Machine Learning for Predictive Modeling Across Critical Domains: A Comparative Analysis of Algorithms in Cybersecurity, Renewable Energy, Pharmaceuticals, and Environmental Science

K.K. Pichik

doi:10.5281/zenodo.21221862

Advance Journal of Science Engineering and Technology

Volume 1 , Issue 2

Research Article • Open Access

Machine Learning for Predictive Modeling Across Critical Domains: A Comparative Analysis of Algorithms in Cybersecurity, Renewable Energy, Pharmaceuticals, and Environmental Science

K.K. Pichik

Pages 119-128

DOI: 10.5281/zenodo.21221862

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Abstract

The proliferation of machine learning algorithms across diverse scientific and industrial domains has revolutionized predictive modeling capabilities, enabling data-driven decision-making in complex systems. This comprehensive review examines the application of machine learning algorithms for predictive modeling across four critical domains: cybersecurity threat detection, photovoltaic system fault diagnosis, pharmaceutical drug delivery systems, and environmental rainfall forecasting. Through a structured literature review and comparative synthesis of peer-reviewed studies published primarily between 2025 and 2026, this paper investigates the performance of supervised learning, ensemble learning, and neural network algorithms for predictive modeling across four critical domains: cybersecurity threat detection, photovoltaic system fault diagnosis, pharmaceutical drug delivery systems, and environmental rainfall forecasting. The review compares algorithm performance, identifies common methodological trends, and evaluates domain-specific strengths, limitations, and emerging research opportunities. The review reveals that ensemble-based learning approaches consistently outperform single-model architectures across domains, with Random Forest achieving F1-scores of 97.5% in cybersecurity threat detection and 98.88% accuracy in photovoltaic fault diagnosis. Neural network architectures, particularly wide neural networks and Long Short-Term Memory networks, demonstrate superior capability in capturing nonlinear interactions, achieving 98.88% accuracy in fault classification and 97% accuracy in predictive maintenance applications. In pharmaceutical applications, ensemble methods achieved 92.3% accuracy in predicting drug release behavior, while in environmental modeling, Random Forest and KNN regression achieved R² values of 0.6155 and 0.6492 respectively. The review identifies key challenges including data quality issues, model interpretability limitations, computational scalability constraints, and the need for standardized benchmarking frameworks. Future research directions include the development of explainable AI methods, federated learning architectures for privacy-preserving distributed modeling, hybrid physics-informed machine learning approaches, and domain-specific optimization strategies. The findings provide a comprehensive framework for algorithm selection across domains, emphasizing that while deep learning excels in complex pattern recognition tasks, ensemble methods offer robust performance with greater interpretability, and simpler models remain valuable for resource-constrained applications with well-structured data.

Keywords

Machine Learning, Predictive Modeling, Cybersecurity Threat Detection, Photovoltaic Fault Diagnosis, Pharmaceutical Drug Delivery, Rainfall Forecasting, Ensemble Learning,

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