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Risk-Based Reliability Assessment of Modern Power Systems using Machine Learning and Probability Theory

Risk-Based Reliability Assessment of Modern Power Systems us...

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2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and alliance technology Conference, ATCON-1 2023

作者： Rajanarayan Prusty, B. Mohan Krishna, S. Bingi, Kishore Gupta, Neeraj Alliance University Alliance College of Engineering and Design Department of Electrical and Electronics Engineering Bengaluru India Universiti Teknologi Petronas Department of Electrical and Electronics Engineering Seri Iskandar Malaysia National Institute of Technology Srinagar Department of Electrical Engineering Srinagar India

ISBN: (纸本)9781665456272

Risk-based reliability assessment is prevalent for modern power systems under higher penetration of renewable generations. This paper highlights the importance of machine learning and probabilistic approaches for risk-based reliability assessment during power system operation and planning. A set of metrics for realistic risk-based reliability assessment considering over-limit probabilities and corresponding severities is suggested. Probabilistic load flow using Monte-Carlo simulation is used to estimate the over-limit probabilities of power system variables. A detailed presentation of steps for the generation of random samples of a set of correlated random variables, development of realistic risk metrics, and portrayal of their significances via critical result analyses for different cases is expected to serve as a reference text for novice researchers in the field of risk-based reliability assessment of modern power systems integrated with photovoltaic generations. © 2023 IEEE.

关键词： Risk assessment

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SecureChainAI: Integrating Blockchain and Artificial Intelligence for Enhanced Security in IoT Environments 5

SecureChainAI: Integrating Blockchain and Artificial Intelli...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Thamaraiselvi, K. Pushpalatha, A. Chidambarathanu, K. Wankhede, Jaishri P. Alagumuthukrishnan, S. Sarveshwaran, Velliangiri New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Karpagam College of Engineering Department of Computer Science and Design Coimbatore India R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India Cmr Institute of Technology Department of Computer Science and Engineering Hyderabad India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798331504403

Internet of Things (IoT) systems develop with paramount importance for security in view of the increased deployment of IoT devices over critical sectors like healthcare, industry automation, and smart cities. The risk exposure of such systems is extremely high to potent cyber threats, including those that may result in data integrity and privacy issues and operational unreliability. Security in IoT systems has thus emerged through heterogeneous integration with blockchain and artificial intelligence models. This blockchain technology provides a decentralized, immutable ledger for secure data transactions. AI models, especially those designed by anomaly detection and prediction algorithms, can identify and mitigate cyber threats. In this paper, the authors propose the SecureChainAI model that synergistically integrates artificial intelligence algorithms with blockchain to safeguard IoT environments. The two core building blocks that will make this proposed model real are CaSNet and the EchoChain algorithm. The CaSNet model integrates LSTM, RNN, and GRU techniques with novelty detection for proficient attack prediction and detection using their peculiar capabilities in sequential data handling and the identification of novel attack patterns. The EchoChain algorithm puts blockchain technology into the IoT infrastructure to provide security in communication and integrity of the values. This makes the architecture lightweight, shifting computational loads to the collector node, and hence suitable for IoT devices with low computational resources. Evaluations of the performance necessary to detect different classes of cyberattacks using SecureChainAI have improved on the detection accuracy, precision, recall, f1-score, and reduced error rates. © 2024 IEEE.

关键词： Network intrusion

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Uncertainty Management in Utility Grid Considering PV Generation: An Operational Planner's Perspective 5

Uncertainty Management in Utility Grid Considering PV Genera...

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5th International Conference on Energy, Power, and Environment, ICEPE 2023

作者： Rajanarayan Prusty, B. Bingi, Kishore Jena, Debashisha Badi, Manjulata Mohan Krishna, S. Alliance College of Engineering and Design Alliance University Department of Electrical and Electronics Engineering Bengaluru India Universiti Teknologi Petronas Department of Electrical and Electronics Engineering Seri Iskandar Malaysia National Institute of Technology Karnataka Department of Electrical and Electronics Engineering Surathkal India

ISBN: (纸本)9798350313123

This paper discusses the importance of adopting a spatiotemporal model-based scenario generation technique by approximating a multivariate continuous stochastic process in a discrete form. The computational steps for such a model development are comprehensibly discussed via a case study using PV generation data collected from the USA. The model accuracy comparison through a detailed discussion of obtained results is likely to help novice researchers to choose the best model amongst the available options and tactically decide to improvise the modeling framework further. This systematically prepared paper is expected to help power engineers with enough confidence to execute operational planning decisions realistically. © 2023 IEEE.

关键词： Multiple linear regression

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Advanced Cyber-Attack Detection in IoT Networks Using Deep Belief Networks and Gradient Boosting Mechanisms 4

Advanced Cyber-Attack Detection in IoT Networks Using Deep B...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohandas, R. Elavarasi, M. Praveen, R.V.S. Chittapragada, Hemanand Nithiya, C. Prabagar, S. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Bca - It Pallavaram India LTIMindtree Limited Digital Engineering and Assurance Hyderabad India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Madanapalle India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Puduvoyal India Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350352931

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems. © 2024 IEEE.

