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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Kumar, Gyanendra Pragya Verma, Lal Pratap Rawat, Sur Singh Singh, Mahesh Kumar School of Computing Sciences and Engineering Galgotias University Greater Noida India Manipal University Jaipur Department of Computer Science and Engineering Rajsthan Jaipur India Jss Academy Department of Computer Science and Engineering Noida India ABES Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350329186

With the rapid advancement in communication technology such as the Internet of Things and machine-to-machine communications, not only humans but a large number of self-adaptive devices are also pouring data into the network. Most of these devices are resource constraint and communicate wirelessly in a lossy, heterogeneous and asymmetric network environment. Recently many multi-home devices are coming with multiple asymmetric network interfaces, so maximum exploitation of these interfaces using concurrent multipath transmission (CMT) protocols is imperative. This paper presents state-of-art of technological details of different variants of CMT protocols as well as an experimental evaluation of basic CMT, CMT-potentially failed (CMT-PF) and Adaptive CMT (A-CMT) in the lossy and asymmetric network environment. The experimental evaluation is performed using NS2.35 simulation tools on widely used network topology under varying network factors like bandwidth, network congestion and round-trip time (RTT). Although these variants are not designed with heavy losses in mind, the simulation results show that the performance of A-CMT is better than CMT and CMT-PF. © 2023 IEEE.

关键词： Machine-to-machine communication

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A survey of energy-efficient strategies for federated learning in mobile edge computing

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Frontiers of Information Technology & Electronic engineering 2024年第5期25卷 645-663页

作者： Kang YAN Nina SHU Tao WU Chunsheng LIU Panlong YANG School of Electronic Engineering National University of Defense TechnologyHefei 230009China Department of Computing Hong Kong Polytechnic UniversityHong Kong 999077China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China

With the booming development of fifth-generation network technology and Internet of Things,the number of end-user devices(EDs)and diverse applications is surging,resulting in massive data generated at the edge of *** process these data eficiently,the innovative mobile edge computing(MEC)framework has emerged to guarantee low latency and enable eficient computing close to the user ***,federated learning(FL)has demonstrated its empirical success in edge computing due to its privacy-preserving ***,it becomes a promising solution for analyzing and processing distributed data on EDs in various machine learning tasks,which are the major workloads in ***,EDs are typically powered by batteries with limited capacity,which brings challenges when performing energy-intensive FL *** address these challenges,many strategies have been proposed to save energy in *** the absence of a survey that thoroughly summarizes and classifies these strategies,in this paper,we provide a comprehensive survey of recent advances in energy-efficient strategies for FL in ***,we first introduce the system model and energy consumption models in FL,in terms of computation and *** we analyze the challenges regarding improving energy efficiency and summarize the energy-efficient strategies from three perspectives:learning-based,resource allocation,and client *** conduct a detailed analysis of these strategies,comparing their advantages and ***,we visually illustrate the impact of these strategies on the performance of FL by showcasing experimental ***,several potential future research directions for energy-efficient FL are discussed.

关键词： Mobile edge computing Federated learning Energy-efficient

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A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

A full privacy-preserving distributed batch-based certificat...

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作者： Rabie, Osama Bassam J. Selvarajan, Shitharth Hasanin, Tawfiq Mohammed, Gouse Baig Alshareef, Abddulrhman M. Uddin, Mueen Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Cybersecurity Center King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University LeedsLS1 3HE United Kingdom Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India College of Computing and Information Technology University of Doha for Science and Technology Doha24449 Qatar

The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority. © 2023, The Author(s).

关键词： Sensor nodes

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Enhancing Object Detection Using Information Theory and Network Coding

Enhancing Object Detection Using Information Theory and Netw...

