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Item Open Access DEVELOPMENT AND OPTIMIZATION OF PHYSICS-INFORMED NEURAL NETWORKS FOR SOLVING PARTIAL DIFFERENTIAL EQUATIONS(Vol. 1 No. 1 (2025): Journal of Emerging Technologies and Computing (JETC), 2025) Sharimbayev B.; Kadyrov Sh.; Kavokin A.This work compares the advantages and limitations of the Finite Difference Method with Physics-Informed Neural Networks, showing where each can best be applied for different problem scenarios. Analysis on the L2 relative error based on one-dimensional and two-dimensional Poisson equations suggests that FDM gives far more accurate results with a relative error of 7.26 × 10-8 and 2.21 × 10-4 , respectively, in comparison with PINNs, with an error of 5.63 × 10-6 and 6.01 × 10-3 accordingly. Besides forward problems, PINN is realized also for forward-inverse problems which reflect its ability to predict source term after its sufficient training. Visualization of the solution underlines different methodologies adopted by FDM and PINNs, yielding useful insights into their performance and applicabilityItem Open Access Text Classification for AI Generated Content with Machine Learning and Deep Learning Models(5th International Conference on Smart Information Systems and Technologies (SIST), 2025) Batyr Sharimbayev; Shirali KadyrovThe rapid development of generative AI models, such as GPT-4, LLaMA, and Gemini, is causing an explosion of AI-generated text that may be akin to human writing. This poses a challenge in differentiating between AI generated content and human-authored text across a range of verticals: academic integrity, misinformation detection, and content moderation. This paper presents a comparison of machine learning and deep learning models on the classifier for AI-generated text. We compare the performance of Logistic Regression with TF-IDF features, a Bi-LSTM model, and a fine-tuned DistilBERT model on data from the COLING Workshop on MGT Detection Task 1, involving text samples from five AI models and human authors. Our experiments showed that Bi-LSTM outperforms other models, yielding the best results in accuracy (90.09%) and F1-score (90.02%). We further present the binary classification performance that distinguishes AI-generated text from human-written content, with an accuracy of 95.9%. It is suggested that deep learning methods are competent in detecting AI-generated text, though there are certain limitations, including adversarial attacks and changing styles of AI-generated writing. Future work will be focused on enhancing model robustness through adversarial training and hybrid architectures.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.