Abhishek Jain

This study aims to present a novel generative AI-driven system for hyper-personalized health monitoring. Dynamic data processing, predictive modeling, and flexible learning improve real-time health evaluations. By combining weighted feature aggregation, iterative least squares estimation, and selective feature extraction, the suggested strategy makes predictions that are more accurate while using less computer power. Abnormality detection methods like adaptive thresholding and Kalman filtering provide accurate health monitoring. Attention, gradient-based optimization, and sequence learning improve health trend forecasts as the model improves. Generative AI-driven wearables outperform conventional and AI-based alternatives in many key performance tests. These evaluations include prediction accuracy (94%), real-time monitoring efficiency (93%), adaptability (92%), data integration quality (95%), and system reaction time (90 ms). These devices are safer (96%), have longer battery life (32 hours), and are simpler, more comfortable, and scalable. The results suggest that creative AI can transform personal healthcare into something more adaptable, safe, and affordable. Generative AI-powered smart gadgets are the most sophisticated means to monitor health in real time and deliver individualized, data-driven medical treatment. Future research will concentrate on improving prediction models and developing AI-driven modification approaches to make them more effective in additional healthcare scenarios.

The realm of human-computer interaction delves deep into understanding how individuals acquire knowledge and integrate technology into their everyday lives. Among the various methods for measuring brain signals, electroencephalography (EEG) stands out for its non-invasive, portable, affordable, and highly time-sensitive capabilities. Some researchers have revealed a consistent correlation between meditation practices and changes in the EEG frequency range, observed across a wide array of meditation techniques. Furthermore, the availability of EEG datasets has facilitated research in this field. This study explores the effectiveness of the One-Dimensional Convolutional Neural Network (CNN-1D) based novel classification method, which impressively achieved an 62% training accuracy, showcasing the robustness of these models in meditation classification tasks. The proposed methodology unveiling a novel method to differentiate neural oscillations in 4 types of meditators and control. This approach analyzes an EEG dataset of highly experienced meditators practicing Vipassana (VIP), Isha Shoonya (SYN), Himalayan Yoga (HYT), and untrained control subjects (CTR) by employing chi-square, CNN, hyperparameter models for data analysis, The outcomes indicate that different meditation types exhibit distinct cognitive features, enabling effective differentiation and classification.