Adaptation of the competence approach to the methodology of training it specialists in the field of programming by E. V. Savina, K. Ya. Radjabov, T. I. Isabekova

“…Substantiation of the expediency of using the proposed methodology for the formation of evaluation materials in the training of IT specialists in the field of programming, followed by its use within the framework of traditional and distance learning technologies in the higher education system. Method. The research methods include: analysis of evaluation materials; methods based on algorithms for decomposing the program code proposed for solving the problem; methods for analyzing data from open sources, methods for analyzing algorithms and data structures. …”
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A multimodal MRI-based machine learning framework for classifying cognitive impairment in cerebral small vessel disease by Guihan Lin, Weiyue Chen, Yongkang Geng, Bo Peng, Surui Liu, Minjiang Chen, Chunying Pang, Pengjun Chen, Chenying Lu, Zhihan Yan, Shuiwei Xia, Yakang Dai, Jiansong Ji

“…Our study provides a multimodal MRI-based machine learning framework, utilizing the AutoGluon platform, that outperforms traditional algorithms in classifying MCI and NCI, offering a promising tool for the early prediction of MCI in CSVD.…”
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WHU-PA3D: Qaidam Basin Planetary Analogous-3D Dataset by A. Jin, A. Jin, A. Jin, C. Chen, C. Chen, C. Chen, Y. Xu, Y. Xu, Y. Xu, Y. Cong, W. Wu, L. Li, Y. Li, Y. Li, Y. Li, B. Yang, B. Yang, B. Yang

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Integrated DDPG-PSO energy management systems for enhanced battery cycling and efficient grid utilization by Oladimeji Ibrahim, Mohd Junaidi Abdul Aziz, Razman Ayop, Wen Yao Low, Nor Zaihar Yahaya, Ahmed Tijjani Dahiru, Temitope Ibrahim Amosa, Shehu Lukman Ayinla

“…Cost evaluations under critical peak tariffs indicate that both EMS algorithms achieved a 34 % cost saving compared to a grid-only system. …”
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Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation by Zongqi Xia, Prerna Chikersal, Shruthi Venkatesh, Elizabeth Walker, Anind K Dey, Mayank Goel

“…Among the best-performing models with the least sensor data requirement, the ML algorithm predicted depressive symptoms with an accuracy of 80.6% (F1-score=0.76), high global MS symptom burden with an accuracy of 77.3% (F1-score=0.78), severe fatigue with an accuracy of 73.8% (F1-score=0.74), and poor sleep quality with an accuracy of 72.0% (F1-score=0.70). …”
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Robust Tube-Based MPC with Piecewise Affine Control Laws by Meng Zhao, Xiaoming Tang

“…This paper presents a tube-based model predictive control (MPC) algorithm with piecewise affine control laws for discrete-time linear systems in the presence of bounded disturbances. …”
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Integration of AI with artificial sensory systems for multidimensional intelligent augmentation by Changyu Tian, Youngwook Cho, Youngho Song, Seongcheol Park, Inho Kim, Soo-Yeon Cho

“…We introduce specialized AI algorithms and raw data processing methods for each function, designed to enhance and optimize sensing performance. …”
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Transcriptome associated with single-cell analysis reveal the role of S-palmitoylation in coronary artery disease by Yiming Xing, Xianhe Lin

“…Nomogram and ROC curve analyses revealed robust diagnostic predictive capabilities grounded in the three hub genes. …”
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Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202 by Amit Kaimkuriya, S. Balaguru

“…WOA outperformed FEA in predictive accuracy, achieving error margins as low as 1.87% for minimum load and 2.31% for compressive strength. …”
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Explicit and Implicit Knowledge in Large-Scale Linguistic Data and Digital Footprints from Social Networks by Maria Pilgun

“…Explicit knowledge analysis is more predictable and suitable for tasks requiring quantitative assessments or classification of explicit data, while implicit knowledge analysis complements it by enabling a deeper understanding of subtle emotional and contextual nuances, particularly relevant for public opinion research, social well-being assessment, and predictive analytics. …”
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A Comparative Evaluation of Texture Features for Semantic Segmentation of Breast Histopathological Images by R. Rashmi, Keerthana Prasad, Chethana Babu K. Udupa, V. Shwetha

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An Image Encryption Scheme Based on DNA Computing and Cellular Automata by Shihua Zhou, Bin Wang, Xuedong Zheng, Changjun Zhou

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Artificial Intelligence-Assisted Clinical Decision-Making: A Perspective on Advancing Personalized Precision Medicine for Elderly Diabetes Patients by Hu J, Ren L, Wang T, Yao P

“…We contend that AI-driven clinical decision support systems (CDSS) offer superior performance in risk prediction and treatment optimization, with studies demonstrating diagnostic accuracy rates of up to 93.07%, supporting our argument for their widespread implementation. …”
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Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam. by Raja Das, Pham Van Tien, Karl W Wegmann, Madhumita Chakraborty

“…The post-event landslide susceptibility models of these three climate extreme events were developed using nine causative factors and a Random Forest machine learning algorithm. The results indicate a notable areal expansion of high to very high landslide susceptibility in the northern and eastern regions and a moderate reduction in the central and southern areas during the post-Molave period compared to the post-Ketsana period. …”
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A Method of Intelligent Driving-Style Recognition Using Natural Driving Data by Siyang Zhang, Zherui Zhang, Chi Zhao

“…Finally, a comparative evaluation of SVM, Random Forest, and KNN recognition indicates the superiority of the SVM algorithm and highlights the effectiveness of dimensionality reduction in optimizing characteristic parameters. …”
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Integrative in Silico modeling for mTOR inhibition: From ridge classifiers to descriptor-free deep neural networks by Seyed Alireza Khanghahi, Hadi Kamkar, Seyedehsamaneh Shojaeilangari, Abdollah Allahverdi, Parviz Abdolmaleki

“…Our findings revealed that while the Random Forest classifier achieved the highest accuracy among all models (0.9290 accuracy, 0.8940 F1-score, 0.9737 AUC), DL methods also demonstrated strong predictive capabilities, with nearly all models attaining an accuracy above 0.90. …”
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Structural integrity and hybrid ANFIS-PSO modeling of the corrosion rate of ductile irons in different environments by Ukoba K., Akinribide O.J., Adeleke O.

“…The percentage error of the ANFIS-PSO predictions is significantly lower than the ANFIS-standalone prediction. …”
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Drosophila Eye Gene Regulatory Network Inference Using BioGRNsemble: An Ensemble-of-Ensembles Machine Learning Approach by Abdul Jawad Mohammed, Amal Khalifa

“…Our validation analysis using the TFLink online database yielded 3703 verified predicted gene links, out of 534,843 predictions. Conclusion: Although the BioGRNsemble approach presents a promising method for inferring smaller, focused regulatory networks, it encounters challenges related to algorithm sensitivity, prediction bias, validation difficulties, and the potential exclusion of broader regulatory interactions. …”
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A comparative artificial neural networks for Schwarzschild black hole (SBH) radius by Khalil Ur Rehman, Wasfi Shatanawi, Weam G. Alharbi

“…Therefore, it remains a challenging task for them to predict the key characteristics of concerned black holes accurately. …”
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Effects of neighborhood streetscape on the single-family housing price: Focusing on nonlinear and interaction effects using interpretable machine learning. by Jaewon Han, Ayoung Woo, Sugie Lee

“…Previous studies using the conventional Hedonic Price Model to predict existing housing prices may have limitations in addressing the relationship between house prices and streetscapes as visually perceived at the human eye level, due to the constraints of streetscape estimations. …”
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