2024· International Journal of Machine Learning and Predictive Analytics· 0 citations
TL;DR
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.
Abstract
Predictive analytics is rapidly evolving through Artificial Intelligence (AI), particularly with the emergence of foundation models that enable scalable, transferable, and context-aware intelligence across multiple domains. Unlike traditional machine learning models, foundation models leverage large-scale multimodal pretraining and efficient task-specific adaptation, enabling superior reasoning, zero-shot learning, and cross-domain knowledge transfer. 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. The framework supports structured and unstructured data while incorporating transfer learning, attention mechanisms, semantic embeddings, and continuous feedback for adaptive decision-making. Mathematical formulations model feature representation, semantic similarity, predictive confidence, and optimization. The proposed framework enhances prediction accuracy, scalability, interpretability, and computational efficiency, providing a robust foundation for next-generation intelligent decision support across healthcare, finance, manufacturing, smart cities, and other enterprise domains.
This paper reviews hybrid KG–LLM frameworks for predictive analytics, highlighting graph embeddings, Retrieval-Augmented Generation (RAG), transformer-based reasoning, and contextual embedding fusion to improve prediction accuracy, interpretability, and robustness.
Meena Krishnan· International Journal of Mac...· 0 citations
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.
Karen Lewis, Steven Young· International Journal of App...· 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
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
Foundation models have significantly improved predictive analytics across healthcare, finance, manufacturing, cybersecurity, transportation, retail, and scientific research by enabling accurate forecasting, classification, anomaly detection, and intelligent decision support. However, their large computational requirements, memory consumption, energy usage, and inference latency limit deployment on resource-constrained platforms such as edge devices, IoT systems, mobile devices, and embedded applications. Knowledge distillation has emerged as an effective approach for developing resource-efficient AI by transferring knowledge from large teacher models to compact student models while preserving predictive performance. Unlike conventional compression techniques, it retains semantic representations and improves model efficiency through advanced strategies such as feature distillation, self-distillation, multi-teacher learning, and adaptive optimization. This paper proposes a Foundation Model Distillation Framework (FMDF) for predictive analytics in heterogeneous computing environments. The framework integrates teacher–student learning, adaptive feature distillation, multi-level knowledge transfer, dynamic loss optimization, and resource-aware inference to enable efficient deployment across cloud, edge, mobile, and embedded platforms. The proposed methodology includes foundation model training, hierarchical knowledge distillation, lightweight model optimization, and continuous performance evaluation. Mathematical formulations support knowledge transfer, prediction optimization, computational efficiency, and model compression. Experimental results demonstrate that FMDF significantly reduces model complexity while maintaining high predictive accuracy, scalability, robustness, and energy efficiency, making it a promising solution for resource-efficient predictive AI, edge intelligence, federated learning, digital twins, and next-generation intelligent decision support systems.
Ken Iverson· International Journal of Mac...· 0 citations