Mathematical Statistician and Engineering Applications

Fog Computing Performance Optimization in Healthcare Environment

Ameenah Mohammad Alharbi, Mohammed Abdullah Al-Hagery — Tue, 12 Sep 2023 00:00:00 +0000

Cloud computing is very powerful and efficient technology, but it suffers from some efficiency issues due to its distributed nature. With the advent of fog, the large cloud is divided into many small cloudlets. Fog computing is one of the latest techniques in today’s cloud computing environment. Each cloudlet is a small and new architectural element extending today’s cloud computing infrastructure. The term” cloudlets” has been combined with fog computing, as it enhances the characteristics of cloud computing. Today, fog computing represents a basic layer and an essential cloud component. Consequently, this technology can help solve efficiency issues. Therefore, this paper aims to improve the efficiency of the cloud computing environment by optimizing the efficiency parameters results of fog computing. The parameters include response time, latency, and network utilization. The solution is to design a simulation system to minimize the redundancy of tasks and increase the performance rate. The results show an improvement in the performance of the proposed system from 40 to 48% based on the three parameters (response time, network utilization, and latency). Therefore, these results are promising and very significant, which encourages the applying the proposed system in the real healthcare environment.

Cyclic Coupled Fixed Point via Interpolative Kannan Type Contractions

Youssef El Bekri, Mohamed Edraoui, Jamal Mouline, Abdelhafid Bassou — Tue, 12 Sep 2023 00:00:00 +0000

In this article, we introduce the notion of cyclic coupled Kannan type contraction via interpolation. Additionally, we prove the existence a strong coupled fixed point theorem for such mappings

Estimators using EWMA Statistic for Estimation of Population Mean

Rajesh Singh, Poonam Singh, Sakshi Rai — Tue, 12 Sep 2023 00:00:00 +0000

In this study, we have put forward a class of estimators for estimating population mean for the cross-sectional and time scaled surveys. It is revealed in the study conducted by [5] that the use of Exponentially weighted moving average (EWMA) statistic improves the performance of the estimators for the time scaled surveys as it makes the use of past as well as the present information. [5] proposed ratio estimators using EWMA statistic, so we have made improvements by introducing a class of modified difference-ratio and product estimators with the use of tri-mean, mid-range, Gini’s mean difference, Downtown’s method and probability weighted moments of auxiliary variable. Our estimators showed better results. Results are verified with the simulation.

MRI and CT Scan Data Based Volume Rendering with Python

A .V. Krishna Rao Padyala , K.Ramkumar — Tue, 12 Sep 2023 00:00:00 +0000

To diagnose a variety of conditions, from torn ligaments to tumors, doctors make use of MRI and CT scans. An application developed using python, installed in a mobile or tablet render the data of MRI and CT scans and let the doctor understand patient health condition. A method for transforming MRI/CT scan data in DICOM form to HDF5 with python. Variety of tools available now to develop android applications for mobile with python.

Wearable Electronics in Health Care Applications and Enery Harvesting

Dr. A S Rathore, Stalin Yanneboina — Wed, 27 Sep 2023 00:00:00 +0000

This research gives a comparative analysis of a link between the magnitude of the received voltage and the received power using three different antennas. The antennas were used to receive radio waves. Conventional etching methods as well as contemporary inkjet printing methods were used during the fabrication of the antennas, which were created with the intention of operating in two separate frequency bands. The relay will then detect the overlaid signal coming from the source, and it will send the destination the signal when it has been decoded. It is possible to compute closed-form outage probability as well as ergodic rates at both the relay and the destination. This issue has been shown to be NP-Complete from a mathematical perspective, and as a result, it cannot be solved with simple effort. In order to achieve this goal, we present a series of approximation algorithms that have been shown to perform well from the point of view of constructing a minimum dominating set (DS). These algorithms combine energy-saving strategies with energy-harvesting strategies in order to extend the lifetime of the network while still meeting the real-time requirements of EH-WBANs. To be more specific, we first provide a centralised method that is able to create DSs with the smallest possible size and find the greatest possible number of DSs. Because the rectifier circuit contains a Schottky diode, which is a non-linear device, the input impedance and echo characteristics will change dramatically depending on the load size and input power. As a result, it has been difficult to design a multi-band high efficiency rectifier with a high dynamic range. As a result, an enhanced impedance compression approach has been presented as a means of reducing the impedance range of nonlinear diodes that have a large dynamic range.

