The present study addresses the rising importance of mental health by devel oping a novel healthcare plan. We integrate physiological data from sensors, such as Heart Rate (HR) and Galvanic Skin Response (GSR), to predict and manage anxiety. These sensors provide non-invasive insights into the com plex relationship between physiological reactions and mental well-being. To analyze the collected data, we developed a novel algorithm, Regression Based Random Forest (RBRF). Using a large-scale dataset, we empirically validated the effectiveness of our approach, achieving an impressive 95 % accuracy in identifying anxiety. Our findings demonstrate the potential of sensor-based technologies and advanced algorithms to empower individuals to proactively monitor and manage their mental health. This approach holds significant promise for improving the precision and effectiveness of mental health care. • The study aims to improve mental healthcare by incorporating physiological data (Heart Rate and Galvanic Skin Response) to detect and potentially treat anxiety. • Employs a novel algorithm, Regression Based Random Forest (RBRF), to analyze the collected data and identify anxiety. • Achieved high accuracy (95 %) in identifying anxiety using the RBRF algorithm on a large dataset.

This study presents a generalized framework of vector calculus for non-integer dimensional spaces, motivated by the prevalence of fractals in nature. The work formulates first- and second-order differential operators, including gradient, divergence, and scalar and vector Laplacian, for scalar and rotationally covariant vector functions. This framework is applied to the thermoelastic response of an infinite fractal medium with a cylindrical cavity, a problem that incorporates thermoelastic mass diffusion and variable thermal conductivity through the Kirchhoff transformation. The system is analyzed under combined thermal and chemical shocks at the boundary, with the medium remaining mechanically fixed. The governing equations are solved using the Laplace transform method, and Zakian technique is employed for numerical inversion. The computational results indicate that parameters such as delay time and fractal dimension significantly influence the material's response. The graphical analysis visually examines the effects of different kernel functions, fractal dimension, variable thermal conductivity, nonlocal length and time scales on the thermoelastic response, providing a clear illustration of their impact. Specifically, an increase in fractal dimension leads to a more pronounced reduction in the thermoelastic response near the cylindrical cavity. Furthermore, an examination of different memory-dependent kernel functions reveals that nonlinear kernels demonstrate superior performance compared to linear kernels within this theoretical framework.