Electromyogram based muscle stress estimation of Gastrocnemius medialis using Machine learning algorithms by Amol Kumar, Manoj Duhan, Poonam Sheoran

“…Moreover, in the present study,  decision trees (DT), random forests (RF) and support vector machines (SVM) have been used as classifiers. …”
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Time-frequency image and high-order spectrum characteristics based radar signal recognition by Shitong LI, Daying QUAN, Zeyu TANG, Yun CHEN, Xiaofeng WANG, Xiaoping JIN

“…Aiming at improving the accuracy of radar signal recognition under a low signal-to-noise ratio, a radar signal recognition algorithm based both on time-frequency image and high-order spectrum feature was proposed.Firstly, the time-frequency image was obtained by Choi-Williams distribution (CWD) transform, based on which the time-frequency image was preprocessed and the texture features were extracted by gray level co-occurrence matrix (GLCM) in sequence.Meanwhile, the symmetrical holder coefficient was used to extract the high-order spectral features of the signal.Then, the texture features and high-order spectrum features were form a new set of joint feature vectors.Finally, with the proposed feature vector the classification and recognition of radar signals were implemented by a support vector machine.The algorithm was verified on the data set with eight typical radar signals.Experimental results show that the recognition accuracy of different radar signals can achieve higher than 90% when the signal-to-noise ratio is -8 dB.…”
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Time-frequency image and high-order spectrum characteristics based radar signal recognition by Shitong LI, Daying QUAN, Zeyu TANG, Yun CHEN, Xiaofeng WANG, Xiaoping JIN

“…Aiming at improving the accuracy of radar signal recognition under a low signal-to-noise ratio, a radar signal recognition algorithm based both on time-frequency image and high-order spectrum feature was proposed.Firstly, the time-frequency image was obtained by Choi-Williams distribution (CWD) transform, based on which the time-frequency image was preprocessed and the texture features were extracted by gray level co-occurrence matrix (GLCM) in sequence.Meanwhile, the symmetrical holder coefficient was used to extract the high-order spectral features of the signal.Then, the texture features and high-order spectrum features were form a new set of joint feature vectors.Finally, with the proposed feature vector the classification and recognition of radar signals were implemented by a support vector machine.The algorithm was verified on the data set with eight typical radar signals.Experimental results show that the recognition accuracy of different radar signals can achieve higher than 90% when the signal-to-noise ratio is -8 dB.…”
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Optimization method improvement for nonlinear constrained single objective system without mathematical models by HOU Gong-yu, XU Zhe-dong, LIU Xin, NIU Xiao-tong, WANG Qing-le

“…Therefore, to improve the optimization accuracy of nonlinear constrained single objective systems that are without accurate mathematical models while considering the cost of obtaining samples, a new method based on a combination of support vector machine and immune particle swarm optimization algorithm (SVM-IPSO) is proposed. …”
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Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision by Wanna Fu, Zhen Chen, Qian Cheng, Yafeng Li, Weiguang Zhai, Fan Ding, Xiaohui Kuang, Deshan Chen, Fuyi Duan

“…The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and k-Nearest Neighbors (KNN). …”
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Risk Warning Method of Corn Cross-border Supply Chain Based on DBN-MFSVM by GE Zhen-lin

“…Finally, the extracted high-dimensional features were input into the multi-class fuzzy support vector machine model for training to realize the risk classification early warning of corn cross-border supply chain. …”
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Estimation of soil free Iron content using spectral reflectance and machine learning algorithms by Wanzhu Ma, Hongkui Zhou, Hao Hu, Zhiqing Zhuo, Kangying Zhu, Guangzhi Zhang

“…The full spectrum, correlated spectrum, and principal components from principal component analysis (PCA) were considered as model variable selection. We used machine learning algorithms, such as partial least squares (PLS), support vector machine (SVM), random forest (RF), and deep neural network (DNN) algorithms for model construction. …”
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Application of machine learning algorithm for prediction of abortion among reproductive age women in Ethiopia by Angwach Abrham Asnake, Alemayehu Kasu Gebrehana, Hiwot Altaye Asebe, Beminate Lemma Seifu, Bezawit Melak Fente, Meklit Melaku Bezie, Mamaru Melkam, Sintayehu Simie Tsega, Yohannes Mekuria Negussie, Zufan Alamrie Asmare

“…In the current study, 7 machine learning algorithm (i.e. logistic regression, decision tree classifier, random forest classifier, support vector machine, K neighbor classifier, XGBoost, and Nave bayes) were applied. …”
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Integrated Forecast of Monthly Saltwater Intrusion at Modaomen Waterway Based on Multiple Models by LU Pengyu, LIN Kairong, YANG Yugui, YUAN Fei, HE Yong

