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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Priya, M. Anitha, A. Nallakaruppan, M.K. Raju, J. Raghavan, R. Srivastava, Divyansh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350325188

Heart disease is one of the destructive infections that an enormous populace of individuals all over the planet grieves. The prediction of heart disease is very much urged because of increasing death rates. The conventional method of prediction isn't adequate for such a disease. Evolving a clinical determination framework based on machine learning concepts gives more exact conclusions than the conventional way for the prediction of such illness. In this paper, we have used different machine learning algorithms to accurately foresee whether an individual has a heart illness or not. The concepts of machine learning like linear prediction KNN, SVM, and, ANN to predict the probability of heart disease in an individual are used. The medical and general details of the person like BMI, alcohol and tobacco consumption, etc. are used to make the prediction. © 2023 IEEE.

关键词： ANN Heart Disease KNN Machine Learning Neural Network SVM

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Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

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China Communications 2024年第3期21卷 230-246页

作者： Tong Minglei Li Song Han Wanjiang Wang Xiaoxiang School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing ***,the network state information is uncertain or *** deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this *** problem of minimizing the average sum task completion delay of all IoT devices over all time periods is *** decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier *** results validate that the proposed scheme performs better than other baseline schemes.

关键词： computing resource allocation mobile edge computing satellite-terrestrial networks task offloading decision

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WLARS: Workload-Aware Recharge Scheduling Mechanism for Improving Surveillance Quality in Wireless Rechargeable Sensor Networks

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IEEE Sensors Journal 2023年第11期23卷 12237-12250页

作者： Dande, Bhargavi Chang, Chih-Yung Wu, Shih-Jung Roy, Diptendu Sinha National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Tamkang University Department of Computer Science and Information Engineering New Taipei25137 Taiwan National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong793003 India

With radio frequency (RF), the mobile charger (MC) can wirelessly transmit energy to the sensor nodes in the network. The wireless energy transfer enhances the lifetime of sensor nodes in wireless rechargeable sensor networks (WRSNs). Most of the studies improved the efficiency of MC or perpetuated the lifetime of WRSNs. At the same time, none of the studies focused on considering the spatial and temporal surveillance qualities (STSQs) of each sensor recharged. Therefore, this article proposed an efficient energy recharge scheduling for MC, aiming to maximize the STSQ of the given network. Initially, sensor routing load (RL) is considered to partition the network to distribute the charging load of the MCs evenly. Second, the busy level of the MC is considered to send the recharging requests to the MC. Based on the busy level of MC, the sensors adjust the sensing rate frequency to manage their energy. Finally, cooperation between the neighboring MCs is proposed to reduce the waiting time for recharging requested sensors. The simulation results present that the proposed work yields the literature in terms of STSQ, traveling distance, average waiting time (AWT), and energy usage efficiency. © 2001-2012 IEEE.

关键词： computer architecture

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A Novel Time Resource Allocation Configuration for Multi-Task Offloading in Mobile Cloud Computing (MCC)

A Novel Time Resource Allocation Configuration for Multi-Tas...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kumar, M. Praveen Meena, M. Kumar, S. P. Santhosh Saravanan, B. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Vivekanandha College of Technology for Women Department of Information Technology Tiruchengode India Rathinam Technical Campus Department of Information Technology Coimbatore India

ISBN: (纸本)9798350300857

The main objective of this research study revolves around reducing remote cloud communication latency and alleviating system load through the implementation of a 'cloudlet.' However, it is important to acknowledge that while cloudlets offer advantages in reducing communication delays, matching the formidable system performance of remote clouds fall short in terms. The cloud dynamically allocates resources to cater to the changing user population, but the vulnerability of cloudlet services leads to interruptions in user access during task execution. Consequently, this renders cloud computing ineffective, as users fail to receive the expected computational outcomes. In response to these challenges, this article introduces a comprehensive framework for real-time resource allocation, enabling users to efficiently distribute essential resources. To overcome issues related to task failures and real-time resource allocation, the paper recommends the adoption of the Particle Swarm Optimization-Motion Log (PSO-ML) method. Remarkably, the PSO-ML method surpasses the traditional PSO method in terms of delivering more robust and impactful results. Keywords: MCC, PSO-ML, PSO, Assets allocation, Task Offloading © 2023 IEEE.

