Digital Empowerment in Social Work: Leveraging AI to Enhance Educational Access in Developing Nations by Zvinodashe Revesai, Benjamin Tungwa, Telson Anesu Chisosa, Vanessa Runyararo Meki

“…However, this research points out the need for addressing the digital divide and ethical issues associated with artificial intelligence, including problems of privacy and algorithmic bias. The study concludes by making a call for further research into models of safe and equitable AI integration in social work education.…”
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METHODOLOGICAL APPROACH TO CONSTRUCTION OF A SYSTEM FOR DETECTION AND PREVENTION OF DESTRUCTION OF THE ACTIVITIES OF CONSTRUCTION PARTICIPANTS by Mykhailo Malykhin, Denys Dubinin, Olha Bielienkova

“…The methodological foundation of the research combines system analysis, risk-oriented modelling, and process mapping techniques. The proposed framework incorporates the utilisation of behavioural indicators, deviation tracking algorithms, and early warning dashboards, which collectively facilitate proactive responses to disruption risks. …”
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Machine Learning-Based Carbon Compliance Forecasting and Energy Performance Assessment in Commercial Buildings by Aditya Ramnarayan, Felipe de Castro, Andres Sarmiento, Michael Ohadi

“…Owing to the need for continuous improvement in building energy performance standards (BEPSs), facilities must adhere to benchmark performances in their quest to achieve net-zero performance. …”
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Deep Mining on the Formation Cycle Features for Concurrent SOH Estimation and RUL Prognostication in Lithium-Ion Batteries by Dongchen Yang, Weilin He, Xin He

“…By integrating diverse datasets with advanced algorithms and models, we perform correlation analyses of parameters such as capacity, voltage, temperature, pressure, and strain, enabling precise SOH estimation and RUL prediction. …”
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Using machine learning for the assessment of ecological status of unmonitored waters in Poland by Andrzej Martyszunis, Małgorzata Loga, Karol Przeździecki

“…Study shows highest efficiency of XGBoost and Random Forest algorithms for classification of unmonitored water bodies. …”
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Comparing Machine Learning-Based Crime Hotspots Versus Police Districts: What’s the Best Approach for Crime Forecasting? by Eugenio Cesario, Paolo Lindia, Andrea Vinci

“…As a result, the efficient allocation of public security resources based on spatio-temporal crime prediction models has become a critical concern for urban management. …”
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YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments by Zihang Xiong, Mei Wang, Ruixiang Kan, Jiayu Zhang

“…As for implementing ship detecting methods in such challenging conditions, the main contributions of this work are as follows: (i) a low-illumination image-enhancement module that adaptively improves multi-scale feature perception in low-illumination scenes; (ii) receptive-field attention convolution to compensate for weak long-range modeling; and (iii) an Adaptively Spatial Feature Fusion head to refine the multi-scale learning of ship features. …”
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Determine train load and speed based on dynamic displacement results by Nhung Thi Cam NGUYEN, An Thai LE, Hoang Nguyen BUI, Thuc Van NGO, Huy Phan BUI, Minh Quang TRAN

“…By applying finite element models (FEM) and real-time displacement data analysis combined with optimization algorithms, the estimation of train load and speed is performed. …”
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Radiomics capabilities in the interpretation of ultrasound and CT data in patients with chronic kidney disease: A review by Alexandra V. Proskura, Khalil M. Ismailov, Alexander G. Smoleevskiy, Amina I. Salpagarova, Irina N. Bobkova, Andrei M. Shestiuk

“…The article discusses the basics of radiomic methods, including texture analysis of images and the creation of diagnostic models using machine learning algorithms. The advantages of radiomic characteristics, in particular statistical features of order II and higher orders, in assessing interstitial fibrosis and other abnormal changes in the renal parenchyma are discussed in detail. …”
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Enhanced Detection of Intrusion Detection System in Cloud Networks Using Time-Aware and Deep Learning Techniques by Nima Terawi, Huthaifa I. Ashqar, Omar Darwish, Anas Alsobeh, Plamen Zahariev, Yahya Tashtoush

