Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review by Habiba Njeri Ngugi, Andronicus A. Akinyelu, Absalom E. Ezugwu

“…This paper presents a review of machine learning (ML) and deep learning (DL) techniques for crop disease diagnosis, focusing on Support Vector Machines (SVMs), Random Forest (RF), k-Nearest Neighbors (KNNs), and deep models like VGG16, ResNet50, and DenseNet121. …”
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Postoperative Apnea‐Hypopnea Index Prediction of Velopharyngeal Surgery Based on Machine Learning by Jingyuan You, Juan Li, Yingqian Zhou, Xin Cao, Chunmei Zhao, Yuhuan Zhang, Jingying Ye

“…Compared with traditional stepwise linear regression (LR) algorithm, machine learning algorithms including artificial neural network (ANN), support vector regression, K‐nearest neighbor, random forest, and extreme gradient boosting were utilized to establish the regression model. …”
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Machine learning as a tool for diagnostic and prognostic research in coronary artery disease by B. I. Geltser, M. M. Tsivanyuk, K. I. Shakhgeldyan, V. Yu. Rublev

“…The advantages and disadvantages of individual ML methods (logistic regression, support vector machines, decision trees, naive Bayesian classifier, k-nearest neighbors) for the development of diagnostic and predictive algorithms are shown. …”
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Assessment of salt tolerance in peas using machine learning and multi-sensor data by Zehao Liu, Qiyan Jiang, Yishan Ji, Rong Liu, Hongquan Liu, Xiuxiu Ya, Zhenxing Liu, Zhirui Wang, Xiuliang Jin, Tao Yang

“…Using this information, aboveground biomass (AGB) and Soil Plant Analyses Development (SPAD) values were estimated under both growth conditions using four machine learning algorithms: CatBoost, Light Gradient Boosting Machine (LightGBM), support vector machines (SVM), and random forest regression (RF). …”
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Identification of key genes as diagnostic biomarkers for IBD using bioinformatics and machine learning by Tianhao Li, Haoren Jing, Xinyu Gao, Te Zhang, Haitao Yao, Xipeng Zhang, Mingqing Zhang

“…Core candidate genes were subsequently prioritized using protein-protein interaction network analysis, further refined through machine learning approaches (Random Forest/Support Vector Machines). …”
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Prediction of Thermal and Optical Properties of Oxyfluoride Glasses Based on Interpretable Machine Learning by Yuhao Xie, Xiangfu Wang

“…Based on the components of glasses, four algorithms, namely K-Nearest Neighbor, Random Forest, Support Vector Machine, and eXtreme Gradient Boosting, were used to construct an optimal machine learning model to predict the thermal and optical properties of oxyfluoride glass, namely glass transition temperature, density, Abbe number, liquidus temperature, thermal expansion coefficient, and refractive index. …”
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Damage prediction of rear plate in Whipple shields based on machine learning method by Chenyang Wu, Xiangbiao Liao, Lvtan Chen, Xiaowei Chen

“…The results demonstrate that the training and prediction accuracies using the Random Forest (RF) algorithm significantly surpass those using Artificial Neural Networks (ANNs) and Support Vector Machine (SVM). …”
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Does machine learning outperform logistic regression in predicting individual tree mortality? by Aitor Vázquez-Veloso, Astor Toraño Caicoya, Felipe Bravo, Peter Biber, Enno Uhl, Hans Pretzsch

“…Here, we compare the performance of five different ML algorithms (Decision Trees, Random Forest, Naive Bayes, K-Nearest Neighbour, and Support Vector Machine) against Logistic binomial Regression in individual tree mortality classification under 40 different case studies and a cross-validation case study. …”
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Advanced Methods for Identifying Counterfeit Currency: Using Deep Learning and Machine Learning by Nama'a Hamed, Fadwa Al Azzo

“…In this work, we offer a thorough investigation of sophisticated methods for detecting counterfeit money that make use of deep learning and machine learning approaches. Using machine learning algorithms like Random Forest, Decision Tree Classifier, XGBoost, CatBoost, and Support Vector Machine (SVM) in addition to deep learning techniques like Convolutional Neural Networks (CNNs), VGG16, MobileNetV2, and InceptionV3, we examine the security characteristics of Iraqi dinar banknotes and build robust models. …”
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Cyberattack detection on SWaT plant industrial control systems using machine learning by Shadi Jaradat, Md Mostafizur Komol, Mohammed Elhenawy, Naipeng Dong

