Interpretable machine learning model of effective mass in perovskite oxides with cross-scale features by Changjiao Li, Zhengtao Huang, Hua Hao, Zhonghui Shen, Guanghui Zhao, Ben Xu, Hanxing Liu

Subjects: “…Machine learning…”
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Integrating pharmacogenetics, multi‐omics and machine learning in the novel therapeutic view of pulmonary hypertension by Chenxi Li, Jipeng Xue, Dongchen Zhou, Xiaomeng Qi, Ting Chen, Zhoubin Li

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Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning training by Abdullah Al Siam, Md Maruf Hassan, Md Atikur Rahaman, Masuk Abdullah

“…The framework comprises four key components: (1) converting DICOM files to GIF format with encryption, (2) decrypting EGIF files for processing, (3) enabling confidentiality-preserving machine learning training using EGIF data, and (4) facilitating physician diagnosis and report generation based on trained machine learning models. …”
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A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity by Victoria Shevchenko, R. Austin Benn, Robert Scholz, Wei Wei, Carla Pallavicini, Ulysse Klatzmann, Francesco Alberti, Theodore D. Satterthwaite, Demian Wassermann, Pierre-Louis Bazin, Daniel S. Margulies

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Common biomarkers of idiopathic pulmonary fibrosis and systemic sclerosis based on WGCNA and machine learning by Ning Shan, Yu Shang, Yaowu He, Zhe Wen, Shangwei Ning, Hong Chen

“…In this study, weighted correlation network analysis (WGCNA), protein‒protein interaction, Kaplan–Meier curve, univariate Cox analysis and machine learning methods were used on datasets from the Gene Expression Omnibus database. …”
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Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning by Zheng-Yong Zhang, Jian-Sheng Su, Huan-Ming Xiong

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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…To enhance predictive accuracy, advanced machine learning models, including Random Forest, Gradient Boosting, Multi-Layer Perceptron, and Categorical Boosting, were applied. …”
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A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand Estimation by Reem Tluli, Ahmed Badawy, Saeed Salem, Mahmoud Barhamgi, Amr Mohamed

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Role of Machine Learning and Data Mining in Internet Security: Standing State with Future Directions by Bilal Ahmad, Wang Jian, Zain Anwar Ali

“…This study highlights the developing research about the application of machine learning and data mining in Internet security. …”
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Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT by Rongqun Peng, Xiuhua Fu, Tian Ding

“…We investigate in detail the impact of variable sampling rate on traffic prediction and propose a high-speed traffic prediction method using machine learning and recurrent neural networks. We first investigate a VSR-NLMS adaptive prediction method to perform time series prediction dataset transformation. …”
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Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics by Krutika Patidar, Nikhil Pillai, Saroj Dhakal, Lindsay B. Avery, Panteleimon D. Mavroudis

“…In the present work, we propose a machine learning-based target pharmacology assessment framework that utilizes minimal physiologically based pharmacokinetic (mPBPK) modeling and machine learning (ML) to infer optimal physicochemical properties of antibodies and their targets. …”
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IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning by Susanto, Deris Stiawan, Budi Santoso, Alex Onesimus Sidabutar, M. Agus Syamsul Arifin, Mohd Yazid Idris, Rahmat Budiarto

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Combining machine learning and single-cell sequencing to identify key immune genes in sepsis by Hao Wang, Linghan Len, Li Hu, Yingchun Hu

“…Subsequently, diagnostic models were constructed using four machine learning algorithms (Logistic regression, AdaBoost, KNN, and XGBoost) based on the identified key genes. …”
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Machine Learning-Assisted Drug Repurposing Framework for Discovery of Aurora Kinase B Inhibitors by George Nicolae Daniel Ion, George Mihai Nitulescu, Dragos Paul Mihai

“…<b>Results:</b> The machine learning models trained for drug repurposing showed satisfying performance and yielded the identification of saredutant, montelukast, and canertinib as potential AurB inhibitors. …”
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From Spectra to Signatures: Detecting Fentanyl in Human Nails with ATR–FTIR and Machine Learning by Aubrey Barney, Václav Trojan, Radovan Hrib, Ashley Newland, Jan Halámek, Lenka Halámková

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Species determination using AI machine-learning algorithms: Hebeloma as a case study by Peter Bartlett, Ursula Eberhardt, Nicole Schütz, Henry J. Beker

“…Based on these data an Artificial Intelligence (AI) machine-learning species identifier has been developed that takes as input locality data and a small number of the morphological parameters. …”
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Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review by Anass Houdou, Imad El Badisy, Kenza Khomsi, Sammila Andrade Abdala, Fayez Abdulla, Houda Najmi, Majdouline Obtel, Lahcen Belyamani, Azeddine Ibrahimi, Mohamed Khalis

“…Abstract Many studies use machine learning to predict atmospheric pollutant levels, prioritizing accuracy over interpretability. …”
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Stock Price Prediction Using Machine Learning: Evidence from Pakistan Stock Exchange by Zafar Akhter, Dr. Hassan Raza

Subjects: “…Machine learning…”
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Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models by Mi Jin Noh, Yang Sok Kim

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Machine learning-driven optimization for predicting compressive strength in fly ash geopolymer concrete by Maryam Bypour, Mohammad Yekrangnia, Mahdi Kioumarsi

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