Defect detection in solar panels remains constrained by the limitations of manual labeling and the inefficiency of traditional inspection methods, which often struggle with large, high-resolution imagery. This study presents a novel self-supervised learning approach using the Momentum Contrast (MoCo) framework to address these challenges without relying on annotated datasets. Leveraging MoCo’s robust feature extraction and K-Nearest Neighbors (KNN) clustering, our method achieves accurate defect identification, bypassing the dependency on labeled data. Evaluated on the ELPV dataset, our approach attained a notable 96.95% accuracy, demonstrating significant improvement over existing unsupervised methods like KDAD, SAOE, DRA, and BGAD-FAS, and even outperforming some supervised models such as Adapted VGG19, Adapted VGG16, and ShuffleNet. Additionally, our model achieved 99.44% accuracy on the EL dataset, underscoring its adaptability and robustness across different environments. This framework offers a scalable, automated solution for image-level defect detection, poised to enhance efficiency and reduce manual intervention in industrial applications.

Postpartum depression (PPD) is a significant mental health concern affecting mothers worldwide, irrespective of demographic factors. Detecting and managing PPD at an early stage is crucial for effective intervention. In the context of mental health, intelligent predictive models based on machine learning (ML) have emerged as valuable tools. However, there remains a relative scarcity of research specifically targeting postpartum mental health due to several prominent factors that collectively impede the widespread adoption and practical implementation of ML in the field of PPD. This paper provides an updated overview of ML approaches for PPD prediction. A systematic search across IEEE Xplore, PubMed, Science Direct, and Scopus yielded 1,074 relevant articles. The performance of ML algorithms varies depending on the dataset and the problem being addressed. Notably, the findings reveal that the random forest (RF) algorithm consistently demonstrates the highest predictive accuracy, followed by support vector machine (SVM), logistic regression (LR), XGBoost, and AdaBoost. The development of advanced data techniques in PPD has encouraged interdisciplinary collaboration between researchers in psychiatry and computer science that holds great potential for refining the accuracy and reliability of PPD predictive models, ultimately resulting in improved outcomes for mothers and their families through early detection, intervention, and support.

Mobile learning is an emerging approach in Asian countries, particularly in the local context, such as the Malaysian universities. This research adds the perspectives of users' experiences to the mobile learning regarding potentials in the study of local culture. The motivation for this research is the limited users' voices -- students for mobile learning -- particularly, their own experiences in learning local cultural studies and the associated potentials. The aim of this study is to investigate if user experience has any mobile learning potentials to assist users in studying local culture. To find in-depth meaning, this research garnered the data from focus group discussions with students. The data were then analyzed using thematic analysis. This research indicates the students' interest in mobile learning demonstrated on the students' perspectives: the desired mobile applications paradigm and usability design concerns.

Mobile learning is a fairly new approach to the educational paradigm where learning is concerned. The usage of mobile learning may also be extended to various fields including Science, Technology, Engineering, and Mathematics (STEM). As Malaysia is lagging behind in STEM, this research is conducted to study preliminary information concerning mobile learning in a local university context, particularly, STEM. The method for this research is a descriptive survey. The results indicate that students already have some skills in using mobile phones, particularly multimedia skills, which can be applied to STEM.