Abstract This paper explores the integration of virtual reality (VR), cloud computing, EEG based neurofeedback, visual attention modeling, and autonomous learning paradigms to advance next generation educational and robotic systems. It compares three immersive learning approaches: traditional VR... This paper explores the integration of virtual reality (VR), cloud computing, EEG based neurofeedback, visual attention modeling, and autonomous learning paradigms to advance next generation educational and robotic systems. It compares three immersive learning approaches: traditional VR (high immersion, limited adaptivity), cloud gaming based VR (scalable but bandwidth intensive), and EEG driven VR (highly personalized but technically complex). A central theme is the need for systems that adapt not only to user interactions but also to real-time cognitive and behavioral signals. The paper reviews visual attention models highlighting their limitations in interactive VR due to dataset bias and examines autonomous learning methods like Learning from Visual Observation (LfVO) and multi agent strategic learning, which show promise but struggle with scalability and open ended tasks. Key research gaps include the lack of cognitive feedback integration in VR education, insufficient attention aware streaming in cloud gaming, poor generalization of attention models to VR, and the absence of real time human intent inference in multi agent systems. The work concludes that future platforms must achieve “intelligence aware immersion” combining cloud infrastructure, biosignal driven personalization, and socially aware AI to deliver scalable, responsive, and human-centered experiences in education and robotics. Success hinges on interdisciplinary advances in neurotechnology, networking, AI, and human AI interaction, ensuring equitable access and meaningful enhancement of human learning and collaboration. Read More Read Less

Abstract This paper presents a comprehensive study on trajectory prediction using Extreme Learning Machines (ELMs), with applications in autonomous systems, surveillance, and robotic networks. It begins by highlighting the importance of time series and trajectory data in pattern... This paper presents a comprehensive study on trajectory prediction using Extreme Learning Machines (ELMs), with applications in autonomous systems, surveillance, and robotic networks. It begins by highlighting the importance of time series and trajectory data in pattern recognition tasks, citing uses in visual surveillance, human behavior analysis, and autonomous navigation. The paper reviews related work, emphasizing ELM's advantages such as fast training, strong generalization, and avoidance of local minima over traditional ANNs and SVMs. Specific studies are discussed, including LB-EBM for multimodal pedestrian prediction, multilayer ELMs for video surveillance, and hybrid models like ELM-HTM for anomaly detection. The core of the work details an ELM-based trajectory prediction algorithm, outlining input/output structures, preprocessing steps, and offline/online phases. It distinguishes between single step, multi step, and rolling prediction strategies, favoring the latter for balancing accuracy and error accumulation. The method is validated on real world network robot data: 90 trajectories were collected, of which 70 were used for training. Results show high prediction accuracy (e.g., X-axis errors 26.6 mm) and ultra fast inference (6 ms per prediction), meeting real time robotic requirements. A spin classification experiment further demonstrates ELM's efficacy, achieving 91.4% accuracy. The study concludes that ELM provides a lightweight, efficient, and adaptable approach to real time trajectory forecasting in intelligent sports robotics and beyond. Read More Read Less

Abstract This paper explores the integration of entropy-driven methods and AI-assisted neural networks in art, design, and education. It highlights how convolutional neural networks (CNNs), enhanced by deeper architectures and adaptive algorithms, enable precise image analysis and generation... This paper explores the integration of entropy-driven methods and AI-assisted neural networks in art, design, and education. It highlights how convolutional neural networks (CNNs), enhanced by deeper architectures and adaptive algorithms, enable precise image analysis and generation in creative fields. Entropy is used as a quantitative measure of information richness to guide image selection, improve classification efficiency, and support objective evaluation in art education where traditional assessment struggles with subjectivity. The study also emphasizes the role of explainable AI (XAI) and concept based models to ensure transparency in AI-generated artistic outputs. A key concern is user trust, which is influenced not only by image realism but also by perceived AI involvement and cultural sensitivity. Empirically, the authors validate a 17-4-1 backpropagation (BP) neural network using NeuroSolutions software to evaluate art design professionals. Trained on expert judgments and enterprise criteria, the model achieves high accuracy, with MAE and MAPE of approximately 1.86%, RMSE of 2.27%, and a maximum error of less than 4.5%. This demonstrates strong generalization and reliability for talent assessment. The research proposes a unified conceptual framework linking digital inputs, entropy based analysis, AI processing, XAI, human perception, and creative outcomes. Ultimately, the study advocates for culturally aware, explainable, and humancentered AI systems that enhance rather than replace human creativity, originality, and pedagogical effectiveness in art and design. Read More Read Less