Polycystic ovary syndrome (PCOS) requires early and precise diagnosis to manage and prevent long-term health consequences effectively. In this research, a large dataset of healthcare data gathered from various hospitals in Kerala, India, was evaluated using multiple machine learning (ML) and deep learning (DL) models to identify a highly reliable and accurate prediction of PCOS. The six algorithms used for comparison with the proposed DL model are support vector classification, random forest, logistic regression, k-nearest neighbors, and gaussian naive Bayes; they were selected due to their strengths in handling features in large datasets. The highly parameterized neural networks were tuned using efficient approaches like Optuna and genetic algorithms. The results indicated that the model implemented using our proposed combination of DL model and Optuna, outperformed the traditional models, achieving 93.55% reliability. This suggests the potential for using deep learning for decision-making in diagnosing PCOS. This method demonstrates the importance of integrating various data types with powerful analytic tools in medical diagnostics to support customized therapy.

Dexterity is the coordination of hands and fingers with the eyes. Existing unilateral movements and dexterity tests use wearable devices for recording hand assessment. It is understandable that whenever some wired devices tag hands, the patient tends to be more conscious, which may not reflect the actual outcome. In this paper, using the finger-tap counter and loop-wire test, an attempt is made to evaluate unilateral movements and dexterity in adults of different age groups without using any wearable device. The test timings are recorded automatically with the help of the ATmega328p microcontroller and transferred to the clinician wirelessly via Bluetooth communication. Healthy 30 subjects between the age range (20–35, 36–50, 51–65, 66–80) were tested for finger-tap counter and loop-wire tests. The experiment is also tested for dominant and non-dominant hands for the same subjects. In this experiment, the result shows as the age increases, the trend in the assessment completion time and number of failures during the performance of the test increases. Female groups can perform both tests quicker than male groups with fewer errors irrespective of age group.

Introduction: This research explores the use of Deep Learning (DL) techniques for hand and gesture recognition to support hand rehabilitation programs. The primary objective is to enhance cognitive function and hand-eye coordination through gamified therapeutic exercises that track and respond to hand gestures in real time. Methods: Pre-trained Convolutional Neural Network (CNN) models were employed for hand recognition using Google's open-source MediaPipe Library. Four classic arcade games-Pong, Tetris, Fruit Ninja, and a Virtual Keyboard-were redeveloped as gesture-controlled rehabilitation tools within a web-based interface built using the Phaser.js framework. A score-based system was implemented to track user performance and progress. Usability was evaluated using the System Usability Scale (SUS), and statistical validation was performed with a one-sample t -test against the industry benchmark. Results: Data collected from 15 participants demonstrated consistent gesture recognition accuracy and stable game control performance. The SUS evaluation indicated favorable user responses, with usability scores exceeding the benchmark threshold, suggesting that participants found the system intuitive and engaging. Discussion: The study confirms the feasibility of using monocular camera-based computer vision for hand rehabilitation. Compared to existing low-cost rehabilitation tools, the proposed system provides an accessible, interactive, and affordable alternative. Integrating gesture recognition with gamified interfaces effectively supports dexterity recovery and motivates users through engaging gameplay. These findings establish a foundation for future development of AI-based rehabilitation platforms using standard camera devices.

Aim/Purpose: This study investigates how personal resources act as moderators and mediators in the Job Demands-Resources (JD-R) model. The study introduced intrinsic motivation as a personal resource and explored the mediating and moderating role relating to job demands, job strain, and psychological well-being in a doctoral education setting. Background: Future scholars and professionals are shaped by doctoral education, an essential phase in an academic career. Students’ psychological well-being is negatively impacted by the stress associated with the rigorous demands of the program, such as long hours of study and academic research publications. Understanding the relationship between intrinsic motivation and doctorate students’ mental health has attracted attention recently. Although fostering intrinsic motivation provides positive outcomes, the relationship with overall well-being is still multifaceted. Methodology: The study collected cross-sectional data from 391 full-time PhD students in India’s social science discipline. The investigation performed direct, moderation and mediation analysis of the study construct. Contribution: The current findings offer validation for nurturing the psychological well-being of scholars in the stressful doctoral study environment through intrinsic motivation. Furthermore, by emphasizing the scholars’ intrinsic motivation, the current study contributes to the body of literature on how scholars might prioritize their work-life balance and handle the demands of their programs. Findings: Demands of the doctoral program increase job strain and negatively impact students’ psychological well-being; however, this effect is conditional on the level of intrinsic motivation. This study highlights the significance of intrinsic motivation as a personal resource in the JD-R model that facilitates doctoral students’ psychological well-being. Recommendations for Practitioners: The study recommends that institutions, supervisors, and practitioners create a learning environment that fosters intrinsic motivation among scholars. The study result highlights interventions to enhance student motivation and psychological well-being to enhance the three psychological needs of scholars, i.e., autonomy, relatedness, and competence. Institutions can enhance the scholar’s autonomy by providing a choice of courses during their coursework stage and by providing an opportunity for research grants and collaboration. Supervisors suggested providing constructive feedback, maintaining open communication, and conducting research writing, methodology and time management skills, and growth mindset workshops for students that foster scholars’ competence. A sense of relatedness can be achieved through shared challenges and successes, peer mentorship programs, study groups, peer reviews, and social events. Recommendation for Researchers: The researchers should consider other personal resources (for example, self-efficacy, grit, and resilience) that scholars can use to improve psychological well-being. Researchers should broaden their studies and interventions on resources rather than strategies to reduce the PhD program demands to tackle the job strain among scholars. Impact on Society: Scholars with better mental health may be more productive and resilient and better able to support their communities. We can ensure they build their excellent research results, assist various fields in advance, and engage in social concerns. Future Research: Researchers can investigate the present relationship using longitudinal studies by tracking students’ well-being over time. Further studies can establish causal links, assess the efficacy of intrinsic motivation interventions, and uncover complex interactions between demands and resources influencing mental health among scholars.