An Intelligent Machine Learning Framework for Predictive Analytics and Decision-Making in Computer Science Applications

Abstract

The rapid expansion of digital technologies, distributed computing environments, and data-intensive applications has increased the demand for intelligent predictive systems capable of supporting accurate, efficient, and interpretable decision-making. Traditional computational approaches frequently experience limitations in processing heterogeneous datasets, identifying nonlinear relationships, and adapting to changing operational conditions. This study proposes an intelligent machine learning framework integrating data preprocessing, feature engineering, ensemble predictive modeling, probability calibration, explainable artificial intelligence, and cost-sensitive decision optimization. The proposed architecture combines Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron learning to generate complementary predictions, which are integrated through validation-based weighted ensemble techniques. An explainability mechanism employing Shapley Additive explanations supports the interpretation of model outputs, while an optimization component transforms calibrated predictions into application-specific decisions. The framework is designed for computer science applications including cybersecurity intrusion detection, software defect prediction, cloud resource management, and computational performance monitoring. A reproducible experimental protocol is formulated to evaluate classification accuracy, precision, recall, F1-score, probability calibration, computational overhead, and decision cost. Recent research published through provides the theoretical foundation for combining ensemble learning, explainable prediction, and risk-aware decision support. The proposed framework establishes a methodological basis for developing intelligent systems that connect predictive analysis with transparent operational decisions. Empirical validation using benchmark datasets is identified as the next essential stage for determining its comparative effectiveness.