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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Viswa, A.V. Dharma Pravardhana, V. Thangavel, Senthil Kumar Jeyakumar, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Coimbatore Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350372120

Understanding the prevalence and severity of accidental falls among the elderly is crucial. Even for those living independently, falls are frequent and can lead to severe injuries, often being a primary cause of elderly mortality. To address this need, the development of a comprehensive platform becomes essential, capable of continuously monitoring individuals' activity status for timely detection of falls and other unexpected behaviors. Real-time monitoring is vital for providing swift and efficient early warnings during a fall. Conventional methods of monitoring, such as manual surveillance, are resource-intensive and may lack the immediacy required to respond effectively to fall incidents. Therefore, to overcome these limitations, innovative solutions have been introduced. Implementing an automated system can significantly reduce the time and energy expended on monitoring while ensuring a prompt and proactive response to potential fall events. Despite advancements in fall detection research, many existing studies rely on wearable devices primarily within home settings. Extending the scope of these technologies to diverse environments could result in more inclusive and versatile fall detection systems, enhancing elderly safety across various settings. Conventional fall detection systems frequently encounter privacy concerns when employing RGB camera-based monitoring due to their intrusive nature. To tackle this issue, alternative non-intrusive methods such as LiDAR technology have been introduced. LiDAR proves to be effective in detecting falls while also upholding privacy, rendering it a promising option for enhancing geriatric safety. © 2024 IEEE.

关键词： Anomaly detection

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An Iterative PRISMA Review of GAN Models for Image Processing, Medical Diagnosis, and Network Security

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computers, Materials & Continua 2025年第2期82卷 1757-1810页

作者： Uddagiri Sirisha Chanumolu Kiran Kumar Sujatha Canavoy Narahari Parvathaneni Naga Srinivasu Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522302India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad501301India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravati522503India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Inti International University Nilai71800Malaysia

The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some

关键词： GAN CGAN WGAN DCGAN image analysis

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A Comparison of Multinomial Naive Bayes and XG Boost for Sentiment Analysis and Bias Detection in Tweets 9

A Comparison of Multinomial Naive Bayes and XG Boost for Sen...

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9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Devadath, R. Alex, Emeric C Sreeja, K. Abhishek, S. Anjali, T. Engineering Amrita School of Computing Department of Computer Science Amritapuri India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350350845

In the context of social platforms, this study explores Twitter sentiment analysis, providing practical insights and directions for future research. In previous days, people conveyed their feelings directly, but now platforms like Twitter have gained popularity for expressing individual and organizational opinions. Understanding these expressions can be challenging. This research focuses on sentiment analysis techniques for Twitter data, a subfield of Natural Language Processing (NLP) where machine learning plays a key role in detecting public emotions. The paper covers methods, tools, pre-processing, sentiment classification techniques, evaluation matrixes, and machine learning algorithms like Naive Bayes. The analysis, employing various keywords and hashtags, evaluates using metrics such as confusion matrix, F1 score, precision, and recall. The study emphasizes the responsible and ethical use of sentiment analysis tools for informed decision-making, promoting inclusivity and empathy online. Through systematic analysis, a million tweets were categorized into positive and negative sentiments, contributing to the growing body of knowledge in sentiment analysis. © 2024 IEEE.

关键词： Sentiment analysis

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Animal Intrusion Detection Using Yolo V8 10

Animal Intrusion Detection Using Yolo V8

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Kathir, M. Balaji, V. Ashwini, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

This project presents an inventive Animal Intrusion Detection System using Yolo v8 object detection algorithm. Addressing the problem of wildlife intrusions in agriculture as its main goal, the presented work focuses on precise animal identification and the activation of appropriate warning systems. The technology effectively identifies animals in agricultural fields, protecting crops as well as guaranteeing the welfare of wildlife. An intelligent monitoring system that can detect the image of an animal entering, sending alert messages to the farmers or landlords is proposed. It may lessen conflicts between people and wildlife, safeguard crops, and maintain biodiversity, all of which would be advantageous to both wildlife conservation and agriculture. The presented work uses YOLO v8, the most recent instantaneous object detector in the YOLO family, because it performs exceptionally well in terms of accuracy and speed. YOLOv8 offers new features and enhancements, building on the strengths of earlier YOLO versions. This makes it perfect for object detection in a range of applications. © 2024 IEEE.

