2025· International Journal of Machine Learning and Predictive Analytics· Vol 8, pp. 01-17· 0 citations
TL;DR
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.
Abstract
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.
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
Modern enterprises rely on intelligent data pipelines to collect, process, transform, and analyze data from diverse sources such as cloud platforms, IoT devices, enterprise systems, and social media. Traditional optimization techniques, including rule-based scheduling and heuristic resource allocation, improve efficiency but struggle to adapt to dynamic workloads, changing resource availability, and evolving business requirements. Artificial Intelligence (AI) addresses these limitations through predictive analytics, adaptive scheduling, anomaly detection, and autonomous resource optimization. However, the opaque nature of many AI models reduces transparency, trust, and regulatory compliance. This paper proposes an Explainable AI (XAI)-based Intelligent Data Pipeline Optimization Framework that integrates data preprocessing, predictive analytics, explainability, and adaptive optimization. The framework continuously monitors pipeline performance, generates optimization recommendations, and provides human-interpretable explanations for AI-driven decisions using feature attribution and model interpretation techniques. An automated feedback mechanism enables continuous learning and improvement. Experimental evaluation demonstrates enhanced optimization accuracy, reliability, scalability, interpretability, and administrator trust with minimal impact on performance. The proposed framework provides a transparent and trustworthy approach for next-generation intelligent data engineering systems.
Per Brinch Hansen, O. Olesen· International Journal of Dat...· 0 citations
This study presents a comprehensive framework that includes data acquisition, preprocessing, feature engineering, predictive modeling, evaluation, and decision support, concluding that predictive analytics is a key enabler of intelligent enterprises and future data-driven innovation.
Pooja Agarwal, Rakesh Chandra· International Journal of Mac...· 0 citations
Machine learning has become a critical technology for large-scale data forecasting across industries such as finance, healthcare, transportation, manufacturing, energy, and e-commerce. While traditional forecasting methods like linear regression, ARIMA, and exponential smoothing perform well on smaller datasets, they struggle to manage the volume, velocity, and complexity of modern big data. Machine learning frameworks such as TensorFlow, Apache Spark MLlib, H2O.ai, Scikit-learn, and XGBoost offer scalable solutions by processing large datasets, identifying complex patterns, and generating accurate predictions through distributed computing.This study reviews machine learning architectures for large-scale forecasting, covering forecasting evolution, key algorithms, framework architectures, data processing pipelines, and evaluation methods. It proposes a scalable forecasting framework consisting of data collection, preprocessing, feature engineering, model training, forecasting, and performance evaluation. Findings indicate that distributed machine learning frameworks improve forecasting accuracy while reducing computational costs. Ensemble learning and gradient boosting techniques demonstrate superior performance, scalability, and robustness compared to traditional methods. The study concludes that machine learning frameworks provide a strong foundation for large-scale forecasting and will play an increasingly important role in future predictive applications.
John Peterson· 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
The rapid proliferation of data-intensive applications, cloud infrastructure, and IoT ecosystems has made proactive resource provisioning critical for maintaining optimal network performance. However, network administrators face a constant battle against capacity constraints, where traditional reactive approaches fail to accurately anticipate traffic fluctuations. This inability to foresee demand leads to costly over-provisioning, unexpected downtime, and degraded quality of service directly impacting operational budgets and business continuity. To achieve efficient capacity planning, accurate forecasting of bandwidth utilization is essential. This study addresses the challenge by evaluating a diverse spectrum of models including seasonal decomposition, Prophet, Random Forest, XGBoost, Support Vector Regression, and advanced deep learning architectures like bidirectional and Convolutional LSTMs - using a common interface dataset benchmarked across MAPE, NRMSE, and R-square metrics. Ultimately, this research delivers actionable insights into the trade-offs between model accuracy and computational efficiency, empowering engineers, operators, and business owners to select the optimal forecasting model for their specific infrastructure needs.
Niraj Gadhe, K. Bhardwaj, M. Jain et al.· arXiv.org· 0 citations
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