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Integrated intelligence and Communication Systems (ICIICS), International Conference on

作者： Madhumitha A Muhammed Al-Fatlawi T. Aditya Sai Srinivas Ramachandra A C R. Venkatasubramanian Department of Computer Science and Business Systems Bannari Amman Institute of Technology Erode India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

In recent days, for strengthening the climate change adaption water features are utilizing as the most crucial environmental element. The integration of Remote Sensing (RS) with artificial intelligence (AI) is becoming the major sought-after approach for extracting the features of water information. In existing, the Faster Regional Convolution Neural Network (FR_CNN) is utilized for the water bodies' extraction. However, the existing FR_CNN model struggled with computational complexity. To overcome this issue, Contrast Vision Transformer (CoVT) is proposed to extract features from water bodies. The transformer with self-attention mechanism reduces the computational complexity by eliminating regional proposal networks. Initially, the satellite images are taken from sentinel-2 dataset and further processed into preprocessing. In preprocessing Gaussian Median Filter (GMF) and Atmospheric Correction (AC) are utilized to reduce the noise and atmospheric effects from images. After that, proposed CoVt extract features from preprocessed images and fed into projection head to generate projected features. Finally, the projected features are further processed with prediction head to predict the satellite image features of water bodies. From the results, the proposed CoVt provided better results when compared to existing FR_CNN by providing results such as accuracy (99.4%), precision (99.1%), recall (98.5%) and F1-score (98.7%) respectively.

关键词： computer vision Head Accuracy Atmospheric modeling Noise Feature extraction Transformers Satellite images Computational complexity artificial intelligence

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No Fear of Representation Bias Graph Contrastive learning with Calibration and Fusion

SSRN

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SSRN 2024年

作者： Li, Jindong Liu, Yixin Xing, Qianli Wang, Qi Pan, Shirui The School of Artificial Intelligence Jilin University Changchun130012 China The Department of Data Science and AI Monash University MelbourneVIC3800 Australia The College of Computer Science and Technology Jilin University Changchun130012 China The School of Information and Communication Technology Griffith University Gold CoastQLD4222 Australia Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education China

Graph contrastive learning (GCL) is receiving growing attention due to its promising self-supervised learning capability on graph-structured data. While GCL approaches have demonstrated significant potential, their inherited stochastic perturbation-based augmentation causes the representation bias on representation hyperspace. Such a serious issue, unfortunately, causes the representation drift between the distributions of original and augmented embeddings, misleading the contrastive training for the graph neural network encoders. Moreover, the information loss caused by bias can further result in incomplete representation problem, hindering the self-supervised framework from capturing comprehensive structural and attributive patterns. To address these challenges, this paper introduces the CAlibratioN aNd FusiON model (CANNON for short). CANNON introduces a cross-view calibrating contrast mechanism to enhance the agreement between the original and augmented representations, which effectively alleviates the representation drift. Concurrently, CANNON overcomes the incomplete representation problem with a fused-view calibrating contrast mechanism for contrastive learning application on the fused embeddings, where a transformer-based cross-view fusion block is used to integrate two augmented views into a comprehensive one. The extensive experimental results, executed on 9 real-world datasets, demonstrate that CANNON significantly outperforms existing state-of-the-art methods. The code is available at https://***/jindongli-Ai/CANNON. © 2024, The Authors. All rights reserved.

关键词： Calibration

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Enhanced EEGNet Optimization Using Lightweight Deep Neural Network for Detecting Human Stress Levels from Raw EEG Signals Dataset

Enhanced EEGNet Optimization Using Lightweight Deep Neural N...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Silabdi Hana Raini Hassan Ahmed Rimaz Faizabadi Abdullah Gubbi Mohammed Zakir Bellary V Manjula Department of Computer Science Kulliyyah of Information & Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Department of Computer Science Kulliyyah of Information & Communication Technology International Islamic University Kuala Lumpur Malaysia Centre for Unmanned Technologies International Islamic University Malaysia Kuala Lumpur Malaysia Department of Electronics and Communication Engineering Bearys Institute of Technolgy (VTU) Mangalore India Department of Artificial Intelligence and Machine learning P.A.College of Engineering(VTU) Mangalore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Accurate stress detection is essential in neuroscience and healthcare since human stress levels have a substantial impact on general well-being. Although EEG-based stress detection is a popular non-invasive method, real-time applications are limited by the high processing costs of classic feature extraction techniques. This work removes the requirement for intricate feature extraction by presenting an efficient EEGNet framework for real-time stress prediction utilizing raw EEG signals. The suggested approach improves accuracy and efficiency by utilizing Z-score normalization and adjusting batch size. Compared to traditional CNNs, the lightweight EEGNet architecture's depth-wise and separable convolutions ensure computational efficiency by reducing trainable parameters by a factor of ten. The proposed method outperformed current EEGNet-based techniques in stress identification by 5% to 18%. Furthermore, the improved system efficiently handles raw EEG signals, which is suitable for real-time, resource-constrained scenarios. The results shows the effectiveness of model-centric optimization in deep learning and open the door to the creation of more effective stress detection techniques. This work provides a workable solution for real-time non-invasive stress monitoring, highlighting the significance of lightweight frameworks customized for particular applications.

