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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： K. Krishnakumar Omprakash Gurrapu Reshma P P. Muthu Krishnammal Huma Qamar Khan Ninad Amane Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha School of Engineering Chennai India Volvo Trucks North America North Carolina USA Department of Artificial Intelligence and Data Science Dhanalakshmi Srinivasan College of Engineering Coimbatore India School of Electronics Engineering (SENSE) VIT-AP University Amaravati India Department of Computer Science and Engineering Joginpally B R Engineering College Hyderabad India Department of AI&ML BMS Institute of Technology and Management Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Diagnostic imaging is an important technique which helps in diagnosing several diseases at the initial stage in order to take necessary treatments. Nevertheless, conventional techniques tend to have issues with regard to precision, speed, and quantitatively assessing multidimensional medical information. In this paper, we reveal the potential of deep convolutional networks (DCNs) in the field of medical image diagnostic. It means that, with the help of deep learning algorithms DCNs can learn meaningful features of medical images and improve diagnostic accuracy and speed. The near proposed technique involves the use of deep convolutional network model to analyse medical images for diagnosis of diseases such as cancer, heart diseases and neurological disorders. The obtained experimental results demonstrate that the presented model has the highest accuracy of 96.5% with very high sensitivity and specificity coefficients compared to other methods. Further, the interpretability of the proposed DCN model and different aspects of it are discussed to support clinical validation. Through this research, the effectiveness of deep learning in revolutionizing the diagnosis for medical imaging diagnostics while early disease detection and treatment is established.

关键词： Deep learning Neurological diseases Accuracy Sensitivity Medical services Sensitivity and specificity Convolutional neural networks Medical diagnostic imaging Diseases Cancer

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Lightweight Pedestrian Detection through Guided Filtering and Deep Learning 10

Lightweight Pedestrian Detection through Guided Filtering an...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

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The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation

The Evolution of Zero Trust Architecture (ZTA) from Concept ...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Md Nasiruzzaman Maaruf Ali Iftekhar Salam Mahdi H. Miraz School of Computing and Data Science Xiamen University Malaysia Malaysia Faculty of Computer Science and IT Universiteti Metropolitan Tirana Albania The Doctoral College University of Wales Trinity Saint David (UWTSD) Wales UK Mahdi H. Miraz is with the School of Computing and Data Science Xiamen University Malaysia Malaysia School of Computing Faculty of Arts Science and Technology Wrexham University UK Faculty of Computing Engineering and Science University of South Wales UK

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

Zero Trust Architecture (ZTA) is one of the paradigm changes in cybersecurity, from the traditional perimeter-based model to perimeterless. This article studies the core concepts of ZTA, its beginning, a few use cases and future trends. Emphasising the always-verify and least privilege access, some key tenets of ZTA have grown to be integration technologies like Identity Management, Multi-Factor Authentication (MFA) and real-time analytics. ZTA is expected to strengthen cloud environments, education, work environments (including from home) while controlling other risks like lateral movement and insider threats. Despite ZTA’s benefits, it comes with challenges in the form of complexity, performance overhead and vulnerabilities in the control plane. These require phased implementation and continuous refinement to keep up with evolving organisational needs and threat landscapes. Emerging technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) will further automate policy enforcement and threat detection in keeping up with dynamic cyber threats.

关键词： Multi-factor authentication Education Machine learning Market research Threat assessment Real-time systems Zero Trust Complexity theory Information technology Identity management systems

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Comparative analysis of alkali-treated natural fibres for improved interfacial adhesion in composite materials

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Interactions 2024年第1期245卷 206-206页

作者： Bhagat, Madhuri S. Jadhav, Varsha D. Kulkarni, Sumant Khanderao Satishkumar, P. Saminathan, Rajasekaran Department of Civil Engineering Yeshawantrao Chavan College of Engineering Maharashtra Wanadongari Nagpur India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Maharashtra Pune India School of Civil Engineering REVA University Rukmini Knowledge Park Karnataka Kattigenahalli Bengaluru India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

