Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis by Marcos Uceta, Alberto del Cerro‐León, Danylyna Shpakivska‐Bilán, Luis M. García‐Moreno, Fernando Maestú, Luis Fernando Antón‐Toro

“…ABSTRACT Background The demand for fresh strategies to analyze intricate multidimensional data in neuroscience is increasingly evident. One of the most complex events during our neurodevelopment is adolescence, where our nervous system suffers constant changes, not only in neuroanatomical traits but also in neurophysiological components. …”
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EXPERIENCE OF PROJECT-BASED LEARNING: AN ATTEMPT AT OBJECTIVE ANALYSIS OF RESULTS AND PROBLEMS by D. A. Trishchenko

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Mining online hotel reviews using big data and machine learning: An empirical study from an emerging country by Hanh Thi My Le, Thuy-An Phan-Thi, Binh T. Nguyen, Thang Quyet Nguyen

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Evolution of AI enabled healthcare systems using textual data with a pretrained BERT deep learning model by Yi Jie Wang, Wei Chong Choo, Keng Yap Ng, Ran Bi, Peng Wei Wang

“…Early technology trend analysis relied heavily on expert judgment. This study is the first to introduce a deep learning self-supervised model to the field of AI in healthcare, effectively improving the accuracy and efficiency of the analysis. …”
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Leveraging Social Media Data to Understand COVID-19 Prevention Measures in Construction: A Machine Learning Approach by Emmanuel B. Boateng, Daniel Oteng, Dan N. O. Bonsu, Vinod Gopaldasani

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Machine learning applications in the analysis of sedentary behavior and associated health risks by Ayat S Hammad, Ayat S Hammad, Ali Tajammul, Ismail Dergaa, Ismail Dergaa, Ismail Dergaa, Maha Al-Asmakh, Maha Al-Asmakh

“…As prolonged inactivity becomes a growing public health concern, researchers are increasingly utilizing machine learning (ML) techniques to examine and understand these patterns. …”
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Integrating local wisdom into learning in school’s curriculum: A bibliometric analysis by K. Khusyairin, Udin Saefudin Sa'ud, S. Sururi, Nani Hartini

“…This study presents a bibliometric analysis of research on integrating local wisdom into school learning, providing a comprehensive overview of scholarly developments from 2010 to 2024. …”
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Soil moisture retrieval and spatiotemporal variation analysis based on deep learning by Zihan Zhang, Jinjie Wang, Jianli Ding, Jinming Zhang, Liya Shi, Wen Ma

“…The Boruta algorithm and correlation analysis were applied to select key variables. Nine deep learning models, including three basic architectures (Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Transformer) and six hybrid structures (CNN-LSTM, LSTM-CNN, CNN-with-LSTM, CNN-Transformer, GAN-LSTM, Transformer-LSTM), were systematically compared to evaluate the impact of neural network structure on model performance. …”
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The Training Method for Digital Data Operation by I. V. Shevtsova

“…As a result, students better learn knowledge and practical skills that are also applicable to working with other types of social media and global data platforms. …”
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Three Decades of Land Cover Dynamics in a Boreal Coastal Basin: A Multisensor Spectral Index and Machine Learning Approach Using Landsat Data and GB-SAR Data by Jinsong Zhang, Bochi Zou, Yifei Yuan, Asad Khan, Muhammad Bilawal Junaid, Qaiser Abbas, Rana Muhammad Zulqarnain, Nazih Y. Rebouh, Olga D. Kucher, Hassan Alzahrani

“…This approach enabled automated preprocessing, atmospheric correction, cloud masking, and multidecadal time-series analysis across the study area. Landsat surface reflectance and spectral index data were fused and subsequently classified using a random forest machine learning algorithm, allowing for enhanced land cover classification accuracy across seven time intervals spanning three decades. …”
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Mixed Layer Depth Estimation From Multisource Remote Sensing Data Using Clustering-Machine Learning Method by Zengxin Guan, Kaijun Ren, Senliang Bao, Hengqian Yan, Huizan Wang, Yanlai Zhao, Jianbin Liu