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Data, Computation and Communication (ICDCC), International Conference on

作者： Sultan Alam Veronika Dhingra Diya Raghuvanshi Srishti Sharma Disha Sengar Anshika Singh School of Computing Science and Engineering VIT Bhopal University Sehore India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

This research explores the challenge of dynamic environment object detection. Changing environment factors adversely affect the feature detection and robustness of the model. The approach proposed homogenizes the principles from Information Theory and Network Coding to enhance the overall final matching accuracy and robustness of the model. The effectiveness is improved by systematically using the concepts of entropy, mutual information, Principal Component Analysis, Random Linear Network Coding and Support Vector Machines. The developed framework maintains distinctive feature recognition and classification performance with rapid environment changes. The proposed and the baseline model is run in python using OpenCV for obtaining the experimental result. By including these robust feature extraction techniques along with Scale-Invariant Feature Transform, in dynamic environments, the model achieves a final matching accuracy of 72.4% and an F1-score of 76.1%. In contrast, the baseline model achieves a final matching accuracy of 43.8% and an F1-score of 44%. The improvement in performance shows the model's enhanced resilience and robustness with the changing environmental conditions, contributing valuable prospects for real-time applications where environmental variability is a factor to be considered.

关键词： Support vector machines Accuracy Object detection Transforms Network coding Feature extraction Robustness Real-time systems Entropy Principal component analysis

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An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

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Computers, Materials & Continua 2024年第7期80卷 389-425页

作者： Nidhi Parashar Prashant Johri Arfat Ahmad Khan Nitin Gaur Seifedine Kadry School of Computer Science and Engineering Galgotias UniversityGreater Noida203201India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction *** learning(DL)and machine learning(ML)models effectively deal with such *** research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March *** addition,it analyses the effectiveness of various input parameters considered in crop yield prediction *** conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop *** total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is *** conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research *** study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel *** also discuss the ethical and social impacts of AI on ***,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven ***,thorough research is required to deal with challenges in predicting agricultural output.

关键词： Machine learning crop yield prediction deep learning remote sensing long short-term memory time series prediction systematic literature review

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Evolving Towards Optimal Cloud Resource Allocation and Cost Management: An In-depth Analysis

Evolving Towards Optimal Cloud Resource Allocation and Cost ...

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Advances in computing, Communication and Networking (ICAC2N), International Conference on

作者： Sourabh Pal Krishna Kant Agrawal Baskar Kasi School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

This research paper explores the field of cloud computing optimisation, with the goal of tackling the two-fold difficulty of efficiently allocating resources and effectively managing costs. The primary objective is to achieve a balance between optimising the allocation of resources to enhance performance and minimising operational expenses in cloud systems. Our approach incorporates a systematic methodology that includes a thorough examination of existing literature, the development of theoretical models, and the execution of substantial experiments. The suggested analysis indicates that dynamic allocation, load balancing, and auto-scaling techniques have significant potential in reaching this balance. Dynamic allocation adjusts resources in real-time to accommodate varying workloads, while load balancing maintains equitable distribution of resources. Auto-scaling stands out as the top performer, with a significant 28% reduction in costs and a noteworthy 30% boost in performance. The proposed research expands upon previous studies by offering a comprehensive evaluation of resource allocation options within the existing approaches. It highlights the feasibility of obtaining cost reductions while concurrently improving performance in cloud systems. However, the suggested study recognises specific constraints, including the utilisation of synthetic workload data and simplified cloud infrastructures. The research has significant ramifications as it provides guidance to organisations on how to optimise their allocation of cloud resources and manage costs. This ultimately promotes efficient and cost-effective cloud operations.

关键词： Cloud computing Costs Systematics Load management Dynamic scheduling Real-time systems Resource management Security Optimization Load modeling

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A branch current estimation and correction method for a parallel connected battery system based on dual BP neural networks

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Green Energy and Intelligent Transportation 2022年第2期1卷 112-123页

作者： Quanqing Yu Yukun Liu Shengwen Long Xin Jin Junfu Li Weixiang Shen School of Automotive Engineering Harbin Institute of TechnologyWeihaiShandong 264209China School of New Energy Harbin Institute of TechnologyWeihaiShandong 264209China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

In the actual use of a parallel battery pack in electric vehicles(EVs),current distribution in each branch will be different due to inconsistence characteristics of each battery *** the branch current is approximately calculated by the total current of the battery pack divided by the number of the parallel branches,there will be a large error between the calculated branch current and the real branch *** current sensors to measure each branch current is not practical because of the high *** estimation of branch currents can give a safety warning in time when the parallel batteries of EVs are seriously *** paper puts forward a method to estimate and correct branch currents based on dual back propagation(BP)neural *** the proposed method,one BP neural network is used to estimate branch currents,the other BP neural network is used to reduce the estimation error cause by current pulse ***,this paper makes discussions on the selection of the best inputs for the dual BP neural networks and the adaptability of the method for different battery capacity and resistence *** effectiveness of the proposed method is verified by multiple dynamic conditions of two cells connected in parallel.

