Problems and perspectives of family doctors training on the undergraduate stage by Yu. M. Kolesnik, V. D. Syvolap, N. S. Mikhaylovskaya, T.O. Kulinich

“…Under healthcare reform activities of all medical schools and post-graduate education institutions are directed to improvement of the effectiveness of high-qualified family doctors training. …”
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Multi‐function radar work mode recognition based on residual shrinkage reconstruction recurrent neural network by Lihong Wang, Kai Xie

“…These features are then processed through a residual shrinkage structure to reduce noise, which significantly improves the model's robustness in non‐ideal scenarios. …”
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Method of automatic coregistration of digital remote sensing images from different sources by A.N. Borisov, V.V. Myasnikov, V.V. Sergeev

“…For additional improvement of the alignment accuracy, we utilized a priori data about the images in the form of their georeferencing information.…”
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The Implementation of BCTrustAI.SL into the Automated Practices of Digital Labour Platforms to Ensure Fairness, Transparency and Accountability by Şaban İbrahim Göksal, Kristi Joamets

“… Since digital labour platforms may infringe upon the rights of platform workers through automated decision-making and monitoring practices, the European Parliament and of the Council has adopted the Directive (EU) 2024/2831 on improving working conditions in platform work (Directive 2024/2831). …”
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Predicting cognitive decline in cognitively impaired patients with ischemic stroke with high risk of cerebral hemorrhage: a machine learning approach by Eun Namgung, Young Sun Kim, Sun U. Kwon, Dong-Wha Kang, Dong-Wha Kang

“…Four machine learning algorithms were trained, Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), and logistic regression, to predict cognitive decliners, defined as a decline of ≥3 K-MMSE points over 9 months, and ranked variable importance using the SHapley Additive exPlanations methodology.ResultsCatBoost outperformed the other models in classifying cognitive decliners within 9 months. …”
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Artificial intelligence in data storage systems by V. V. Zhilin, O. A. Safar'yan

“…Minimization of the human factor in the system operation contributes to the improvement of its work.…”
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A network intrusion detection method designed for few-shot scenarios by Weichen HU, Congyuan XU, Yong ZHAN, Guanghui CHEN, Siqing LIU, Zhiqiang WANG, Xiaolin WANG

“…Existing intrusion detection techniques often require numerous malicious samples for model training.However, in real-world scenarios, only a small number of intrusion traffic samples can be obtained, which belong to few-shot scenarios.To address this challenge, a network intrusion detection method designed for few-shot scenarios was proposed.The method comprised two main parts: a packet sampling module and a meta-learning module.The packet sampling module was used for filtering, segmenting, and recombining raw network data, while the meta-learning module was used for feature extraction and result classification.Experimental results based on three few-shot datasets constructed from real network traffic data sources show that the method exhibits good applicability and fast convergence and effectively reduces the occurrence of outliers.In the case of 10 training samples, the maximum achievable detection rate is 99.29%, while the accuracy rate can reach a maximum of 97.93%.These findings demonstrate a noticeable improvement of 0.12% and 0.37% respectively, in comparison to existing algorithms.…”
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Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques by Suat Özdemir, Feyza Yıldırım Okay, Duygu Altunkaya

“…Recent studies show that transforming IoT data into images improves the performance of the learning model. In this paper, we present a review of these studies which use image transformation/encoding techniques in IoT domain. …”
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Enhancing Predictive Capabilities for Identifying At-Risk Stocks Using Multivariate Time-Series Classification: A Case Study of the Thai Stock Market by Katsamapol Petchpol, Laor Boongasame

“…Despite improvements from specialized techniques, the study identifies challenges in long-term financial risk prediction. …”
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Artificial intelligence in drug development: reshaping the therapeutic landscape by Sarfaraz K. Niazi, Zamara Mariam

“…Although, to date, no AI-generated medicinal drug has been FDA-approved, HLX-0201 for fragile X syndrome and new molecules for idiopathic pulmonary fibrosis have entered clinical trials. However, AI models are generally considered “black boxes,” making their conclusions challenging to understand and limiting the potential due to a lack of model transparency and algorithmic bias. …”
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Synergistic Integration of LQR Control and PSO Optimization for Advanced Active Suspension Systems Utilizing Electro-Hydraulic Actuators and Electro-Servo Valves by Trong Tu

