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IEEE 802.15.6 Relaying Protocol for MBANs 1

IEEE 802.15.6 Relaying Protocol for MBANs

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Waheed, Tabassum Aqeel-Ur-Rehman Shaikh, Faisal Karim Hamdard University Dept. of Computer Engineering Karachi Pakistan Sir Syed University of Engineering Technology Dept. of Software Engineering Karachi Pakistan Mehran University of Engieneering Technology Dept. of Telecommmunication Engineering Jamshoro Pakistan

ISBN: (纸本)9781665424134

During the recent COVID-19 pandemic, Medical Body area networks (MBANs) emerged as an essential tool for the early diagnosis and treatment of infected patients. MBANs played an instrumental role in the remote treatment of COVID patients through early diagnosis using machine learning and IoT (Internet of Things). MBANs have paved their way in the health care industry and are being used in remote monitoring, emergency handling, and elderly assistance. The availability of the IEEE 802.15.6 standard has opened doors for new wearable consumer devices and IoMT (Internet of Medical Things) enabled applications to facilitate disease management. This standard was developed for wireless communication at a short-range, around the human body with low power requirements. This standard allows 2hop star topology within an intra-MBAN so that when direct communication is not possible, alternate routes are available through relays. This paper proposes a protocol for an intra-MBAN topology, which is a novel T-Relay mechanism that implements the 2-hop star topology. To validate the proposed protocol, it was tested on OMNeT ++ framework with Castalia Simulator for various scenarios. The simulation results show that the T-Relay mechanism enhances the PDR when the channel has variations. © 2021 IEEE.

关键词： Wireless local area networks (WLAN)

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Investigating Principle Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health Informatics Application

Investigating Principle Component Analysis Impact on the Per...

引用

Information Technology (ACIT)

作者： Ahmed BaniMustafa Sattam Almatarneh Olla Bulkrock Raed Alazaidah Hossam Almahasneh Ghassan Samara Computer Information Science Dept. Higher Colleges of Technology Abu Dhabi UAE Data Science & Artificial Intelligence Dept. Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah KSA Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Science Dept. Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. The dataset used in this study consists of thousands of images that represent four types of blood cells. These images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. The study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. The performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. The recorded performance is then compared to drawing a decisive conclusion.

关键词： Training Logistic regression Cells (biology) Nearest neighbor methods Vectors Numerical models Classification algorithms Random forests Principal component analysis Biomedical imaging

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Integrating AI and Cybersecurity: Advancing Autonomous Vehicle Security and Response Mechanisms

Integrating AI and Cybersecurity: Advancing Autonomous Vehic...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Venkata Lakshmi Namburi Srinivasa Rao Adapa Shanti Swaroop Kumar Chamala Manish Yerram Pranjal Gupta Kamal Upreti Software Systems Engineer Danlaw Inc Michigan USA Software Engineer OpenLogix LLC Michigan USA Software Developer Software Bots Inc Michigan USA Computer Science with IOT VNRVJIET Hyderabad India Computer Science Engineering ADGIPS Delhi India Dept. of Computer Science Christ University Delhi NCR Campus India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The rapid evolution of autonomous and connected vehicles has led to their integration with numerous technologies and software, rendering them vulnerable targets for cybersecurity attacks. While efforts have traditionally focused on preventing these attacks, the escalating risk underscores the importance of also vindicating their wallop. Nevertheless, this procedure is often onerous & facade scalability confronted, particularly due to connectivity issues in automobiles. This research advises a vehicle-based vibrant imposition response scheme, enabling swift responses to a variety of incidents and reducing reliance on external security centers. The classification encompasses an inclusive range of probable retorts, a procedure for evaluating retorts, & innumerable assortment approaches. Implemented on an embedded platform, the solution was evaluated using two distinct cyberattack use cases, highlighting its adaptability, responsiveness, volume for dynamic arrangement constraint alterations & nominal memory trail. Concurrently, this paper presents an innovative (AVSF) that synergistically integrates (AI) and cybersecurity techniques to fortify AV resilience against evolving threats. Additionally, the framework incorporates advanced cybersecurity measures such as encryption, authentication, and intrusion detection to mitigate vulnerabilities and safeguard critical AV systems. The fusion of AI and cybersecurity not only enhances AV security posture but also enables intelligent cyber threat monitoring and response capabilities. Extensive simulations and experimental evaluations demonstrate the efficacy of the AVSF in real-time scenarios, contributing to the development of robust security solutions for autonomous vehicle deployment and advancing safer transportation systems in the era of AI-driven mobility.

关键词： Scalability software Smart transportation Safety Time factors computer security Vehicle dynamics Artificial intelligence Autonomous vehicles Resilience

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Ontological View-driven Intensional Semantic Integration for Information Systems in a Decentralized Environment 14

Ontological View-driven Intensional Semantic Integration for...