关键词： Cyber attacks

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Development of a Nail-based Application for the Detection of Disease using Image Processing 2

Development of a Nail-based Application for the Detection of...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gote, Pradnyawant M. Kumar, Praveen Dhale, Trishul Mishra, Gaurav Vedprakash Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Jawaharlal Nehru Medical College Department of Radiodiagnosis Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Disease diagnosis using non-invasive techniques is a key determinant in the improvement of outcomes in patients and the enhancement of precautionary health measures. The study focuses on the design and development of a nail-based detection application employing image processing techniques to detect diseases such as systemic disease, fungal disease, Leukonychia (White spots on nails) and Paronychia (Nail bed infection) through analysis of the patterns of the fingernails and other related symptoms. The developed system integrates several techniques, comprising a customized imaging application, a user-friendly interface, and software for real-time image processing and disease classification. A variety of algorithms proposed for image processing, such as Median filtering, Sobel Mask and HOG features, are designed to extract important features from nail images and then use machine learning models that match these features against a database of established markers of various diseases. Comprehensive testing utilizing an annotated dataset of nail images demonstrated the system's accuracy in classifying certain diseases, further showcasing its potential for possible usage as a proficient tool for early disease diagnosis. Thus, this application is easy to use, user-friendly interface, and provides advancements in diagnosing diseases both in clinical settings and in the comfort of one's home. The incorporation of this technology into routine clinical practice might facilitate the possibility of early diagnosis and early intervention, which could improve patient outcomes and reduce healthcare system burdens. © 2024 IEEE.

关键词： Median filters

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Minimization of energy consumption in MANET using optimized k-medoid PSO Clustering Model 3

Minimization of energy consumption in MANET using optimized ...

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3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Jebakumar Immanuel, D. Thomas, Aby K Prasath, B. Kanchana, M. Narasimharaj, V. Anandhasilambarasan, D. SNS College of Engineering Department of Computer Science and Engineering Coimbatore India Alliance College of Engineering and Design Alliance University Department of Electronics and Communication Engineering Bangalore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Engineering and Technology Department of Mechatronics Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665456647

The (MANET) mobile ad hoc network connect with one another across wireless networks without relying on a central hub for network operations. In a decentralised network, individual mobile nodes take on more and more responsibility for the overall architecture of the system. Given that the routing and data transmission between nodes are impacted by the network's changing topology. The nodes' role as routers in the constant routing and transmission of data bundles is made necessary by this inherent volatility. The problem is that the mobile nodes, which rely on batteries instead of a constant power source, are the biggest consumers of energy in the network because they have to constantly recharge their power. The proposed work takes into account the delinquent of energy consumption in MANET, and finds that the optimization of energy constraint completely dominates the problem space. To begin, the MANET's mobile nodes are clustered using the suggested K-medoid clustering procedure, which helps to lessen the high cost of data routing in extremely large and dense networks. Modification of discrete particle swarm optimization procedure is a metaheuristic algorithm used to bargain the best possible value for the k-medoid algorithm, which is then used to develop a low-power protocol for exchanging information. packet delivery ratio, and throughput are the metrics used to compare different protocols. The results of the simulation analysis show that using this approach reduces the time needed for the method to run and boosts the nodes' energy efficiency. © 2022 IEEE.

关键词： Energy efficiency

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Real-Time Object Detection on DJI Ryze Tello Quadrotor Drone With YOLO and SSD Algorithms

Real-Time Object Detection on DJI Ryze Tello Quadrotor Drone...

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International Conference on Green Energy, Computing and Intelligent technology 2024, GEn-CITy 2024

作者： Madhani, Aryaan Bingi, Kishore Omar, Madiah Korah, Reeba Kumar, Gaurav Rajanarayan Prusty, B. Department of Information Technology Pimpri Chinchwad College of Engineering Pune India Department of Electrical and Electronics Engineering Universiti Teknologi PETRONAS Seri Iskandar Malaysia Department of Chemical Engineering Universiti Teknologi PETRONAS Seri Iskandar Malaysia Department of Electronics and Communication Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Electrical Electronics and Communication Engineering School of Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781837242689

Drones equipped with real-time object detection offer a novel perspective on our environment, yet they encounter challenges related to hardware limitations and the demands of complex applications. These challenges necessitate innovative solutions, particularly given the potential benefits in areas such as search and rescue operations, agriculture, infrastructure monitoring, and environmental conservation. This study investigates the feasibility of real-time object recognition on the DJI Ryze Tello drone using two advanced algorithms: YOLOv3 and Single Shot Multibox Detector (SSD). The evaluation of these algorithms focuses on three key metrics: accuracy, processing speed, and resource consumption. Experiments were carried out in a controlled indoor setting and in the field, with changing lighting as well as background complexity. The results showed that SSD outperformed YOLOv3 in precision, recall, and F1-Score (60-70%), making it more suitable for accuracy-focused applications. In contrast, YOLOv3's faster processing speed (50-60 FPS) is better suited for scenarios requiring quick decision-making. This research offers valuable guidance for selecting these models based on specific application needs, particularly in agriculture, search and rescue, and surveillance. © The Institution of engineering & technology 2024.

