Hybrid machine learning algorithms accurately predict marine ecological communities by Luciana Erika Yaginuma, Luciana Erika Yaginuma, Fabiane Gallucci, Danilo Cândido Vieira, Paula Foltran Gheller, Simone Brito de Jesus, Thais Navajas Corbisier, Gustavo Fonseca

“…In the supervised stage, these associations were modeled as a function of the environmental features by five supervised algorithms (Support Vector Machine, Random Forest, k-Nearest Neighbors, Naive Bayes, and Stochastic Gradient Boosting), using 80% of the samples for training, leaving the remaining for testing. …”
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Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China by Lei-Lei Liu, Aasim Danish, Xiao-Mi Wang, Wen-Qing Zhu

“…Initially, we employ an ensemble stacking technique that combines the strengths of three machine learning classifiers. This combination leverages the support vector classifier (SVC) as the key meta-classifier to optimize and refine predictions. …”
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Genomic selection in pig breeding: comparative analysis of machine learning algorithms by Ruilin Su, Jingbo Lv, Yahui Xue, Sheng Jiang, Lei Zhou, Li Jiang, Junyan Tan, Zhencai Shen, Ping Zhong, Jianfeng Liu

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Machine Learning Algorithms for Nondestructive Sensing of Moisture Content in Grain and Seed by Arthur P. LeBlanc, Samir Trabelsi, Khaled Rasheed, John A. Miller

“…Performance of this model is investigated and compared with models trained on an individual grain or seed by using different algorithms, including artificial neural network (NN), support vector regression (SVR), ElasticNet, among other algorithms. …”
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FEATURE-BASED IMPLEMENTATION OF MACHINE LEARNING ALGORITHMS FOR CARDIOVASCULAR DISEASE PREDICTION by H. Singh, R. Tripathy, P. Kumar Sarangi, U. Giri, S. Kumar Mohapatra, N. Rameshbhai Amin

“…This study employs various machine learning algorithms, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Random Forest, Decision Tree, and Naïve Bayes, to assess their accuracy in predicting cardiovascular disease and related conditions This paper makes use of the UCI repository dataset for coaching and testing including some basic parameters such as age and sex. …”
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Live Weight Prediction in Norduz Sheep Using Machine Learning Algorithms by Cihan Çakmakçı

“…The objective of this study was to compare predictive performances of four machine learning (ML) models: Support Vector Machines with Radial Basis Function Kernel (SVMR), Classification and Regression Trees (CART), Random Forest (RF) and Model Average Neural Networks (MANN) to predict live weight from morphological measurements of Norduz sheep (n=93). …”
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Water Level Classification for Detect Flood Disaster Status Using KNN and SVM by Jiwa Akbar, Muchtar Ali Setyo Yudono

“…In this context, the flood disaster classification system uses water surface elevation data from the Water Resources Agency to predict the likelihood of floods using the K-Nearest Neighbors (KNN) Algorithm. This research aims to classify flood status based on water surface elevation using the K-Nearest Neighbors and Support Vector Machine(SVM) methods in the Ciliwung River. …”
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Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms by Nizom Farmonov, Khilola Amankulova, Jozsef Szatmari, Alireza Sharifi, Dariush Abbasi-Moghadam, Seyed Mahdi Mirhoseini Nejad, Laszlo Mucsi

“…A Wavelet-attention convolutional neural network (WA-CNN), random forest and support vector machine (SVM) algorithms were utilized to automatically map the crops over the agricultural lands. …”
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Optimal control strategy based on artificial intelligence applied to a continuous dark fermentation reactor for energy recovery from organic wastes by Kelly Joel Gurubel Tun, Elizabeth León-Becerril, Octavio García-Depraect

“…This strategy has demonstrated to be a viable algorithm for real-time control applications. First, experimental data from continuous dark fermentation are modeled using support vector machine (SVM) algorithm for response prediction and then, optimization algorithms are employed to identify the key parameters that enhance H2 production. …”
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A Methodology for Situation Assessing of Space-Based Information Networks by Sai Xu, Jun Liu, Jiawei Tang, Xiangjun Liu, Zhi Li