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14th International Conference on Knowledge engineering and Ontology Development, KEOD 2022 as part of 14th International Joint Conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2022

作者： Ali Adhnouss, Fateh Mohamed Ali El-Asfour, Husam M. McIsaac, Kenneth El-Feghia, Idris Aburukba, Raafat Wahaishi, Abdulmutalib Dept. of Electrical & Computer Engineering University of Western London Canada Faculty of Information Technology Misurata University Libya Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates Software Engineering Rochester Institute of Technology New York United States

ISBN: (纸本)9789897586149

Ontologies are an essential component of semantic integration approaches for information systems . In a decentralized environment, each specification of the domain reflects an Ontological view. However, the semantics characterization of information systems in such decentralized environment poses a significant issue related to their integration. Information systems are viewed as independent intensional entities with their own beliefs, distinct from those held by others. Such autonomy is distorted by traditional extensional semantics. Other entities’ beliefs are introduced into a given entity, thus affecting their beliefs. Additionally, the information that one entity provides to another entity may not be consistent with the information known by the latter. We need an alternative semantics for information integration, which is not dependent on the extension, but rather on the underlying conceptualization. This paper proposes a classification of the environment where the information systems lives and a novel modelling paradigm for information integration using intensional logic to model the ontological views. The model comprises a formal modeling approach for the conceptualization as well as for the semantic integration process. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Semantics

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Effective Disease Recognition in Cucumbers: A Web-Based Application Using Transfer Learning Models

Effective Disease Recognition in Cucumbers: A Web-Based Appl...

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Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE International Conference on

作者： Md Shahidur Rahman Asif Ahamed Md Nuruzzaman Pranto Md Ariful Islam Abdullah Al Masum Abdullah Al-Sakib S M Arafat Rahman Rezaul Haque Shafiur Rahman Dept. of Engineering Management Westcliff University CA USA College of Technology and Engineering Westcliff University CA USA Dept. of Business Administration International American University CA USA Dept. of Information Technology Westcliff University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health.

关键词： Deep learning Accuracy Animals Biological system modeling Transfer learning Feature extraction Real-time systems Chemicals Diseases Farming

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Eggplant Disease Diagnosis Using a Robust Ensemble of Transfer Learning Architectures

Eggplant Disease Diagnosis Using a Robust Ensemble of Transf...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md Ismail Hossain Siddiqui Zishad Hossain Limon Shakil Khan Mahbub Alam Khan Hamdadur Rahman Shimanto Krishna Chakroborty Shamim Forhad Abdullah Al-Sakib S M Arafat Rahman Shafiur Rahman Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Business Analytics International American University CA USA Dept. of Management Information System Pacific State University USA Dept. of Management of Information System International American University USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Eggplant diseases present major challenges to agricultural productivity, leading to yield losses and threatening farmers livelihoods. Traditional diagnostic methods tend to be resource-intensive, subjective, and often inaccurate, making them unsuitable for large-scale use. This study addresses these shortcomings by proposing an Ensemble Transfer Learning (ETL) approach for precise classification of eggplant diseases. The model combines the strengths of ConvNeXt, EfficientNetV2, NFNet, and RegNet architectures, enhancing generalization and minimizing overfitting. Two datasets containing over 19,800 images were utilized for training and validation, with advanced preprocessing and data augmentation techniques applied to ensure high input data quality and model robustness. To assess performance, evaluation metrics such as accuracy, F1-score, MCC, and PR-AUC were used. The model achieved an impressive classification accuracy of 99.76%, surpassing state-of-the-art methods and demonstrating strong generalization across various conditions. A web-based application was also developed, enabling real-time disease diagnostics through a user-friendly interface. This application provides farmers with actionable insights, promoting sustainable farming practices by reducing crop losses and minimizing the need for chemical interventions. However, its reliance on high-quality labeled images limits its applicability in uncontrolled environments. Future research will aim to integrate more diverse datasets and develop mobile-based solutions to enhance scalability and usability. This study emphasizes the potential of ensemble transfer learning to transform agricultural disease diagnosis and improve crop productivity.

关键词： Productivity Accuracy Transfer learning Crops computer architecture Real-time systems Medical diagnosis Chemicals Diseases Farming

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Credit Card Fraud Detection Using Synthetic Minority Oversampling Technique and Deep Learning Technique

Credit Card Fraud Detection Using Synthetic Minority Oversam...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Abdullah M. Elmangoush Hanein O. Hassan Ayyah A. Fadhl Malak Ahmed Alshrif dept. of computer networks Libyan International University Benghazi Libya dept. of software engineering Libyan International University Benghazi Libya dept. of artificial intelligent Libyan International University Benghazi Libya dept. of telecommunication Libyan International University Benghazi Libya

