Remaining useful life prediction of Lithium-ion batteries based on data preprocessing and CNN-LSSVR algorithm by Ti Dong, Yiming Sun, Jia Liu, Qiang Gao, Chunrong Zhao, Wenjiong Cao

“…Targeting the thorny issues of limited battery historical cycle data and the impact of uncertainty in the data collection process in practical applications, this study proposes a Remaining useful life (RUL) prediction method for lithium-ion batteries based on the data preprocessing and the joint convolutional neural network (CNN)-least squares support vector regression (LSSVR) algorithm. Based on the performance degradation characteristics of the battery, the method proposes new RUL assessment indexes and corresponding health factors. …”
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Hydrological and chemical characteristics of karst groundwater and carbon flux estimation by Qing Wang, Yin Zhang, Ningtao Wang, Mengru Li, Yu Zhang

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An Algorithm for Sound Velocity Error Correction Using GA-SVR Considering the Distortion Characteristics of Seabed Topography Measured by Multibeam Sonar Mounted on Autonomous Unde... by Xiaohan Yu, Junsen Wang, Yang Cui, Shaohua Jin, Gang Bian, Na Chen

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Research on COP Prediction Model of Chiller Based on PSO-SVR by Zhou Xuan, Cai Panpan, Lian Sizhen, Yan Junwei

“…Since the difficulty of building mechanism model and the structure of COP model of chiller is complex, greatly affected by operating parameter, a COP prediction model of chiller is proposed based on Support Vector Regression, and the parameters are optimized by Particle Swarm Optimization algorithm. …”
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Fast Prediction for Roll Motion of a Damaged Ship Based on SVR by LIU Han, SU Yan, ZHANG Guoqiang

“…Additionly, the rolling motion response database for the damaged ship is constructed. The support vector regression (SVR) algorithm is used to model the rolling motion database for identification, exploring the relationship between the operating condition factors and coefficients in the equation of roll motion. …”
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RELIABILITY ANALYSIS OF REACTION FORCE DEVELOPED IN THE LUBRICATED REVOLUTE JOINT FOR A SLIDER-CRANK SYSTEM INCLUDING JOINT WITH CLEARANCE AND LUBRICATION by ZHAO Kuan, XUE He, CHEN JianJun, QIAO XinZhou

“…The system dynamic model was set up based on Newton-Euler method,The prediction accurary of Support Vector Machine Regression is difficult to reach the target accurary because the selection of parameters isn’t accurate. …”
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Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India by Srikanth Bhoopathi, Nitish Kumar, Somesh, Manali Pal

“…Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. …”
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An UWB LNA Design with PSO Using Support Vector Microstrip Line Model by Salih Demirel, Filiz Gunes, A. Kenan Keskin

“…A rigorous and novel design procedure is constituted for an ultra-wideband (UWB) low noise amplifier (LNA) by exploiting the 3D electromagnetic simulator based support vector regression machine (SVRM) microstrip line model. …”
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Comparative analysis of regression algorithms for drug response prediction using GDSC dataset by Soojung Ha, Juho Park, Kyuri Jo

“…Results In the experiments, Support Vector Regression algorithm and gene features selected with LINC L1000 dataset showed the best performance in terms of accuracy and execution time. …”
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Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment by Heru Agus Santoso, Nur Setiawati Dewi, Susilo, Arga Dwi Pambudi, Hanif Pandu Suhito, Iman Dehzangi

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A Model for Fat Content Detection in Walnuts Based on Near-Infrared Spectroscopy by Langqin Luo, Honghua Zhang, Yu Wang, Jianliang Zhang, Rui Zhang, Shan Gao, Yuanyong Dian, Zijin Bai, Chunhui Feng, Ze Zhang

