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Wellness tourism represents the intersection of a powerful lifestyle trend and a growing global industry. The purpose of this study is to design a pattern of wellness tourism with socio-cultural approach to improve the tourist’s health in (Baraghan Alborz village). In order to conduct the research, a Mixed Method has been used. The statistical population includes experts in the fields of tourism, health tourism, spiritual health, psychoanalysts and psychologists (qualitative part) and in the quantitative part, tourists of Baraghan Alborz village   period of July to August 2024 are considered. The size of the sample was determined in the qualitative method by the logical method of theoretical and judgmental sampling and until the theoretical saturation was reached, a total of 12 people, and in the quantitative part, 384 tourists (n = 384) were serveyed. For sampling, random sampling method was used. In the qualitative section using semi-structured interviews and Qualitative data coding was done in order to identify the components of wellness tourism in improving the health of tourists in Baraghan Alborz village, by theme analysis method. The questionnaire data were analyzed using factor analysis techniques with SPSS 26 software to assess the impact of the identified components. The findings indicate that the model of wellness tourism in improving the health of tourists, consists of 11 sub-themes, quality management of the wellness tourism destination, the necessity of linking science with wellness tourism, nature, physical dimension, mental dimension, social dimension and spiritual dimension, professional and expert team work, training and empowerment, nostalgia and memory-making, the characteristics of the unique wellness tourism model. Additionally, 7 main categories including destination management, the necessity of linking science with wellness tourism, nature, physical dimension, mental dimension, social dimension and spiritual dimension, which have the greatest impact in improving  the health of tourists in Baraghan village,has been achieved.The development of wellness tourism, while introducing Baraghan as a wellness tourism destination ,not only helps to improve the health of the community and tourists (which is one of the basic pillars of a healthy society), but also can create a sustainable livelihood for local communities and is focused on the support  and protection of natural, cultural and historical resources. By increasing the participation of local people in related activities and businesses, while expanding employment, it brought countless economic benefits to the tourist destination and ultimately country.

With an increasing reliance on mobile devices, continuous and assured user authentication is essential to protect sensitive personal data and digital interactions from unwanted access. Based on this background, this research proposed the development of the HybridTouch framework for smartphone-based continuous and passive user authentication. The proposed HybridTouch combines Convolutional Neural Networks for spatial feature extraction and Gated Recurrent Units for temporal sequence analysis. It uses accelerometer, gyroscope, and touch data to take advantage of the unique behavioral patterns captured by it. Innovative preprocessing techniques have been incorporated into the proposed approach: Discrete Wavelet Transform is used for signal denoising, and Variable-Length Adaptive Temporal windowing is used for segmentation based on signal entropy to enhance feature representation. To eliminate the data scarcity limitation, Generative Adversarial Networks were used to synthesize realistic behavioral data that considerably augmented the dataset and enhanced model generalization capability. Extensive experiments conducted on the Hand Movement, Orientation, and Grasp (HMOG) dataset showed that the proposed HybridTouch achieved excellent results with authentication accuracy up to 98.8% with real data, growing up to 99% with GAN-augmented data. The hybrid model further has an equal error rate of 1.4% on real data and 1.25% on synthetic data, which is better than any other models currently present (Sağbas et al., 2024; Siddiqui et al., 2022; Abuhamad et al., 2020) and all implementations of standalone convolutional neural networks and gated recurrent units.