“…This research remarks the fundamentals of the experimental validation and further refinement of these control algorithms to adapt to various driving conditions and vehicle models, ultimately aiming to transition these optimized controllers from theoretical frameworks to practical, real-world applications. …”
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Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects by Maryam Yeganegi, Mahsa Danaei, Sepideh Azizi, Fatemeh Jayervand, Reza Bahrami, Seyed Alireza Dastgheib, Heewa Rashnavadi, Ali Masoudi, Amirmasoud Shiri, Kazem Aghili, Mahood Noorishadkam, Hossein Neamatzadeh

“…AI integration with genomic analysis has identified key biomarkers associated with NTDs, such as Growth Associated Protein 43 (GAP43) and Glial Fibrillary Acidic Protein (GFAP), with logistic regression models achieving 86.67% accuracy. Current AI-assisted ultrasound technologies have improved diagnostic accuracy, yielding sensitivity and specificity rates of 88.9% and 98.0%, respectively, compared to traditional methods with 81.5% sensitivity and 92.2% specificity. …”
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A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students by Flórián Gubics, Ádám Nagy, József Dombi, Antónia Pálfi, Zoltán Szabó, Zsolt János Viharos, Anh Tuan Hoang, Vilmos Bilicki, István Szendi

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Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks by Musallam M. AlZubi, Mohamed A. Mohamed, Hanan M. Amer, A. A. Salama

“…The results are validated through numerical examples and extensive simulations, demonstrating NFPM's superiority over traditional fuzzy logic, with significant improvements such as a 25% reduction in power failures, 95% enhanced network connectivity, a 15% increase in data transmission success rates, and overall improvements in energy efficiency and robustness to fluctuations and noise. …”
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MULTILEVEL RESEARCH OF INNOVATIVE AND REPRODUCTION DEVELOPMENT OF REGIONS ON THE BASIS OF THE STATISTICAL ANALYSIS by Yu. A. Dmitriev, D. Yu. Fraymovich, Z. V. Mishchenko

“…The nomenclature of the factors included in model can be corrected during improvement and improvement. …”
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Pioneering the Security of EHRs Using an Immersive Blockchain Conceptual Framework by Rihab Benaich, Youssef Gahi, Saida El Mendili

“…Findings indicate that this hybrid blockchain model, enhanced with quantum-resistant cryptography and decentralized governance, significantly improves EHR security. …”
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The new era of artificial intelligence in neuroradiology: current research and promising tools by Fabíola Bezerra de Carvalho Macruz, Ana Luiza Mandetta Pettengil Dias, Celi Santos Andrade, Mariana Penteado Nucci, Carolina de Medeiros Rimkus, Leandro Tavares Lucato, Antônio José da Rocha, Felipe Campos Kitamura

“…If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.…”
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Hybrid Filtering Technique for Accurate GNSS State Estimation by Jahnvi Verma, Nischal Bhattarai, Thejesh N. Bandi

“…We demonstrate that this hybrid method outperforms classical algorithms both in terms of accuracy and flexibility for easier implementation, showing better than 30% improvement in the variance of the horizontal position for the simulated as well as the real-world dynamic receiver dataset. …”
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Supervised Learning-Based Fault Classification in Industrial Rotating Equipment Using Multi-Sensor Data by Aziz Kubilay Ovacıklı, Mert Yagcioglu, Sevgi Demircioglu, Tugberk Kocatekin, Sibel Birtane

“…The results demonstrate the machine learning model’s ability to retrieve valuable information from multi-sensor data integration, improving predictive maintenance strategies. …”
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Transductive Radar Clutter Segmentation Network for Ionospheric Inhomogeneity Clutter Suppression by Yuanbiao Li, Lei Yu, Yinsheng Wei

“…Experiments demonstrate the effectiveness of TRCS-Net in improving clutter suppression performance compared to individual algorithms, leveraging transductive transfer learning and simulation data.…”
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