“…The research employs a Long Short-Term Memory (LSTM) network alongside traditional machine learning algorithms like Random Forest (R.F.), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN) to classify cyberattacks. …”
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Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques by Oumoulylte Mariame, El Allaoui Ahmad, Farhaoui Yousef, Boughrous Ali Ait

“…We assess the effectiveness of algorithms such as Random Forest, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, and Gradient Boosting. …”
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Analysis of signals from air conditioner compressors with ordinal patterns and machine learning by Keila Barbosa, Alejandro C Frery, George DC Cavalcanti

“…We analyze the expressiveness of the Ordinal Patterns and identify those variables that best differentiate the two machines. Furthermore, we incorporate machine learning algorithms, such as Artificial Neural Networks, Support Vector Machines, and Decision Trees, to evaluate and validate the effectiveness of Ordinal Patterns as discriminative features. …”
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Parametric optimization of the slot waveguide characteristics using a machine-learning approach by Yadvendra Singh, Suraj Jena, Harish Subbaraman

“…Three different ML techniques, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF), were tested to compute performance parameters for the slot waveguide. …”
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Machine learning techniques for predictive modelling in geotechnical engineering: a succinct review by Shrikant M. Harle, Rajan L. Wankhade

“…Techniques such as aRVM, Random Forest (RF), PSO-ANN, Support Vector Machines (SVM), and numerical methods are discussed for their effectiveness in predicting settlement, building responses, and safety risks. …”
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The Use of Selected Machine Learning Methods in Dairy Cattle Farming: A Review by Wilhelm Grzesiak, Daniel Zaborski, Marcin Pluciński, Magdalena Jędrzejczak-Silicka, Renata Pilarczyk, Piotr Sablik

“…A description of ML methods (linear and logistic regression, classification and regression trees, chi-squared automatic interaction detection, random forest, AdaBoost, support vector machines, k-nearest neighbors, naive Bayes classifier, multivariate adaptive regression splines, artificial neural networks, including deep neural networks and convolutional neural networks, as well as Gaussian mixture models and cluster analysis), with some examples of their application in various aspects of dairy cattle breeding and husbandry, is provided. …”
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A framework based on mechanistic modelling and machine learning for soil moisture estimation by Sabri Kanzari, Sana Ben Mariem, Samir Ghannem, Safouane Mouelhi, Hiba Ghazouani, Bechir Ben Nouna

“…These values were used as inputs to the Hydrus 1D model to generate soil water content profiles over a 27-year period. 109 profiles were calculated using machine learning algorithms (regression trees; random forest; support vector machine) to predict soil moisture content from daily values of rainfall and evaporation. …”
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Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models by Michael Contreras-Ramírez, Jhonathan Sora-Cardenas, Claudia Colorado-Salamanca, Clemencia Ovalle-Bracho, Daniel R. Suárez

“…The model with the best results was a Support Vector Machine (SVM), achieving a sensitivity of 91.87% and a specificity of 89.21% at the crop level. …”
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Prediction of tea leaf characteristics using spectral data and machine learning techniques by Sum Tateh, Suyog Balasaheb Khose, Damodhara Rao Mailapalli, Chandranath Chatterjee, Narendra Singh Raghuwanshi

“…Random forest and eXtreme gradient boost performed well for predicting leaf chlorophyll and sugar contents, respectively. Support vector machine and Decision tree classifiers accurately identified infested leaves based on LCC and sugar contents, while logistic regression classifier classifies disease well using vegetation indices. …”
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Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review by Mohammad Faiz, Bakkanarappa Gari Mounika, Mohd Akbar, Swapnita Srivastava

“…This analysis covers both machine learning models (ML), such as support vector machine (SVM) & random forest (RF), as well as deep learning algorithms (DL), including convolution neural network (CNN), AlexNet, ResNet50, ShuffleNet, MobileNet, RNN. …”
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Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing by Katarzyna Antosz, Lucia Knapčíková, Jozef Husár

“…A variety of classification algorithms, including neural networks (NNs), bagged trees (BT), and support vector machines (SVMs), were employed to efficiently analyse and predict defects. …”
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