

K. Saritha, K.Thilagavathi — 2022-08-19

The main object of this article is to obtain the coefficient estimate using q-∣ difference error function related to shell shaped region. Also we estimate ∣b3 − μb2 2 ∣ ∣ with the function h ∈ ESλ q,`(ψ(ζ)) and h ∈ ECλ q,`(ψ(ζ)) using subordination and quasi subordination.