Differentiating Mental Stress Levels: Analysing Machine Learning Algorithms Comparatively For EEG-Based Mental Stress Classification Using MNE-Python

Mental stress is a prevalent and consequential condition that impacts individuals' well-being and productivity. Accurate classification of mental stress levels using electroencephalogram (EEG) signals is a promising avenue for early detection and intervention. In this study, we present a comprehensive investigation into mental stress classification using EEG data processed with the MNE-Python library. Our research leverages a diverse set of machines learning algorithms, including Random Forest (RF), Decision Tree, K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Adaboost, and Extreme Gradient Boosting (XGBoost), to discerndifferences in classification performance. We employed a single dataset to ensure consistency in our experiments, facilitating a direct comparison of these algorithms. The EEG data were pre-processed using MNE-Python, which included tasks such as signal cleaning, and feature selection. Subsequently, we applied the selected machine learning models to the processed data and assessed their classification performance in terms of accuracy, precision, recall, and F1-score. Our results demonstrate notable variations in the classification accuracy of mental stress levels across the different algorithms. These findings suggest that the choice of machine learning technique plays a pivotal role in theeffectiveness of EEG-based mental stress classification. Our study not only highlights the potential of MNE-Python for EEG signal processing but also provides valuable insights into the selection of appropriate machine learning algorithms for accurate and reliable mental stress assessment. These outcomes hold promise for the development of robust and practical systems for real-time mental stress monitoring, contributing to enhanced well-being and performance in various domains such as healthcare, education, and workplace environment

Cloud computing, emerging computing technology of new age

Cloud computing is seen as an emerging technology highly utilized by different industries and businesses to optimize their processes conservation resources. Its popularity opens various loopholes and barriers, which are useful, especially when improving its overall performance. This study is conducted cloud computing further technological improvements suggested. This technology has been attractive developers and end-users because it provides both parties with an increased revenue opportunity. Entailed for and maintenance of physical hosting has been drastically eliminated from them for the end-user. Also true for the service providers since the increasing number integrating cloud computing to their systems increased revenue. However, the technology is facing challenges with security and privacy, workflow optimization, provisioning, cloud interoperability, data management, need or architecture modifications. These barriers inhibit cloud computing from achieving their maximum potential in terms of Service (QoS) and cost-effectiveness. Nevertheless, metrics monitored and measurements be done cloud computing's overall performance for of both the operation and economic aspects future studies is good & interesting

Beneficiary satisfaction with mental health care services: A cross sectional study at district mental health programme OPD of Ganjam District

contact drop outs. Active participation of people with mental illness (PWMI) & their caregivers is of utmost important to achieve the objectives of DMHP and reduce the longstanding mental & neurological disorder (MND) cases. Aim: To describe the beneficiary satisfaction with mental health care services under DMHP Ganjam. Materials & Methods: Descriptive theoretical framework & cross-sectional study design. Beneficiaries were selected by probability sampling. Beneficiary satisfaction was measured by using questionnaire in a Likert scale. Results: Communication skills of doctor, waiting time for consultation, availability of drugs at drug distribution centre were in the 4th quartile, cleanliness of OPD and Drug distribution centre functioning were in 3rd quartile. Adequacy of information available at hospital and waiting time at registration were in 2nd quartile. The functioning of NIDAN diagnostic centre and behaviour of hospital staff other than doctor got lowest score and were in 1st quartile. Conclusion: The distribution score in quartiles gave a preliminary evidence on components of beneficiary satisfaction on mental health care services at DMHP OPD. Recommendation: Counselling on service availability at NIDAN, training on communication skill for hospital staff, steps to reduce waiting time & need assessment of beneficiaries. Participatory research to explore the beneficiary perception needs to be carried out. Keywords: Mental Health, PWMI, MN