Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability by Md. Abu Jabed, Masrah Azrifah Azmi Murad

“…The research paper also examines the algorithms frequently utilized in the machine learning domain, including Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). …”
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Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms by Quancheng Liu, Jun Zhou, Zhaoyi Wu, Didi Ma, Yuxuan Ma, Shuxiang Fan, Lei Yan

“…This study integrates hyperspectral imaging with intelligent optimization algorithms—Zebra Optimization Algorithm (ZOA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)—and a Support Vector Machine (SVM) model to classify jujube varieties. …”
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A Novel Anomaly Forecasting in Time‐Series Data: Feedback Connection between Forecasting and Detecting Algorithms with Applications to Power Systems by Hyung Tae Choi, Hae Yeon Park, Taewan Kim, Jung Hoon Kim

“…More precisely, Xgboost and long short‐term memory are used for forecastor and one‐class support vector machine and robust random cut forest are used for detector. …”
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Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm by Ling Li, Tao Yue, Hui Wu, Yanping Zhao, Qinmei Liu, Hairong Zhang, Wei Xu

“…Experimental results demonstrate that the proposed system, based on support vector machine (SVM) with the EN-ICSA feature selection method, performs exceptionally well compared to other traditional machine learning and feature selection methods, achieving an accuracy of 91.94%, precision of 92.32%, recall of 89.85%, and F1-score of 90.82%.…”
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What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection by Lu Liang, Jacob Daniels

“…This study comprehensively assessed ten widely used data techniques, namely AdaBoost, Bayesian ridge, gradient tree boosting, K-nearest neighbors, Lasso, multivariable linear regression, neural network, random forest, ridge regression, and support vector machine. We compared their performance using a standardized baseline dataset and their responses to various parameter combinations. …”
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Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A SHAP-Driven Framework to Accelerate Urban Carbon Neutrality by Zhihui Zhao, Minjuan Wang, Jin Wei, Xiao Cen, Shengnan Du, Ziwen Wu, Huanying Liu, Weiqiang Wang

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Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico by K. D. Rodríguez González, L. E. Arista Cázares, F. D. Yépez Rincón

“…This study evaluates four Machine Learning Algorithms—Random Forest (RF), K-Means Clustering, Support Vector Machine (SVM), and Classification and Regression Trees (CART)—for precise land use and land cover (LULC) classification in the Monterrey Metropolitan Area. …”
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Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014-2019, by clinicians or machine learning alg... by David M Vu, Amy R Krystosik, Bryson A Ndenga, Francis M Mutuku, Kelsey Ripp, Elizabeth Liu, Carren M Bosire, Claire Heath, Philip Chebii, Priscilla Watiri Maina, Zainab Jembe, Said Lipi Malumbo, Jael Sagina Amugongo, Charles Ronga, Victoria Okuta, Noah Mutai, Nzaro G Makenzi, Kennedy A Litunda, Dunstan Mukoko, Charles H King, A Desiree LaBeaud

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Identification of GJC1 as a novel diagnostic marker for papillary thyroid carcinoma using weighted gene co-expression network analysis and machine learning algorithm by Jingshu Zhang, Ping Sun

“…Enrichment analysis was performed on differentially expressed genes (DEGs) using Gene Ontology (GO), Disease Ontology (DO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Subsequently, three machine learning algorithms Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) were used to identify the core genes. …”
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Short-term salinity prediction for coastal areas of the Vietnamese Mekong Delta using various machine learning algorithms: a case study in Soc Trang Province by Le Thi Thanh Dang, Hiroshi Ishidaira, Ky Phung Nguyen, Kazuyoshi Souma, Jun Magome

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Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditi... by Seda Günaydın, Necati Çetin, Cevdet Sağlam, Kamil Sacilik, Ahmad Jahanbakhshi

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Rapid differentiation of patients with lung cancers from benign lung nodule based on dried serum Fourier-transform infrared spectroscopy combined with machine learning algorithms by Huanyu Li, Lixue Dai, Shaomei Guo, Hongluan Wang, Lei Lei, Jie Yu, Xiaoyun Li, Jun Wang

“…Five machine learning models, linear discriminant analysis (LDA), support vector machine (SVM), random forest, multilayer perceptron (MLP), and LightGBM, were optimized using FTIR spectral data (1800–900 cm−1 band). …”
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The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population–Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis... by Musa Jaiteh, Edith Phalane, Yegnanew A Shiferaw, Refilwe Nancy Phaswana-Mafuya

“…Logistic regression, support vector machines, random forests, and decision trees will be used. …”
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Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung... by FangHao Cai, Zhengjun Guo, GuoYu Wang, FuPing Luo, Yang Yang, Min Lv, JiMin He, ZhiGang Xiu, Dan Tang, XiaoHui Bao, XiaoYue Zhang, ZhenZhou Yang, Zhi Chen

“…Radiomic features were extracted from intratumoral, peritumoral, and integrated intratumoral-peritumoral regions by manually delineating the gross tumor volume (GTV) and an additional 3 mm surrounding area. Three machine learning algorithms—Support Vector Machine (SVM), XGBoost, and Gradient Boosting—were employed to construct radiomic models for each region. …”
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Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning by Bruno Matos Porto, Flavio Sanson Fogliatto

“…Three of them – Light Gradient Boosting Machine (LightGBM), Support Vector Machine with Radial Basis Function (SVM-RBF), and Neural Network Autoregression (NNAR) – were never before reported for predicting ED patient arrivals. …”
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Enhanced Binary Kepler Optimization Algorithm for effective feature selection of supervised learning classification by Amr A. Abd El-Mageed, Amr A. Abohany, Khalid M. Hosny

“…BKOA-MUT was evaluated using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) on 25 UCI benchmarks, including three large-scale ones. …”
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Accurate and robust prediction of Amyloid-β brain deposition from plasma biomarkers and clinical information using machine learning by Jiayuan Xu, Andrew J. Doig, Sofia Michopoulou, Sofia Michopoulou, Petroula Proitsi, Petroula Proitsi, Fumie Costen, The Alzheimer's disease neuroimaging initiative

“…Decision tree, random forest, support vector machine, and multilayer perceptron machine learning methods were used to combine all this information. …”
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Comparative analysis of machine learning models for predicting river water quality: a case study of the Zayandeh Rood River by Elham Fazel Najafabadi, Paria Shojaei, Mojgan Askarizadeh

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K-Gen PhishGuard: an Ensemble Approach for Phishing Detection with K-Means and Genetic Algorithm by Ali Al-Hafiz, Adnan Jabir, Shamala Subramaniam

“…In the second phase, the best set of features in each group is identified through the Genetic algorithm to enhance the classification process. Finally, a voting ensemble technique is applied, in which the Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Adaptive boosting (AdaBoost) models are combined. …”
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AI-Driven Identification of Grapevine Fungal Spores via Microscopic Imaging and Feature Optimization with Cuckoo Search Algorithm by Xin Shi, Seyed Mohamad Javidan, Yiannis Ampatzidis, Zhao Zhang

“…Advanced image processing techniques were applied to segment spores and extract texture, color, and shape features. A support vector machine (SVM) classifier achieved 97.5% overall accuracy after preprocessing, a substantial improvement over the initial 81.25% without image preprocessing. …”
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