关键词： Resource allocation

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Maximizing a κ-Submodular Maximization Function under an Individual Knapsack Constraint 12

Maximizing a κ-Submodular Maximization Function under an In...

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12th International Symposium on information and Communication technology, SOICT 2023

作者： Tran, Tan Pham, Canh Ha, Dung Faculty of Information Technology Vnu University of Engineering and Technology Hanoi Viet Nam ORLab Faculty of Computer Science Phenikaa University Hanoi Viet Nam Faculty of Information Technology Vnu University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9798400708916

In this work, we consider a novel problem of maximizing monotone k-submodular functions under the individual knapsack constraint () over the ground set V, which has been found numerous applications in machine learning, including data summarization and information propagation. We propose an approximation algorithm that has approximation ratio and takes O(nklog (n)/μ) query complexity, where μ is an input parameter. Alongside theoretical analysis, we conduct extensive experiments on our proposed algorithm via some applications, such as Influence Maximization and Sensor Placement. The experimental results demonstrate that our algorithm gives competitive solution quality with state-of-the-art techniques but significantly reduces the required queries. © 2023 ACM.

关键词： Combinatorial optimization

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Chaotic Analysis of Pedestrian Flow Series for Vehicular Networking in Complex Interaction Environment 9

Chaotic Analysis of Pedestrian Flow Series for Vehicular Net...

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9th International Conference on computer and Communications, ICCC 2023

作者： Qi, Siqi Wang, Lusheng Kai, Caihong Zhu, Kongjin Hefei University of Technology School of Computer Science and Information Engineering Hefei China Hefei University of Technology School of Automotive and Transportation Engineering Hefei China

ISBN: (纸本)9798350317251

Interaction between self-driving cars and pedestrians is a key issue for the design of vehicular networking and auto-drive algorithms. Pedestrian movement usually exhibits irregular and complex behaviors, dramatically increasing the difficulty of such design. By choosing a suitable method to analyze the pedestrian flow and obtain its major features, self-driving cars and service robots could intelligently avoid collision with pedestrians. Therefore, this paper analyzes the chaotic characteristics of the pedestrian flow time series of different locations in real campus scenes qualitatively and quantitatively. The optimal delay and optimal embedding dimension of pedestrian flow time series are obtained by the mutual information method and Cao's method, respectively. The largest Lyapunov exponent, correlation dimension, and Kolmogorov entropy are used to quantitatively analyze its chaos, and recurrence plot is used for qualitative demonstrations. According to this study, pedestrian flow time series are definitely chaotic, which provides essential theoretical support for pedestrian flow prediction and auto-drive algorithm design in future vehicular networking. © 2023 IEEE.

关键词： Lyapunov methods

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Metaverse Creation And Decentralized Land NFTs Over Blockchain 9

Metaverse Creation And Decentralized Land NFTs Over Blockcha...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Janet, S. Shaji, Ayda Sara D'Cruz, Brian Cedric Abraham, Eldhose Philip, Abin Oommen Kamal, Leda Toc H Institute of Science and Technology Department of Computer Science and Engineering Kochi India Indian Institute of Information Technology Kottayam IIIT-K Department of Computer Science and Engineering Cybersecurity India

ISBN: (纸本)9798350314090

A metaverse is a rapidly evolving concept in the realm of virtual reality that refers to a network of three-dimensional virtual environments centered on social interaction. The word is widely used in futurism and science fiction to depict a potential iteration of the internet as a single, universal virtual environment assisted by the usage of virtual and augmented reality headgear. There are several Metaverse currently available, such as Sandbox and Decentraland, each offering unique and immersive experiences for users. Non-Fungible Tokens (NFTs) are a novel form of digital assets that are stored on a blockchain, a decentralized digital ledger that keeps track of transactions. NFTs can prove ownership and validity for these digital assets via a blockchain, increasing their value and collectability. NFTs can be linked to digital assets that can be reproduced, such as images, movies, and audio. To give a public certificate of authenticity or proof of ownership, NFTs use a digital ledger. As part of our ongoing project, the creation of the Metarealm, a metaverse for Kochi city, promises to provide an engaging and cutting-edge platform for users to buy, sell, and trade virtual land and assets. This Metaverse will enable users to explore the city and its landmarks in a fully immersive 3D environment, fostering a sense of community and engagement among its users. As the Metaverse and NFTs continue to evolve, the possibilities for virtual experiences and digital ownership are limitless. © 2023 IEEE.

