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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Mahto, Ashish Gaur, Aryan Sachdeva, Vrinda Department of Computer Science and Engineering India

ISBN: (纸本)9798350356816

In contemporary times, there has been a notable shift among youth and young adults towards prioritizing their health, encompassing both physical and mental well-being. Recognizing this trend, innovative solutions have emerged, including developing digital platforms aimed at tracking and enhancing personal health metrics. This paper proposes the establishment of a dedicated website tailored to this demographic, facilitating the monitoring of progress and fostering consistency in health-related endeavors. The primary objective of this platform is to motivate individuals to embark on a journey of self-improvement by adhering to a structured regimen. Drawing inspiration from the principles of the 75 HARD Challenge, participants are encouraged to commit to a rigorous plan devoid of indulgences or deviations. By embracing this challenge, individuals can experience multifaceted benefits, spanning improved nutritional habits, heightened physical fitness, bolstered cardiovascular health, optimized hydration levels, cognitive enhancement, and enhanced mental *** empirical analysis and anecdotal evidence, this research underscores the transformative potential of consistent adherence to the 75 HARD Challenge. Notably, participants stand to gain not only tangible health improvements but also invaluable intangible rewards, such as habit formation and personal growth. This initiative equips individuals with the tools needed to thrive in their academic pursuits or professional endeavors by instilling a sense of accountability and resilience. © 2024 IEEE.

关键词： Electronic health record

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Energy consumption dynamic prediction for HVAC systems based on feature clustering deconstruction and model training adaptation

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Building Simulation 2024年第9期17卷 1439-1460页

作者： Huiheng Liu Yanchen Liu Huakun Huang Huijun Wu Yu Huang School of Civil Engineering and Transportation Guangzhou UniversityGuangzhou510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China

The prediction of building energy consumption offers essential technical support for intelligent operation and maintenance of buildings,promoting energy conservation and low-carbon *** paper focused on the energy consumption of heating,ventilation and air conditioning(HVAC)systems operating under various modes across different *** constructed multi-attribute and high-dimensional clustering vectors that encompass indoor and outdoor environmental parameters,along with historical energy consumption *** enhance the K-means algorithm,we employed statistical feature extraction and dimensional normalization(SFEDN)to facilitate data clustering and *** method,combined with the gated recurrent unit(GRU)prediction model employing adaptive training based on the Particle Swarm Optimization algorithm,was evaluated for robustness and stability through k-fold *** the clustering-based modeling framework,optimal submodels were configured based on the statistical features of historical 24-hour data to achieve dynamic prediction using multiple *** dynamic prediction models with SFEDN cluster showed a 11.9%reduction in root mean square error(RMSE)compared to static prediction,achieving a coefficient of determination(R2)of 0.890 and a mean absolute percentage error(MAPE)reduction of 19.9%.When compared to dynamic prediction based on single-attribute of HVAC systems energy consumption clustering modeling,RMSE decreased by 12.6%,R2 increased by 4.0%,and MAPE decreased by 26.3%.The dynamic prediction performance demonstrated that the SFEDN clustering method surpasses conventional clustering method,and multi-attribute clustering modeling outperforms single-attribute modeling.

关键词： HVAC system energy consumption clustering analysis deep learning model adaptation dynamic prediction

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A fast and highly scalable frequent pattern mining algorithm

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Future Generation computer Systems 2024年 160卷 854-868页

作者： Cheng, Wan-Shu Lin, Yi-Ting Huang, Peng-Yu Chen, Ju-Chin Lin, Kawuu W. Department of Computer Science and Information Management Providence University Taichung Taiwan Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

As the use of big data and its potential benefits become more widespread, public and private organizations around the world have realized the imperative of incorporating comprehensive and robust technologies into their business processes. In particular, companies are implementing more and more intelligent systems into their business processes, resulting in an exponential increase in the amount of data being collected and used in everyday life on a quarterly basis. Therefore, an algorithm that can efficiently explore the frequent patterns from big data will be able to productively analyze and utilize the generated data to better optimize resources and even develop new business models. Thus, in this urban data era, it is not only essential to make more efficient and accurate decisions to improve business profitability and customer satisfaction, but also a highly challenging core issue. In this study, a new approach, scalable FP mining, is proposed for the first time to achieve a certain level of performance and more efficient memory utilization despite large amounts of data. Experiment results for different data characteristics show that the best performance of the proposed method requires only 48.9% of the DP algorithm's execution time. In further experiments with increased exploration difficulty, the optimal execution time is only 37%. On specific associated databases, the proposed method maintains stable and excellent performance with 12.7% for different data characteristics. The experimental results with increasing exploration difficulty at each stage demonstrate the stability, robustness, and reliability of the proposed SFP, especially under high data complexity and exploration difficulty. © 2024

