Combining Deep Learning and Street View Images for Urban Building Color Research by Wenjing Li, Qian Ma, Zhiyong Lin

“…In this document, we focus on “building color” and present a deep learning-based framework that combines geospatial big data with AI technology to extract and analyze urban color information. …”
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Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms by Abdurrahman Burak Guher, Sakir Tasdemir, Bulent Yaniktepe

“…In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), K-Nearest Neighbors (K-NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. …”
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Driving heterogeneity identification using machine learning: A review and framework for analysis by Xue Yao, Simeon C. Calvert, Serge P. Hoogendoorn

“…This paper presents a systematic review of current ML-based methods for driving heterogeneity identification. …”
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TEACHERS' AND STUDENTS' PERSPECTIVES ON USING DIGITAL EDUCATIONAL TOOLS DURING THE PROCESS OF LEARNING by Grendi Hendrastomo, Nur Endah Januarti

“…The article presents the proportion and variety of digital learning media used by teachers and students in sociology education. …”
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General and Specific Problems of the Formation of Intellectual Operations in Younger Schoolchildren with Learning Difficulties by A.I. Andriashina, L.A. Tishina

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Reducing Data Volume in News Topic Classification: Deep Learning Framework and Dataset by Luigi Serreli, Claudio Marche, Michele Nitti

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Machine learning study on predicting depressive symptoms and genetic correlations in Parkinson’s disease by Haijun Zhang, Yifan Zhang, Guihua Li, Guihua Li

“…A comparative analysis of multiple machine learning models was conducted to predict depression in PD patients, ultimately selecting the most effective model. …”
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Privacy Issues, Attacks, Countermeasures and Open Problems in Federated Learning: A Survey by Blessing Guembe, Sanjay Misra, Ambrose Azeta

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Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy by Ansam Khraisat, Ammar Alazab, Moutaz Alazab, Areej Obeidat, Sarabjot Singh, Tony Jan

“…PEIoT-DS use federated learning to create a comprehensive intrusion detection model without necessitating the transmission of raw data to a central server. …”
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Active Learning for Medical Article Classification with Bag of Words and Bag of Concepts Embeddings by Radosław Pytlak, Paweł Cichosz, Bartłomiej Fajdek, Bogdan Jastrzębski

“…Systems supporting systematic literature reviews often use machine learning algorithms to create classification models to assess the relevance of articles to study topics. …”
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Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning by Alfredo Ordinola, David Abramian, Magnus Herberthson, Anders Eklund, Evren Özarslan

“…However, such applications demand repeated acquisitions of MRI volumes with varied experimental parameters demanding long acquisition times and/or limited spatial resolution. In this work, we present a deep-learning-based approach for increasing the spatial resolution of diffusion MRI data in the form of fODFs obtained through constrained spherical deconvolution. …”
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Self-Regulation Strategies in Online EFL/ESL Learning: A Systematic Review by Şadıman Hunutlu

“…At this point, self-regulation strategies (hereafter, SRS), also used in face-to-face learning, are essential in improving the process. This study (n=24) aims to present a broad perspective of the studies based on students’ SRS by focusing on online EFL/ESL learning research articles published in the last five (2018-2022) years. …”
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Active learning algorithm for alleviating the user cold start problem of recommender systems by Toon De Pessemier, Bruno Willems, Luc Martens

“…A popular solution for this problem is to use active learning strategies. These strategies request ratings for a small set of carefully selected items to reveal the preferences of new users. …”
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Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors by Abubakar Wakili, Sara Bakkali, Ahmed El Hilali Alaoui

“…These threats disrupt network integrity by manipulating routing information, attracting traffic through malicious nodes and tunneling data to malicious endpoints. This paper presents a novel machine learning-based framework to enhance RPL's security and QoS. …”
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Collaborative Music Learning : Utilizing n-Track Application in Private Music Education by Piter Sembiring, Yudi Sukmayadi, Ayo Sunaryo

“…Unlike conventional music learning tools, n-Track enables real-time digital collaboration between teachers and students, facilitating asynchronous learning. …”
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Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors by Chibuike Chiedozie Ibebuchi

“…This study introduces a machine learning framework to predict the DAEP with a 24 h lead time, leveraging historical data and forecasts available at the prediction time. …”
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Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing by Katarzyna Antosz, Lucia Knapčíková, Jozef Husár

“…This article presents a discussion of the application of machine learning methods to enhance the quality of drive shaft production, with a particular focus on the identification of critical quality issues, including cracks, scratches, and dimensional deviations, which have been observed in the final stages of machining. …”
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The Role of Math Games for Children’s Early Math Learning: A Systematic Review by Mary DePascale, Geetha B. Ramani

“…However, research on what games are effective and who can benefit from playing them often focuses on single elements or contexts of gameplay, and there is little research summarizing the effects of math games on children’s learning. The current systematic review presents research on the impact of math games on preschool through third grade children’s math development, examining what game contexts, types, and content areas are effective for math learning, who can learn from games, and what features of math games effectively promote learning in early childhood. …”
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Extending Power Electronic Converter Lifetime in Marine Hydrokinetic Turbines with Reinforcement Learning by Samuel Barton, Ted K. A. Brekken, Yue Cao

“…The power electronic converter, a key element in the electrical energy conversion system, is a common point of failure in direct-drive turbine applications—leading to increased maintenance efforts. This work presents a reinforcement learning (RL) method built within a quadratic feedback torque control framework to balance energy generation with power electronic device lifetime. …”
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Programmed learning in chemistry education: a critical review of theory, application, and effectiveness by Pavlo P. Nechypurenko, Tetiana V. Tashtan, Serhiy O. Semerikov

“…Critical gaps include limited longitudinal studies examining knowledge retention and insufficient research on teacher experiences and implementation fidelity. We present an integrative framework for future programmed learning applications in chemistry education that emphasises adaptive scaffolding, contextualised learning, and metacognitive development. …”
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