关键词： Drones

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Smart City Survey on AIoT Using Machine Learning, Deep Learning, and Its Computing Tools 5th

Smart City Survey on AIoT Using Machine Learning, Deep Lear...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Priakanth, P. Jothimani, K. Gopikrishnan, S. Linga Reddy, C. Department of Computer Science and Design Kongu Engineering College Erode India School of Computer Science and Engineering VIT-AP University Amaravati India Department of Information and Communication Technology University of Agder Kristiansand Norway

ISBN: (纸本)9789819735228

Urban-city administrators are proceeding on smart applications with the fully integrated IoT-artificial intelligence analytics explication assists intelligent-safer workstation, immediate happenstance response, disaster control management including smart electricity, trash, air and water allowing them to produce more prominent levels of flexibility, yielding, sustainability and profitability. Though smart city notion is gradually developing and yet not established all over the world owing to economical, technological, and administrating blockades. This perspective paper aims to accomplish a review of IoT-enabled artificial intelligence regarding the usage of machine learning and deep learning for the development of smarter cities in addition to different computing tools to employ methods, platforms, architectures, and its real-world enactment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Attention-Based RNN for Alzheimer's Diagnosis: A Multi-Modal Approach with Enhanced Feature Retention Algorithm 1

Attention-Based RNN for Alzheimer's Diagnosis: A Multi-Modal...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Ramalingam, S. Gowsikraja, P. Loganathan, E. Logeswaran, A. Ramkumar, N. Geetha, K. Sri Eshwar College of Engineering Department of ECE Coimbatore India Kongu Engineering College Department of Computer Science and Design India Erode Sengunthar Engineering College Department of CSE Erode India Velalar College of Engineering and Technology Department of IT India SR University Department of ECE Warangal India Excel Engineering College Department of CSE India

ISBN: (纸本)9798331518943

Alzheimer's disease (AD) affects millions, and early detection is key. This paper introduces a new deep learning algorithm, Attention-Based Recurrent Neural Network (ABRNN), to detect AD using multi-modal data, including MRI scans and clinical features. ACO is used for preprocessing to enhance image quality and retain important patterns in MRI scans for diagnosis. SegNet isolates the brain regions that indicate the progression of AD through image segmentation. ResNet is used for feature extraction to capture deep hierarchical features from the segmented MRI scans to identify subtle but important features of brain degeneration. A memory-augmented block is added to store key feature patterns so the network can recall important information for AD diagnosis. A hybrid attention mechanism combines imaging and non-imaging features to focus on the relevant brain regions and clinical indicators. ABRNN is compared to 4 existing algorithms: CNN, LSTM, VGG-16, and Random Forest. In experiments, ABRNN achieved 97.85% accuracy, outperforming CNN (94.2%), LSTM (93.5%), VGG-16 (95.1%), and Random Forest (92.3%). ABRNN's performance is better because it retains important features through memory augmentation, extract features well with ResNet, and segments well with SegNet, so it's more accurate than traditional methods © 2024 IEEE.

关键词： Image segmentation

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IoT-Enabled Automated Smart Irrigation System Incorporating Sensor Parameters 5th

IoT-Enabled Automated Smart Irrigation System Incorporating ...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Neeraj Srinivas Reddy, P. Hasan, Dler Salih Alsahlanee, Abbas Thajeel Rhaif Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Mechanical Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This article presents the experimental findings of a smart irrigation system evaluated in two separate agricultural settings—small-scale farming and large-scale agriculture. The system combines sensors, microcontrollers, and actuators to precisely monitor and regulate the irrigation process in order to optimum water use and crop output. Water savings range from 20 to 45% when using smart irrigation technology in small-scale agriculture. By only turning on the irrigation pump when necessary, the technology optimizes water use and fosters sustainable farming methods. Furthermore, by sustaining perfect growing conditions, the approach boosts crop output, with yield benefits ranging from 10 to 22%. These improvements assist small-scale farmers in becoming more profitable and raising their level of living. In large-scale agriculture, smart irrigation systems allow excellent water management and resource optimization across huge agricultural fields. The technology consistently saves a large quantity of water, often between 25 and 50%, addressing water scarcity issues. Additionally, this method has led to a 12–25% increase in aggregate crop yields, thereby raising farm income and increasing agricultural firm profitability. Experiments shows that this smart irrigation system could bring cost-effective, sustainable techniques to extensive and intensive agriculture alike. Intelligence-guided decision-making technology is applied here, and cutting-edge technologies are brought to bear on the water use problem. The system conserves precious resources and increases agricultural output at the same time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart agriculture

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