Machine Learning for Anomaly Detection in Electric Transportation Networks

E3S Web of Conferences, Год журнала: 2024, Номер 511, С. 01039 - 01039

Опубликована: Янв. 1, 2024

This study introduces a sophisticated anomaly detection system based on machine learning. The is specifically developed to enhance the dependability and safeguard security of electric transportation networks, with particular emphasis charging infrastructure for vehicles (EVs). Utilizing extensive datasets, research examines several facets stations, records, identified abnormalities, following maintenance measures. examination station demonstrates system’s versatility in accommodating many circumstances, as seen by range power ratings, consumption patterns, energy provided. Further records provides comprehensive understanding individual sessions, enabling irregularities such atypical surges extended durations. learning system, having been trained verified using this data, has commendable degree precision identifying anomalies, shown congruence between anticipated abnormalities real results. repair measures carried out reaction highlight practical ramifications proactive tactics utilized reduce downtime operations. performance measures, including accuracy, recall, F1 score, unequivocally validate resilience guaranteeing precise identification while mitigating occurrence false positives negatives. seamless incorporation into results, not only amplifies safeguarding EV but also establishes an invaluable instrument implementations. research, addition offering thorough performance, elucidates forthcoming avenues scalability, real-time monitoring, interpretability, thereby making valuable contribution wider discussion revolutionary capabilities ever-changing realm transportation.

Язык: Английский

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Advancements in AI-Based Information Technologies: Solutions for Quality and Security

Systems, Год журнала: 2024, Номер 12(2), С. 58 - 58

Опубликована: Фев. 9, 2024

At the current stage of development and implementation information technology in various areas human activity, decisive changes are taking place, as there powerful technical resources for accumulation processing large amounts [...]

Язык: Английский

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Modeling and Upgrade of Disaster-Resilient Interdependent Networks Using Machine Learning

Опубликована: Янв. 1, 2024

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Язык: Английский

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Ltl-Based Runtime Verification Framework for Cyber-Attack Anomaly Prediction in Cyber-Physical Systems

Опубликована: Янв. 1, 2024

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Язык: Английский

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Current Applications and Future Trends of Artificial Intelligence and Machine Learning in the Resilience of Interdependent Critical Infrastructures

Опубликована: Фев. 12, 2024

Critical infrastructures, such as power and water networks, are vital for society the economy. However, they vulnerable to various disruptions component failures, cyber-attacks, natural disasters. These can cascade across critical infrastructure networks (CINs), causing significant socioeconomic losses. Decision-makers face challenge of protecting CINs before restoring their functions afterward, considering interdependencies uncertainties. Current methods struggle model big data, complex interactions, multilayer dependencies between CINs. Artificial intelligence (AI) machine learning (ML) applications be used overcome these challenges, systems discover data patterns representing a promising research trend that could benefit both private companies governments. This article undertakes comprehensive review literature on in improving resilience interdependent (ICISs). The aim is address existing knowledge gap dispersed articles this area, following Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) protocol. primary goal assess current state ML ICISs engineering field by examining available literature, order future opportunities trends. findings summarized, potential trends listed, aiming inspire practitioners explore directions field.

Язык: Английский

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Improving Earth surface temperature forecasting through the optimization of deep learning hyper-parameters using Barnacles Mating Optimizer

Franklin Open, Год журнала: 2024, Номер 8, С. 100137 - 100137

Опубликована: Июль 14, 2024

Time series forecasting is crucial across various sectors, aiding stakeholders in making informed decisions, planning for the short and long term, managing risks, optimizing profits, ensuring safety. One significant application of time predicting Earth surface temperatures, which vital civil environmental sectors such as agriculture, energy, meteorology. This study proposes a hybrid model temperature using Deep Learning (DL). To improve DL model's performance, an optimization algorithm called Barnacles Mating Optimizer (BMO) integrated to optimize both weights biases. The trained on global dataset with seven inputs compared models optimized by Particle Swarm Optimization (PSO), Harmony Search Algorithm (HSA), Ant Colony (ACO). Additionally, comparison made Autoregressive Moving Average (ARIMA) method. Evaluation Mean Absolute Error (MAE), Root Square (RMSE), coefficient determination (R2) demonstrates superior performance BMO, showing minimal errors.

Язык: Английский

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Future Trends and Opportunities of Artificial Intelligence and Machine Learning in the Resilience of Interdependent Critical Infrastructure Networks in the Kingdom of Saudi Arabia

Lecture notes in civil engineering, Год журнала: 2024, Номер unknown, С. 232 - 239

Опубликована: Окт. 25, 2024

Язык: Английский

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Modeling and upgrade of disaster-resilient interdependent networks using machine learning

Optical Switching and Networking, Год журнала: 2024, Номер 55, С. 100791 - 100791

Опубликована: Ноя. 9, 2024

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