Enhanced E-commerce decision-making through sentiment analysis using machine learning-based approaches and IoT. by Yasser Filahi, Omer Melih Gul, Ali Elghirani, Erkut Arican, Ismail Burak Parlak, Seifedine Kadry, Kostas Karpouzis

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Artificial Intelligence Approach in Machine Learning-Based Modeling and Networking of the Coronavirus Pathogenesis Pathway by Shihori Tanabe, Sabina Quader, Ryuichi Ono, Hiroyoshi Y. Tanaka, Akihisa Yamamoto, Motohiro Kojima, Edward J. Perkins, Horacio Cabral

“…Data on coronaviral infection held in a database were analyzed with Ingenuity Pathway Analysis (IPA), a network pathway analysis tool. …”
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Computational hybrid analysis of drug diffusion in three-dimensional domain with the aid of mass transfer and machine learning techniques by Mohammed Alqarni, Ali Alqarni

“…The mass transfer equation including diffusion is solved in the domain and then the data is extracted for building machine learning models. …”
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Analysis of science learning outcomes on motion and force topics among seventh-grade students from gender perspective by Dian Nugraheni, Aisyah Az-Zahro, Agung Mulyo Setiawan, Erni Yulianti, Isnanik Juni Fitriyah, Fatimah Az-Zahro

“…However, students' understanding of science material, particularly motion and force, often varies and impacts learning outcomes. This study aims to analyze the science learning outcomes for motion and force material among seventh-grade MTs students and to investigate whether there are significant differences based on gender. …”
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Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors by Fatih Orhan, Mehmet Nurullah Kurutkan

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Early PCOS Detection: A Comparative Analysis of Traditional and Ensemble Machine Learning Models With Advanced Feature Selection by Khandaker Mohammad Mohi Uddin, Md. Tofael Ahmed Bhuiyan, Md. Mahbubur Rahman, Md. Manowarul Islam, Md Ashraf Uddin

“…In this study, we examined a dataset consisting of 541 patient records to enhance the detection of PCOS using advanced machine learning techniques. We established a data preprocessing pipeline that rigorously addressed missing values and identified outliers, while also normalizing the data to ensure it was ready for input. …”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

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Analysis and modeling of COVID-19 epidemic dynamics in Saudi Arabia using SIR-PSO and machine learning approaches by Rafat Zrieq, Sahbi Boubaker, Souad Kamel, Mohamed Alzain, Fahad D. Algahtani

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Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method by Jinxi Su, Rong Tang, Huilong Lin

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Anticipatory moral distress in machine learning-based clinical decision support tool development: A qualitative analysis by Clare Whitney, Heidi Preis, Alessa Ramos Vargas

“…The current inquiry applied a framework of the symbolic interaction of participatory experience-based co-design and used an interpretive descriptive approach to analysis of qualitative data, investigating the ethical issues brought to light by clinicians participating in three participatory experience-based co-design focus groups, as a part of the initial development of a CDS tool for detecting risk factors for adverse health outcomes in outpatient obstetric care at a single academically affiliated medical institution. …”
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Machine Learning-Enabled Prediction and Mechanistic Analysis of Compressive Yield Strength–Hardness Correlation in High-Entropy Alloys by Haiyu Wan, Baobin Xie, Hui Feng, Jia Li

“…The yield strength and hardness are critical performance metrics, yet their interrelationships in diverse HEAs remain incompletely understood, partly due to data limitations. This work employs an integrated machine learning framework to investigate the compressive yield strength (σ_y) and hardness (HV) correlation across a dataset of cast HEAs. …”
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Insights from novel word learning: Error analysis and its relevance in understanding fast versus slow mapping by Abhishek BP, Spoorthi Jain

“…The present study was carried out with the aim of carrying out of error analysis on data involving fast and slow mapping. 20 participants were divided into two sub groups of 10 each. …”
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Insights from novel word learning: Error analysis and its relevance in understanding fast versus slow mapping by Abhishek BP, Spoorthi Jain

“…The present study was carried out with the aim of carrying out of error analysis on data involving fast and slow mapping. 20 participants were divided into two sub groups of 10 each. …”
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Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis by Somayeh Farahani, Marjaneh Hejazi, Sahar Moradizeyveh, Antonio Di Ieva, Emad Fatemizadeh, Sidong Liu

“…<b>Results:</b> Of 728 articles, 43 were qualified for qualitative analysis, and 30 were included in the meta-analysis. …”
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Effect of discontinuing antipsychotic medications on the risk of hospitalization in long-term care: a machine learning-based analysis by Mikko Nuutinen, Riikka-Leena Leskelä, Daniela Fialova, Ira Haavisto, Harriet Finne-Soveri, Jokke Häsä, Johanna Edgren, Hein van Hout, Daniel E. da Cunha Leme, John P. Hirdes, Graziano Onder, Rosa Liperoti

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Machine learning-assisted radiogenomic analysis for miR-15a expression prediction in renal cell carcinoma by Yulian Mytsyk, Paweł Kowal, Yuriy Kobilnyk, Mateusz Lesny, Michał Skrzypczyk, Dmytro Stroj, Victor Dosenko, Olena Kucheruk

“…Conclusions Radiogenomic analysis using machine learning provides a robust, non-invasive approach to predicting miR-15a expression, enabling enhanced tumor stratification and personalized RCC management. …”
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Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

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State-of-Health Estimation for Lithium-Ion Batteries via Incremental Energy Analysis and Hybrid Deep Learning Model by Yan Zhang, Anxiang Wang, Chaolong Zhang, Peng He, Kui Shao, Kaixin Cheng, Yujie Zhou

“…Incremental Energy Analysis (IEA) was conducted on the charging data to extract various incremental energy characteristics. …”
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A Multi-Scale Deep Learning Framework Combining MobileViT-ECA and LSTM for Accurate ECG Analysis by Abduljabbar S. Ba Mahel, Mehdhar S. A. M. Al-Gaashani, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Mohammed Saleh Ali Muthanna, Ammar Muthanna, Ahmed Aziz

“…This paper introduces a novel deep learning (DL) architecture designed to enhance the processing, feature extraction, and analysis of ECG signals. …”
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A machine learning model for predicting obesity risk in patients with diabetes mellitus: analysis of NHANES 2007–2018 by Wenqiang Wang, Ruiqing Mo, Xingyu Chen, Sijie Yang

“…This study aimed to identify key predictors of obesity and develop a machine learning-based predictive model for patients with T2DM using data from the National Health and Nutrition Examination Survey (NHANES).MethodsData from adults with diabetes were extracted from the NHANES 2007–2018 cycles. …”
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