2024· International Journal of Applied Data Science & Modern Computing· 0 citations
TL;DR
The proposed framework includes four stages: knowledge acquisition, data preprocessing, hybrid model integration, and predictive decision support, which improves prediction accuracy, reliability, transparency, and decision-making of next-generation intelligent systems.
Abstract
Predictive intelligence enables systems to forecast future events using historical data, domain knowledge, and advanced analytics. Traditional approaches are either knowledge-driven, offering interpretability and reasoning, or data-driven, providing strong learning capabilities but facing challenges in explainability and adaptability. Hybrid predictive intelligence combines both paradigms to overcome their limitations. The proposed framework includes four stages: knowledge acquisition, data preprocessing, hybrid model integration, and predictive decision support. By integrating expert knowledge with machine learning techniques, it improves prediction accuracy, reliability, transparency, and decision-making. Applications span healthcare, industrial automation, cybersecurity, finance, smart cities, and intelligent transportation systems. Comparative studies show that hybrid models outperform conventional approaches in accuracy, robustness, and interpretability. Future developments in federated learning, digital twins, graph neural networks, and autonomous reasoning are expected to further enhance predictive intelligence for next-generation intelligent systems.
This paper proposes the Foundation Model-Based Predictive Analytics Framework for Multi-Domain Decision Intelligence (FMPA-MDI), an integrated architecture that combines heterogeneous data acquisition, multimodal preprocessing, semantic representation learning, transformer-based predictive reasoning, retrieval-augmented learning, knowledge graph integration, explainable AI (XAI), and intelligent decision optimization.
Seshagiri N· International Journal of Mac...· 0 citations
Decision Intelligence (DI) combines Artificial Intelligence (AI), Machine Learning (ML), analytics, and domain expertise to improve organizational decision-making. However, the growing volume of multimodal data, including text, images, sensor data, and numerical information, presents significant challenges for conventional decision support systems. This study proposes a Multimodal AI Framework for Decision Intelligence Systems that integrates diverse data sources to enhance prediction accuracy, contextual understanding, and operational efficiency. The proposed architecture consists of four layers: data ingestion, multimodal processing, fusion intelligence, and decision orchestration. It employs Natural Language Processing (NLP), Computer Vision (CV), time-series analytics, and transformer-based fusion techniques to generate predictive insights, automated recommendations, and explainable decisions. The framework is applicable across healthcare, finance, manufacturing, retail, and intelligent governance. Performance evaluation demonstrates that multimodal AI significantly outperforms traditional unimodal systems by improving prediction accuracy, reducing decision latency, and enhancing contextual awareness. The proposed framework supports faster, more reliable, and explainable decision-making, providing a scalable solution for next-generation enterprise decision intelligence and data-driven strategic planning.
Seshagiri N· International Journal of Art...· 0 citations
Data-driven applications across healthcare, manufacturing, finance, transportation, cybersecurity, smart cities, and Industrial Internet of Things (IIoT) require intelligent predictive systems that are accurate, explainable, and capable of real-time decision-making. While conventional machine learning (ML) pipelines effectively automate tasks such as data preprocessing, feature engineering, model training, and deployment, they often lack contextual reasoning, adaptive intelligence, and explainability when handling heterogeneous and multimodal data. Recent advances in Large Language Models (LLMs) offer new opportunities to enhance ML pipelines through semantic reasoning, intelligent feature generation, automated model optimization, and explainable predictions. This research proposes a Large Language Model-Augmented Machine Learning Pipeline (LLM-MLP) that integrates data acquisition, intelligent preprocessing, semantic feature engineering, automated model selection, hyperparameter optimization, explainable AI, continuous monitoring, and feedback-driven refinement within a unified framework. By combining LLM-based reasoning with traditional ML techniques, the proposed architecture improves predictive accuracy, interpretability, scalability, and computational efficiency. The framework supports continuous learning through reinforcement-based optimization and is applicable to diverse domains, including healthcare diagnosis, predictive maintenance, financial risk assessment, cybersecurity, customer analytics, and smart infrastructure management. Overall, the proposed LLM-MLP provides an adaptive, trustworthy, and scalable predictive intelligence framework for next-generation AI-driven decision support systems.
Andrey Ershov· International Journal of Mac...· 0 citations
Decision Support Systems (DSS) are widely used in healthcare, finance, manufacturing, education, transportation, and public administration to support data-driven decision-making. Traditional DSS based on rule-based expert systems and statistical models often struggle to adapt to dynamic and complex environments. To address these limitations, Knowledge-Based Machine Learning (KBML) integrates machine learning with symbolic knowledge representation techniques such as ontologies, semantic networks, expert rules, and domain constraints. By incorporating prior knowledge into the learning process, KBML enhances reasoning, interpretability, transparency, and predictive performance while reducing training requirements. This paper reviews knowledge-based machine learning approaches for intelligent DSS and examines the integration of knowledge engineering principles with supervised, unsupervised, reinforcement, and ensemble learning methods. The roles of ontologies, rule-based inference, semantic reasoning, and knowledge graphs in improving learning effectiveness are also discussed. A comprehensive DSS framework is proposed, consisting of knowledge acquisition, data preprocessing, feature engineering, knowledge representation, model training, inference generation, and decision recommendation modules. Experimental results demonstrate that knowledge-enhanced models achieve higher accuracy, improved decision consistency, reduced uncertainty, and greater interpretability than conventional machine learning approaches. The study also highlights challenges related to knowledge acquisition, scalability, ontology maintenance, and system integration. Future research directions include explainable AI, deep knowledge graphs, federated learning, cognitive computing, and autonomous reasoning systems.
Kevin Taylor· International Journal of Mac...· 0 citations
Artificial Intelligence (AI) has become a transformative technology for enhancing Intelligent Decision Support Systems (IDSS) by enabling accurate, adaptive, and data-driven decision-making across diverse computer science applications. This review examines the fundamental concepts of AI, its major techniques, including machine learning, deep learning, expert systems, fuzzy logic, reinforcement learning, explainable AI, and generative AI, and their roles in modern decision support systems. It further discusses emerging trends such as human-centered AI, edge AI, federated learning, digital twins, hybrid AI models, and responsible AI that are reshaping intelligent decision-making. The review also highlights the applications of AI-enabled IDSS in cybersecurity, software engineering, cloud computing, the Internet of Things, healthcare informatics, robotics, big data analytics, smart manufacturing, and education technologies. In addition, key challenges related to data quality, explainability, scalability, privacy, ethics, computational complexity, and user trust are critically discussed, followed by future research directions emphasizing foundation models, neuro-symbolic AI, Green AI, Quantum AI, AI governance, and autonomous decision intelligence. Overall, the review provides a comprehensive overview of recent advancements and identifies promising opportunities for developing trustworthy and efficient AI-driven intelligent decision support systems.
S. Poonguzhali, M. Arora, Didit Abdul Majid et al.· Journal of Intelligent Decis...· 0 citations
This paper presents a comprehensive framework integrating graph embeddings, retrieval-augmented generation (RAG), transformer-based reasoning, attention mechanisms, and contextual embedding fusion to improve prediction accuracy, explainability, and robustness.
Mahabala H.N· International Journal of Int...· 0 citations