Background/Objectives: Autism spectrum disorder (ASD) is a neurodevelopmental disorder distinguished by an extensive range of symptoms, including reduced social interaction, communication difficulties and tiresome behaviors. Early detection of ASD is important because it allows for timely intervention, which significantly improves developmental, behavioral, and communicative outcomes in children. However, traditional diagnostic procedures for identifying autism spectrum disorder (ASD) typically involve lengthy clinical examinations, which can be both time-consuming and costly. This research proposes leveraging automated machine learning (AUTOML) to streamline the diagnostic process and enhance its accuracy. Methods: In this study, by collecting data from various rehabilitation centers across Pakistan, we applied a specific AUTOML tool known as Tree-based Pipeline Optimization Tool (TPOT) for ASD detection. Notably, this study marks one of the initial explorations into utilizing AUTOML for ASD detection. The experimentations indicate that the TPOT provided the best pipeline for the dataset, which was verified using a manual machine learning method. Results: The study contributes to the field of ASD diagnosis by using AUTOML to determine the likelihood of ASD in children at prompt stages of evolution. The study also provides an evaluation of precision, recall, and F1-score metrics to confirm the correctness of the diagnosis. The propose TPOT-based AUTOML framework attained an overall accuracy 78%, with a precision of 83%, a recall of 90%, and an F1-score of 86% for the autistic class. Conclusions: In summary, this research offers an encouraging approach to improve the detection of autism spectrum disorders (ASD) in children, which could lead to better results for affected individuals and their families.

The impact of competitiveness on the mental health of Pakistani male and female adolescents is an important area of study, as the pressures from academic, social, and familial expectations significantly influence the psychological well-being of adolescents. This research aims to examine the relationship between gender differences in competitiveness and mental health outcomes, particularly depression, anxiety, and stress, among adolescent males and females. A cross-sectional design was employed involving a sample of 387 adolescents (227 boys and 160 girls) aged 14 to 19, recruited through a convenience sampling method. Quantitative data were obtained on the Revised Competitiveness Index (RC1; Housten et al., 2023) and the DASS-21 (Lovibond & Lovibond, 1995). The study employed two-way and mixed factorial ANOVA to examine gender differences in competitiveness and mental health outcomes. The results demonstrated that adolescent boys displayed markedly greater levels of competitiveness than their female peers. Furthermore, girls reported elevated levels of depression, anxiety, and stress compared to boys. The findings indicate that the rise in competitiveness correlates with an increase in depression, anxiety, and stress, especially in female adolescents. The study highlights the importance of recognizing gender differences in developing mental health interventions for adolescents, suggesting that tailored approaches may be necessary to address the distinct needs of boys and girls.

Doctoral programs demand rigorous intellectual commitment and an unrelenting pursuit of knowledge, often at the cost of scholars’ mental health. Mental health and Positive Psychological Functioning (PPF) are crucial components of the overall well-being of individuals. Pakistani Ph.D. scholars, like any other students, face a range of challenges and stressors during their academic journey. The present study evaluated the current mental health and PPF of Pakistani scholars by utilizing a cross-sectional research design. In order to carry out this investigation, 240 participants (male = 111 & female = 129) were selected using a snowball sampling technique. To assess the mental health and PPF of study participants, DASS-21 and the Positive Psychological Functioning Scale (PPFS) were used to collect data. Results of two-way ANOVA illustrated that there is a significant main effect of PPFS and DASS-21 across gender, socioeconomic status (SES), and employment status. Further results of two-way and three-way mixed factorial ANCOVA revealed significant main effect of PPFS with (F(2,147) = [29.137], p < .001) , and DASS-21 (Depression (F(2,147) = [17.736], p < .001) , Anxiety (F(2,147) = [7.578], p = .001) , and stress (F(2,147) = [10.066], p < .001) across Social sciences, Computer science and Engineering majors. The study concludes that mental health and PPF are important areas to consider when supporting the well-being of Pakistani PhD students.