Data Science and its Usage in the Application of Smart Home Environment

Sujata Veerangouda Patil, Dr. Neetu Agarwal — Wed, 27 Sep 2023 00:00:00 +0000

The Internet of Things (IoT) and Data Science are two of the most talked-about fields in today's rapidly developing digital industry, thanks to the qualities that have made them so attractive to businesses and industries across the board. Using IoT technology, we built a Smart Home Automation System with two distinct types of modules that control a wide range of sensors. The term "Internet of Things" refers to a technology that enables the transmission and reception of data between computing devices embedded in everyday things through the use of the internet. The initial section of this project is dedicated to the automatic regulation of fan speed in response to the ambient temperature. Similarly, the second component of our technology automatically modifies the intensity of the lights based on the ambient light. Sensors are just one of the many parts of these modules that help to generate data values at regular intervals. The numbers in that data set are now being analyzed via a web application. We have built a real-time data monitoring system within our web app. We can learn more about the modules' energy usage from the findings of the analyses we run. It is also helpful for power management. The addition of these modules elevates the overall quality of the project and helps to ensure uniformity in this technologically advanced era.

Investigating Solutions of Volterra Integral Equations Using the Successive Approximations

Zana Mohammed Hassan, Sadeq Taha Abdulazeez, Sarbast Kamal Rasheed — Tue, 12 Sep 2023 00:00:00 +0000

This paper explores the subject of integral equations with an emphasis on Volterra integral equations (VIEs). Through the use of the method of successive approximations (MOSA), the main goal of this work is to investigate and analyze the solutions to (VIEs). Furthermore, we show how to transform a normal differential equation's initial value problem into a (VIEs) and a Volterra integral differential equation. We seek to provide a greater knowledge of the behavior and solutions of (VIEs) by a thorough investigation of the proposed method.

Data-Backed Policy Formulation: Utilizing Big Data Analytics to Anticipate and Adapt to Customer Behavior

Devalappa D Lamani, Neetu Agarwal — Mon, 23 Oct 2023 00:00:00 +0000

This paper examines the integration of big data analytics into policy formulation for anticipating and adapting to customer behavior. By leveraging diverse data sources and advanced analytics, businesses can proactively align policies with evolving customer preferences. The study showcases methodologies, real-world cases, and ethical considerations, emphasizing how data-driven policies enhance customer satisfaction and operational efficiency. The paper underscores the importance of responsible data governance and iterative policy refinement to stay competitive in a dynamic market.

Inventory control problem using Metaheuristic Techniques: Application to Two Warehouse Inventory Model

Mohammed Safiya, D. Gopinath, G. Shankar Lingam — Tue, 12 Sep 2023 00:00:00 +0000

Inventory management necessitates striking a balance between the benefits of having stockpiles of items, which allow you to meet client requests, and the expenses of carrying inventory. Methods for managing inventories are essential, and by using these methods, most businesses may greatly cut their expenditures related to the flow of goods. In today's marketplaces, firms must efficiently manage their supply chains to preserve market contributions and increase profits. Inventory control in the most effective manner is a critical component of supply chain control. When dealing with inventory control concerns, strategic decisions must be taken, such as the ordering times and the product order quantities. These choices can either reduce overall costs or increase total earnings. Because of the considerable costs correlated with preserving inventory and the fact that it degrades over time, it is generally not economically practical for a company to keep a large inventory on its business premises for an extended time. On the other hand, keeping a limited inventory is not profitable because it increases the risk of running out of stock at the point of sale during periods of high demand for the commodities being sold. According to the outcomes, excellent inventory management is necessary to ensure that the firm is conducted in the most cost-effective manner possible. This study's author used the Multi-objective Particle Swarm Optimization (MOPSO) and the Firefly algorithms to adhere to the EOQ inventory model. As part of his evaluation of inventory costs, the author of this paper performed a comparison analysis. As a result, the author determined that the APSO algorithm outperformed the Firefly approach in performance.

Effectiveness of Digital India and Artificial Intelligence in Indian Education System

Jitendra Sheetlani, Shilpa Shivanand Jadimath — Tue, 12 Sep 2023 00:00:00 +0000

The Indian education system is undergoing a significant transformation through the "Digital India" initiative and the integration of artificial intelligence (AI) technologies. This paper investigates the multifaceted impact of these innovations on education in India, examining their influence and effectiveness. The integration of "Digital India" and AI holds the potential to revolutionize education delivery and accessibility, enhancing personalized learning and administrative efficiency. However, challenges such as the digital divide and privacy concerns must be addressed. Striking a balance between innovation and overcoming these challenges is essential for establishing an equitable and effective educational system in India.

