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IEEE Southeastcon

作者： Deborsi Basu Ritu Chaudhary Uttam Ghosh Raja Datta G. S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Dept. of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India Dept. of Computer Science and Data Science School of Applied Computational Sciences MMC TN USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The advancement in distributed Federated Learning (FL) techniques is transforming next-generation edge computing, especially in e-Helathcare systems where data integrity and security are critical concerns. Collecting, processing and analyzing such substantial medical data is highly challenging due to their non-identical and versatile nature. Conventional FL approaches often show unsatisfactory and inaccurate results while operating on those data. Traditional FL methods also lack optimal dynamic management mechanisms for edge devices in ehealthcare, which is crucial to ensure that only authorized devices participate in handling any system failures and new registrations. To tackle such important aspects, in this work, we propose a personalized FL technique to handle abrupt label shifts among clients' data. The proposed approach further targets to achieve personalized models for local edge clients. A statistical batch normalization is proposed on customized edge servers, keeping the data specificity intact. To make the proposed system more secure and robust, a Software Defined Network-based (SDN-based) centralized server is used that allows only authorized and efficient edge clients to participate. The proposed comprehensive experiments on two healthcare datasets demonstrate better accuracy than traditional FL methods with enhanced security and privacy.

关键词： data privacy Federated learning data integrity Medical services Servers Security Software defined networking Next generation networking Batch normalization Edge computing

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An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions

arXiv

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arXiv 2023年

作者： Tung, Caleb Eliopoulos, Nicholas Jajal, Purvish Ramshankar, Gowri Yang, Chen-Yung Synovic, Nicholas Zhang, Xuecen Chaudhary, Vipin Thiruvathukal, George K. Lu, Yung-Hsiang Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Computer and Data Sciences Department Case Western Reserve University ClevelandOH United States

computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements. Producing more energy-efficient CNNs often requires model training which can be cost-prohibitive. We propose a novel, automated method to make a pretrained CNN more energy-efficient without re-training. Given a pretrained CNN, we insert a threshold layer that filters activations from the preceding layers to identify regions of the image that are irrelevant, i.e. can be ignored by the following layers while maintaining accuracy. Our modified focused convolution operation saves inference latency (by up to 25%) and energy costs (by up to 22%) on various popular pretrained CNNs, with little to no loss in accuracy. © 2023, CC BY.

关键词： Convolution

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Biologically Interpretable Model for Precise Recurrence Prediction of Non-Small Cell Lung Cancer

Biologically Interpretable Model for Precise Recurrence Pred...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yang Ai Xiuju Du Jing Liu Yinhao Li Lanfen Lin Jingsong Li Yen-Wei Chen Graduate School of Information Science and Engineering Ritsumeikan University Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Information Science and Engineering Ritsumeikan University Shiga Japan College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The precise prediction of preoperative recurrence in non-small cell lung cancer (NSCLC) that is suitable for clinical application is still an open question. Recent advancements integrating genomic data with deep learning have shown promise in enhancing recurrence analysis in NSCLC patients. However, the lack of interpretability in the decision-making process of DNN models has hindered their clinical trustworthiness. In this paper, we propose a novel Biologically Informed Pathway-Aware Neural Network (BioPAN). By automatically extracting biological prior knowledge to guide the architecture of DNN models, we design a unified architecture of gene-pathway-biological process-disease. This approach endows each neuron with entity meaning and learns a multi-level view of biological pathways and processes related to recurrence for fully interpretable NSCLC recurrence prediction. We demonstrated that the proposed model well explains the molecular mechanisms linking genes to NSCLC recurrence, identifies several genes that significantly promote and inhibit recurrence, and elucidates the pivotal roles of various gene pathways at the biological process level. Moreover, it outperforms classical machine learning methods, provides fully interpretable biological information, and requires fewer parameters. Broadly, BioPAN enables clinical discovery and prediction, and may have general applicability across cancer types.

关键词： Deep learning Biological system modeling Microprocessors Neurons Decision making Lung cancer Genomics Biological processes computer architecture Predictive models

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Customer Segmentation Using Credit Card data Analysis

Customer Segmentation Using Credit Card Data Analysis

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International Conference on Software engineering Research, Management and Applications (SERA)

作者： Saikat Raj Santanu Roy Surajit Jana Soumyadip Roy Takaaki Goto Soumya Sen Computer Science and Engineering (Data Science) Future Institute of Engineering and Management Kolkata India A.K. Choudhury School of Information Technology University of Calcutta Kolkata India Faculty of Information Sciences and Arts Toyo University Japan

Customer segmentation is a separation of a market into multiple distinct groups of consumers who share the similar characteristics. Segmentation of market is an effective way to define and meet Customer needs and also to identify the future business plan. Unsupervised machine learning algorithms are suitable to analyze and identify the possible set of customers when the labeled data about the customers are no available. In this research work the spending of different customers who have credit cards are analyzed to segment them into different clusters and also to plan further business improvements based on the different characteristics of these identified clusters.

