A Machine Learning-Based Parameterized Tropical Cyclone Precipitation Model by Yi Lu, Jie Yin, Peiyan Chen, Hui Yu, Sirong Huang

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An integrated machine learning and fractional calculus approach to predicting diabetes risk in women by David Amilo, Khadijeh Sadri, Evren Hincal, Muhammad Farman, Kottakkaran Sooppy Nisar, Mohamed Hafez

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Spatio-Temporal Habitat Dynamics of Migratory Small Yellow Croaker (<i>Larimichthys polyactis</i>) in Hangzhou Bay, China by Xiangyu Long, Dong Wang, Pengbo Song, Mengwen Han, Rijin Jiang, Yongdong Zhou

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A machine learning model for early detection of sexually transmitted infections by Juma Shija, Judith Leo, Elizabeth Mkoba

“…The dataset was split into a 70%:15%:15% ratio for training, testing, and validation, respectively, and five machine learning algorithms were evaluated: AdaBoost, Support Vector Machine, Random Forest, Decision Tree, and Stochastic Gradient Descent. …”
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Performance Evaluation of Robotic Harvester with Integrated Real-Time Perception and Path Planning for Dwarf Hedge-Planted Apple Orchard by Tantan Jin, Xiongzhe Han, Pingan Wang, Yang Lyu, Eunha Chang, Haetnim Jeong, Lirong Xiang

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Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis by Faisal Abdulaziz Almisned, Natacha Usanase, Dilber Uzun Ozsahin, Ilker Ozsahin

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Impacts of farming and herding activities on land use and land cover changes in the north eastern corridor of Ghana: A comprehensive analysis by Nanii Yenibehit, Abubakari Abdulai, Joseph Amikuzuno, James Kofi Blay

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Rapid and accurate multi-phenotype imputation for millions of individuals by Lin-Lin Gu, Hong-Shan Wu, Tian-Yi Liu, Yong-Jie Zhang, Jing-Cheng He, Xiao-Lei Liu, Zhi-Yong Wang, Guo-Bo Chen, Dan Jiang, Ming Fang

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Detection and Analysis of Malicious Software Using Machine Learning Models by Selman Hızal, Ahmet Öztürk

“…Our analysis encompasses binary and multi-class classification tasks under various experimental conditions, including percentage splits and 10-fold cross-validation. The evaluated algorithms include Random Tree (RT), Random Forest (RF), J-48 (C4.5), Naive Bayes (NB), and XGBoost. …”
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Sysmon event logs for machine learning-based malware detection by Riki Mi’roj Achmad, Dyah Putri Nariswari, Baskoro Adi Pratomo, Hudan Studiawan

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Mapping Antarctic Blue Ice Areas With Sentinel-2A/B Images and LightGBM Model by Xiaolong Teng, Jiahui Xu, Xiangbin Cui, Guitao Shi, Zhengyi Hu, Qingyu Gu, Bailang Yu, Jianping Wu, Yan Huang

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Immune status assessment based on plasma proteomics with meta graph convolutional networks by Min Zhang, Nan Xu, Qi Cheng, Jing Ye, Shiwei Wu, Haoliang Liu, Chengkui Zhao, Lei Yu, Weixing Feng

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Converging efficiency: Computational and fractal insights into parallel non-linear schemes by Mudassir Shams, Nasreen Kausar, Ali Akgül, Tonguç Çağın

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Lightweight Deepfake Detection Based on Multi-Feature Fusion by Siddiqui Muhammad Yasir, Hyun Kim

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Multi-scenario Dynamic Simulation and Optimization of Urban Ventilation Environment: A Case Study of Taiyuan Metropolitan Area by Junda HUANG, Yuncai WANG

“…Then, a prediction model is constructed based on the random forest algorithm. The land use types and ventilation environment of multiple scenarios in 2010 and 2020 are input into the validated prediction model to simulate changes in the future ventilation environment. …”
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Enhancing liver disease diagnosis with hybrid SMOTE-ENN balanced machine learning models—an empirical analysis of Indian patient liver disease datasets by Ritu Rani, Garima Jaiswal, Nancy, Lipika, Shashi Bhushan, Fasee Ullah, Prabhishek Singh, Manoj Diwakar, Manoj Diwakar

“…Immediate action is necessary for timely diagnosis of the ailment before irreversible damage is done.MethodsThe work aims to evaluate some of the traditional and prominent machine learning algorithms, namely, Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Gaussian Naïve Bayes, Decision Tree, Random Forest, AdaBoost, Extreme Gradient Boosting, and Light GBM for diagnosing and predicting chronic liver disease. …”
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Downscaling of Soil Moisture Map using Sentinel Radar Satellite Images and Distribution Analysis in the West of Iran by Seyed Hossein Mirmosavi, kohzad Raispour, Muhammad Kamangar

“…The results of this study also confirm that the algorithm used in this research can effectively lead to the extraction of the soil surface moisture layer with a higher resolution…”
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Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases by Huai-wen Zhang, Yi-ren Wang, Bo Hu, Bo Song, Zhong-jian Wen, Lei Su, Xiao-man Chen, Xi Wang, Ping Zhou, Xiao-ming Zhong, Hao-wen Pang, You-hua Wang

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Effective tweets classification for disaster crisis based on ensemble of classifiers by Christopher Ifeanyi Eke, Kholoud Maswadi, Musa Phiri, Mulenga, Mohammad Imran, Dekera Kwaghtyo, Akeremale Olusola Collins

“…A range of supervised learning algorithms like Decision Trees, Logistic Regression, Support Vector Machines, and Random Forests, were evaluated individually and as part of ensemble methods like AdaBoost, Bagging, and Random Subspace. …”
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Data fusion-based improvements in empirical regression and machine learning for global daily ∼ 8 km resolution sea surface nitrate estimation and interpretation by Aifen Zhong, Difeng Wang, Fang Gong, Jingjing Huang, Zhuoqi Zheng, Xianqiang He, Qing Zhang, Qiankun Zhu

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