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Experimental results demonstrate that the proposed approach outperforms conventional static learning models by improving predictive accuracy, adaptability, and computational efficiency under dynamic conditions.
Real-Time Business Intelligence enables enterprises to analyze and act on data instantly, unlike traditional BI systems that rely on historical batch processing, offering major benefits including faster decisions, greater operational visibility, improved resource utilization, and competitive advantage.
The proposed framework combines data preprocessing, predictive modeling, optimization, and business intelligence dashboards to enhance financial forecasting accuracy and operational efficiency and supports intelligent, scalable, and real-time financial planning for modern enterprises.
Results indicate that AI-driven predictive models outperform traditional statistical approaches in stock market volatility forecasting, providing a reliable foundation for data-driven investment strategies and next-generation intelligent financial analytics systems.
A Smart Healthcare Monitoring through Edge Intelligence framework that integrates Artificial Intelligence (AI), Edge Computing, the Internet of Things (IoT), and Machine Learning (ML) to enable real-time and secure healthcare services.
A complete architecture that integrates embedded sensing, adaptive control algorithms, edge intelligence with predictive analytics, and real-time communication within a single framework to achieve autonomous parameter optimization is proposed.
The proposed autonomous calibration framework integrates multi-sensor fusion, machine vision, laser measurement, inertial sensing, machine learning, and adaptive optimization to continuously estimate and compensate for calibration errors in real time to enable self-learning, real-time robotic calibration that supports...
A Retrieval-Augmented Intelligent Data Warehousing (RAIDW) framework that integrates Retrieval-Augmented Generation, vector databases, knowledge graphs, semantic search, machine learning, and large language models with conventional data warehousing is proposed.
This study examines the ethical challenges of Human–AI co-creation through a literature review and proposes a conceptual framework encompassing fairness, accountability, transparency, privacy, intellectual property protection, trustworthiness, and human oversight.
A Hybrid Deep Learning Framework that integrates Convolutional Neural Networks, Vision Transformers, Vision Transformers, attention mechanisms, and multimodal sensor fusion to enhance recognition accuracy and efficiency is proposed.
This paper presents an AI-based predictive framework for urban air quality management that combines data preprocessing, feature engineering, machine learning, deep learning, and ensemble models, and demonstrates that AI-based models outperform conventional statistical approaches in prediction accuracy and computational...
An Integrated Intelligent Edge Computing Architecture for Real-Time Smart Factory Operations is proposed, combining edge computing, Artificial Intelligence (AI), digital twins, and predictive analytics to enable real-time local data processing with seamless cloud integration.
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