“…After first optimizing the initial spectrum data using five preprocessing methods, we established separate prediction models for walnut kernel fat content based on either a back propagation neural network or a support vector regression (SVR) algorithm. The results show that the correction set and validation set coefficients of determination of the walnut kernel fat content prediction model using the back propagation neural network algorithm were 0.86 and 0.89, respectively, with root mean square errors of 1.56 and 1.58, and an RPD value of 2.57; the coefficients of determination for the calibration and validation sets of SVR were 0.90 and 0.83, respectively, with root mean square errors of 1.76 and 1.70, respectively, and an RPD value of 1.70. …”
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Explainable Machine Learning in the Prediction of Depression by Christina Mimikou, Christos Kokkotis, Dimitrios Tsiptsios, Konstantinos Tsamakis, Stella Savvidou, Lillian Modig, Foteini Christidi, Antonia Kaltsatou, Triantafyllos Doskas, Christoph Mueller, Aspasia Serdari, Kostas Anagnostopoulos, Gregory Tripsianis

“…The study employed four machine learning (ML) methods to assess depression: logistic regression (LR), support vector machine (SVM), XGBoost, and neural networks (NNs). …”
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Bearing Response Prediction in Hydrothermal Aged Carbon Fiber Reinforced Epoxy Composite Joints Using Machine Learning Techniques by Mohit Kumar, Govind Vashishtha, Babita Dhiman, Sumika Chauhan

“…In this research, an innovative support vector regression approach is present that leverages machine learning algorithms to forecast the bearing response of CFREC joints after undergoing hydrothermal aging. …”
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Machine learning approach for water quality predictions based on multispectral satellite imageries by Vicky Anand, Bakimchandra Oinam, Silke Wieprecht

“…The main objective of this study to retrieve and map the water quality parameters from Sentinel-2 and ResourceSat-2 [Linear Imaging Self-Scanning Sensor (LISS)–IV] multi-spectral satellite data, using Support Vector Machines (SVM), Random Forests (RF), and Multi-Linear regression (MLR) models. …”
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COMPARATIVE STUDY OF SURVIVAL SUPPORT VECTOR MACHINE AND RANDOM SURVIVAL FOREST IN SURVIVAL DATA by Ni Gusti Ayu Putu Puteri Suantari, Anwar Fitrianto, Bagus Sartono

“…Random Survival Forest is tree based method that using boostrapping algorithm, and Survival Support Vector Machine using hybrid approaches between regression and ranking constrain. …”
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Hyperspectral estimation of soil organic carbon content in the west lakeside oasis of Bosten Lake based on successive projection algorithm by NIU Fangpeng, LI Xinguo, MAMATTURSUN•Eziz, ZHAO Hui

“…Taking the west lakeside oasis of Bosten Lake as the study area, using the measured soil organic carbon content and hyperspectral data, the successive projection algorithm (SPA) was used to filter the characteristic variables from the full-band spectral data, and then the full-band and characteristic bands were used to construct partial least square regression (PLSR) and support vector machine (SVM) models to estimate soil organic carbon content. …”
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Optimization of urban green space in Wuhan based on machine learning algorithm from the perspective of healthy city by Xuechun Zhou, Xiaofei Zou, Wenzuixiong Xiong

“…Adopting a healthy city development perspective, the research aims to assess the impact of green space optimization on urban health, economic performance, and social structure.MethodsA multivariable model was constructed using random forest and Support Vector Machine (SVM) algorithms to evaluate the influence of key indicators on urban green space. …”
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Prediction of Rheological Parameters of Polymers by Machine Learning Methods by T. N. Kondratieva, A. S. Chepurnenko

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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

“…The time-invariant phenology features were calculated from composite Sentinel-2 normalized difference vegetation index (NDVI) series using linear cosine regression (LCR). They integrated as training samples to construct a one-class support vector machine (OCSVM) classifier, generating Jun.…”
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Short-Term Load Forecasting Based on Feature Selection and Combination Model by Yusong XU, Shanhua ZOU, Xianling LU

“…Bayesian information criterion is used to get the dimension of the optimal feature vector. And then, three different simple-kernel based support vector regression models are built using three kernel functions respectively and complete prediction. …”
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