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Data Driven Stochastic Game Network-Based Smart Home Monitoring System Using IoT-Enabled Edge Computing Environments

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Verma, Prabal Sood, Sandeep K. Kaur, Harkiran Kumar, Mohit Wu, Huaming Gill, Sukhpal Singh Department of Information Technology National Institute of Technology Srinagar India Department of Computer Applications National Institute of Technology Kurukshetra India Department of Computer Science Guru Nanak Dev University Amritsar India Department of Information Technology National Institute of Technology Jalandhar India Center for Applied Mathematics Tianjin University Tianjin China School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Edge computing plays a crucial role in the processing of Consumer Internet of Things (IoT)-enabled latency-sensitive applications. In smart homes, dynamic action strategies based on multiple IoT objects with edge processing can be the best solution for handling adverse events. To overcome these challenges, the use of Stochastic Game Net (SGN) forming IoT devices as players with predefined action sets is one of the feasible solutions. Relative to this context, the edge-assisted IoT-enabled data-driven SGN model is proposed to handle various events in the smart home environment. Stochastic Petri Nets (SPNs) and game theory are integrated into our proposed model to build data-driven dynamic SGNs for the smart home environment. Dynamic SGNs for a comprehensive smart home system are generated in real-time through transitions based on sensor data, enhancing interoperability and scalability in smart home environments. We use the Net logo tool and state-of-the-art smart home sensor datasets to generate dynamic SGNs for various events. Experimental results demonstrate the effectiveness of the proposed model within a data-driven smart home environment. It shows that the present work significantly outperforms other state-of-the-art techniques in terms of decision-making at the edge layer. Moreover, using the proposed system the energy efficacy increased to around 39mJ/K nodes, and the average temporal delay for different events was reduced significantly. IEEE

关键词： Game theory

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MAXIMIZING BIG DATA FOR DATA-DRIVEN DECISION MAKING: AN IN-DEPTH ASSESSMENT

MAXIMIZING BIG DATA FOR DATA-DRIVEN DECISION MAKING: AN IN-D...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kumar, Maneesh Kaur, Dilrajpreet Sanghi, Akash Yuvaraj, K. Mukuntharaj, C. Tamkhade, Jayashree School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand 248007 India Department of Physics School of Basic & Applied Sciences K. R. Mangalam University Haryana Gurugram122103 India Department of Computer Science & Engineering Invertis University Uttar Pradesh Bareilly243123 India Department of Computer Science Karpagam Academy of Higher Education Coimbatore641021 India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore641032 India E&TC Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350355338

In this essay, we examine the history and current condition. We begin by outlining the history of big information reviewing similar technologies including centers, and Hadoop. The four stages of the chain-data production, data capture, - are then the main emphasis. [1] We lay out the overall context, talk about the difficulties, and cover the most recent developments for each step. Finally, we look at a number of exemplary big business. These conversations are meant a thorough introduction and overall picture of this fascinating field. A discussion of unresolved issues and prospective directions follows this survey's conclusion. © 2024 IEEE.

关键词： Data centers

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Enhanced English Sentence Pair Classification Through KIRINLog Technique 5

Enhanced English Sentence Pair Classification Through KIRINL...

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5th International Conference on Advances in Electrical, Electronics and Computing technology, EECT 2025

作者： Ounprasert, Patthanan Anekboon, Khantharat Department of Computer and Information Sciences Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798331541545

Natural language processing is a rapidly evolving technology in an era of quick technological growth. Conventional rule-based methods are insufficient to meet the demands of today's complex challenges. One of the significant tasks in natural language processing is recognizing textual entailment. Recognizing textual entailment is vital in natural language processing as it enables systems to understand the relationship between texts, which is crucial for applications like text summarization, sentiment analysis, information verification, question answering, text classification, and machine translation. One challenge is reducing the size of the input vectors while maintaining good prediction results, F1 scores. To overcome this challenge, this research presents the novel encoding technique by reducing the encoding size for the sentences involving compositional knowledge dataset. This dataset is sentence pairs in English to classify textual entailments into three categories: entailment, neutral, and contradiction. This research proposes the KIRINLog technique, which consists of six main methods: preprocessing, removing the same word, max-length padding and truncation for sentence alignment, adding a flag value, word embedding by Word2Vec, and dimensionality re-duction with principal component analysis to reduce the size of input vector. All of six main processes are proposed to create feature vectors for recognizing textual entailment (the sentences involving compositional knowledge dataset). This study used an attention-based bidirectional LSTM as a classification model. This model combines bidirectional LSTM with an attention to make analyzing sequences in both forward and backward directions and to help the model focus on important information in the sentence. This proposed technique achieved an accuracy of 92.4%, with precision at 88.8%, recall at 88.6%, and an F1-score of 88.6% on the 2014 sentences involving compositional knowledge dataset. © 2025 IEEE.

