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A Graph-Assisted Digital-Twin-Driven Multiagent Shared Offloading for Internet of Vehicles

IEEE Internet of Things Journal 2025年第11期12卷 17349-17363页

作者： Zahangir Alam, Md Rahman, Suryaia Asif Bin Khaled, Md Islam, Ashraful Jamalipour, Abbas Independent University Bangladesh Department of Computer Science and Engineering Center for Computational and Data Sciences Dhaka1229 Bangladesh The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Vehicular edge computing (VEC) allows vehicles to process part of the tasks locally at the network edge while offloading the rest of the tasks to a centralized cloud server for processing. A massive volume of tasks generated by the Internet of Vehicles (IoV) leads to buffer overflow that causes higher latency. Elevating latency, in turn, can increase network energy consumption. Both higher latency and energy consumption lead to a degradation of network performance. Therefore, VEC design requires a balance between latency and energy consumption tradeoff. To reduce overwhelming amount of offloading to edge servers, a cooperative cluster-based shared offloading strategy has been proposed in this work. We use digital twin technology in VEC for managing and adapting to environmental dynamic changes. Then, we leverage Lyapunov (Ly) optimization to transform the stochastic offloading problem into a more manageable deterministic form. Finally, we present a decentralized coordination graph (CG)-driven Ly-based multiagent deep deterministic policy gradient (CG-LyMADDPG) algorithm that trains agents toward energy efficient optimal offloading policy while maintaining queue stability at a maximum delay constraint. The experimental result shows that the proposed learning significantly outperforms the baseline algorithms for energy savings while maintain queue stability. © 2014 IEEE.

关键词： Stochastic systems

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Unsupervised machine learning approach for tailoring educational content to individual student weaknesses

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High-Confidence Computing 2024年第4期4卷 91-104页

作者： Shabab Intishar Rahman Shadman Ahmed Tasnim Akter Fariha Ammar Mohammad Muhammad Nayeem Mubasshirul Haque Sriram Chellappan Jannatun Noor Department of Computer Science and Engineering BRAC UniversityDhaka 1212Bangladesh Department of Computer Science and Engineering University of South FloridaFL 33620USA Computing for Sustainability and Social Good(C2SG)Research Group Department of Computer Science and EngineeringSchool of Data and SciencesBRAC UniversityDhaka 1212Bangladesh

By analyzing data gathered through Online Learning(OL)systems,data mining can be used to unearth hidden relationships between topics and trends in student ***,in this paper,we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system *** our implementation,we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%.By focusing on marks below this threshold,we aim to identify and establish associations based on the patterns of weakness observed in the past ***,we leverage K-means clustering to provide instructors with visual representations of the generated *** strategy aids instructors in better comprehending the information and associations produced by the *** clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights,enabling them to support the verification of the relationship between *** can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their *** paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

关键词： Recommendation system Clustering F-P growth Apriori Associative pattern E-learning sphere Online learning

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Identification of domain-specific euphemistic tweets using clustering

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International Journal of Information Technology (Singapore) 2024年第1期16卷 21-31页

作者： Devi, Maibam Debina Saharia, Navanath Data Engineering Lab Department of Computer Science & Engineering IIIT Senapati Manipur Imphal 795002 India

Social media platforms (SMPs) are frequently utilised as a readily accessible and comprehensive medium for expressing personal opinions nowdays. The use of euphemism, a linguistic strategy in which the underlying feeling of expressive content is veiled by the use of mild language, has been a longtime practise in the realm of SMPs for the purpose of reducing harshness or to discuss sensitive topics [1]. The identification of masked contents [2] in euphemism is challenging due to their inherent nature. This study presents a proposed identification mechanism aimed at detecting domain-specific euphemisms through the utilisation of clustering techniques. The pattern categorization feature is created utilising domain-specific lexical features combined with frequency-based features. In order to identify the most suitable match, the hybrid feature extraction algorithms incorporate uni-gram and bi-gram features dependent on frequency based feature, in conjunction with a lexicon. The objective of the dimension reduction phase is to address the issue of sparsity and to identify the most significant words for each sample in order to classify them into different domains using centroid and density-based clustering techniques. The DBSCAN algorithm is employed with an epsilon value of 2.5 and a minimum number of points set to 6, resulting in the identification of 7 distinct clusters. To calculate the optimal value for k in the K-means algorithm, the Silhouette score is utilised. The clusters that were obtained are examined by manual means. We compare our model to FLUTE dataset with epsilon value of 0.2, minpoints of 5 for DBSCAN, and obtain validation score of 0.55. The DBSCAN clustering algorithm generates distinct clusters that extend beyond the scope of the inquiry domain. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： DBSCAN Domain-specific euphemistic text Euphemism K-means Silhouette score

