Software module for automatic correction of drawings obtained as a result of graphic data exchange between CAD systems, using the furniture production example by A. V. Kalinichenko, A. Yu. Alikov, T. A. Yurosheva

“…As part of the study, methods of geometric modeling, analytical geometry and graphical presentation of results were used. …”
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A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides by Xiaomeng Geng, Xiaomeng Geng, Xiaomeng Geng, Huiru Li, Huiru Li, Le Wang, Weidong Sun, Yize Li

“…Additionally, it identifies the limitations and unresolved challenges in current approaches, such as constraints on data resolution, algorithmic biases, and environmental variability. The potential for integrating multi-source remote sensing data with marine environmental parameters and deep learning techniques is discussed, emphasizing their roles in improving the accuracy and reliability of monitoring and predicting Ulva prolifera green tides. …”
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Optimization of sports injury treatment through artificial intelligence: Methods for effective prevention, diagnosis and rehabilitation by Kimi Milić Marko, Sinanović Šćepan, Jestrović Vladimir

“…The research methodology includes big data analysis, image processing, machine learning, and customized algorithms for prediction and rehabilitation monitoring. …”
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Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising by Rudy Gunawan, Yvonne Tran, Jinchuan Zheng, Hung Nguyen, Rifai Chai

“…This evidence indicates a possibility of using the NIQE as a new option in evaluating denoised quality to find improvement or compare the quality between CNN models.…”
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Homework, Households, and Hurdles: The Unexpected Drivers of Student Graduation Perceptions by Daniel Alhassan, Zahra Fatah, Priscilla Mansah Codjoe, Caroline Bena Kuno, Dorcas Ofori-Boateng

“…Among the tested models, random forest provided the best classification performance. …”
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Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques by Athanasios Angeioplastis, John Aliprantis, Markos Konstantakis, Alkiviadis Tsimpiris

“…Five machine learning algorithms—k-nearest neighbors, random forest, logistic regression, decision trees, and neural networks—were applied to identify correlations between courses and predict grades. …”
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Enhancing emergency response efficiency through advanced urban logistics: The role of driver psychophysiology and vehicle dynamics in mitigating socio-economic impacts by Yurii Davidich, Dávid Földes, Andrii Galkin

“…Utilising regression models and physiological assessments, we developed predictive models that link these factors to emergency response times. …”
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Radiology AI and sustainability paradox: environmental, economic, and social dimensions by Burak Kocak, Andrea Ponsiglione, Valeria Romeo, Lorenzo Ugga, Merel Huisman, Renato Cuocolo

“…Socially, AI risks perpetuating healthcare disparities through biases in algorithms and unequal access to technology. On the other hand, AI has the potential to improve sustainability in healthcare by reducing low-value imaging, optimizing resource allocation, and improving energy efficiency in radiology departments. …”
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An ensemble machine learning framework with explainable artificial intelligence for predicting haemoglobin anaemia considering haematological markers by Dhruva Darshan B S, Punit Sharma, Krishnaraj Chadaga, Niranjana Sampathila, G. Muralidhar Bairy, Sushma Belurkar, Srikanth Prabhu, Swathi K S

“…The ML models' judgments are interpreted using explainable artificial intelligence (XAI) methods. …”
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Prediction of Voice Therapy Outcomes Using Machine Learning Approaches and SHAP Analysis: A K-VRQOL-Based Analysis by Ji Hye Park, Ah Ra Jung, Ji-Na Lee, Ji-Yeoun Lee

“…The emotional domain shows relatively low predictive power across the models. SHAP analysis reveals interpretable patterns, highlighting jitter and speaking fundamental frequency (SFF) as key contributors in high-performing models. …”
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Research on the Enhancement and Development of the Resilience Assessment System for Underground Engineering Disaster Risk by Weiqiang Zheng, Zhiqiang Wang, Bo Wu, Shixiang Xu, Jiacheng Pan, Yuxuan Zhu

“…The integration of intelligent static assessment models based on artificial intelligence algorithms can effectively enhance the accuracy of resilience evaluations. …”
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A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides by Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Ashutosh Mishra, Sachin Kumar Gupta, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…Abstract Current artificial intelligence (AI) trends are revolutionizing medical image processing, greatly improving cervical cancer diagnosis. Machine learning (ML) algorithms can discover patterns and anomalies in medical images, whereas deep learning (DL) methods, specifically convolutional neural networks (CNNs), are extremely accurate at identifying malignant lesions. …”
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Recent Advances in Multi-Agent Reinforcement Learning for Intelligent Automation and Control of Water Environment Systems by Lei Jia, Yan Pei

“…From the perspective of cooperative control, this paper investigates the modeling mechanisms and policy coordination strategies of MARL in key tasks such as water supply scheduling, hydro-energy co-regulation, and autonomous monitoring. …”
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ELEQTRONeX: A GPU-accelerated exascale framework for non-equilibrium quantum transport in nanomaterials by Saurabh S. Sawant, François Léonard, Zhi Yao, Andrew Nonaka

“…By customizing algorithms for GPU multithreading, we achieve significant improvement in computational time, and excellent scaling on up to 512 GPUs and billions of spatial grid cells. …”
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Fruit Detection Methods Based on Deep Learning in Agricultural Planting: A Systematic Literature Review by Xinyu Gong, Qiufeng Wu

“…Based on a comprehensive analysis of existing research, we classify deep learning-based fruit detection models into four application scenarios: few-shot detection (addressing limited data availability and high annotation costs), complex scene detection (resolving issues arising from object occlusion, overlapping, and variable illumination), small-target detection (improving performance on low-resolution and densely clustered objects), and real-time detection (designing lightweight algorithms for faster inference). …”
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Artificial Intelligence in medicine: Potential and Application Possibilities - Comprehensive literature review by Michał Kotowicz, Magdalena Bieniak-Pentchev, Maria Koczkodaj

“…In psychiatry, generative AI models support mental health through personalized interventions and symptom tracking, while in ophthalmology, multimodal learning improves early detection of eye diseases. …”
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Visual recognition of honeybee behavior patterns at the hive entrance. by Tomyslav Sledevič, Artūras Serackis, Dalius Matuzevičius, Darius Plonis, Gabriela Vdoviak

“…Utilizing advanced YOLOv8 models for detection and segmentation, our approach analyzes various aspects of bee behavior, including location, direction, path trajectory, and movement speed within a designated area on the hive's landing board. …”
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Electricity nowcasting and forecasting efficiency in blockchain-integrated energy trading platforms by Ameni Boumaiza

“…Our methodology integrates real-time nowcasting algorithms and machine learning-based forecasting models to optimize electricity distribution, battery storage utilization, and cost efficiency within the marketplace. …”
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Artificial intelligence as one of the key drivers of the economy digital transformation by O.I. Pizhuk

“…Artificial intelligence technologies represent a unique opportunity for us to transform business operations by reducing the number of repetitive, routine tasks, significantly increase productivity, create new products, create new models of economic growth, and improve the efficiency of government agencies, especially at the level of development of solutions aimed at ensuring the well-being of citizens. …”
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Transforming Farming: A Review of AI-Powered UAV Technologies in Precision Agriculture by Juhi Agrawal, Muhammad Yeasir Arafat

“…In this paper, we present a comprehensive overview of the integration of multispectral, hyperspectral, and thermal sensors mounted on drones with AI-driven algorithms to transform modern farms. Such technologies support crop health monitoring in real time, resource management, and automated decision making, thus improving productivity with considerably reduced resource consumption. …”
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