关键词： Sentiment analysis

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Tai Chi Exercise Posture Detection and Assessment for the Elderly Using BPNN and 2 Kinect Cameras

Tai Chi Exercise Posture Detection and Assessment for the El...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Kanchanapaetnukul, Sarawin Aunkaew, Rungarun Charernmool, Piyanuch Daoudi, Mohamed Saraubon, Kobkiat Visutsak, Porawat King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Bangkok Thailand Chaopraya University Faculty of Science and Technology Nakhon Sawan Thailand Imt Lille Douai Univ. Lille Cnrs Umr 9189 CRIStAL LilleF-59000 France

ISBN: (纸本)9798350326413

Exercise and recreation are beneficial to all genders and ages, exercise reduces stress and makes people healthy. Physical limitation among the elderly is the major concern and needed to be taking care for the elderly exercise. Low-impact exercises such as walking, slow jogging in the park, and Tai Chi are recommended for the elderly. Tai Chi is a slow and gentle exercise, which can help the circulatory system and dementia in the elderly;it also helps the elderly to get socialized and make new friends. Unfortunately, in the COVID-19 pandemic, people must stay in the house and avoid social activities including outdoor exercises and recreation. This paper aims to develop Tai Chi exercise posture detection and assessment system for helping the elderly to practice Tai Chi at home by themselves. The system provides Tai Chi video clips for demonstration and the graphics user interface (GUI) for capturing the movement of the elderly while they are exercising Tai Chi. The system will detect and assess the elderly's movement whether it is correct or not by using 2 Kinect cameras. The Kinect is used for joints detection and the series of joints movement will be used to compare with the correct Tai Chi postures stored in the system. The questionnaire, which was developed based on the usability criteria defined by the ISO 9241-11 and the users' experience, was used to evaluate the system. The precision, recall, F1-score, and accuracy of our system are 0.94, 0.98, 0.96, and 0.93 respectively. © 2023 IEEE.

关键词： Parks

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Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

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computer Systems science & Engineering 2023年第3期44卷 2277-2292页

作者： Ashit Kumar Dutta Mazen Mushabab Alqahtani Yasser Albagory Abdul Rahaman Wahab Sait Majed Alsanea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd Diriyah13713RiyadhKingdom of Saudi Arabia Physical Therapy Department Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl Ahsa31982HofufKingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational *** LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning *** recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for *** the different processes involved in ML based LMS,feature selection and classification processesfind *** this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for *** key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in *** pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal ***,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)*** design of GSO and MFO techniques result in reduced computation complexity and improved classification *** presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several *** simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

关键词： Learning management system data mining performance monitoring machine learning feature selection

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Automating the Identification of High-Value Datasets in Open Government Data Portals

arXiv

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arXiv 2024年

作者： Quarati, Alfonso Nikiforova, Anastasija Institute for Applied Mathematics and Information Technology National Research Council Genoa Italy Institute of Computer Science Faculty of Science and Technology University of Tartu Tartu Estonia

Recognized for fostering innovation and transparency, driving economic growth, enhancing public services, supporting research, empowering citizens, and promoting environmental sustainability, High-Value Datasets (HVD) play a crucial role in the broader Open Government Data (OGD) movement. However, identifying HVD presents a resource-intensive and complex challenge due to the nuanced nature of data value. Our proposal aims to automate the identification of HVDs on OGD portals using a quantitative approach based on a detailed analysis of user interest derived from data usage statistics, thereby minimizing the need for human intervention. The proposed method involves extracting download data, analyzing metrics to identify high-value categories, and comparing HVD datasets across different portals. This automated process provides valuable insights into trends in dataset usage, reflecting citizens’ needs and preferences. The effectiveness of our approach is demonstrated through its application to a sample of US OGD city portals. The practical implications of this study include contributing to the understanding of HVD at both local and national levels. By providing a systematic and efficient means of identifying HVD, our approach aims to inform open governance initiatives and practices, aiding OGD portal managers and public authorities in their efforts to optimize data dissemination and utilization. © 2024, CC BY.