Analysing the Effects of OSI Presentation Layer in Data Mining, with Emphasis on SEO

Jitendra Sheetlani, Suneeta Salimath — Tue, 12 Sep 2023 00:00:00 +0000

In this paper, investigate into the OSI Presentation Layer's influence on data mining, with a specific focus on SEO. This layer is pivotal for data formatting and transmission between systems. Our investigation explores the impact of the Presentation Layer on various data mining techniques, including clustering, classification, and association rule mining. Additionally, we assess its role in optimizing search engine rankings. Search engines rely on data mining to rank web pages accurately. Through experiments, we confirm that the Presentation Layer significantly affects data mining techniques and SEO applications. Our study contributes insights into the OSI Presentation Layer's implications for data mining and SEO, offering a pathway to enhance search engine rankings and data mining accuracy.

Optimization of B. O. T Contracts Using a Hybrid Model of Multi-Objective Programming and Game Theory

Meisam Gharedaghi, Mohammad Reza Zarrabi, Omid Solaymani Fard — Tue, 12 Sep 2023 00:00:00 +0000

In the current research, construction, operation and transfer or BOT contracts have been studied, which are of great importance to legal and economic experts today. Different countries, especially developing countries, consider these contracts as an efficient tool for attracting foreign capital and using private sector management in infrastructure projects. In this type of contracts, the public sector entrusts the construction of the design to the private sector, and the private sector is obliged to return the ownership of the design to the public sector after its construction and operation. For the success of BOT, the government must provide the economic, political and especially legal basis for private investment. Contract B. O. TIt is a suitable mechanism for the government and the private sector and the use of proper management of this sector, as well as the use of public funds to carry out construction projects faster. In the presented model and its mechanism, the role of banks and the use of loans were especially considered, because in our country, banks' investment is mainly related to the construction of residential and commercial units, and when the housing market Banks will face serious problems if they go into recession. But in this way, by investing in construction projects, they can earn future and long-term profits, and their funds, which are mainly people's deposits, can be used for the development of the country, and in fact, a win-win game will occur.

Detour Polynomials of Some Certain Graphs

Haveen J. Ahmed — Tue, 12 Sep 2023 00:00:00 +0000

The length of a longest u–v path in a connected graph between two distinct vertices u and v is called detour distance and denoted by , the detour distance is an important type of distances types in graph theory because of its importance in chemical applications and other sciences. In this paper, we find detour polynomials, detour index and study some properties for some certain graphs such as: Wagner graph, friendship graph, double cycles and double paths graphs.

Reliability Analysis and Statistical Fitting for the Transmuted Weibull Model in R

Sameera Abdulsalam Othman, Kurdistan M. Taher Omar, Sadeq Taha Abdulazeez — Tue, 12 Sep 2023 00:00:00 +0000

Accelerated life testing is a fundamental practice in reliability engineering, allowing the evaluation of component or device performance over extended lifetimes impractical to encounter during design. This study delves into the application of the transmuted Weibull distribution to model lifetime data, showcasing its versatility in real-world scenarios. The evaluation includes critical metrics such as Akaike’s information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination, and standard error for distribution comparison. Utilizing Maximum Likelihood Estimation (MLE) for parameter estimation, a simulation study is conducted with varying sample sizes, and the R programming language is employed for in-depth analysis. Real data analysis involves comparing the Transmuted Weibull (twd) model with other models using goodness-of-fit criteria. Maximum Likelihood Estimates (MLEs) are obtained, and the likelihood ratio test demonstrates the (twd) model's superior alignment with the data. The study concludes with the simplicity of producing Quick Fit plots for analysis using R software. The presented approach provides a comprehensive understanding of reliability characteristics, combining theoretical insights with practical applications and numerical analyses

Applications of Artificial Intelligence in the Aviation Industry and Air Accidents

Captain Mohammad Youseftorkaman, Captain Ali Kangaranifarahani, Danial Dana — Tue, 12 Sep 2023 00:00:00 +0000

The purpose of this research is to identify and explain the capabilities of artificial intelligence on the aviation industry and air accidents. To review and analyze the strengths and weaknesses, opportunities and threats, the opinions of 25 managers, professors, experts and experts in the field of artificial intelligence have been used. Selected people in this field include people who have books, articles, authorships, translations in the field of aviation and artificial intelligence, and also have work experience as university professors, managers and consultants in terms of education and positions. Opportunities, threats, strengths and weaknesses were collected from the expert community based on an open questionnaire and interviews using the grand theory method. Then, using the Shannon entropy technique, weighting and ranking have been done. After ranking, 18 opportunities, 13 threats, 15 strengths, and 14 weaknesses were identified. Then, using the quantitative matrix of strategic planning, the indicators were scored and weighted. The total score of the internal factors was 2.469 and the total of the external factors was 2.316, which was drawn on the matrix, and Aggressive strategy was chosen to formulate strategies for the use of artificial intelligence in the aviation industry and air accidents. Therefore, the strategy was presented with an aggressive approach.