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Spatio-Temporal Weighted Graph Reason Learning for Multivariate Time-Series Anomaly Detection

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IEEE Internet of Things Journal 2025年

作者： Zhang, Huaqi Pang, Huaxin Zhao, Yufeng Gao, Guandong Song, Yang Wei, Shikui Zhao, Yao National Police University for Criminal Justice Baoding China China Academy of Chinese Medical Sciences Chinese Medicine Data Center Beijing China Hebei Agricultural University School of Mechatronical and Electrical Engineering Baoding China Beijing Jiaotong University School of Computer and Information Technology Beijing China

Constructing an efficient and deployable anomaly detection system requires achieving high accuracy, low latency, and reliability. Existing methods either spend considerable time extracting rich spatio-temporal features to enhance anomaly detection performance, or blindly integrate multi-source features to boost accuracy, often neglecting the reliability of feature aggregation. The trade-off between the three objectives must be carefully considered when developing the model. To address these challenges, we introduce a novel Spatio-Temporal Weighted Graph Reasoning Learning (STWGRL) framework for multivariate time-series anomaly detection. Specifically, we propose a series-denoising receptance-weighted key value (D-RWKV) module to efficiently capture and model expressive long-term sequence information through a linear scaling mechanism. D-RWKV ensures compatibility by alleviating the memory bottleneck and enabling parallelized training. Furthermore, we design a targeted-awareness graph adaptive aggregation (TaGAA) module to learn the directed graph and adaptively enhance the signal's intrinsic characteristics. Two graph-constraint losses are employed to strengthen the consistency and sparsity of the learned graphs. Experimental results on multiple benchmark tasks clearly demonstrate the effectiveness of the proposed framework. STWGRL achieves more accurate scores than most baselines, while containing fewer than 10K parameters. © 2014 IEEE.

关键词： Graph neural networks

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Visible Thermal Person Re-Identification Via Multi-Branch Modality Residual Complementary Learning

SSRN

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SSRN 2024年

作者： Chen, Long Sun, Rui Yu, Yiheng Du, Yun Zhang, Xudong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer and Information Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei230009 China The School of Computer and Information Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei230009 China

Visible-thermal Person Re-identification (VT Re-ID) is a challenging task in all-weather surveillance system. Existing methods focus on extracting the modality-shared features, ignoring the discriminative inter-modality complementary features. To tackle this issue, we propose the multi-branch modality residual complementary learning method consisting of modality residual complementary learning (MRCL) module and multi-branch feature learning (MBFL) module. The MRCL module can be easily integrated into existing CNN and Transformer baselines and drive the network to focus on both intra-modality and inter-modality information simultaneously. On one hand, we adopt the basic two-stream network to obtain the intra-modality features, on the other hand, we feed the residual image acquired by cross-modality correlation saliency erasing operation to another two-stream network to capture the inter-modality complementary features. Moreover, we employ the MBFL module to capture local spatial features and local channel features and integrate them with global features to increase the discriminability and robustness of the ultimate representations for handling intra-modality variations. The extensive experiments on RegDB and SYSU-MM01 datasets demonstrate the superiority of our proposed method compared with state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Learning systems

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Optimizing Network Management and Virtualization Using Machine Learning Approach: Network Slice Prediction

Optimizing Network Management and Virtualization Using Machi...

引用

Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Lateef Adesola Akinyemi Olamide Peter Oshinuga Sunday Oladayo Oladejo Stephen Obono Ekwe Mbuyu Sumbwanyanbe Ernest Mnkandla Oluwagbemiga Omotayo Shoewu Departments of Computer Science and Elect. Engineering CSET University of South Africa Johannesburg South Africa Department of Electronic and Computer Engineering Lagos State University Lagos Nigeria School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Department of Elect. Electronic and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa Department of Electrical Engineering CSET University of South Africa Johannesburg South Africa Department for Computer Science CSET University of South Africa Johannesburg South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often lack efficiency and result in inaccuracies. By leveraging ML algorithms such as Naive Bayes and Random Forest, an intelligent framework that automates network slice prediction is developed. This framework enhances network virtualization and management, facilitating resource allocation. The ML algorithms take real-time network conditions and usages, such as packet delay and smart city, as input and output for selecting the most suitable network slice. data analysis is further conducted to reveal the connections between the input parameters and how these parameters influence the selection of the accurate network slice. Network slicing plays a crucial role as it enables the customization of services and facilitates efficient scaling to meet the specific needs of different applications and industries. The accuracy scores of the employed ML algorithms were generally perfect, except for the KNN and SVM classifiers, which achieved an accuracy of 94.30% and 92.16%, respectively, for the prediction of network slices based on incoming network connections and usages.