关键词： Livestock

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Symbolic Regression Using Dynamic Structured Grammatical Evolution with Digit Concatenation and Linear Scaling 2nd

Symbolic Regression Using Dynamic Structured Grammatical Evo...

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2nd International Conference on Advances in Data-driven computing and Intelligent Systems, ADCIS 2023

作者： Geethani, V. Velayutham, C. Shunmuga Balraj, Aditya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India

ISBN: (纸本)9789819995301

Symbolic Regression’s vast search space can lead to computational inefficiencies. However, Grammatical Evolution (GE) narrows down the search by focusing on solutions adhering to specific grammar and guiding the algorithm toward more promising regions. Dynamic Structured Grammatical Evolution (DSGE), a variant of GE, enables the evolution of variable-length structures, addressing the issue of finding concise and interpretable solutions as problem complexity increases. Integrating constants is challenging, as disruption of values may occur. DSGE, by using digit concatenation as a constant creation method, enables better preservation and accurate representation of constant values during the evolutionary process. The evolved equations can overfit in certain cases. The search algorithm mitigates overfitting by incorporating Linear Scaling with error minimization, prioritizing equations with lower errors to promote better generalization and produce more reliable models. Experiments were demonstrated on the Feynman equations, one of the benchmark datasets. The combined methodology results in capturing the exact and symbolically equivalent equations. These findings highlight the potential of the proposed approach for symbolic regression and related problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Regression analysis

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Blockchain-Enabled Mitigation Strategies for Distributed Denial of Service Attacks in IoT Sensor Networks:An Experimental Approach

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computers, Materials & Continua 2024年第12期81卷 3679-3705页

作者： Kithmini Godewatte Arachchige Mohsin Murtaza Chi-Tsun Cheng Bader M.Albahlal Cheng-Chi Lee Department of Engineering Technologies School of ScienceComputing and Engineering TechnologiesSwinburne UniversityMelbourne3122Australia School of Engineering Science Technology Engineering and Mathematics CollegeRMIT UniversityMelbourneVIC 3001Australia Department of Information Technology College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Asia UniversityTaichung City41354TaiwanChina

Information security has emerged as a crucial consideration over the past decade due to escalating cyber security threats,with Internet of Things(IoT)security gaining particular attention due to its role in data communication across various ***,IoT devices,typically low-powered,are susceptible to cyber ***,blockchain has emerged as a robust solution to secure these devices due to its decentralised ***,the fusion of blockchain and IoT technologies is challenging due to performance bottlenecks,network scalability limitations,and blockchain-specific security ***,on the other hand,is a recently emerged information security solution that has great potential to secure low-powered IoT *** study aims to identify blockchain-specific vulnerabilities through changes in network behaviour,addressing a significant research gap and aiming to mitigate future cybersecurity *** blockchain and IoT technologies presents challenges,including performance bottlenecks,network scalability issues,and unique security *** paper analyses potential security weaknesses in blockchain and their impact on network *** developed a real IoT test system utilising three prevalent blockchain applications to conduct *** results indicate that Distributed Denial of Service(DDoS)attacks on low-powered,blockchain-enabled IoT sensor networks cause measurable anomalies in network and device performance,specifically:(1)an average increase in CPU core usage to 34.32%,(2)a reduction in hash rates by up to 66%,(3)an increase in batch timeout by up to 14.28%,and(4)an increase in block latency by up to 11.1%.These findings suggest potential strategies to counter future DDoS attacks on IoT networks.

关键词： Blockchain cyber intrusions DDoS IoT low powered sensors vulnerabilities wireless network

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Comparative Analysis of Machine Learning Models for Smart Pothole Detection: Enhancing Road Safety and Maintenance Efficiency 2

Comparative Analysis of Machine Learning Models for Smart Po...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Dhanvanth Prasath, M.P. Avinash, M. Prabu, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388916

In this paper, we propose a Smart Pothole Detection System designed to enhance road safety and improve maintenance efficiency through machine learning and geospatial analysis. Using smartphone applications, road images with corresponding XYZ coordinates are collected and labeled as either "pothole"or "no pothole". This dataset is utilized to train and evaluate multiple machine learning models, including Support Vector Machine (SVM), Logistic Regression, and Logistic Regression integrated with the AdaBoost ensemble method. These models are compared based on their accuracy, precision, recall, and F1-score, to determine the most effective solution for pothole detection. SVM emerged as the best-performing model, demonstrating high-dimensional data handling capabilities, while the AdaBoost-enhanced Logistic Regression model also performed competitively. Our system holds promise in contributing to real-time, automated pothole detection, which is essential for proactive road safety management and efficient maintenance planning. © 2024 IEEE.