“…The proposed method first applies principal component analysis for dimensionality reduction, followed by pre-labeling situational factor data using an improved K-means clustering algorithm. The on-orbit assessment of individual satellites is then performed using a particle swarm optimization-support vector machine algorithm. …”
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Parameter Calculation of Steam Pipeline Based on Hybrid Modeling by Hongwei CHEN, Zherui MA, Chunwang LV, Yuqiang LIU, Wei ZHANG, Ruikun WANG

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Rolling Bearing Fault Diagnosis Method Based on Fusion of CNN and CSSVM by LI Yunfeng, LAN Xiaosheng, SHEN Hongchang, XU Tongle

“…Finally, the extracted feature vectors were input into the support vector machine classification layer with optimized parameters by cuckoo search algorithm to realize the fault classification of rolling bearings. …”
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In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery by Hui Li, Liping Di, Chen Zhang, Li Lin, Liying Guo, Ruopu Li, Haoteng Zhao

“…They integrated as training samples to construct a one-class support vector machine (OCSVM) classifier, generating Jun.…”
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Two-level feature selection method based on SVM for intrusion detection by Xiao-nian WU, Xiao-jin PENG, Yu-yang YANG, Kun FANG

“…To select optimized features for intrusion detection，a two-level feature selection method based on support vector machine was proposed.This method set an evaluation index named feature evaluation value for feature selection，which was the ratio of the detection rate and false alarm rate.Firstly，this method filtrated noise and irrelevant features to reduce the feature dimension respectively by Fisher score and information gain in the filtration mode.Then，a crossing feature subset was obtained based on the above two filtered feature sets.And combining support vector machine，the sequential backward selection algorithm in the wrapper mode was used to select the optimal feature subset from the crossing feature subset.The simulation test results show that，the better classification performance is obtained according to the selected optimal feature subset，and the modeling time and testing time of the system are reduced effectively.…”
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An Exploratory Application of Machine Learning Algorithms in Estimating Net Salaries in Romania by Adriana Aiftincăi

“…Its practical utility lies in its potential to serve as a forecasting tool for wage policies, a support for employers in budget planning, and a foundation for extending the analysis to regional or sectoral levels. …”
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Improvement of metaphor understanding via a cognitive linguistic model based on hierarchical classification and artificial intelligence SVM by Dongmei Zhu

“…It proposes a metaphor recognition algorithm that combines a Convolutional Neural Network (CNN) with a Support Vector Machine (SVM). …”
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Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification by Anas Bilal, Muhammad Shafiq, Waeal J. Obidallah, Yousef A. Alduraywish, Alishba Tahir, Haixia Long

“…The classification process, QCOA optimizes the Support Vector Machine (SVM) parameters and performs feature selection. …”
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Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype by Linlin Sun, Xiubo Chen, Zixu Chen, Linlong Jing, Jinxing Wang, Xinpeng Cao, Shenghui Fu, Yuanmao Jiang, Hongjian Zhang

“…A particle swarm optimization–support vector machine (PSO-SVM) model was then developed to predict the crushing force based on fertilizer shape features. …”
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Adaptive Track Association Method Based on Automatic Feature Extraction by Zhaoyue Zhang, Guanting Dong, Chenghao Huang

“…Fuzzy mathematical techniques are subsequently applied to extract discriminative features from both definite association and nonassociation sets, followed by training a support vector machine (SVM) model. Finally, the SVM performs classification and association of trajectories in the fuzzy association group. …”
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Functional Connectivity Changes in Primary Motor Cortex Subregions of Patients With Obstructive Sleep Apnea by Lifeng Li, Qimeng Shi, Bowen Fang, Yuting Liu, Xiang Liu, Yongqiang Shu, Yingke Deng, Yumeng Liu, Haijun Li, Junjie Zhou, Dechang Peng

“…Additionally, we employed three machine learning algorithms—support vector machine (SVM), random forest (RF), and logistic regression (LR)—to distinguish patients with OSA from HC based on FC features. …”
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