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Artificial Intelligence Support for the Prompt Identification and Understanding of the Broader Spectrum of Autism in Children 5

Artificial Intelligence Support for the Prompt Identificatio...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Julaiha, A.G. Noorul Hemamalini, S. Preiya, V. Sathya Sathyamoorthy, K. Priyanka, V. Rajalakshmi Institute of Technology Centre for Data Science Department of Computer Science & Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

This research work presents a novel language intervention system for Tamil-speaking children with autism spectrum disorder (ASD). The system satisfies the considerable requirement for tools aimed at one more section of population that has actually been forgotten by mainstream economics. Traditional language learning applications often lack specificity for diverse linguistic contexts, focusing on general learning rather than the unique challenges faced by ASD children. The proposed solution leverages Deep Learning (DL) modules to transform partial spoken Tamil words into complete, child-friendly vocabulary. This system goes beyond simple word completion;it incorporates interactive features to engage children with ASD and promote active learning. Preliminary evaluations yielded promising results, suggesting the system's potential in enhancing communication skills among Tamil-speaking children with ASD. This paper research study reviews the limitations of existing language learning tools and the rationale behind employing a DL-based approach. We discuss the specific DL architecture (e.g., Recurrent Neural Networks) envisioned for the system and how it will be trained on a corpus of Tamil speech data. This study also investigate the potential advantages beyond vocabulary learning, such as improved sentence formulation and the development of practical language abilities. Future ambitions include broadening the system's scope to include more languages, refining the DL model through ongoing training with enriched datasets, and conducting extensive user research with varied ASD populations. Validation across demographics is critical to ensuring the system's efficiency and generality. This research has the potential to bridge the language acquisition gap for Tamil-speaking children with ASD, empowering them to develop crucial communication skills and fostering their social inclusion. © 2024 IEEE.

关键词： Recurrent neural networks

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Leveraging XGBoost and Clinical Attributes for Heart Disease Prediction 5

Leveraging XGBoost and Clinical Attributes for Heart Disease...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Susmitha, Kota Bhargavi, Maridu Sri, Achyuta Mohitha Sai Reddy, Bogala Devi Prasaad Satyanarayana, Paladugu Siva Rao, K V Ranga Vignan's Foundation for Science Technology and Research Department of Computer Science and Engineering Guntur India Department of Computer Science and Engineering-Data Science Hyderabad India

ISBN: (纸本)9798331530013

Despite the various advances in medical technology, heart disease continues to rank among the leading causes of mortality in the world, killing millions each year. There is hope that the risks involved with heart disease can be reversed in time and the outcomes of patients improved with earlier prediction and diagnosis. However, the use of conventional methods of diagnostic techniques tends to be very resource-intensive and inaccessible to other more disadvantaged areas. The present work defines a model for machine learning in predicting heart disease and illustrates a more efficient and accessible solution. There is a risk for heart disease that is determined using a set of various clinical features. The algorithm used in the prediction of heart disease is XG Boost that are applied to the dataset. The method provides a reliable tool for the early detection of heart disease, thus helping healthcare providers to improve preventive care and treatment strategies. The results of this study will be helpful in advancing research to identify other avenues for prediction of heart disease. © 2024 IEEE.