关键词： Open Data

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Renewable Energy Efficiency of Unmanned Aerial Vehicles Operating with Wireless Sensor Networks and Mobile Ad Hoc Networks

Renewable Energy Efficiency of Unmanned Aerial Vehicles Oper...

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Smart Technologies for Power and Renewable Energy (SPECon), International Conference on

作者： Wakeel Ahmad Sarfaraz Ahmed Adeel Ahmad Shams Tabrez Siddiqui Mohammad Khamruddin Haneef Khan Deptartment of Electrical Engineering Technology College of Applied Industrial Tech Jazan University Jazan Saudi Arabia Dept. of CNET College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Computer Science Faculty of Science Aligarh Muslim University Aligarh U.P India Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (数字)9798350383041

ISBN: (纸本)9798350383058

The amalgamation of Unmanned Aerial Vehicles (UAVs) into Wireless Sensor Networks (WSNs) and Mobile Ad Hoc Networks (MANETs) provides significant prospects to improve network capabilities. This study examines the energy renewable efficiency and performance of UAVs operating in WSNs and MANETs. The assessment considers a variety of elements that influence energy consumption, including the possibility of integrating renewable energy, energy management measures, and the impact on overall network performance. This seeks to provide insights into optimizing energy usage and improving the operating efficiency of UAVs in these dynamic network situations through extensive analysis and evaluation. A special emphasis is placed on the management of mobility, the optimization of energy usage, and the effectiveness of routing protocols. The paper explores existing protocols such as LEACH, AODV and OLSR highlighting their benefits and challenges for UAV’s. A comparative analysis evaluates adapted protocols based on metrics such as energy efficiency, latency, and reliability.

关键词： Measurement Wireless sensor networks Renewable energy sources Autonomous aerial vehicles Routing protocols Reliability Vehicle dynamics

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Retraction Note: Anticipating network failures and congestion in optical networks a data analytics approach using genetic algorithm optimization

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Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Kiran, Ajmeera Kalpana, V. Madanan, Mukesh Ramesh, Janjhyam Venkata Naga Alfurhood, Badria Sulaiman Mubeen, Suraya Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science College of Arts and Applied Sciences Dhofar University Salalah Sultanate of Oman Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of ECE CMR Technical Campus Hyderabad India

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Novel vision transformer and data augmentation technique for efficient detection of monkeypox disease

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Multimedia Tools and Applications 2024年 1-19页

作者： Alarfaj, Aisha Ahmed Ahmad, Salman Hakeem, Abeer M. Alabdulqader, Ebtisam Abdullah PERO, Chiara Alsubai, Shtwai Innab, Nisreen Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Post Graduate Resident Department of Urology Allama Iqbal Teaching Hospital Dera Ghazi Khan Pakistan Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University P. O. Box 800 Riyadh11421 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Via Giovanni Paolo II 132 Fisciano Salerno Italy Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh13713 Saudi Arabia Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Recent technological advancements have paved the way for the optimization of medical processes, particularly automated disease detection. Moreover, the adoption of machine learning (ML) has greatly helped in automating disease detection. Such approaches can detect various diseases early, enabling timely treatment to save countless lives. Early and accurate diagnosis is very important for diseases like monkeypox, to curb its spread. Monkeypox is a viral disease caused by double-stranded DNA and can be transmitted through close contact with infected humans or animals. It’s early identification and accurate lesion diagnosis are critical to contain the disease. This study proposes an automated approach to optimize the diagnosis of monkeypox disease using a novel vision transformer, which is utilized due to its effectiveness for feature extraction. The Proposed approach’s efficiency and accuracy are tested on a public benchmark dataset comprising a variety of skin lesions of different ages and genders. In addition, data augmentation involves rotation, scaling, and flipping thereby enhancing the density of the training data set for better generalization of ML models. Experiments involve binary, as well as, multi-class classification. For the binary class, the proposed model achieves an accuracy of 97.63%, outperforming traditional ML and deep learning (DL) techniques. In the case of multi-class classification with monkeypox, measles, normal, HFMD, cowpox, and chickenpox classes, the proposed model archives an accuracy of 90.61% while precision, recall, and F1 scores are 91.39%, 89.17%, and 90.28%, respectively. Furthermore, the proposed approach shows average accuracy, precision, recall, and F1 scores of 97.54%, 96.19%, 95.16%, and 95.67%, respectively for five-fold cross-validation. Experiments demonstrate that the combination of data augmentation techniques and the vision transformer model significantly optimizes diagnostic performance. In brief, advanced DL architectur

关键词： Medical image processing

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