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International Conference on Machine Learning and Big data Analytics, ICMLBDA 2023

作者： Dekka, Satish Raju, K. Narasimha Sai, D. Manendra Pallavi, M. Sambana, Bosubabu JNTU-GV Andhra Pradesh Vizianagaram India Andhra University Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Vignan’s Institute of Engineering for Women Visakhapatnam JNTU-GV Andhra Pradesh Vizianagaram India Department of Data Science School of Computing Mohan Babu University Andhra Pradesh Tirupathi India

ISBN: (纸本)9783031513374

One of the most important and complex issues in the IoT context is energy usage. Because the many nodes utilized in IoT contexts are energy-constrained and must operate for long periods of time without human interaction, their batteries quickly run out of power. As a result, nodes must manage their energy effectively. Sensing, communicating, and processing are regarded as the primary functions of IoT sensors. IoT connectivity uses more energy at the node level than at other times. The main motto of this study is to reduce energy consumption and lengthen network life. Although routing also has a major impact on energy usage, the first one deals with employing an energy model, and the second one deals with selecting an effective routing protocol. When picking a protocol, various other performance indicators such as throughput, delay, packet delivery ratio, and packet loss are also considered. The Network Simulator 3 is used as a tool for testing these strategies (NS3). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy Model Green IoT Green networks MANET NS3 Routing

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STAR: Stepwise Task Augmentation and Relation Learning for Aspect Sentiment Quad Prediction

arXiv

引用

arXiv 2025年

作者： Lai, Wenna Xie, Haoran Xu, Guandong Li, Qing Department of Computing Hong Kong Polytechnic University Hong Kong School of Data Science Lingnan University Hong Kong School of Computer Science Data Science Institute University of Technology Sydney SydneyNSW Australia Department of Computing The Hong Kong Polytechnic University Hong Kong

Aspect-based sentiment analysis (ABSA) aims to identify four sentiment elements, including aspect term, aspect category, opinion term, and sentiment polarity. These elements construct the complete picture of sentiments. The most challenging task, aspect sentiment quad prediction (ASQP), predicts these elements simultaneously, hindered by difficulties in accurately coupling different sentiment elements. A key challenge is insufficient annotated data that limits the capability of models in semantic understanding and reasoning about quad prediction. To address this, we propose stepwise task augmentation and relation learning (STAR), a strategy inspired by human reasoning. STAR constructs auxiliary data to learn quadruple relationships incrementally by augmenting with pairwise and overall relation tasks derived from training data. By encouraging the model to infer causal relationships among sentiment elements without requiring additional annotations, STAR effectively enhances quad prediction. Extensive experiments demonstrate the proposed STAR exhibits superior performance on four benchmark datasets. © 2025, CC BY-NC-SA.

关键词： Prediction models

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LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup 42

LOPO: An Out-of-order Layer Pulling Orchestration Strategy f...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Gu, Lin Huang, Junhao Huang, Shaoxing Zeng, Deze Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China China University of Geosciences School of Computer Science Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions. © 2023 IEEE.

关键词： Containers

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Incorporating 5G NR Sidelink with MQTT to Maximize Energy Efficiency in IoT-Enabled Microgrids 1

Incorporating 5G NR Sidelink with MQTT to Maximize Energy Ef...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Neelamegam, G. Ashforn, Hermina J.M. Raja, Mohamed S. Priyadharshini, J. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Engineering Department of Information Technology Trichy India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350377002

IoT for smart microgrids is an innovative disruption at the cutting edge of energy management, relying on interconnectedness provided by smart sensors and devices to enhance power generation and distribution precision in metropolitan areas. The problems of the real-time operational capabilities required for efficient energy resource management thus progress, inhibit rapid control and management. The main goal of this research proposal consists of to use of the advanced technology of 5G NR Sidelink combined with MQTT to conduct a revolutionary communication system of Microgrid running through the IoT devices. The 5G NR (New Radio) Sidelink enables direct D2D communication, which leads to a dramatic lowering of the latency in the absence of traditional network infrastructures for communication, while the MQTT protocol improves the communication efficiency among networks of numerous IoT devices. The introduction of this system was followed by a 15% decrease in energy transmission losses and by 20% improvement in response times in comparison to conventional models specified in the quantitative evaluation thus increasing operational efficiency by far. These efficiencies not only reduce operating expenses and power consumption but also increase the solution's scalability, pointing out the possibility of building the solution for bigger urban microgrid implementations. In consideration of that, the suggested mechanism with 5G NR Sidelink along with MQTT overcomes elaborate barriers of IoT-based microgrids and establishes a new potency in advanced energy management in smart cities which could make the distribution network in the urban areas on the edge of the verge of a change. © 2024 IEEE.