关键词： Deep learning Accuracy Feature extraction Brain modeling Electroencephalography Real-time systems Data models Stress Optimization Monitoring

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Unified Source-Free Domain Adaptation

arXiv

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arXiv 2024年

作者： Tang, Song Su, Wenxin Ye, Mao Zhang, Jianwei Zhu, Xiatian The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The TAMS Group Department of Informatics Universität Hamburg Hamburg Germany The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China The Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

In the pursuit of transferring a source model to a target domain without access to the source training data, Source-Free Domain Adaptation (SFDA) has been extensively explored across various scenarios, including closed-set, open-set, partial-set, and generalized settings. Existing methods, focusing on specific scenarios, not only address only a subset of challenges but also necessitate prior knowledge of the target domain, significantly limiting their practical utility and deployability. In light of these considerations, we introduce a more practical yet challenging problem, termed unified SFDA, which comprehensively incorporates all specific scenarios in a unified manner. To tackle this unified SFDA problem, we propose a novel approach called Latent Causal Factors Discovery (LCFD). In contrast to previous alternatives that emphasize learning the statistical description of reality, we formulate LCFD from a causality perspective. The objective is to uncover the causal relationships between latent variables and model decisions, enhancing the reliability and robustness of the learned model against domain shifts. To integrate extensive world knowledge, we leverage a pre-trained vision-language model such as CLIP. This aids in the formation and discovery of latent causal factors in the absence of supervision in the variation of distribution and semantics, coupled with a newly designed information bottleneck with theoretical guarantees. Extensive experiments demonstrate that LCFD can achieve new state-of-the-art results in distinct SFDA settings, as well as source-free out-of-distribution generalization. Our code and data are available at https://***/tntek/source-free-domain-adaptation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Automated Personalized Health Analytics using IoT and machine learning Algorithms

Automated Personalized Health Analytics using IoT and Machin...

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Augmented intelligence and Sustainable Systems (ICAISS), International Conference on

作者： T M Sivanesan Smitha J A U. Arul K. Balasubramanian Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Sairam College of Engineering Bengaluru Karnataka India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Business Administration Kalasalingam Business School Kalasalingam Academy of Research and Education Srivilliputhur Tamilnadu India

This research work develops and implements an integrated system using IoT technologies and machine learning algorithm to measure Body Mass Index (BMI) and perform health analytics in hospitals. Obesity, overweight, and chronic diseases are major public health issues. Healthcare workers need accurate and quick BMI readings to monitor weight status and health issues. To overcome these limitations, the proposed system uses IoT sensors to automate BMI evaluations and collect real-time health data. Wireless IoT devices interface with hospital information systems. The Support Vector machine (SVM) algorithm analyzes BMI, physical activity, and vital sign data. It can identify and predict BMI values from data. SVM can predict BMI for customized weight loss recommendations. The proposed setup helps hospitals to automate BMI measuring, thus reducing medical staff burden and manual intervention. Real-time monitoring and analysis enable early identification and individualized treatment of obesity-related health concerns. IoT devices with the SVM algorithm enable data-driven decision-making, improving patient outcomes and healthcare efficiency.