“…This paper builds the regression model by Random Forest (RF) algorithm, Support Vector Machine (SVM) and Elman Neural Network (ENN), and conducts a monthly integrated forecast through Bayesian Model Averaging (BMA) method. …”
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Transfer learning based feature selection for feedforward neural network for speech emotion classifier by D. V. Krasnoproshin, M. I. Vashkevich

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APPLICATION OF IMPROVED GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS by HU Xuan, LI Chun, YE KeHua, ZHANG WanFu

“…The improved gray wolf algorithm is used to optimize the support vector machine to diagnose the gearbox fault feature set after dimensionality reduction. …”
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SVR Data-Driven Optimization of Generator Leading Phase Operation Limit by Dengfeng LI, Mincai YANG, Yuming LIU, Ruilin XU, Xia YU, Zhaojiong LI

“…In view of the difficulty in modeling the mechanism caused by the complex and strong coupling nonlinearities between the multiple variables in the limiting conditions of leading phase operation, a novel method is proposed in this paper to optimize the leading phase operation limit of generator based on data-driven support vector machine regression (SVR). The limit calculation of generator leading phase operation is converted to the minimization of reactive power subject to the multiple constraints of leading phase. …”
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Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer’s disease diagnosis by Qian Zhang, Jinhua Sheng, Rougang Zhou, Qiao Zhang, Binbing Wang, Rong Zhang

“…In this paper, an enhanced Particle Swarm Optimization (PSO) algorithm, which integrates opposition-based Latin squares sampling initialization (OL) with dynamic inertia weights and learning factors (D), termed OLDPSO, is proposed to improve feature selection and classification within a Support Vector Machine (SVM) model for AD diagnosis using magnetic resonance imaging (MRI) data. …”
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Integrated approach to land degradation risk assessment in arid and semi-arid Ecosystems: Applying SVM and eDPSIR/ANP methods by Ehsan Moradi, Hassan Khosravi, Pouyan Dehghan Rahimabadi, Bahram Choubin, Zlatica Muchová

“…To predict LD hazard, the Support Vector Machine (SVM) algorithm was used with 179 LD locations and twelve variables, including land use, lithology, rainfall, temperature, distance to the stream, elevation, aspect, slope, curvature, distance to the road, Normalized Difference Moisture Index (NDMI), and population density. …”
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Detecting cognitive motor dissociation by functional near-infrared spectroscopy by Yan Wang, Yan Wang, Yan Wang, Wentao Zeng, Wentao Zeng, Wentao Zeng, Leyao Zou, Leyao Zou, Leyao Zou, Qijun Wang, Bingkai Ren, Bingkai Ren, Bingkai Ren, Qi Xiong, Yang Bai, Yang Bai, Yang Bai, Zhen Feng, Zhen Feng, Zhen Feng

“…Seven features of hemodynamic responses were extracted during the task and the rest conditions. The support vector machine combined with genetic algorithm was employed to classify and predict the brain's response to spoken commands and to identify CMD patients among prolonged DOC individuals.ResultsWe identified seven CMD patients using fNIRS, of whom four were in VS/UWS and three were in MCS–. …”
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Simulating Future Land Use/Land Cover of Tigris River Basin Assuming the Continuation of the Conditions During 2018 and 2023. by Abolfazl Ghanbari, Ayat Khaleel-Gharibawi, Hala Abdulkareem-Rubaiee, Mehrdad Jeihouni

“…Based on this, the present study developed multi-temporal (2003-2023) LULC maps for TRB through classifying Landsat images using the random forest (RF) and support vector machine (SVM) algorithms, and simulating future LULC states (2028) employing the cellular automata (CA)-Markov model. …”
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Enhancing heart disease prediction accuracy by comparing classification models employing varied feature selection techniques by Balliu Lorena, Zanaj Blerina, Basha Gledis, Zanaj Elma, Meçe Elinda Kajo

“…It includes the analysis of different algorithms such as Decision Tree, Logistic Regression, Support Vector Machine, Random Forest and hybrid models. …”
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Machine learning and microfluidic integration for oocyte quality prediction by Hassan Saffari, Davood Fathi, Peyman Palay, Hamid Gourabi, Rouhollah Fathi

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Development of a Forecasting Framework Based on Advanced Machine Learning Algorithms for Greenhouse Gas Emissions by Seval Ene Yalçın

“…This paper presents a forecasting framework for greenhouse gas emissions based on advanced machine learning algorithms: multivariable linear regression, random forest, k-nearest neighbor, extreme gradient boosting, support vector, and multilayer perceptron regression algorithms. …”
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