Gradient Boosting
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LightGBM-Based Grid Demand Estimation and Prediction for Intelligent Electricity Load Forecasting
Abstract: Correctly predicting electricity demand in emerging energy markets like India is the most effective way to maintain grid stability. This is particularly important. This article offers a comprehensive data- based structure that captures both the broad temporal dynamics and regional diversity. This technique utilizes a large-scale multi-dimensional feature space that includes temporal indicators, autoregressive lag terms, sliding-window rolling statistics, and state-level administrative markers. Our systematic benchmarking approach involves the use …
Published in Journal of Power Electronics and Power Systems · Vol. 16, Issue 3, 2026 Read article
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Controlled Temporal Feature Ablation and Explainable Ensemble Learning for Next- Hour Residential Electricity Forecasting
Abstract: Short-term residential load forecasting is increasingly used in demand response, home energy management, and data-driven grid operation, yet reported gains are often difficult to attribute to either the forecasting algorithm or the temporal representation supplied to it. This study presents a controlled, leakage-aware evaluation of temporal feature engineering for one-hour-ahead household electricity forecasting. The public UCI Individual Household Electric Power Consumption dataset was aggregated to hourly resolution, causally imputed using …
Published in Trends in Electrical Engineering · Vol. 13, Issue 3, 2023 Read article
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Ensemble-Based Detection of Phishing URLs Using Machine Learning: A Cybersecurity Approach
Abstract: Phishing websites are among the most frequently used tactics by cybercriminals to trick people into revealing sensitive information, including usernames, passwords, banking details, and other personal data. As attackers continuously modify website structures and Uniform Resource Locators (URLs), conventional blacklist and signature-based detection techniques often fail to identify newly emerging phishing websites. This research introduces an ensemble machine learning method designed to identify phishing URLs more accurately and consistently. The …
Published in Journal Of Network security · Vol. 14, Issue 3, 2026 Read article
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Intelligent Big Data Processing: Optimizing Analytics Pipelines Through Ensemble Machine Learning, Distributed Computing, and Adaptive AI Architectures
Abstract: Traditional data analytics methods are facing significant challenges due to the explosive rise of digital data. Global data generation is expected to reach nearly 175 zettabytes by 2025. This puts significant pressure on existing computing infrastructure. Conventional analytics methods often struggle with scalability, processing speed, and smart decision-making. Rule-based systems and standalone machine learning models frequently cannot handle the complexity and scale of modern big data environments effectively. To tackle …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 2, 2026 · pp. 1–11 Read article