Homomorphism and Isomorphism into a Complex Fuzzy Number Group

Durai Ganesh A, Charles Sagayaraj A C, K. Rajaannam — Tue, 12 Sep 2023 00:00:00 +0000

Fuzzy mathematics forms a branch of mathematics including fuzzy set theory & fuzzy logic. It started in 1965 after the publication of LotfiAskeZadeh's seminal work Fuzzy sets.

A fuzzy number is a generalization of a regular, real number in the sense that it does not refer to one single value but rather to a connected set of possible values, where each possible value has its own weight between 0 and 1. This weight is called the membership function. Calculations with fuzzy numbers allow the incorporation of uncertainty on parameters, properties, geometry, initial conditions, etc.

In this article, we define the concepts of homomorphism and isomorphism of complex fuzzy number groups. And the properties of complex fuzzy number homomorphism and fuzzy number isomorphism are studied.

Image Feature Encoding Using Lownerization Tensor

K. Pavan Kumar , Y. Suresh , PESN Krishna Prasad ,K. Upendra Raju — Tue, 12 Sep 2023 00:00:00 +0000

Nowadays, Technology is improving day by day. These days, vulnerability is the most important factor for any real-time application. There is a need to decrease the vulnerability of any application that is up to date. For many applications, till now we are using finger prints and faces as security. This paper advocates multimodal authentication using face and finger print. The image analysis process is divided into three parts. Preprocessing of images, Extracting the features from the image and Classification and interpretation of features. To save these extracting features, we need a technique so that these features cannot be vulnerable to anyone else. In this paper, Preprocessing can be done using Mean Square Error and Features can be extracted using Non-negative Matrix factorization. The proposed tensor technique called the Lownerization Tensor for encoding these extracted features

A New Model Proposed for Secure Cluster Management in Software Defined Networks

Chakali Maddilety, Pankaj Kawad Kar, M. V. Narayana — Mon, 15 Jan 2024 00:00:00 +0000

Software Defined Networking (SDN) has become a revolutionary method for managing networks due to the fast-paced advancement of communication technology. Clusters are crucial in the context of Software-Defined Networking (SDN) since they enable effective allocation of resources and optimization of the network. Nevertheless, the security issues related to cluster management in SDN settings have gained more attention, requiring the creation of strong models to protect against such risks. This study introduces an innovative paradigm specifically developed for the secure administration of clusters in Software Defined Networks. The suggested approach aims to enhance the security of cluster based SDN systems by addressing crucial problems such as unauthorized access, data breaches, and cyber threats. The primary areas of emphasis in the model include authentication, authorization, and encryption procedures, which are implemented to guarantee the confidentiality, integrity, and availability of cluster resources. The authentication module of the model utilizes sophisticated cryptographic algorithms and multi-factor authentication protocols to authenticate the identity of network entities involved in cluster activities. Authorization methods are incorporated to establish and enforce access controls, guaranteeing that only authorized entities possess the requisite permissions to engage with cluster resources. The architecture presents dynamic access control policies that adjust to the changing network conditions, improving flexibility and response to developing security concerns. In addition, the suggested paradigm integrates resilient encryption methods to safeguard communication routes both inside and between clusters. This measure aids in reducing the likelihood of unauthorized interception and manipulation of data, hence strengthening the overall security of the SDN infrastructure. The model incorporates anomaly detection and intrusion prevention algorithms to detect and mitigate harmful actions inside the cluster, hence improving the overall resilience of the network. In order to verify the effectiveness of the suggested paradigm, thorough simulations and tests were carried out inside a regulated Software-Defined Networking (SDN) environment. The results indicate a notable enhancement in the security stance of cluster management, as the model successfully prevents several typical methods of attack. Furthermore, the model demonstrates its capacity to scale and adapt, guaranteeing its suitability for various SDN implementations. The suggested methodology for safe cluster management in Software Defined Networks offers a complete approach to tackling the changing cybersecurity concerns in contemporary networking settings. This approach provides a strong solution to protect the integrity and availability of cluster resources in SDN infrastructures by including advanced authentication, authorization, and encryption procedures. As a result, it significantly enhances overall security.

