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Advancements in electrical, electronics, Communication, Computing and Automation (ICAeCA), International Conference on

作者： Sanjith Krishna S Premchand V H Shri Ram b Sakthi Satchidhanandan S Anitha N Hariharan S.V B.E. Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India B.E. Computer Science Engineering Kumaraguru College of Technology Coimbatore India B.E. Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India Department of Mathematics Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

battery Monitoring Systems (bMS) have played a pivotal role in our trajectory towards greener and sustainab.e.living, especially in our commutation methods. The rise of electric cars and two-wheelers has put renewed focus on high density energy storage and their safety. existing bMS’s often provide the bare minimum in terms of protection and regulation of energy, often falling short of certain safety standards or lack thereof. Within this research, we task ourselves to identify and provide an alternative, viab.e. cost-effective method to ensure safe monitoring and especially safe debugging of faulty battery packs. We employ off-the-shelf components and integrate them into existing bMS to provide an easier way for debugging via Internet of Things (IoT) to allow local technicians access to the same information as the Original equipment Manufacturer (OeM) themselves. Therefore, our aim is to provide a platform for people to troub.e.hoot their bMS’ on electric vehicles and perform predictive maintenance via remote monitoring, providing the same flexibility of a regular combustion car.

关键词： Temperature measurement buildings Debugging Programming electric vehicles batteries State of charge Internet of Things Remote monitoring Predictive maintenance

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A Framework for Video based Sign Language Interpretation Using Machine Learning and Statistical Methods 4

A Framework for Video Based Sign Language Interpretation Usi...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swathi, D. Susmetha, K. Sreeji, S. Mangairkarasi, S. B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri TamilNadu Chennai600119 India Sathyabama Institute of Science and Technology Semmancheri Department of Computer Science and Engineering TamilNadu Chennai6001109 India

ISBN: (纸本)9798331523923

The Sign Language Recognition System has b.e. designed to capture video input, process it to detect hand gestures, and translate these gestures into readab.e.text. The project consists of several key components and steps: Video Processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frame extraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. The accurate detection and tracking of the hand movements are critical for the sub.e.uent recognition of the sign language gestures. Sign Language Recognition: The core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. The model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. This can be done through a console output or a graphical user interface (GUI) built with Tkinter. The GUI provides a user friendly experience, allowing users to see the translated text in real-time. © 2025 Ieee.

关键词： Convolutional neural networks

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edgeCrypt Tracker: Ob.e.t Tracking With Differential encryption for IoAAV Surveillance

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Ieee Internet of Things Journal 2025年第9期12卷 11511-11528页

作者： Thiyagarajan, Karthik Hammad, Issam Lu, Rongxing Dalhousie University Faculty of Engineering Mathematics and Internetworking HalifaxNSB3H 4R2 Canada University of New Brunswick Faculty of Computer Science FrederictonNBE3B 5A3 Canada

This article proposes edgeCrypt Tracker, an ob.e.t-tracking algorithm combined with differential encryption to provide better accuracy and runtime efficiency for battery-operated Internet of Autonomous Aerial Vehicles (IoAAV). Specifically, edgeCrypt Tracker operates directly on high-efficiency video coding (HeVC) and has three stages: 1) preprocessing;2) ob.e.t tracking;and 3) differential encryption. The preprocessing stage separates intra frames and removes artificial camera motion caused by camera movement from inter frames. Next, the ob.e.t tracking stage utilizes a hybrid neural network, combining a single-shot multibox detector (SSD) network with a MobileNetV3 backbone that processes intra coded blocks and a fast gated recurrent neural network (FastGRNN) network that processes inter coded blocks. Finally, the tracked information is passed to the differential encryption stage, which encrypts all syntax elements within moving ob.e.ts and alternate syntax elements related to the background. experimental results demonstrate that edgeCrypt Tracker achieves an average ob.e.t tracking accuracy of 92%, real-time inference with a 35% lower encryption overhead compared to state-of-the-art methods. This work demonstrates the potential of integrating ob.e.t tracking and encryption within video compression for secure, efficient AAV-based surveillance. © 2014 Ieee.

