Integration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction by Ahya Radiatul Kamila, Johanes Fernandes Andry, Francka Sakti Lee, Felliks F. Tampinongkol

“…Predicting turnover is crucial for companies to anticipate and take appropriate actions to retain potential employees. This study aims to optimize the employee turnover prediction model by integrating hash encoding techniques and machine learning. …”
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Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood Navigation by Amirmasoud Amiran, Behrouz Behnam, Sanaz Seyedin

“…This research is first to introduce a machine learning-based method to enhance the quality and speed of selecting emergency evacuation centers in Tehran, optimizing the use of the city’s current capacities. …”
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Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis by Fatma Hilal Yagin, Cemil Colak, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…EBM, LightGBM, and AdaBoost algorithms were applied to generate a discriminatory model between RA and controls. Comprehensive performance metrics were calculated, and the interpretability of the optimal model was assessed using global and local feature descriptions. …”
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Deep machine learning identified fish flesh using multispectral imaging by Zhuoran Xun, Xuemeng Wang, Hao Xue, Qingzheng Zhang, Wanqi Yang, Hua Zhang, Mingzhu Li, Shangang Jia, Jiangyong Qu, Xumin Wang

“…Convolutional neural network (CNN), quadratic discriminant analysis (QDA), support vector machine (SVM), and linear discriminant analysis (LDA) models perform well on cross-validation and test data. …”
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Tether Force Estimation Airborne Kite Using Machine Learning Methods by Akarsh Gupta, Yashwant Kashyap, Panagiotis Kosmopoulos

“…By analyzing key input features such as wind speed and kite dynamics, our model predicts optimal locations for Airborne Wind Energy System installation, offering a promising alternative to traditional wind turbines.…”
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Integration of UAV Remote Sensing and Machine Learning for Taro Blight Monitoring by Yushuai Wang, Yuxin Chen, Zhou Shu, Shaolong Zhu, Weijun Zhang, Tao Liu, Chengming Sun

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Automated Detection of Poor-Quality Scintigraphic Images Using Machine Learning by Anil K. Pandey, Akshima Sharma, Param D. Sharma, Chandra S. Bal, Rakesh Kumar

“…Receiver operator characteristic (ROC) analysis was used to select the optimal model using the largest value of area under the ROC curve. …”
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Machine Learning and Explainable AI for Thai Basil Growth Prediction in Hydroponics by Sankalp Kadam, Vinaya Gohokar, Rupali Kute

“…The green area and plant height were recorded as growth parameters. Six machine learning (ML) models are employed to estimate the growth of Thai Basil. …”
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Predicting 24-hour intraocular pressure peaks and averages with machine learning by Ranran Chen, Jinming Lei, Yujie Liao, Yiping Jin, Xue Wang, Xiaomei Li, Danping Wu, Hong Li, Yanlong Bi, Haohao Zhu

“…This study aimed to develop and assess a machine learning model for predicting 24-hour peak and average IOP, leveraging advanced techniques to enhance prediction accuracy. …”
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Automatic mosaic method of remote sensing images based on machine vision by S.P. Gao, M. Xia, S.J. Zhang

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Machine Learning Reveals Magmatic Fertility of Skarn-Type Tungsten Deposits by Rui-Chang Tan, Yong-Jun Shao, Yi-Qu Xiong, Zhi-Wei Fan, Hong-Fei Di, Zhao-Jun Wang, Kang-Qi Xu

“…Then we trained and validated machine learning (ML) models, specifically support vector machine (SVM) and random forests (RF), to classify magmatic fertility based on apatite chemistry in igneous rocks. …”
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Determination of cervical vertebral maturation using machine learning in lateral cephalograms by Shahab Kavousinejad, Asghar Ebadifar, Azita Tehranchi, Farzan Zakermashhadi, Kazem Dalaie

“…Results. The proposed model achieved an accuracy of 99.49% and demonstrated a loss of 0.003 on the test set. …”
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Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy by Xiangyu Shao, Wenjun Yan, Chaoying Yan, Wen Zhao, Yixuan Wang, Xia Shi, Hongchang Dong, Tianjiang Li, Junpo Yu, Peng Zuo, Zeyu Zhou, Jiming Jin

“…Traditional statistical models have limitations in multi-variable modeling, while machine learning models face interpretability challenges. …”
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Random Oversampling-Based Diabetes Classification via Machine Learning Algorithms by G. R. Ashisha, X. Anitha Mary, E. Grace Mary Kanaga, J. Andrew, R. Jennifer Eunice

“…In this work, we propose an e-diagnostic model for diabetes classification via a machine learning algorithm that can be executed on the Internet of Medical Things (IoMT). …”
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An ensemble-driven machine learning framework for enhanced water quality classification by Preet Singh, Taniya Hasija, Salil Bharany, Hafiza Nazra Tun Naeem, B. Chinna Rao, Seada Hussen, Ateeq Ur Rehman

“…This study applies machine learning (ML) models to classify water quality using an integrated dataset from Telangana, India. …”
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A Machine Learning Approach to Credit Card Transaction Fraud Prediction by Liu Zixuan

“…This study explores machine learning techniques to address category imbalances in credit card fraud detection datasets to mitigate economic losses while improving model performance. …”
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Machine learning to improve HIV screening using routine data in Kenya by Jonathan D. Friedman, Jonathan M. Mwangi, Kennedy J. Muthoka, Benedette A. Otieno, Jacob O. Odhiambo, Frederick O. Miruka, Lilly M. Nyagah, Pascal M. Mwele, Edmon O. Obat, Gonza O. Omoro, Margaret M. Ndisha, Davies O. Kimanga

“…We generated a stratified 60‐20‐20 train‐validate‐test split to assess model generalizability. We trained four machine learning algorithms including logistic regression, Random Forest, AdaBoost and XGBoost. …”
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A Social Media Sentiment Analysis Using Machine Learning Approaches by Noor Salah Irzooqi Al-Agele, Didem KIVANÇ TÜRELİ

“…With an accuracy of 93%, the Random Forest (RF) model proved to be the most effective among other models, Because of its exceptional capacity and generating accurate and dependable results on textual data, the Random Forest (RF) model proves in the study to be the most optimal choice for sentiment analysis textual. …”
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Aircraft Engine Remaining Useful Life Prediction Using Machine Learning by Michael Kimollo, Xudong Liu

“…To this end, using the NASA C-MAPSS dataset, we trained several machine-learning models to address the aforementioned two problems. …”
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Combination of dynamic TOPMODEL and machine learning techniques to improve runoff prediction by Pin‐Chun Huang

“…The present study aims to evaluate the optimal combination of these parameters within the dynamic TOPMODEL framework using machine learning techniques to improve the accuracy of runoff predictions and bolster the model's reliability. …”
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