关键词： Microgrids

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FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection 42

FedMoS: Taming Client Drift in Federated Learning with Doubl...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, Xiong Chen, Yuxin Li, Yuqing Liao, Xiaofei Jin, Hai Li, Bo Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients' selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal O(T - 2/3) convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques. © 2023 IEEE.

关键词： Momentum

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Evaluating the Cultural Sensitivity of Large Language Models in Mental Health Support: A Framework Inspired by Ubuntu Values 12th

Evaluating the Cultural Sensitivity of Large Language Models...

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12th International Conference on Big data and Artificial Intelligence, BDA 2024

作者： Forane, Sontaga G. Ezugwu, Absalom E. Igwe, Kevin Unit for Data Science and Computing North-West University 11 Hoffman Street North-West Potchefstroom2520 South Africa Department of Psychology Faculty of Humanities University of Johannesburg Auckland Park Bunting Road Johannesburg2092 South Africa

ISBN: (纸本)9783031818202

Africa’s growing mental health crisis underscores a significant lack of accessible and culturally relevant mental health services, with conditions such as depression and anxiety increasingly prevalent. While Cognitive Behavioral Therapy (CBT) has proven effective, it often lacks cultural resonance in African contexts, highlighting the need for innovative approaches. This study proposes a framework for enhancing AI-driven mental health applications by integrating CBT principles with Ubuntu and fine-tuning large language models (LLMs) to embed cultural context. The goal is to improve user engagement and the effectiveness of mental health interventions in South Africa. This framework emphasizes diversity, equity, and inclusion (DEI), with a particular focus on cultural adaptation. Through theoretical exploration, the study demonstrates the potential of Africa-centric LLM applications to offer culturally sensitive support, helping to bridge the mental health service gap across the continent. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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SMOTE Oversampling and Near Miss Undersampling Based Diabetes Diagnosis from Imbalanced dataset with XAI Visualization 28

SMOTE Oversampling and Near Miss Undersampling Based Diabete...

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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Nayan, Nasim Mahmud Islam, Ashraful Islam, Muhammad Usama Ahmed, Eshtiak Hossain, Mohammad Mobarak Alam, Md Zahangir University of Information Technology and Sciences Department of Computer Science and Engineering Bangladesh Independent University Center for Computational and Data Sciences Bangladesh School of Computing and Informatics University of Louisiana at Lafayette United States Independent University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350300482

This study investigated the predictive ability of ten different machine learning (ML) models for diabetes using a dataset that was not evenly distributed. Additionally, the study evaluated the effectiveness of two oversampling and undersampling methods, namely the Synthetic Minority Oversampling Technique (SMOTE) and the Near-Miss algorithm. Explainable Artificial Intelligence (XAI) techniques were employed to enhance the interpretability of the model's predictions. The results indicate that the extreme gradient boosting (XGB) model combined with SMOTE oversampling technique exhibited the highest accuracy and an F1-score of 99% and 1.00 respectively. Furthermore, the utilization of XAI methods increased the dependability of the model's decision-making process, rendering it more appropriate for clinical use. These results imply that integrating XAI with ML and oversampling techniques can enhance the early detection and management of diabetes, leading to better diagnosis and intervention. © 2023 IEEE.

关键词： Machine learning

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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Computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Development of Advanced Noise Filtering Techniques for Medical Image Enhancement 5

Development of Advanced Noise Filtering Techniques for Medic...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Kushwaha, Sumit Amuthachenthiru, K. Geetha, K. Narasimharao, Jonnadula Dileep Kumar, M. Gadde, Sai Sudha University Institute of Computing Chandigarh University Department of Computer Applications Punjab 140413 India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam626117 India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603 203 India Cmr Technical Campus Department of Computer Science and Engineering Telangana Hyderabad501401 India Hensard University Faculty of Management Sciences Bayelsa 561101 Nigeria Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Vaddeswaram500075 India

ISBN: (纸本)9798350385649

Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. This work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. The Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. The study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality. © 2024 IEEE.

关键词： Medical imaging

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Differentiable Convex Polyhedra Optimization from Multi-view Images

arXiv

引用

arXiv 2024年

作者： Ren, Daxuan Mei, Haiyi Shi, Hezi Zheng, Jianmin Cai, Jianfei Yang, Lei College of Computing & Data Science Nanyang Technological University Singapore Sensetime Research China Department of Data Science & AI Monash University Monash University Australia

This paper presents a novel approach for the differentiable rendering of convex polyhedra, addressing the limitations of recent methods that rely on implicit field supervision. Our technique introduces a strategy that combines non-differentiable computation of hyperplane intersection through duality transform with differentiable optimization for vertex positioning with three-plane intersection, enabling gradient-based optimization without the need for 3D implicit fields. This allows for efficient shape representation across a range of applications, from shape parsing to compact mesh reconstruction. This work not only overcomes the challenges of previous approaches but also sets a new standard for representing shapes with convex polyhedra. © 2024, CC BY.

关键词： Geometry

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