关键词： Data mining

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Load balancing routing in RPL for the internet of things networks: a survey

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International Journal of Wireless and Mobile Computing 2023年第3-4期24卷 243-257页

作者： Venugopal, Kala Basavaraju, T.G. Department of Computer Science and Engineering Government Engineering College Karnataka Hassan India

Presently, when the Internet of Things (IoT) makes virtually everything smart by improving every aspect of our life, continuous development in this area is imperative. As IoT deals with the Low-Power Lossy Networks (LLNs) with constrained resources, Routing in such constrained networks is considered an acute problem. The Internet engineering Task Force (IETF) has come out with a de facto routing protocol for IoT networks called the IPv6 Routing Protocol for Low-Power and Lossy networks (RPL). Unfortunately, though RPL is formulated with numerous salient characteristics, load balancing is a principal concern that is left unaddressed. Load balancing in IoT guarantees fair dissemination of traffic load amid the nodes in the network and affects the connectivity, stability, reliability and lifetime of LLNs. This paper elucidates the state-of-the-art load balancing routing protocols and issues of load balancing, aiding researchers to design efficient and reliable load balancing routing protocols for IoT networks. © 2023 Inderscience Enterprises Ltd.

关键词： Internet of things

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Enhanced Wi-Fi-Li-Fi system

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International Journal of Wireless and Mobile Computing 2023年第2期25卷 183-189页

作者： Gowtham, M. Department of Computer Science and Engineering The National Institute of Engineering Karnataka Mysuru India

Today's world is fully dependent on data. Data are individual packets or units of information which on process leads to a useful information which intend helps in decision making. So these data are to be shared among the multiple user devices and we come across some of the most commonly used transmission media. Currently, Wireless Fidelity (Wi-Fi) is dominating over other media because of its ease of remote accessing methods without any physical connection. But these do have some loopholes, as they have limited speed of transmission or very low bit rate. So in order to overcome these drawbacks we come up with an emerging concept where the speed of transmission can be enhanced up to 100 Gbps with the help of light as the source of transmission, called Light-Fidelity (Li-Fi). Li-Fi range is of large bandwidth that enables the communication in radiofrequency areas and shows data transmission efficiently. By using both Wi-Fi and Li-Fi we propose a new system, the hybrid Wi-Fi-Li-Fi system. © 2023 Inderscience Enterprises Ltd.

关键词： Decision making

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The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年第12期12卷 21473-21487页

作者： Yi, Meng Lee, Vincent C.S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS and AI ClaytonVIC3800 Australia Southeast University School of Computer Science and Engineering the Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Suzhou215123 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying Unmanned Aerial Vehicles (UAVs) as aerial base stations enhances the coverage and performance of communication networks in Vehicular Edge Computing (VEC) scenarios. However, due to the limited communication range and energy capacity of UAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of UAVs and efficient task offloading remains a significant challenge. To address this problem, the UAV-assisted Two-stage Intelligent Collaboration (UTIC) method is proposed in this paper to tackle the joint position optimization and task scheduling issue. Firstly, a UAV-assisted Two-stage Task Scheduling (UTTS) system model is designed to optimize the allocation process. Secondly, an Enhanced Particle Swarm Optimization (E-PSO) algorithm is designed to determine the optimal positions of UAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of UAVs. Thirdly, Deep Deterministic Policy Gradient (DDPG) method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in UAV deployment and outperform three other classical reinforcement learning (RL) algorithms in terms of reducing system cost. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A review of challenges and solutions in the design and implementation of deep graph neural networks

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International Journal of computers and Applications 2023年第3期45卷 221-230页

作者： Mohi ud din, Aafaq Qureshi, Shaima Department of Computer Science and Engineering NIT Srinagar Srinagar India

