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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.
The overall framework outlines the potential impact of Green AI in areas such as cost reduction, carbon footprint minimization, resource efficiency, and sustainable digital transformation, making it a promising avenue for advancing the sustainability of computing systems.
Overall, personalized content algorithms can improve information consumption, but maintaining diversity of information is essential for a well-informed society, and responsible algorithm design, ethical AI governance, diversity-aware recommendations, and user-controlled personalization settings are emphasized.
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 unified CML framework that combines data preprocessing, causal graph construction, structural causal modeling, causal feature optimization, predictive learning, intervention analysis, and continuous model adaptation is proposed, providing a scalable foundation for trustworthy and adaptive AI in dynamic environments.
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.
This study reviews recent AI-driven predictive maintenance approaches, identifies key research gaps, and proposes an intelligent framework integrating IoT, edge computing, cloud platforms, deep learning, and Explainable AI (XAI), which improves fault prediction, reduces downtime, and extends equipment lifespan.
Overall, the proposed framework offers a scalable, secure, and explainable solution for intelligent financial management, enabling organizations to improve financial planning, investment decisions, risk mitigation, risk mitigation, and long-term sustainability.
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.
Experimental evaluation demonstrates improved relationship discovery, query performance, and knowledge extraction compared with conventional relational approaches, making the proposed framework suitable for intelligent applications in healthcare, cybersecurity, finance, smart manufacturing, and enterprise knowledge man...
This paper highlights key challenges, including interoperability, security, heterogeneous resource management, and sustainable computing, while demonstrating the potential of Edge–Cloud collaboration to improve computational efficiency, response time, energy efficiency, scalability, and privacy for next-generation smar...
A resilient, adaptive routing framework for the well-timed delivery of independent logistics robots over uniquely temporary traffic and sudden tangible obstructions is offered, providing evidence of clinical feasibility for inclusion of decentralized, reactive real-time routing models into heavy-duty industrial automat...
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