关键词： early diagnosis Heart disease UCI (University California Irvine) Heart Disease dataset XG Boost

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Modified light GBM based classification of malicious users in cooperative cognitive radio networks

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Cyber-Physical Systems 2024年第1期10卷 104-122页

作者： Sekar, S. Jeyalakshmi, S. Ravikumar, S. Kavitha, D. Department of Information Technology SRM Valliammai Engineering College Chennai India Department of Artificial Intelligence and Data Science SRM Valliammai Engineering College Chennai India Department of Computer Science and Engineering SRM Valliammai Engineering College Chennai India

This paper proposes the Modified Light GBM to classify the Malicious Users (MUs) and legitimate Secondary Users (SUs) in the cognitive-radio network. The proposed method is to avoid the consequences of malicious users in Cooperative Spectrum Sensing (CSS) without the detection of MUs. The method is tested against the occurrence of Always No Malicious User (ANMU), always yes malicious user (AYMU), the Random Malicious User (RMU), and Opposite Malicious User (OMU) transmitting spectrum sensing data to the Fusion Center (FC) by normal secondary users. The efficiency of the proposed method is expressed via simulations and compared with other existing methods. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Signal to noise ratio

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A Mathematical and Dynamical Model for Analyzing H-Shaped PM Vernier Motor for Electric Motorcycle Mid-Drive Applications

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IEEE Transactions on Energy Conversion 2025年第1期40卷 529-543页

作者： Ghods, Mehrage Faiz, Jawad Bazrafshan, Mohammad Amin Gorginpour, Hamed Toulabi, Mohammad Sedigh University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran Persian Gulf University Department of Intelligent Systems Engineering and Data Science Bushehr*** Iran Oakland University Department of Electrical and Computer Engineering RochesterMI48309 United States

The permanent magnet (PM) Vernier machines enhance torque density and decrease cogging torque compared to conventional permanent magnet synchronous motor. This paper presents a novel fractional-slot H-shaped PM Vernier machines, which incorporates unique design for the PM housing and rotor core construction. These designs are specifically optimized for the small-scale electric vehicles application. The choice of the PM arrangement is based on the combination of V-shaped and spoke-array PM topologies. The aim is to achieve a higher torque density than V-shaped PM machines, while reducing cogging torque compared to spoke-array PM machines. By using this methodology, leakage fluxes in the terminal region of the PMs reduce, resulting in an augmentation of both flux-linkage and power factor. A prototype 680 W motor was fabricated with 12 slots and 8 poles to verify the accuracy and effectiveness of the proposed model. Mathematical model and dynamical analysis are proposed for the analytical prediction of machine performance. Due to the unique shape feature of the machine, the conformal mapping approach is applied to design the rotor core. To validate the model, the predicted performance of the motor was compared with those obtained by finite element analysis and experiments. © 1986-2012 IEEE.

关键词： Permanent magnets

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RagaMoodSync: a deep learning framework for Hindustani Raga recommendations driven by facial emotions

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International Journal of Information Technology (Singapore) 2025年 1-14页

作者： Pingle, Yogesh Prabhakar Ragha, Lakshmappa K. Department of Computer Engineering Terna Engineering College University of Mumbai Maharashtra Mumbai India Department of Computer Science & Engineering (Data Science) Vidyavardhini’s College of Engineering & Technology University of Mumbai Maharashtra Mumbai India

RagaMoodSync presents an innovative framework for personalized music therapy through Hindustani Raga recommendations based on facial emotion recognition. Utilizing a Fusion-DenseNet (FusDenseNet) architecture with advanced multi-pooling for emotion detection, the system achieves high precision in identifying user moods. Integrated with a Focus Deep Collaborative Filtering (FDCF) recommendation system, RagaMoodSync delivers tailored raga suggestions that address emotional well-being and stress management. Validated with the FER2013 dataset, the framework demonstrates superior performance over existing approaches. By merging AI-driven emotion recognition with the therapeutic potential of Indian classical music, this system pioneers a novel intersection of technology and mental health care, offering a promising pathway for non-invasive emotional healing solutions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Focus Deep Collaborative Filtering FusDenseNet RagaMoodSync

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Enhancing wireless sensing via a target-mounted intelligent reflecting surface

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National science Review 2023年第8期10卷 13-14页

作者： Xiaodan Shao Rui Zhang School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].

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Inductive Lottery Ticket Learning for Graph Neural Networks

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Journal of computer science & Technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

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