Natural fibers such as bamboo, kenaf, flax, ramie and hemp were chemically treated to improve their adherence with hydrophobic matrices. One common chemical process for altering the surface of these natural fibers is alkali treatment (AT). The experiment involves soaking bamboo fibers (BF) in solutions of 1, 10, and 30 wt % sodium hydroxide (NaOH) for one day, half an hour, and twenty minutes at room temperature. These alkali-treated and untreated bamboo fibers was compared with their physical and mechanical qualities. Using scanning electron microscopy (SEM) and the circular fiber assumption, the CSA of bamboo fibers both before and after AT were compared. Improving the AT procedures for BF at room temperature using varying NaOH concentrations is the focus of this effort. Additionally, the tensile characteristics of both untreated and variously treated AT Bamboo fibres are examined using a two-parameter Weibull distribution. Instead of just showing the average tensile strength, probabilistic tensile strength has shown to be a more effective method. This study proves without a reasonable doubt that adding 1 wt % NaOH to bamboo fiber makes natural fiber reinforced composites mechanically better. According to the SEM, the CSA of bamboo fibers decreases by about 13.62–20.24% and about 18.42–22.63%, respectively, after alkali treatment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile strength

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A Reconfigurable Subarray Architecture and Hybrid Beamforming for Millimeter-Wave Dual-Function-Radar-Communication Systems

arXiv

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arXiv 2024年

作者： Jin, Xin Lv, Tiejun Ni, Wei Lin, Zhipeng Zhu, Qiuming Hossain, Ekram Poor, H. Vincent The School of Communication and Information Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Data61 Commonwealth Scientific and Industrial Research Organization Marsfield SydneyNSW2122 Australia The College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Department of Electrical and Computer Engineering University of Manitoba Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks. By integrating hybrid analog-digital (HAD) beamforming into a multi-user millimeter-wave (mmWave) DFRC system, we design a new reconfigurable subarray (RS) architecture and jointly optimize the HAD beamforming to maximize the communication sum-rate and ensure a prescribed signal-to-clutter-plus-noise ratio for radar sensing. Considering the non-convexity of this problem arising from multiplicative coupling of the analog and digital beamforming, we convert the sum-rate maximization into an equivalent weighted mean-square error minimization and apply penalty dual decomposition to decouple the analog and digital beamforming. Specifically, a second-order cone program is first constructed to optimize the fully digital counterpart of the HAD beamforming. Then, the sparsity of the RS architecture is exploited to obtain a low-complexity solution for the HAD beamforming. The convergence and complexity analyses of our algorithm are carried out under the RS architecture. Simulations corroborate that, with the RS architecture, DFRC offers effective communication and sensing and improves energy efficiency by 83.4% and 114.2% with a moderate number of radio frequency chains and phase shifters, compared to the persistently- and fully-connected architectures, respectively. © 2024, CC0.

关键词： Beamforming

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Finding Global Homophily in Graph Neural Networks When Meeting Heterophily

arXiv

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arXiv 2022年

作者： Li, Xiang Zhu, Renyu Cheng, Yao Shan, Caihua Luo, Siqiang Li, Dongsheng Qian, Weining School of Data Science and Engineering East China Normal University Shanghai China Microsoft Research Asia Shanghai China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node’s neighborhood with multi-hop neighbors to include more nodes with homophily. However, it is a significant challenge to set personalized neighborhood sizes for different nodes. Further, for other homophilous nodes excluded in the neighborhood, they are ignored for information aggregation. To address these problems, we propose two models GloGNN and GloGNN++, which generate a node’s embedding by aggregating information from global nodes in the graph. In each layer, both models learn a coefficient matrix to capture the correlations between nodes, based on which neighborhood aggregation is performed. The coefficient matrix allows signed values and is derived from an optimization problem that has a closed-form solution. We further accelerate neighborhood aggregation and derive a linear time complexity. We theoretically explain the models’ effectiveness by proving that both the coefficient matrix and the generated node embedding matrix have the desired grouping effect. We conduct extensive experiments to compare our models against 11 other competitors on 15 benchmark datasets in a wide range of domains, scales and graph heterophilies. Experimental results show that our methods achieve superior performance and are also very efficient. © 2022, CC BY.

关键词： Graph neural networks

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Personalized Federated Learning for Smart Healthcare Using SDN-Driven Edge Computing

Personalized Federated Learning for Smart Healthcare Using S...

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2025 IEEE SoutheastCon, SoutheastCon 2025

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

ISBN: (纸本)9798331504847

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. © 2025 IEEE.