关键词： Nuclear power plants

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efficiency boosting of Secure Cross-Platform Recommender Systems Over Sparse Data

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Ieee Transactions on Dependab.e.and Secure Computing 2025年第3期22卷 2146-2160页

作者： Ren, Hao Xu, Guowen Zhang, Tianwei Ning, Jianting Huang, Xinyi Li, Hongwei Lu, Rongxing School of Cyber Science and Engineering Sichuan University Chengdu610017 China State Key Laboratory of Cryptology Beijing100878 China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education Chengdu610017 China Cyber Science Research Institute Sichuan University Chengdu610017 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Cyber Science and Engineering Wuhan University Wuhan430072 China Faculty of Data Science City University of Macau 999078 China Colledge of Cyber Security Jinan University Guangzhou510632 China School of Computer Science University of New Brunswick New BrunswickE3B 5A3 Canada

Fueled by its successful commercialization, the recommender system (RS) has gained widespread attention. However, as the training data fed into the RS models are often highly sensitive, it ultimately leads to severe privacy concerns, especially when data are shared among different platforms. In this paper, we follow the tune of existing works to investigate the prob.e. of secure sparse matrix multiplication for cross-platform RSs. Two fundamental and critical issues are addressed: preserving the training data privacy and b.e.king the data silo prob.e.. Specifically, we propose two concrete constructions with significantly boosted efficiency. They are designed for the sparse location insensitive case and location sensitive case, respectively. State-of-the-art cryptography building blocks including homomorphic encryption (He) and private information retrieval (PIR) are fused into our protocols with non-trivial optimizations. As a result, our schemes can enjoy the He acceleration technique without privacy trade-offs. We give formal security proofs for the proposed schemes and conduct extensive experiments on both real and large-scale simulated datasets. Compared with state-of-the-art works, our two schemes compress the running time roughly by 10×10× and 2.8×2.8×. They also attain up to 15×15× and 2.3×2.3× communication reduction without accuracy loss. © 2024 Ieee.

关键词： Differential privacy

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A Vision Transformer Approach to efficient brain Tumor Detection and Segmentation in Medical Imaging

A Vision Transformer Approach to Efficient Brain Tumor Detec...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： ebinesh K Aashish D balaji Sougoumar Anand Karuppannan B.E. Electronics Engineering (VLSI Design and Technology) Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

brain tumor detection and segmentation are important tasks in the field of medical imaging that have significant potential to enhance treatment planning and accurate diagnosis. The construction of a Vision Transformer (ViT) model for the purpose of identifying brain tumor from medical images specifically MRI scans has b.e. covered in this work. Renowned for its outstanding performance recognition challenges, the Vision Transformer will be utilized to improve brain tumor detection accuracy and efficiency. The proposed system will be evaluated in terms of accuracy, performance, and inference time against conventional deep learning models. This research aims to improve real-time medical image processing with ViT, resulting in faster diagnosis and more efficient tumor identification in clinical practice.

关键词： computer vision Image segmentation Accuracy Hospitals Magnetic resonance imaging brain tumors Transformers brain modeling Real-time systems Planning

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Image Super-Resolution Using Auto encoders

Image Super-Resolution Using Auto Encoders

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International Conference on Trends in electronics and Informatics (ICeI)

作者： K. Sandhya Rani Pancheti bhavya R. Vignesh e. Murali D. Deepa J. Cruz Antony B.E Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Image super-resolution (SR) remains a crucial prob.e. in computer vision to obtain high resolution images from low-resolution ones. In this paper, a novel approach towards SR is presented with the help of auto-encoders, a specific kind of neural network whose task is to learn compressed representations of the high resolution images. In the context of the proposed model, there is an encoder, which, alternately, compresses the input and a decoder which reconstruct the output is proposed as capturing as well as reproducing all the details of an image. experimental results show that the proposed method is one of the most efficient and accurate ones on benchmark datasets in terms of better details preservation and artefact reduction besides reducing processing time compared to the conventional methods. This makes this approach to be flexib.e.and efficient to be used in medical imaging, satellite and digital photography making it an effective solution to improving image quality and resolution.

关键词： Image quality computer vision Visualization Image coding Superresolution Neural networks Signal processing algorithms Signal processing Optical flow Image reconstruction

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A Framework for Video based Sign Language Interpretation Using Machine Learning and Statistical Methods

A Framework for Video Based Sign Language Interpretation Usi...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Swathi D Susmetha K Sreeji. S S. Mangairkarasi B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri Chennai TamilNadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Semmancheri Chennai TamilNadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

关键词： Hands Deep learning Sign language Translation Tracking Text recognition Speech recognition Streaming media Real-time systems Graphical user interfaces

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Attractor and Slicing Analysis of a T Cell Differentiation Model based on Reaction Systems 11th

Attractor and Slicing Analysis of a T Cell Differentiatio...