关键词： Support vector machines Machine learning algorithms Accuracy data analysis Smart cities Network slicing Prediction algorithms

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Dts-Adapstnet: An Adaptive Spatiotemporal Neural Networks for Traffic Prediction with Mulit-Graph Fusion

SSRN

引用

SSRN 2023年

作者： Zhang, Jing Shi, Wenlong Ye, Xiucai Tsai, Peiwei School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science University of Tsukuba Tsukuba Japan Department of Computer Science and Software Engineering Swinburne University of Technology Hawthorn Australia

Traffic prediction is of vital importance in intelligent transportation systems. It can realize efficient route planning, avoid traffic congestion and reduce travel time, etc. However, it is difficult to make accurate road traffic prediction, due to the complex spatiotemporal dependencies of the traffic network. The construction and learning of spatial dependencies play a pivotal role in road traffic prediction, laying the foundation for accurate traffic prediction. Regrettably, most of the existing methods for capturing spatial dependencies only consider a single spatial relationship, ignoring other potential temporal and spatial correlations in the traffic network. Moreover, the end-to-end training methods are difficult to control the training direction during graph learning. Additionally, the result of traffic forecasting lacks of fusing multiple traffic data information. All of these have adverse effects on road traffic prediction. In other to capture the spatiotemporal dependencies of the traffic network accurately, a novel traffic prediction framework, Adaptive Spatio-Temporal Graph Neural Network based on Multi-graph Fusion (DTS-adapSTNet), is proposed. First of all, in order to better extract the hidden spatial dependencies, a method of fusing multiple factors is designed, which includs the distance relationship, transfer relationship and same-road segment relationship of traffic data, etc. Secondly, an adaptive learning method is proposed, which can control the learning direction of parameters better by the adaptive matrix generation module and traffic prediction module. Thirdly, an improved loss function is designed for training processes and a multi-matrix fusion module is designed to perform weighted fusion of the learned matrices, updating the spatial adjacency matrix continuously, which fuses as much traffic information as possible for more accurate traffic prediction. Finally, based on the experiments by two large real-world datasets, the DTS-adapSTNe

关键词： Travel time

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Twin Auto-Encoder Model for Learning Separable Representation in Cyberattack Detection

arXiv

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arXiv 2024年

作者： Phai, Vu Dinh Uy, Quang Nguyen Hoang, Dinh Thai Nguyen, Diep N. Son, Pham Bao Dutkiewicz, Eryk The School of Electrical and Data Engineering The University of Technology Sydney SydneyNSW2007 Australia The Computer Science Department Faculty of Information Technology Le Quy Don Technical University Hanoi Viet Nam The University of Engineering and Technology Vietnam National University Hanoi Viet Nam

Representation Learning (RL) plays a pivotal role in the success of many problems including cyberattack detection. Most of the RL methods for cyberattack detection are based on the latent vector of Auto-Encoder (AE) models. An AE transforms raw data into a new latent representation that better exposes the underlying characteristics of the input data. Thus, it is very useful for identifying cyberattacks. However, due to the heterogeneity and sophistication of cyberattacks, the representation of AEs is often entangled/mixed resulting in the difficulty for downstream attack detection models. To tackle this problem, we propose a novel model called Twin Auto-Encoder (TAE). TAE deterministically transforms the latent representation into a more distinguishable representation namely the separable representation and then reconstruct the separable representation at the output. The output of TAE called the reconstruction representation is input to downstream models to detect cyberattacks. We extensively evaluate the effectiveness of TAE using a wide range of benchmarking datasets. Experiment results show the superior accuracy of TAE over state-of-the-art RL models and well-known machine learning algorithms. Moreover, TAE also outperforms state-of-the-art models on some sophisticated and challenging attacks. We then investigate various characteristics of TAE to further demonstrate its superiority. © 2024, CC BY.

关键词： Learning algorithms

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Multi-scale Variable Window Attention Network for Continuous Sign Language Recognition

Multi-scale Variable Window Attention Network for Continuous...

引用

Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Nan Zhang Chao Wei Leming Guo Zeyu Cheng Zhao Zhao Yangcan Wu Wanli Xue School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Emergency Centre Tianjin China School of Mechanical Engineering Tianjin University of Technology Tianjin China Shaanxi Big Data Group Co. Ltd Shaanxi China Shaanxi Logistics Group Industry Research Institute Co Shaanxi China

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

Sign language is primarily conveyed through hands and face movements. Therefore, a pivotal challenge in continuous sign language recognition (CSLR) is the meticulous modeling and interpreting of visual cues emanating from hands and facial movements. However, contemporary CSLR methods have exhibited limitations in adequately capturing information from both the hands and facial regions, often overlooking the continuity, and correlations across multiple consecutive frames. To mitigate these shortcomings, we propose the Multi-scale Variable Window Attention Network (MVAN). This framework fuses the strengths of an improved Efficient Multi-scale Attention (iEMA) with Variable Window Adaptation (VWA) attention. Specifically, iEMA first scans information spatially at multiple scales to simultaneously capture information from areas such as hands and faces, and models spatial continuity interactions between them, enhancing spatial features. Secondly, VWA dynamically changes its window size at the temporal level to adaptively capture complex temporal changes in hands and face movements in successive frames. Thus, VWA can effectively model the continuity and correlation between hands and face movements to have the ability to capture sign language movements of different lengths. Comprehensive experimental results demonstrate that our proposed MVAN strengthens the visual feature extraction of hands and face, improving the recognition performance.

关键词： Hands Sign language Adaptation models Visualization Correlation Face recognition Neural networks Feature extraction Robustness Social intelligence

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