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Item Open Access DETECTING SOCIAL CONFLICTS IN KINDERGARTENS USING DEEPLEARNING AND COMPUTER VISION(SDU University, 2025) Dina KengesbayEarly conflict detection in kindergartens plays a significant role in ensuring a harmonious learningatmosphere and in promoting the social growth of young children. While most previous works have onlyaddressed conflict detection through adults, in this paper, we specifically address conflict detection inkindergartens using deep learning, utilizing both spatial and temporal information to improve performance.The application of deep learning and computer vision in automatically detecting and analyzing earlyconflicts among young children is discussed in this paper. Using video footage, we leverage state-of-the-art RNNs and 3D CNNs for high-accuracy detection of conflict instances. Crucial visual cues—facialexpressions, gestures, poses, vocal tone, and movement—are examined for the extraction of tension oraggression signs. The model is evaluated on real kindergarten video data, with promising conflict detectionand classification results. The findings indicate the potential of AI-supported tools in assisting teachers inclass management, child behavior monitoring, early intervention mechanisms, and the fostering of a goodsocial environment.Item Open Access Development of method to analyzefactors of kidney disease by the use of fuzzy logic(SDU University, 2025) Assel Yembergenova; Azamat Serek; Bauyrzhan BerlikozhaThe study introduces a new strategy for the analysis of kidney disease parameters based on fuzzy logic.Fuzzy logic is a more accurate way to categorize clinical parameters than statistical analysis because thereis uncertainty and variability in medical data. The data is comprised of an extensive amount of clinicalparameters including age, blood pressure, specific gravity, albumin, sugar, random blood glucose, bloodurea, serum creatinine, sodium, potassium, hemoglobin, packed cell volume, white blood cell count, andred blood cell count.The methodology utilizes fuzzy logic centroid computation to categorize these parameters into low,medium, and high levels to provide a more dynamic and interpretable assessment of renal health. Fuzzymemberships give the current work the capability to discover intricate interrelationships between clinicalvariables, which may have been otherwise unattainable by conventional mean, median, and standarddeviation-based analyses.The findings confirm that fuzzy logic and conventional statistical methods enhance the comprehensionof kidney disease by incorporating intricate interactions between clinical variables. The method is employedto achieve more accurate prediction and diagnostic models, offering insight to be used in kidney diseaseassessment and medical decisions.Item Open Access A Survey on Multimodal Approaches for Lung Disease Diagnosis using Deep Learning(SDU University, 2025) Zhaniya MedeuovaLung disorders are a major global health issue. A quick and accurate diagnosis is essential for proper treatment. In order to increase diagnostic accuracy, recent multimodal techniques are gaining popularity. This study carried out a comprehensive analysis of research articles on multimodal approaches that were published between 2020 and 2024 in Scopus and Google Scholar. The results show that there is limited study on the multimodal approach and on a variety of lung disorders such as asthma, TB, pneumonia, and chronic obstructive pulmonary disease. Several studies concentrated mainly on the detection and binary classification of COVID-19. The field has several challenges, including limited datasets, high computing costs, difficulties in integrating multiple modalities, and lack of accessibility of the models. Future studies should look at a wider range of lung diseases, increase the accessibility of datasets, improve fusion methods for merging data from many sources, and create models that are easier to understand and use fewer resources. Resolving these issues will improve patient outcomes by advancing the real-world use of deep learning in medical diagnosis.Item Open Access Bias and Fairness in Automated Loan Approvals: A Systematic Review of Machine Learning Approaches(SDU University, 2025) Suraiyo Raziyeva; Meraryslan MeraliyevArtificial intelligence (AI) is increasingly transforming credit approval processes, enabling financial institutions to assess risk more efficiently and at greater scale. As these systems become more embedded in lending decisions, concerns around fairness, bias, and accountability have grown significantly. Many of these concerns stem from the use of historical data, proxy variables, and model optimization choices that can unintentionally reinforce existing social and economic inequalities. This work presents a systematic overview of the types and sources of bias in AI - driven loan approval systems and critically examines how machine learning techniques attempt to address them. It also highlights emerging solutions, including explainable AI, federated learning, human-in-the-loop frameworks, and intersectional fairness approaches. Despite ongoing advancements, unresolved challenges remain - particularly the need for dynamic fairness monitoring and for addressing intersectional biases affecting individuals from multiple marginalized groups. To bridge these gaps, the paper emphasizes the importance of interdisciplinary collaboration among AI developers, regulatory bodies, and social scientists. It advocates embedding fairness as a core design principle in the development and deployment of future AI systems. Overall, this study contributes to the growing effort to develop more transparent, inclusive, and socially responsible financial technologies.