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11th International Symposium on From Data to Models and back, DataMod 2023

作者： brodo, Linda bruni, Roberto Falaschi, Moreno Gori, Roberta Milazzo, Paolo Dipartimento di Scienze Economiche e Aziendali University of Sassari Via Muroni 25 Sassari07100 Italy Department of Computer Science University of Pisa Largo B. Pontecorvo 3 Pisa56127 Italy Department of Information Engineering and Mathematics University of Siena Via Roma 56 Siena53100 Italy

ISBN: (纸本)9783031872167

Reaction Systems (RSs) were introduced in the field of natural computing as a qualitative model inspired by biological systems. In a RS, each reaction comprises a set of reactants that generate products unless inhibited by reaction inhibitors. We propose a method for analyzing the attractors of a RS model (the states to which the system converges) and for identifying the entities responsib.e.for their attainment. Our approach builds on (i) the construction of a Labeled Transition System (LTS) based on a formal SOS semantics of RSs, and (ii) the application of a slicing method to the trajectories within the LTS. Our model analysis provides new insights with respect to previous studies. We illustrate our methodology on a case study that demonstrates its capability to identify stimulus combinations that lead to specific phenotypes, but also to elucidate the involvement of proteins within T cells in each scenario. These findings allow for a better understanding of the phenomenon and for the identification of potential drug targets for diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differentiation (calculus)

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Reinforcement Learning With Selective exploration for Interference Management in mmWave Networks

IEEE Transactions on Machine Learning in Communications and ...

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Ieee Transactions on Machine Learning in Communications and Networking 2025年 3卷 280-295页

作者： Dinh-Van, Son Nguyen, van-Linh Cebecioglu, berna bulut Masaracchia, Antonino Higgins, Matthew D. The University of Warwick School of Engineering Warwick Manufacturing Group CoventryCV4 7AL United Kingdom Advanced Institute of Manufacturing With High-Tech Innovations Chiayi Minxiong621301 Taiwan Birmingham City University School of Computing and Digital Technology BirminghamB4 7XG United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom

The next generation of wireless systems will leverage the millimeter-wave (mmWave) bands to meet the increasing traffic volume and high data rate requirements of emerging applications (e.g., ultra HD streaming, metaverse, and holographic telepresence). In this paper, we address the joint optimization of beamforming, power control, and interference management in multi-cell mmWave networks. We propose novel reinforcement learning algorithms, including a single-agent-based method (bPC-SA) for centralized settings and a multi-agent-based method (bPC-MA) for distributed settings. To tackle the high-variance rewards caused by narrow antenna beamwidths, we introduce a selective exploration method to guide the agent towards more intelligent exploration. Our proposed algorithms are well-suited for scenarios where beamforming vectors require control in either a discrete domain, such as a codebook, or in a continuous domain. Furthermore, they do not require channel state information, extensive feedback from user equipments, or any searching methods, thus reducing overhead and enhancing scalability. Numerical results demonstrate that selective exploration improves per-user spectral efficiency by up to 22.5% compared to scenarios without it. Additionally, our algorithms significantly outperform existing methods by 50% in terms of per-user spectral effciency and achieve 90% of the per-user spectral efficiency of the exhaustive search approach while requiring only 0.1% of its computational runtime. © 2025 The Authors.

关键词： Reinforcement learning

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TrustChain: A blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning

arXiv

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arXiv 2025年

作者： Hallaji, ehsan Razavi-Far, Roozbeh Saif, Mehrdad Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada Faculty of Computer Science University of New Brunswick FrederictonNBE3B 5A3 Canada

The server-less nature of Decentralized Federated Learning (DFL) requires allocating the aggregation role to specific participants in each federated round. Current DFL architectures ensure the trustworthiness of the aggregator node upon selection. However, most of these studies overlook the possibility that the aggregating node may turn rogue and act maliciously after being nominated. To address this prob.e., this paper proposes a DFL structure, called TrustChain, that scores the aggregators before selection based on their past behavior and additionally audits them after the aggregation. To do this, the statistical independence between the client updates and the aggregated model is continuously monitored using the Hilbert-Schmidt Independence Criterion (HSIC). The proposed method relies on several principles, including blockchain, anomaly detection, and concept drift analysis. The designed structure is evaluated on several federated datasets and attack scenarios with different numbers of byzantine nodes. © 2025, CC bY-NC-ND.

关键词： Federated learning

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