关键词： mHealth

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Flow-TSVAD: Target-Speaker Voice Activity Detection via Latent Flow Matching for Speaker Diarization

Flow-TSVAD: Target-Speaker Voice Activity Detection via Late...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhengyang Chen Bing Han Shuai Wang Yidi Jiang Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speaker diarization is typically considered as a discriminative task, using discriminative approaches to produce fixed diarization results. In this paper, we explore for the first time the use of neural network-based generative methods for speaker diarization. We implement a Flow-Matching (FM) based generative algorithm within the sequenceto-sequence target speaker voice activity detection (Seq2Seq-TSVAD) diarization system. Our experiments reveal that applying the generative method directly to the original binary label sequence space of the TS-VAD output is ineffective. To address this issue, we propose mapping the binary label sequence into a dense latent space before applying the generative algorithm, and our proposed Flow-TSVAD method can significantly outperform the traditional Seq2Seq-TSVAD system. Additionally, we observe that the FM algorithm converges rapidly during the inference stage, only requiring two inference steps to achieve promising results. Moreover, as a generative model, Flow-TSVAD allows for sampling different diarization results by running the model multiple times, so the ensemble system combining the results from various sampling instances can further boost the diarization performance.

关键词： Voice activity detection Frequency modulation Computational modeling Signal processing algorithms Signal processing Inference algorithms Acoustics Computational efficiency

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INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings

arXiv

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arXiv 2023年

作者： Kazerouni, Amirhossein Azad, Reza Hosseini, Alireza Merhof, Dorit Bagci, Ulas School of Electrical Engineering Iran University of Science and Technology Iran Institute of Imaging and Computer Vision RWTH Aachen University Germany School of Electrical and Computer Engineering College of Engineering University of Tehran Iran Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Department of Radiology Northwestern University Chicago United States

Implicit Neural Representations (INRs) have revolutionized signal representation by leveraging neural networks to provide continuous and smooth representations of complex data. However, existing INRs face limitations in capturing fine-grained details, handling noise, and adapting to diverse signal types. To address these challenges, we introduce INCODE, a novel approach that enhances the control of the sinusoidal-based activation function in INRs using deep prior knowledge. INCODE comprises a harmonizer network and a composer network, where the harmonizer network dynamically adjusts key parameters of the activation function. Through a task-specific pre-trained model, INCODE adapts the task-specific parameters to optimize the representation process. Our approach not only excels in representation, but also extends its prowess to tackle complex tasks such as audio, image, and 3D shape reconstructions, as well as intricate challenges such as neural radiance fields (NeRFs), and inverse problems, including denoising, super-resolution, inpainting, and CT reconstruction. Through comprehensive experiments, INCODE demonstrates its superiority in terms of robustness, accuracy, quality, and convergence rate, broadening the scope of signal representation. Please visit the project’s website for details on the proposed method and access to the code. © 2023, CC BY.

关键词： Inverse problems

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An Enhanced Prediction of Ovarian Cancer Based on Ensemble Classifier Using Explainable AI

An Enhanced Prediction of Ovarian Cancer Based on Ensemble C...

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Communication & Computing (WCONF), World Conference on

作者： Gopal Kumar Thakur Shridhar Kulkarni K Senthil Kumar P Sudarsanam Meruva Sreenivasulu Pundru Chandra Shaker Reddy Department of Data Sciences Harrisburg University of Science & Technology Pennsylvania United States of America Department of Information Technology V.S.B. Engineering College Karur Tamil Nadu India Information Science and Engineering BMS Institute of Technology and Management Karnataka India Computer Science and Engineering Matrusri Engineering College Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

For women's general health and well-being, ovarian cancer detection and prevention are of paramount importance. Opposite uterine cancer, sometimes known as the “silent killer,” presents with subtle signs and symptoms in the early stages, making it difficult to detect in a timely manner. The chance of successful treatment and survival from ovarian cancer is greatly reduced when caught in its later stages. In order to catch the disease in its early, more curable phases, it is crucial to conduct screenings on a regular basis. These screenings can include pelvic exams, ultrasounds, and blood testing for specific biomarkers. This study employs the ovarian cancer dataset from Soochow University, which includes 50 features for precise cancer identification. In order to improve the accuracy and dependability of predictions, the suggested model employs a stacked ensemble-model, which combines the advantages of bagging &boosting classifiers. Better ovarian cancer prediction results are a result of this combination's use of variance reduction and enhanced generalization. With all features considered, the suggested model achieves the greatest model result to date on this dataset, with an accuracy of 96.87%. The outcomes are further explained utilizing SHAPly, an explainable-AI approach. The proposed model's superiority is proven by contrasting its results with those of another state-of-the-art model.

关键词： Accuracy Ultrasonic imaging Computational modeling Predictive models Boosting Real-time systems Medical diagnostic imaging Bagging Ovarian cancer Testing

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