The study of graph neural networks has revealed that they can unleash new applications in a variety of disciplines using such a basic process that we cannot imagine in the context of other deep learning designs. Many limitations limit their expressiveness, and researchers are working to overcome them to fully exploit the power of graph data. There are a number of publications that explore graph neural networks (GNNs) restrictions and bottlenecks, but the common thread that runs through them all is that they can all be traced back to message passing, which is the key technique we use to train our graph models. We outline the general GNN design pipeline in this study as well as discuss solutions to the over-smoothing problem, categorize the solutions, and identify open challenges for further research. Abbreviations: CGNN: Continuous Graph Neural Networks;CNN: Convolution NeuralNetwork;DeGNN: Decomposition Graph Neural Network;DGN: Directional GraphNetworks;DGN: Differentiable Group Normalization;DL: Deep Learning;EGAI:Enhancing GNNs by a High-quality Aggregation of Beneficial Information;GAT: GraphAttention Network;GCN: Graph Convolutional Network;GDC: Graph Drop Connect;GDR: Group Distance Ratio;GNN: Graph Neural Network;GRAND: GraphRandom Neural Networks;IIG: Instance Information Gain;MAD: Man AverageDistance;PDE-GCN: Partial Differential Equations-GCN;PTDNet: ParameterizedTopological Denoising network;TDGNN: Tree Decomposition Graph NeuralNetwork;. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Graph neural networks

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CM-GCN: Crossbred Method based-Graph Convolution Networks for identifying influential spreaders from directed networks 16

CM-GCN: Crossbred Method based-Graph Convolution Networks fo...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Saha, Nilanjana Namtirtha, Amrita Dutta, Animesh National Institute of Technology Computer Science and Engineering Durgapur India Jis College of Engineering Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350383119

Influential spreaders play a critical role, either maximizing information dissemination or controlling epidemic spreads. Much of the existing research concentrates on identifying optimal spreaders in undirected networks. However, recognizing the significance of edge direction in spreading processes is essential when pinpointing influential spreaders. Graph Convolutional Neural Networks (GCNs) have shown remarkable efficacy in various graph-based tasks. Nevertheless, the challenge of over-smoothing in GCNs leads to nodes converging to similar representations with increasing depth, restricting the practical usage of shallow models. In response, this paper introduces a novel approach, the Crossbred Method-based Graph Convolutional Network (CM-GCN), to identify superior spreaders, considering the intricacies of directed networks. The proposed CM-GCN method considers the spreading dynamics specific to directed networks. Through a series of experiments on real-world datasets, our results demonstrate that the CM-GCN model outperforms prevailing baseline approaches. This advancement is particularly unable compared to crossbred method and other centrality methods in directed networks. Moreover, the CM-GCN method effectively addresses over-smoothing in deep GCNs while mitigating information loss attributable to DropEdge. The spreading performance of the proposed method, evaluated using the Directed Susceptible-Infected-Recovered (DBIR) epidemic model on six real networks, showcases significant enhancements in spreading dynamics compared to the established crossbred method. © 2024 IEEE.

关键词： Crossbred method Directed Network DSIR epidemic model Graph Convolution Network Influential Spreaders Kendall τ rank correlation

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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Energy Storage System Using Digital Twins with AI and IoT for Efficient Energy Management and Prolonged Battery Life in Electric Vehicles

Energy Storage System Using Digital Twins with AI and IoT fo...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Lakshmi, M. Sri Sarma, Vanam Ajay G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India Department of Computer Science and Engineering Kurnool India

ISBN: (纸本)9798331509828

This research proposes an integrated framework of a digital twin, incorporating artificial intelligence and the Internet of Things to optimize energy management and prolong the lifespan of the battery in electric vehicles. Under typical operating conditions, the system exhibits predictive accuracy with mean absolute error relative to actual data equal to 0.042 and root mean square error equal to 0.055. These values reflect improvement in responsiveness of the system since latency has been reduced to 0.12 seconds with feedback response times established at 0.45 seconds. Moreover, it has increased by 15% in energy efficiency while promoting its longevity with a capacity fade rate measured at 0.0025% per cycle. Besides, the model shows strong anomaly detection capability - maintenance alert precision is found to be 95% while detection rate reaches76%. All these results point out the possibility that combining Digital Twin technology with AI and IoT may end up significantly boosting energy management systems for electric vehicles compared to more traditional approaches. © 2025 IEEE.

关键词： Artificial Intelligence (AI) Digital Twin Technology Electric Vehicle (EV) Battery Management Energy Efficiency Optimization Internet of Things (IoT) Predictive Maintenance

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