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Intrusion Detection in Cloud Architecture Using machine learning

Intrusion Detection in Cloud Architecture Using Machine Lear...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Govinda Rajulu. G R. Santhoshkumar D. Venkatesan Kalvikkarasi. S P. Santosh Kumar Patra Department of Computer Science and Design St. Martin’s Engineering College Department of Artificial Intelligence and Data Science St. Martin’s Engineering College Department of Artificial Intelligence and Machine Learning Department of CSE St. Martin’s Engineering College Professor in ECE Department Podhagai College of Engineering & Technology St. Martin’s Engineering College

The research of Intrusion Detection Systems is known to be among the most challenging aspects of Cloud Security. In the context of the cloud, security breaches such as denial of service, scanning, injection of malicious code, viruses, worms, and the breaking of passwords are becoming increasingly commonplace. In the event that these attacks are not stopped in a timely manner, the company's reputation may be put in jeopardy, and it may sustain financial harm. As a consequence of this, it is essential to safeguard the cloud against dangers of this kind. In this research, we provide solutions to defend cloud systems by incorporating some of the most successful methods of intrusion detection. Within IDS, our primary focus has been placed on two critical aspects: the effectiveness of the detection techniques and the pace of detection. In order to overcome these obstacles, we suggest utilising machine learning in conjunction with parallelization. Approaches based on machine learning gain information from the data itself, hence diminishing the significance of the human expert. In order to identify intrusions, statistical models, safe system approaches, neural networks, and a variety of other methods are utilised. The purpose of this study is to improve the accuracy of intrusion detection in cloud computing by presenting a new approach to intrusion detection that is based on existing intrusion detection systems and the various architectures that those systems employ.

关键词： Databases Cloud computing security Neural networks Intrusion detection computer architecture Passwords Decision trees

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Analysing Blockchain-Based Cryptography: Enhancing Security, Transparency, and Practical Implementations

Analysing Blockchain-Based Cryptography: Enhancing Security,...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Jasim, Laith H. Mishra, Anshoo Joshi, Sanjeev Kumar Sapaev, I.B. Primmia, D.R. Suseela, D. Babu, Aadi Nagender The Islamic University Najaf Iraq IES Insititute of Technology and Management IES University Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Uttaranchal Institute of Technology Uttaranchal University Department of Mechanical Engineering Dehradun248007 India Tashkent Inst. of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Western Caspian University Scientific Researcher The Department Physics and Chemistry Baku Azerbaijan Prince Shri Venkateshwara Padmavathy Engineering College CSE Chennai127 India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning India GRIET Telangana Hyderabad India

ISBN: (纸本)9798350336474

This paper explores the many uses of blockchain-based cryptography for building trust, improving transparency, and providing security in a variety of contexts. The article begins with a thorough Introduction that lays the groundwork for a thorough analysis of the cryptographic underpinnings of blockchain technology. In "Cryptographic Foundations,"the fundamental components of blockchain security are thoroughly dissected. This includes a detailed examination of Digital Signatures (2.1.2), Cryptographic Keys (2.1.3), Data Encryption (2.1.4), Cryptographic Hashes (2.1.1), and Merkle Trees (2.1.5), explaining their vital functions in guaranteeing the validity and integrity of data on the blockchain. After that, the study goes into great length to analyse symmetric encryption in blockchain (3), highlighting how important it is to protecting the privacy of transaction data. Safe Communication (3.2), Privacy Protection (3.1), Next, Asymmetric Encryption in Blockchain (4) takes front stage, explaining how to improve security and trust in decentralised networks using Public and Private Keys (4.1), Secure Communication (4.2), Digital Signatures (4.3), Access Control (4.4), and Transaction Integrity (4.5). After that, the study delves into the specifics of Local Peer Network Components (5.1), illuminating the design and function of local peers in supporting blockchain transactions. Expanding on this basis, the section Cryptographic Foundations in Blockchain (6) goes over the essential cryptographic components, namely Cryptographic Hashing (6.1), Digital Signatures (6.2), and Cryptographic Keys (6.3), emphasising their critical role in the data integrity and immutability of blockchains. The actions that Internet of Things devices need to take in order to register with the Transaction Processing and Device Registration (7) process are described. A fascinating examination of Blockchain Success Stories (8) completes the article. Three noteworthy case studies are covered: Food Trac

关键词： Blockchain

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Detection and Classification of Faults in a Power System with Inverter Penetration Based on a Real-Time machine learning Application

Detection and Classification of Faults in a Power System wit...

引用

Smart Technologies, Communication and Robotics (STCR)

作者： R. Geetha P. Thilagavathi Jose Anand A. Anitha Govindaram Jerril Gilda S. D. Parameswari Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamilnadu Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai Tamil Nadu India Department of ECE KCG College of Technology Karapakkam Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Muthukumaran Institute of Technology Tamil Nadu India Department of Artificial Intelligence and Machine Learning Jerusalem College of Engineering Pallikaranai Chennai Tamil Nadu India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

This study investigates the implementation of a machine learning (ML) algorithm for the classification and localization of faults in a power system with inverter penetration. The approach utilizes the Clarke transform and cosine distance prediction method to detect fault types and insertion distances. However, the results indicate that the general objective was not met, as the algorithm exhibited significant errors in fault classification, frequently predicting multiple fault types per observation, leading to indecisive fault identification. Additionally, the fault insertion distance detection showed poor performance, achieving only 36% accuracy with a tuned window of =1250. The study concludes that the decision criterion using L2 normal for characteristic eigenvalues and cosine distance for eigenvectors holds potential, but improvements in the regression model are necessary for reliable fault classification and localization in inverter-penetrated power systems.