The Impact of Leadership Style on the Human Resources Efficiency in the Organization

Farnoosh Javadian — Tue, 12 Sep 2023 00:00:00 +0000

This study aims at investigating the impact of leadership style on the human resources efficiency in the organization. This study is applied in terms of objective and has the survey type according to data collection, and it is also the descriptive-analytical in terms of data analysis. The statistical population of this study consists of all managers and senior experts in National Iranian Oil Company and 120 subjects are selected as the sample size through Cochran formula due to the uncertain and high population size. The researcher-made questionnaire is utilized for evaluating the relationship between the research variables, thus it is applied for measuring Fiedler's leadership style with three indices of relationship-oriented, task-oriented and relationship-task oriented leadership styles as well as the human resources efficiency with three indices of learning and development, attraction and preservation of human resources. According to the obtained results, the leadership style has an impact of 94% on the human resources attraction, 71% on human resources learning and development, and 79% on human resources preservation. Therefore, the maximum impact of leadership style is on the human resources attraction.

Adaptive Machine Learning Approaches for Time Series Pattern Detection: A Comprehensive Study

Vijayanand Hunachyali, Kamal Kr. Srivastava — Tue, 12 Sep 2023 00:00:00 +0000

Time series data is prevalent across several domains, encompassing fields such as finance and healthcare. The capacity to identify complex patterns within this data has the potential to reveal valuable insights and enhance prediction capabilities. The present paper offers a complete overview of adaptive machine learning techniques specifically designed for the purpose of detecting patterns in time series data. In this study, we assess a range of algorithms, encompassing conventional time series analysis methods as well as modern deep learning models. Our primary emphasis is on examining their capacity to adapt to evolving data dynamics. The experimental findings indicate that some adaptive algorithms exhibit exceptional resilience and precision when applied to a diverse range of synthetic and real-world datasets. In addition, we present a unique adaptive mechanism that utilizes principles of transfer learning, demonstrating its effectiveness in situations where the pattern structure undergoes temporal evolution. The present study offers a comprehensive comparative analysis that is valuable for both scholars and practitioners. Additionally, it sets the stage for future investigations into the integration of adaptive principles and machine learning techniques, aiming to improve the effectiveness of time series analysis.

A Comparative Study on Pre-Engineered Building and Conventional Steel Structures

D. S. Pawar, A. Y. Kankuntala, Anjana Kone — Tue, 12 Sep 2023 00:00:00 +0000

This paper aims to provide comparative analyzed study between two outstanding structural construction methods i.e. Pre-Engineered Buildings and Conventional Steel Buildings. In recent years, introduction of Pre Engineered Building in the design steel of structures has helped in enhancingthe design. Adoptability ofPre Engineered Buildings in place of Conventional Steel Building resulted in many advantages, including long span, column free structures, economy and easier fabrication, are the most essential requirements in any type of industrial structures. Pre Engineered Buildings fulfills all structural requirement along with reduced time and cost as compared to conventional structures. Pre-Engineered Buildingsmethodology is versatile not only due to its quality pre-designing and prefabrication, but also due to its light weight and economical construction.In this paper a detailed comparative study between Pre Engineered Building & Conventional Steel Structure is carried out. Discussed on Design, Structure Weight, Delivery, Foundations, Construction cost, performance, Resistance, Architecture, Responsibility and Future expansion of both are Pre Engineered Building & Conventional Steel Structure. Through the comparative study it is observed that the Pre-engineered Building structures are more economical than Conventional Steel Building structures.

New Methods of Business Income Generation Under Emerging Technologies

Mohammad Hossein Hosseini, Azadeh Khodaei — Tue, 12 Sep 2023 00:00:00 +0000

Emerging technology promises groundbreaking improvements in business revenue generation by reducing costs and improving ease of use. Therefore, in the current research, it has been tried to design new ways of generating income for businesses under emerging technologies by using the combination of past studies. For this purpose, using the qualitative approach and the strategy of Sandelowski and Barso (2007), the studies that were published in the period (2011 to 2023 for domestic documents and 2010 to 2024 for Latin documents) were searched in authoritative quarterly journals and 30 documents were selected. Using evaluation criteria, the title, abstract, content, and critical evaluation skills were selected and analyzed with the three-stage coding method (open, central, and selective) in MAXQDA software, and finally, the research model was designed using EdrawMax software. The research findings were evaluated based on Guba and Lincoln's (1989) qualitative research quality control criteria and the Holstein formula. Based on the research findings, 289 indicators, 26 components and 6 dimensions were identified for the model. As a result, businesses can boost their business income by using new methods such as artificial intelligence, Internet of Things, digital marketing, branding, cryptocurrencies and financial ecosystem and social media.

New Methods of Business Income Generation Under Emerging Technologies

Mohammad Hossein Hosseini Azadeh Khodaei — Tue, 12 Sep 2023 00:00:00 +0000