“…In contrast, Deep Neural Network (DNN) models showed significant improvements with optimization, with DNN combined with GS (DNN-GS) reaching 89% accuracy. …”
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Marker-Less Navigation System for Anterior Cruciate Ligament Reconstruction with 3D Femoral Analysis and Arthroscopic Guidance by Shuo Wang, Weili Shi, Shuai Yang, Jiahao Cui, Qinwei Guo

“…The system’s two-stage registration framework, combining SIFT-ICP algorithms, achieves accurate alignment between preoperative models and arthroscopic views. …”
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An intelligible AI-driven decision support system for poststroke mobility assessment by Jin Cheng Liaw, Dominik Raab, Malte Weber, Mario Siebler, Harald Hefter, Dörte Zietz, Marcus Jäger, Andrés Kecskeméthy, Francisco Geu Flores

“…Results: The models yielded good to very good (Cohen) coefficients of determination. …”
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Critical review of patient outcome study in head and neck cancer radiotherapy by Jingyuan Chen, Yunze Yang, Chenbin Liu, Hongying Feng, Jason M. Holmes, Lian Zhang, Steven J. Frank, Charles B. Simone, II, Daniel J. Ma, Samir H. Patel, Wei Liu

“…Looking ahead, we envision that combining these complementary approaches, especially the interventional prediction models, will enable more personalized treatment strategies, ultimately improving both tumor control and quality of life for head and neck cancer patients treated with radiation therapy.…”
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Parametric Optimization and Assessment of Modern Heritage Shading Screen for a Mid-Rise Building in Arid Climate: Modernizing Traditional Designs by Anwar Ahmad, Lindita Bande, Waleed Ahmed, Kheira Tabet Aoul, Mukesh Jha

“…This research explores how parametric design and optimization based on genetic algorithms (GAs) can improve shading structures to reduce solar radiation and lower cooling energy consumption. …”
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Decentralized Multi-Agent Search for Moving Targets Using Road Network Gaussian Process Regressions by Brady Moon, Christine Akagi, Cameron K. Peterson

“…The target density is modeled using a Gaussian process, which is iteratively updated as the UAVs search the environment. …”
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ML-Based Materials Evaluation in 3D Printing by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Jakub Kopowski

“…Furthermore, by integrating real-time data from sensors during the printing process, ML can continuously monitor and adjust parameters, ensuring optimal material utilization and reducing waste. ML models can identify and correct defects in printed materials by recognizing patterns associated with defects, thus improving the reliability of 3D-printed objects. …”
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Comparison of Random Forest, XGBoost, and LightGBM Methods for the Human Development Index Classification by Yunna Mentari Indah, Rafika Aristawidya, Anwar Fitrianto, Erfiani Erfiani, L.M. Risman Dwi Jumansyah

“…These findings contribute to the field of statistical modeling by demonstrating how ensemble methods can improve classification accuracy and provide valuable insights for data-driven policymaking, thus enhancing regional development planning and supporting future HDI-related research.…”
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Wavelet-Based Topological Loss for Low-Light Image Denoising by Alexandra Malyugina, Nantheera Anantrasirichai, David Bull

“…Despite significant advances in image denoising, most algorithms rely on supervised learning, with their performance largely dependent on the quality and diversity of training data. …”
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Two Power Allocation and Beamforming Strategies for Active IRS-aided Wireless Network via Machine Learning by Q. Cheng, J. Bai, X. Wang, B. Shi, W. Gao, F. Shu

“…Simulation results show that the proposed algorithms can effectively improve the rate performance compared to fixed PA strategies, only optimizing PA factor, aided by passive IRS, and without IRS.…”
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Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production by Hyeongjun Lim, Sojung Kim

“…Particularly, this study reviews feedstock yield estimation approaches using machine learning technologies that focus on gradually improving estimation accuracy by using big data and computer algorithms from traditional statistical approaches. …”
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