关键词： Logistic regression

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A Deep Q-Learning Approach to Intrusion Detection and Prevention Systems: Enhancing Cybersecurity through Intelligent Adaptation 4

A Deep Q-Learning Approach to Intrusion Detection and Preven...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Chandre, Pankaj Shinde, Manoj Bapurao Tilwant, Shruti Vilas Ghandat, Arun Bhau Shendkar, Bhagyashree D Dhotre, Prashant Mit Art Design and Technology University Mit School of Computing Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331518981

A Deep Q-Learning approach to Intrusion Detection and Prevention Systems (IDPS) offers a cutting-edge solution for enhancing cybersecurity by leveraging intelligent machine learning models. This method dynamically adapts to evolving threats, learning optimal defense strategies through a reward-punishment system. The presented architecture integrates sensor-based traffic monitoring, feature extraction, and real-time decision-making to strengthen network resilience. The system leverages sensor nodes to monitor traffic and uses traffic analyzers to extract critical features from network data. A Deep Q-Learning model is employed to analyze these features, dynamically adjusting security policies by learning through reward and punishment mechanisms. The system interacts with network infrastructure, such as firewalls, routers, and switches, to mitigate threats in real time. Threat intelligence is continuously updated in a dedicated database, while an action executor enforces security measures and policies. This intelligent adaptation ensures proactive threat prevention and network resilience. © 2024 IEEE.

关键词： Cybersecurity

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Efficient Method For Automatic Irrigation System using IOT 10

Efficient Method For Automatic Irrigation System using IOT

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Rajagopal, R. Senbagavalli, M. Sampath, P.V.V.S.S. Sanjeevi Kumar, V. Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bangalore India Alliance School of Advanced Computing Alliance University Department of CSE Bangalore India

ISBN: (纸本)9798350353778

The precise and effective management of water using Internet of Things (IoT) technology in Automatic Irrigation Systems (AIS) has revolutionized agricultural practices. The primary objective is to enhance the watering methods, reduce labor costs, and conserve water. Through the literature review, various components are used in this automatic irrigation system. Identifying gaps in existing approaches leads to the proposal of an integrated framework. This process encompasses the availability and cost-effectiveness of the product. The testing and evaluation of the system will be conducted by testing trails with different soil moisture levels. As a result, we can see an improvement in the quality of the product. The purpose of this project is to show the importance of water conservation in the modern deforested environment. Future work will focus on integrating based on weather conditions by getting the datasets from the weather station. © 2024 IEEE.

关键词： Soil moisture

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VisioRenalNet: Spatial Vision Transformer UNet for enhanced T2-Weighted Kidney MRI Segmentation 2

VisioRenalNet: Spatial Vision Transformer UNet for enhanced ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Afnaan, K. Palaniswamy, Suja Singh, Tripty Prakash, Bhanu Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India Singapore Singapore

Chronic Kidney Disease (CKD) afflicts approximately 20% of the global population, underscoring the pressing need for precise detection and diagnosis. Accurate kidney segmentation in medical imaging is pivotal for capturing intricate details, facilitating effective evaluation and treatment planning. This work consists implementation of U-Net Variants along with enhanced variant for kidney segmentation in Magnetic Resonance Images (MRI). We systematically assess the performance of various U-Net variants, including U-Net, VGGUNet, DenseUNet, ResUNet, U-Net++, and AttentionUNet, utilizing a publicly available dataset of T2-weighted abdomen MRI scans from 100 subjects. Subsequently, we propose an enhanced U-Net framework that seamlessly integrates transformer blocks, spatial Attention Mechanism, and swish activation to enhance performance and robustness. Enhanced UNet provides improved feature learning, enhanced contextual information, selective focus and improved performance metrices for Kidney Segmentation. This significant advancement in medical image segmentation can contribute to enhanced diagnosis, more effective treatment planning, and improved disease management for individuals suffering from kidney diseases. Notably, our enhanced U-Net variant, after 50 epochs, achieved an average Dice coefficient of 89.1%, a Jaccard score of 80.4%, and an impressive accuracy of 96.2%, surpassing all baseline U-Net variants in all evaluation metrics, thereby affirming its superiority in kidney segmentation. © 2024 Elsevier B.V.. All rights reserved.

关键词： Chemical activation

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