关键词： Blockchain

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Precision Cardiac Risk Evaluation through Flexible Classification Mining Strategies 3

Precision Cardiac Risk Evaluation through Flexible Classific...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Das, Neeraj Ganorkar, Akshaya M Jayaprakash, B. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350343274

Accurate methods of measuring cardiac risk are desperately needed, as cardiovascular diseases remain a major global health concern. The aim of a newly released study titled 'Precision Cardiac Risk Evaluation through Flexible Classification Mining Strategies' was to establish new standards in the field of cardiac risk assessment. This work suggests a novel method to increase the accuracy and precision of cardiac risk assessment by utilizing flexible category mining algorithms. The study included a proposed strategy. There are clear limitations to traditional risk assessment methods, especially with regard to how accurately they can identify those who are at risk. As a result of this work, innovative new methods for improving cardiac risk assessment without imposing these limitations have been created. Each of these enhancements is supported by the research's conclusions. Combining state-of-the-art data analysis and machine learning technology allows the recommended strategy to enhance forecast precision and adjust to changing risk indicators. The most significant and remarkable outcome of this study is the development of a prediction model with enhanced AUC-ROC values over previous methods and exceptional sensitivity, specificity, and accuracy. The suggested approach shows a higher ability to recognize individuals who may have cardiovascular diseases. By reducing the likelihood of missed diagnoses and the frequency of false negatives, this is achieved. The research also emphasizes the model's interpretability, which gives medical practitioners a better understanding of the variables that affect the predictions. This interpretability increases the technique's potential impact in clinical settings by ensuring that medical practitioners can provide individualized treatment and make correct diagnoses. To put it another way, interpretability ensures that health care providers can provide each patient with the individualized attention they require. © 2023 IEEE.

关键词： Diagnosis

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A fatigue assessment method based on attention mechanism and surface electromyography

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 112-120页

作者： Dang, Yukun Liu, Zitong Yang, Xixin Ge, Linqiang Miao, Sheng College of Computer Science & Technology Qingdao University Qingdao266071 China School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China School of Computer Science Columbus State University GA31907 United States Faculty of Environmental Engineering The University of Kitakyushu Kitakyushu808-0135 Japan

Surface electromyography (sEMG) signals can be used to quantitatively assess muscle fatigue, thereby directly and objectively reflecting the functional state of neuromuscular activity. Effective fatigue diagnosis can prevent muscle damage, thereby improving the safety of rehabilitation exercise. Traditional fatigue diagnosis has certain limitations, including strong subjectivity and poor accuracy. This paper designs a sEMG signals acquisition circuit and collects the sEMG signals of the upper limb biceps brachii and triceps brachii in the force-relaxation state in a dual-channel form. Muscle fatigue classification assessment using Dynamic Time Warping-K Nearest Neighbor (DTW-KNN) and three deep learning algorithms. The experimental results show that compared with traditional machine learning algorithms, deep learning algorithm can achieve higher accuracy and time efficiency. In addition, this study introduces an attention mechanism to dynamically and reasonably assign network weights to achieve high level feature learning. The Attention-Long Short-Term Memory (Attention Based LSTM) neural network achieves 93.5% assessment accuracy with a time overhead of only 3.73s, allowing for real-time assessment of muscle fatigue. © 2023 The Authors

关键词： Long short-term memory

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Narrative Paragraph Generation for Photo Stream Using Neural Networks 4th

Narrative Paragraph Generation for Photo Stream Using Neura...

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Anjali, M.N. More, Tejash Misa, Kumari Nath, Keshab Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kerala Kottayam686635 India

ISBN: (纸本)9789819920990

Humans have the innate ability to perceive an image just by looking at it, for us images are not just a collection of objects but a network of interconnected object relationships. The problem arises when a machine tries to inspect an image, hence we try to convert image data to textual data. Despite major achievements in the image captioning field, there is a lack of models that provide concise captions of a given image;moreover, already existing models are so much bigger in size that the number of learning parameters is very high. The objective of this paper is to fill that gap;hence, we propose a model that incorporates an advanced deep convolution neural network to extract image features and an attention GRU with a local attention network to generate captions. We have also identified a class imbalance problem with Flickr dataset. We rectified this problem by adding images of some specific classes. The model has been trained on our improvised Flickr dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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