关键词： Location awareness Fault diagnosis machine learning algorithms Transforms machine learning Prediction algorithms Inverters Real-time systems Data models Power system reliability

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Time Series Domain Adaptation via Latent Invariant Causal Mechanism

arXiv

引用

arXiv 2025年

作者： Cai, Ruichu Huang, Junxian Yang, Zhenhui Li, Zijian Eldele, Emadeldeen Wu, Min Sun, Fuchun School of Computer Science Guangdong University of Technology Guangzhou510006 China Peng Cheng Laboratory Shenzhen518066 China School of Computing Guangdong University of Technology Guangzhou510006 China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Institute for Infocomm Research A*STAR Singapore Centre for Frontier AI Research A*STAR Singapore Department of Computer Science and Technology Tsinghua University Beijing China

Time series domain adaptation aims to transfer the complex temporal dependence from the labeled source domain to the unlabeled target domain. Recent advances leverage the stable causal mechanism over observed variables to model the domain-invariant temporal dependence. However, modeling precise causal structures in high-dimensional data, such as videos, remains challenging. Additionally, direct causal edges may not exist among observed variables (e.g., pixels). These limitations hinder the applicability of existing approaches to real-world scenarios. To address these challenges, we find that the high-dimension time series data are generated from the low-dimension latent variables, which motivates us to model the causal mechanisms of the temporal latent process. Based on this intuition, we propose a latent causal mechanism identification framework that guarantees the uniqueness of the reconstructed latent causal structures. Specifically, we first identify latent variables by utilizing sufficient changes in historical information. Moreover, by enforcing the sparsity of the relationships of latent variables, we can achieve identifiable latent causal structures. Built on the theoretical results, we develop the Latent Causality Alignment (LCA) model that leverages variational inference, which incorporates an intradomain latent sparsity constraint for latent structure reconstruction and an inter-domain latent sparsity constraint for domain-invariant structure reconstruction. Experiment results on eight benchmarks show a general improvement in the domain-adaptive time series classification and forecasting tasks, highlighting the effectiveness of our method in real-world scenarios. Codes are available at https: //***/DMIRLAB-Group/LCA. Copyright © 2025, The Authors. All rights reserved.

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An Improved GPS Assisted Fishermen Tracking System Using Internet of Things with Smart Sensors Association

An Improved GPS Assisted Fishermen Tracking System Using Int...

引用

Innovation in Technology (ASIANCON), Asian Conference on

作者： G.M. Karthik Samuthira Pandi V Gayathri S Anni Princy B Kiran Kumar Bhadavath S. Sujatha Department of Information Security School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Panimalar Engineering College Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad India Department of EEE Sri Sairam College of Engineering Bangalore Karnataka India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

This research proposes a border alert system for fisherman that is based on the Internet of Things. As a result of their survival tactics, fisherman often unknowingly crosses international boundaries, putting themselves in danger of being seized or killed. The inability to reliably determine international maritime borders is the root source of this problem. This system aims to prevent such incidents and provide prior warning to fisherman about border regions by utilizing cutting-edge GPS and GSM technologies along with Internet of Things (IoT) monitoring. Protecting fishermen from harm is an implanted gadget that uses GPS to notify them to the country's boundaries. In this research, a novel approach to tracking fisherman using the Internet of Things (IoT) is presented; the IoTGPSTM model is cross-validated with the standard AGPST model to assess the effectiveness of the suggested scheme. The ESP8266 Wi-Fi module is used to transfer the obtained GPS position data to the IoT cloud. The navy may then track the data. If the boat's GPS coordinates are within the danger zone, an alarm will be heard through the buzzer, and the motor speed will be decreased to 25%. The fundamental concept is to use a GPS receiver module and a WiFi-enabled NodeMCU microcontroller to notify the fisherman when they are out at sea.

关键词： Technological innovation Receivers Safety Internet of Things Reliability Relays Monitoring Wireless fidelity Standards Global Positioning System

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