Methane is the second most important anthropogenic greenhouse gas after carbon dioxide, with a global warming potential roughly 28–34 times greater over a 100-year timescale. Major sources include ...

Researchers have developed an integrated gray wolf optimization algorithm-based hybrid estimation framework that combines sample entropy, localized voltage area, and fuzzy entropy to accurately ...

Trained on historical consumption data spanning a decade, the model demonstrated strong predictive performance. It achieved a training error of 0.182 and a forecasting accuracy of 95.2 percent, ...

Decision tree regression is a fundamental machine learning technique to predict a single numeric value. A decision tree regression system incorporates a set of virtual if-then rules to make a ...

ABSTRACT: Detecting behavioural signatures of depression from everyday digital traces is a central challenge in computational psychiatry. Real-world datasets from smartphones and wearables often ...

Diabetic peripheral neuropathy (DPN) is a prevalent and highly disabling complication of diabetes mellitus, associated with markedly increased rates of disability and mortality. Timely intervention ...

ABSTRACT: This study presents a comparative analysis of machine learning models for threat detection in Internet of Things (IoT) devices using the CICIoT2023 dataset. We evaluate Logistic Regression, ...

Abstract: Load forecasting, a crucial aspect of energy management, involves predicting the future electricity demand based on historical data. In the context of monthly time series analysis, this ...

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Abstract: This research seeks to compare the accuracy of the novel Random Forest (RF) and Logistic Regression (LR) methods for forecasting software problems which avoids security threats based on a ...