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

recently, credit card frauds are raised to huge figures due to the reliance on online shopping by users. E-commerce and many other online sites have increased online payment modes which increased the risk of online fraud. There are many techniques have been used by researchers to protect against and detect credit card fraud. Different machine-learning algorithms were used to detect and analyze fraud in online transactions. However, existing solutions suffer from two main issues: class imbalance and insufficient feature extraction methods. Furthermore, extracting relevant features for fraud detection need human-based features engineering which time-consuming and complex problem. The aim of this study is to design and develop an effective credit card fraud detection model. The class imbalance problem has been addressed using a synthetic minority oversampling algorithm. Then, using the sequential deep learning techniques, a credit card detection model was designed and developed to utilize the data generated from SMOTE to improve the feature extraction and representation to solve the problem of insufficient features. The performance of the proposed model has been evaluated by comparing it with the related work in terms of accuracy, detection rate, and f-measure. Results show that the proposed model outperforms the existing state of the arts related models. It achieves a 0.99924 Accuracy, and 0.75976 F-measure. The proposed credit card fraud detection model, which addresses the class imbalance problem using synthetic minority oversampling and utilizes sequential deep learning techniques for improved feature extraction and representation, has shown to outperform existing models in terms of accuracy, detection rate, and f-measure. This could potentially have a significant impact on online markets as it may lead to a decrease in credit card fraud, which would increase consumer trust and confidence in online transactions. Additionally, the proposed model’s improved feature e

关键词： Deep learning Accuracy Costs Credit cards Feature extraction Data models Fraud

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Exploring Relationships among Bitcoin’s Market Price, Energy Consumption and Carbon Dioxide Emissions: A Machine Learning Approach

Exploring Relationships among Bitcoin’s Market Price, Energ...

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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Aadil Jamali Najma Imtiaz Ali Imtiaz Ali Brohi Nadeem Ahmed Kanasro Muhammad Umar Murad Asad Ali Jamali IMCS University of Sindh Jamshoro Pakistan Dept. of Computer Science Government College University Hyderabad Pakistan Dept. of Software Engineering Istanbul Aydin University Istanbul Turkey Dept. of Economics University of Essex Colchester United Kingdom

The cryptocurrency boom has resulted in a substantial rise in energy usage and carbon emissions. The mining process of Bitcoin is notorious for its energy intensive process because it requires huge computational power. The carbon footprint of Bitcoin mining is a growing concern that’s why we have to analyze the number of CO2 emissions resulting from the mining procedure. In this study, we have utilized machine learning techniques to examine the number of CO2 emissions generated by Bitcoin mining, analyzed the correlations between Bitcoin price, electricity consumption, and CO2 emissions, and pinpoint the factors during the process of Bitcoin mining that leads to CO2 emissions. We gathered global data set on Bitcoin electricity consumption, prices, and CO2 emissions. We trained supervised machine learning model (Regression) in Python to analyze and investigate the data. Our research findings have great implications for policymakers and researchers worried about the impact of the latest technologies on the environment, and in future Bitcoin policies and practices; they should prioritize sustainability and reduce the carbon footprint of mining operations. In the end, our research will provide valuable insights into the environmental impact of Bitcoin mining, highlight the factors that cause CO2 emissions and underscored the need for further research and action to reduce the carbon footprint of cryptocurrencies.

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Application to Enhance the Programming Knowledge of Children

Application to Enhance the Programming Knowledge of Children

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Advancements in Computing (ICAC), International Conference on

作者： Anuga Dissanayake Vishwa Lakpriya Mahendran Sanjeevan Ahamed Fahmi Samanthi E.R. Siriwardana Mihiri Samaraweera Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This proposal outlines the development of a comprehensive educational platform aimed at bridging the Information Technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical Information Technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances Information Technology education, equipping Sri Lankan students with essential programming skills to succeed globally.

关键词： Adaptation models Translation Machine learning algorithms Computational modeling Retrieval augmented generation Learning (artificial intelligence) Real-time systems Proposals Information technology Programming profession

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Reliability Analysis of a Monitoring System for Extraterrestrial Habitats

Reliability Analysis of a Monitoring System for Extraterrest...

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Saurabh Band Florian Stechmann Malavika Unnikrishnan Shadi Attarha Christiane Heinicke Andreas Willig Anna Foerster dept. Sustainable Communication Networks University of Bremen Germany Center of Applied Space Technology and Microgravity University of Bremen Germany Dept. of Computer Science and Software Engineering University of Canterbury New Zealand

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

This paper presents a robust and dependable monitoring system designed for space missions where reliability is critical. Theoretically, we use the Markov chain model to evaluate the availability of the system, considering multiple instances of backup devices. The results indicate a substantial improvement in system availability with the inclusion of backup devices. The findings highlight the significance of redundancy in enhancing system dependability while considering practical constraints. Based on this insight, we design an algorithm to deploy backup devices in the system that would improve the system's availability without putting strain on the resources (e.g., channel congestion). It uses backup devices at the sensor node and gateway levels. The algorithm involves backup devices that actively monitor and take over in case of failures, enhancing the system's ability to recover lost or faulty data. We test the system in three failure scenarios: hard failures, sensor failures, and gateway failures. We perform the experiments on our hardware prototypes. Experimental results demonstrate that using backup devices significantly improves the Packet Reception Ratio (PRR) in failure scenarios. We observed an improvement of 30 - 34% in packet reception for different failure scenarios.

关键词： Wireless communication Wireless sensor networks Space missions Redundancy Prototypes Logic gates Reliability theory Reliability engineering Monitoring Strain

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