This study aimed to develop an advanced ensemble approach for automated classification of mental health disorders in social media posts. The research question was: can an ensemble of fine‐tuned transformer models (XLNet, RoBERTa, and ELECTRA) with Bayesian hyperparameter optimization improve the accuracy of mental health disorder classification in social media text. Three transformer models (XLNet, RoBERTa, and ELECTRA) were fine‐tuned on a dataset of social media posts labelled with 15 distinct mental health disorders. Bayesian optimization was employed for hyperparameter tuning, optimizing learning rate, number of epochs, gradient accumulation steps, and weight decay. A voting ensemble approach was then implemented to combine the predictions of the individual models. The proposed voting ensemble achieved the highest accuracy of 0.780, outperforming the individual models: XLNet (0.767), RoBERTa (0.775), and ELECTRA (0.755). The proposed ensemble approach, integrating XLNet, RoBERTa, and ELECTRA with Bayesian hyperparameter optimization, demonstrated improved accuracy in classifying mental health disorders from social media posts. This method shows promise for enhancing digital mental health research and potentially aiding in early detection and intervention strategies. Future work should focus on expanding the dataset, exploring additional ensemble techniques, and investigating the model's performance across different social media platforms and languages.

Background: The common cause of hemiplegia is stroke which is of two types: hemorrhagic and infarct. However; there may be other causes like tumor, a space occupying lesion, thrombus or an embolus etc. Aims and Objectives: Integrated therapy approach which is Panchakarama, physiotherapy, acupuncture, occupational therapy in the management for stroke, is planned to facilitate the movement of Vata Dosha, by the obstruction of which the symptoms are manifested. Materials and Methods: 5 cases of Cerebrovascular Accidents presenting with stroke reported in Yenepoya Ayurveda College and Hospital are taken in the study, wherein an explorative observational study design was framed on basis of objective and subjective parameters of motor and sensory functions of the brain to observe the before and after effects of treatment. Discussion: The line of treatment is to bring back the motor and sensory functions of the brain. The treatment protocol included Panchakarma procedures like Snehana (Abhyanga) Swedana, Basti, physiotherapy, accupunture, occupation therapy and diet. Internal medicines boost the blood supply and natural antioxidants increase the oxygenation to the brain, rejuvenates the brain cells, and decrease cell death. There was a marked improvement in motor function soon after starting the Bhahya Abhyanga and Swedana procedures with Basti and the modalities of Physiotherapy and occupational therapy helped in fast recovery to facilitate and improve motor control and hand functions in affected upper limb, to maximize the person's ability to undertake his or her own personal self-care tasks and domestic tasks.