Machine learning techniques for imbalanced multiclass malware classification through adaptive feature selection by Binayak Panda, Sudhanshu Shekhar Bisoyi, Sidhanta Panigrahy, Prithviraj Mohanty

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Identification of bacterial key genera associated with breast cancer using machine learning techniques by Md. Kaderi Kibria, Isteaq Kabir Sifat, Md. Bayazid Hossen, Farhana Hasan, Md Parvez Mosharaf, Md Ziaul Hassan

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A Human-Centered Approach to Academic Performance Prediction Using Personality Factors in Educational AI by Muhammad Adnan Aslam, Fiza Murtaza, Muhammad Ehatisham Ul Haq, Amanullah Yasin, Muhammad Awais Azam

“…This research highlights the importance of designing AI systems that are not only accurate but also interpretable and aligned with human values, thereby fostering a more equitable educational landscape. Future work will expand on these findings by exploring the interaction effects of personality traits and applying more sophisticated machine learning techniques.…”
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Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study by Katerina Lawrie, Petr Waldauf, Peter Balaz, Radoslav Bortel, Ricardo Lacerda, Emma Aitken, Krzysztof Letachowicz, Mario D’Oria, Vittorio Di Maso, Pavel Stasko, Antonio Gomes, Joana Fontainhas, Matej Pekar, Alena Srdelic, VAVASC Study Group, Stephen O’Neill

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Understanding the creep behaviors and mechanisms of Mg-Gd-Zn alloys via machine learning by Shuxia Ouyang, Xiaobing Hu, Qingfeng Wu, Jeong Ah Lee, Jae Heung Lee, Chenjin Zhang, Chunhui Wang, Hyoung Seop Kim, Guangyu Yang, Wanqi Jie

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Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil by Binghui Xu, Tzu-Chia Chen, Danial Ahangari, S. M. Alizadeh, Marischa Elveny, Jeren Makhdoumi

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Identification and Evaluation of Lipocalin-2 in Sepsis-Associated Encephalopathy via Machine Learning Approaches by Hu J, Chen Z, Wang J, Xu A, Sun J, Xiao W, Yang M

“…On the basis of various machine learning algorithms, we discovered that Lipocalin-2 (LCN2) may be the key gene involved in neutrophil-induced SAE. …”
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Applications of machine learning to high temperature and high pressure environments: A literature review by Hengkai Wang, Zengtao Lv, Santosh Kumar, Qinglin Wang

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Machine Learning-Based Classification of Programming Logic Understanding Levels by Mouse-Tracking Heatmaps by Attaporn Khaesawad, Yosuke Fukuchi, Vibol Yem, Nobuyuki Nishiuchi

“…We collected actual learning-task data from first-year university students and compared the performance of five machine learning models. …”
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Advanced Machine Learning for Preschooler Magnetic Resonance Imaging Analysis in Classification of Anxiety Disorders by Salik Mian, Pranav Kunderu, Shivm Patel

“…These data were used to train machine learning models: Support Vector Machines (SVMs), decision trees, and Logistic Regression. …”
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Design and research of English material archives management system based on machine learning technology by Yi Qin

“…After training and testing on a large-scale English corpus, the classification accuracy of the model has increased from 75 % of the traditional method to 90 %, and the F1 score of keyword extraction has increased from 60 % to 85 %, demonstrating the strong application potential of machine learning in English data management.…”
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Cognitive performance classification of older patients using machine learning and electronic medical records by Monika Richter-Laskowska, Ewelina Sobotnicka, Adam Bednorz

“…Developing efficient diagnostic tools is critical, and machine learning (ML) algorithms have shown potential for improving the accuracy of cognitive impairment classification. …”
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What factors enhance students' achievement? A machine learning and interpretable methods approach. by Hui Mao, Ribesh Khanal, ChengZhang Qu, HuaFeng Kong, TingYao Jiang

“…This study addresses these limitations by employing an ensemble of five machine learning algorithms (SVM, DT, ANN, RF, and XGBoost) to model multivariate relationships between four behavioral and six instructional predictors, using final exam performance as our outcome variable. …”
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Hybrid Deep Learning Models for Sentiment Analysis by Cach N. Dang, María N. Moreno-García, Fernando De la Prieta

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Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study by Jiani Liu, Xin Zhang, Wei Li, Francis Manyori Bigambo, Dandan Wang, Xu Wang, Beibei Teng

“…This study revealed that parental height, caregiver education, and children’s weight significantly influenced the prediction of normal-variant short stature risk, and both the random forest model and gradient boosting machine model exhibited the best discriminatory ability among the 9 machine learning models. …”
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Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data by Guihua Zhang, Jiayue Liu, Yuxin Wu, Guangxi Yue

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Rethinking the 'best method' paradigm: The effectiveness of hybrid and multidisciplinary approaches in chemoinformatics by José L. Medina-Franco, Johny R. Rodríguez-Pérez, Héctor F. Cortés-Hernández, Edgar López-López

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A multi-dimensional evaluation study of visitor perceptions in suburban parks based on machine learning: a case study of Wuhan garden expo park in Wuhan city by Yu Zhao, Yilei Wang

“…Abstract While traditional visitor perception studies rely heavily on questionnaire surveys with limited samples, there is a lack of data-driven approaches that can comprehensively capture and analyze large-scale visitor feedback. This study employs machine learning methods, particularly the Latent Dirichlet Allocation topic model, to analyze large-scale User Generated Content data collected from Xiecheng.com, one of China's largest online review platforms. …”
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AUTOMATIC SCORING OF KNOWLEDGE GAINED AND SHARED THROUGH DISCUSSION FORUMS: BASED ON THE COMMUNITY OF INQUIRY MODEL by P.A.L. Nadeesha, T.A. Weerasinghe, W.R.N.S Abeyweera

“…Our work contributes to the ongoing conversation on leveraging machine learning for cognitive analysis in online learning environments, highlighting the importance of context-specific methodologies in advancing educational assessment practices. …”
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Can heart rate sequences from wearable devices predict day-long mental states in higher education students: a signal processing and machine learning case study at a UK university by Tianhua Chen

“…Using signal processing, both original heart rate sequences and their representations, via Fourier transformation and wavelet analysis, have been modeled using advanced machine learning algorithms. …”
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