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Identifying inflammatory bowel disease subtypes: a comprehensive exploration of transcriptomic data and machine learning-based approaches by Niyati Saini, Animesh Acharjee

“…Despite advances in transcriptomic analysis and machine learning (ML), consistent molecular subtyping across datasets remains a challenge. …”
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A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks by Mohsen Shayanmehr, Sepehr Aarabi, Ahad Ghaemi, Alireza Hemmati

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Prediction and analysis of toxic and side effects of tigecycline based on deep learning by Yin Xiong, Guoxin Liu, Xin Tang, Boyang Xia, Yalian Yu, Guangjun Fan

“…Based on these results, the length of hospital stay was predicted by the deep learning prediction model with an error within 1 day.ConclusionIn this study, the hospitalization days of infected patients were predicted by deep learning model with low error. …”
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Automated oil spill detection using deep learning and SAR satellite data for the northern entrance of the Suez Canal by Mohamed Zakzouk, Abdulaziz M. Abdulaziz, Islam Abou El-Magd, Abdel Sattar Dahab, Elham M. Ali

“…In addition, the area prediction analysis confirmed the superior performance of the Egyptian-data-trained model, which estimated a total affected area of 421.20 km2, closely aligning with the ground truth of 425.20 km2, whereas the EMSA-CSN-data-trained model underestimated oil spills of around 323.98 km2. …”
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Analysing learning behaviour: A data-driven approach to improve time management and active listening skills in students by Vinayak Hegde, Vishrutha M, Pallavi M. Shanthappa, Rekha Bhat, Nisha Raveendran, Roshin C

“…The findings emphasize the pivotal role of non-cognitive skills like time management and active listening in fostering academic achievement. • Significance of Non-Cognitive Skills: The study underscores the critical role of non-cognitive skills, such as time management and active listening, in fostering academic success and overall learning effectiveness. • Survey and Dataset Analysis: The survey, conducted with 350 participants using Google Forms, provided valuable data for training models and highlighted challenges like student failures, which can be addressed through predictive analysis. • Impact on Academic Success: By analyzing learning behaviors and identifying barriers, the findings emphasize the potential of machine learning approaches in understanding and mitigating factors that negatively affect academic performance.…”
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DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography by Ismael Santarrosa-López, Giner Alor-Hernández, Maritza Bustos-López, Jonathan Hernández-Capistrán, Laura Nely Sánchez-Morales, José Luis Sánchez-Cervantes, Humberto Marín-Vega

“…To address these limitations, this study introduces DETEC-ADHD, a web-based application that combines machine learning (ML) techniques with multi-source data to enhance diagnostic accuracy. …”
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Heterogeneous network-based algorithms in the biomedical data mining: A review from technical perspective by Shirui Yu, Aihua Li, Yifei Chen, Dechao Wang, Xiaoli Tang

“…Findings: The heterogeneous network-based algorithms are analyzed from technical perspective. The detailed analysis of these algorithms contributes to a deeper understanding of their features and applicability, and promotes their use in data mining tasks. …”
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Effect of natural and synthetic noise data augmentation on physical action classification by brain–computer interface and deep learning by Yuri Gordienko, Nikita Gordienko, Vladyslav Taran, Anis Rojbi, Sergii Telenyk, Sergii Telenyk, Sergii Stirenko

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Application of Machine Learning Methods for Gravity Anomaly Prediction by Katima Zhanakulova, Bakhberde Adebiyet, Elmira Orynbassarova, Ainur Yerzhankyzy, Khaini-Kamal Kassymkanova, Roza Abdykalykova, Maksat Zakariya

“…Gravity anomalies play critical roles in geological analysis, geodynamic monitoring, and precise geoid modeling. …”
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