MACHINE LEARNING AND DEEP LEARNING APPROACHES FOR LARGE-SCALE DATA ANALYTICS AND INTELLIGENT DECISION SUPPORT SYSTEMS

Abstract

The integration of Machine Learning (ML) and Deep Learning (DL) into Intelligent Decision Support Systems (IDSS) has fundamentally transformed large-scale data analytics by enabling automated pattern recognition, predictive modeling, and prescriptive recommendations in high-velocity, high-dimensional environments. Traditional rule-based or statistical DSS are increasingly supplemented or replaced by hybrid architectures that combine supervised (SVM, Random Forest, XGBoost), unsupervised (K-Means, PCA), semi-supervised, and reinforcement learning techniques with advanced DL models (CNNs, LSTMs, Transformers, GNNs) and physics-informed approaches. These systems excel in handling unstructured and relational data through self-attention mechanisms, graph-based relational learning, and edge computing for real-time inference, achieving benchmark accuracies of 91–98% across domains such as healthcare diagnostics, financial fraud detection, smart manufacturing (predictive maintenance), and urban planning (energy optimization, traffic management). Key architectural evolutions from Lambda/Kappa to Data Lakehouse and Data Fabric support scalable MLOps pipelines, while emerging trends like AutoML, Small Language Models, Sparse Models, and Quantum ML promise further democratization and efficiency gains. Critical challenges include data quality and silos, model interpretability (addressed by XAI techniques such as SHAP), algorithmic bias, privacy concerns (mitigated by Federated Learning), and the “black-box” nature of deep models in high-stakes decisions. Sector-specific case studies demonstrate substantial improvements in decision accuracy (up to 22%), latency reduction (up to 30%), and operational efficiency. Future trajectories emphasize hybrid neuro-symbolic systems, edge AI for low-latency applications, and ethical governance frameworks to ensure responsible deployment. Overall, ML/DL-driven IDSS represent a powerful paradigm for turning big data into actionable intelligence, driving competitive advantage and societal benefit in an increasingly data-centric world.