关键词： BP neural Network Branch current estimation and correction Electric vehicles Lithium-ion battery pack

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A Survey of Machine Learning Algorithms for Defective Steel Plates Classification 8th

A Survey of Machine Learning Algorithms for Defective Steel ...

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8th International Conference on computing, Control and Industrial engineering, CCIE 2024

作者： Gao, Meiyan Wei, Yuanzhou Li, Zhenglin Huang, Bolin Zheng, Changyang Mulati, Aliya College of Engineering and Computing Florida International University Miami United States Department of Computer Science and Engineering Texas A&M University College Station United States Viterbi School of Engineering University of Southern California CaliforniaCA United States School of Engineering Brown University Rhode Island United States Biological Science Florida International University Florida United States

ISBN: (纸本)9789819769339

This paper aims to explore various classification models and data processing techniques for classifying defects on steel plates. The study is based on a dataset of steel plates with seven different types of defects and seeks to identify the most effective combination of machine learning models and data processing techniques for automatic defect recognition. Key methodologies include data augmentation, feature selection, and dimensionality reduction techniques like SMOTE, PCA, and REF. Various models, including Naive Bayes, Logistic Regression, SVM, Decision Trees, Random Forests, and Neural Networks, are evaluated. The results indicate that Hyperparameter Tuning and REF significantly enhance model performance, with Random Forest and Neural Networks achieving the highest accuracy. Random Forest, due to its interpretability and efficiency, is identified as the best model for this classification task. This research lays a foundation for applying machine learning algorithms in industrial defect detection, promising improvements in automation, efficiency, and production quality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis 13th

Dependency-Type Weighted Graph Convolutional Network on End...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Mu, Yusong Shi, Shumin School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031579189

Previous studies consider little on using dependency-type messages in the E2E-ABSA task. Studies using dependency-type messages just contact the dependency-type message and word embedding vectors, which may not fully fuse the context feature and information from the dependency type. This paper proposes a new model called Dependency-Type Weighted Graph Convolution Network (DTW-GCN) to compose dependency-type messages and word embedding. We use a type-weighted matrix to combine the dependency-type message, and DTW-GCN could fuse the dependency-type message and word embedding vectors. Experiments conducted on three benchmark datasets verify the effectiveness of our model. © IFIP International Federation for Information Processing 2024.

关键词： Sentiment analysis

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IPL Data Analysis and Visualization for Team Selection and Profit Strategy 7

IPL Data Analysis and Visualization for Team Selection and P...

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7th International Conference on computing Methodologies and Communication, ICCMC 2023

作者： Saranya, G. Swaminathan, Aravind Joel Benjamin, J. Surendran, R. Nelson, Leema Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai campus Chennai India Data Science Middlesex University Dubai United Arab Emirates Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9781665464086

Day by day, the role of data science and machine learning in cricket is increasing due to the large amount of data generated from a single player on a whole line. The field of data science is the intensive study of data to extract insights and knowledge from the data and apply the acquired knowledge and actionable insights. These available data and statistics are used to predict things like the team's first-innings score and the probability of winning the second team, etc. This study works on Indian Premier League (IPL) Data Analysis 2022 and Data Visualization for the duration (2008-2020) using Python. This application modules are followed by preprocessing, data analysis, and visualization, and finally create a model that predicts the team's overall score and the probability of winning. When building models, python algorithms such as Numpy is used for S cientific computing, Pandas used to perform Data Analysis, and finally Matplotlib and S eaborn to enable Data Visualization. Finally, this paper helps the trainers and owners of the team during the auction to select an emerging player to win the matches in the entire season and to win the trophy and to get profit to the owners by the profit strategy done in this analysis report. © 2023 IEEE.

关键词： Data visualization

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