Predicting the Tensile Strength of Plant Leaves Based on GA-SVM by Wei Chang, Meihong Liu, Yayu Huang, Junjie Lei, Kai Wu

“…Subsequently, correlation analysis is performed on the characteristic indicators, followed by dimensionality reduction using principal component analysis (PCA). A genetic algorithm (GA) is then applied to optimize the structural parameters of the support vector machine (SVM), establishing a GA-SVM-based predictive model for the tensile strength of plant leaves. …”
Get full text

Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier by Ashutosh Kumar Singh, SVN Sreenivasu, U.S.B. K. Mahalaxmi, Himanshu Sharma, Dinesh D. Patil, Evans Asenso

“…These features are classified by a Bayesian optimized support vector machine classifier and the results attained in terms of precision, sensitivity, f-score, and accuracy. …”
Get full text

Estimation of drought-induced forest decline of Scots pine in the Navarrese Pyrenees (Spain) by Marina Rodes-Blanco, Paloma Ruiz-Benito, Miguel A. Zavala, Inmaculada Aguado, Mariano García

“…We parameterised a canopy damage model using 20 m grids from Sentinel-2 imagery and a Support Vector Machine algorithm. The model was calibrated with data from a site experiencing severe drought-induced forest decline and a healthy site. …”
Get full text

Grading evaluation method for inter-turn short circuit of permanent magnet traction motor based on deep Gaussian processes by DAI Jisheng, HU Dean, XU Hailong, ZHU Wenlong, LIAO Qishu

“…Compared with classification methods such as support vector machine (SVM) and back-oropagation neural network (BPN), it exhibits high accuracy and suitability for engineering practical environments with variable operating conditions and small samples, addressing industry challenges in early fault detection and severity evaluation of inter-turn short circuits in permanent magnet traction motors.…”
Get full text

Assessing the impact of land use and land cover change on soil erosion potential in Gimbora river catchment of Gubalafto Woreda, Amhara Region, Ethiopia by Alemu Ale, Abebe Mohammed Ali, Semaigzer Ayalew, Nurhussen Ahmed

“…To do this, Landsat images of 1994, 2009 and 2024 were used. The Support Vector Machine (SVM) algorithm was applied to classify the images into different LULC types. …”
Get full text

Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study by Jorge Marques Silva, Susana M. Vieira, Duarte Valério, João C. C. Henriques, Paul D. Sclavounos

“…This work intends to exploit the short‐term wave forecasting potential on an oscillating water column equipped with the innovative biradial turbine. A Least Squares Support Vector Machine (LS‐SVM) algorithm was developed to predict the air chamber pressure and compare it to the real signal. …”
Get full text

Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques by Mustafa Basthikodi, M. Chaithrashree, B. M. Ahamed Shafeeq, Ananth Prabhu Gurpur

“…This research addresses the challenge of multiclass categorization by employing Support Vector Machine (SVM) as the core classification algorithm and analyzing its performance in conjunction with feature extraction techniques such as Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP), as well as the dimensionality reduction technique, Principal Component Analysis (PCA). …”
Get full text

Monitoring Construction Workers’ Mental Workload Due to Heat Exposure Using Heart Rate Variability and Eye Movement: A Study on Pipe Workers by Shiyi He, Dongsheng Qi, Enkai Guo, Liyun Wang, Yewei Ouyang, Lan Zheng

“…Their HRV and eye movement features were recorded as the inputs of training models classifying mental workload between the two thermal conditions, using supervised machine learning algorithms, including Support Vector Machines (SVM), KNearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF). …”
Get full text

Potential analysis and energy prediction of photovoltaic power plants using satellite-based remote sensing and artificial intelligence techniques by Hadis Ghaedrahmati, Saeed Talebi, Amirmohammad Moradi, Aref Eskandari, Parviz Parvin, Mohammadreza Aghaei, Mohammadreza Aghaei

“…Satellite data from global sources is used to analyze PV energy production based on specific geographic coordinates. Several machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), Decision Tree (DT), and XGBoost, are applied to predict PV energy production from meteorological variables. …”
Get full text

Predicting the Response of Patients Treated with 177Lu-DOTATATE Using Single-photon Emission Computed Tomography-Computed Tomography Image-based Radiomics and Clinical Features by Baharak Behmanesh, Akbar Abdi-Saray, Mohammad Reza Deevband, Mahasti Amoui, Hamid R. Haghighatkhah, Ahmad Shalbaf

“…These selected features were modeled using a decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) classifiers to predict the treatment response in patients. …”
Get full text

Discrimination of Free-Range and Caged Eggs by Chemometrics Analysis of the Elemental Profiles of Eggshell by Shunping Xie, Chengying Hai, Song He, Huanhuan Lu, Lu Xu, Haiyan Fu

“…Outlier diagnosis is performed by robust Stahel–Donoho estimation (SDE) and the Kennard and Stone (K-S) algorithm for training and test set partitioning. Partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) were used for classification of the two types of eggs. …”
Get full text

Integrated bulk and single cell sequencing with experimental validation identifies type 2 diabetes biomarkers by Yan Cao, Liqi Chen, Yurui Zhuang, Yuzhe Shi, Haoru Dong, Ziyi Guo, Jinwei Li

“…Through the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), SLC2A2 emerged as the most likely candidate biomarker of T2D. …”
Get full text

Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model by Gang Li, Ruijiang Ran, Jun Fang, Hao Peng, Shengmin Wang

“…The proposed model integrates a rough set (RS), the sparrow search algorithm (SSA), and the least squares support vector machine (LSSVM). …”
Get full text

Enhanced Alzheimer’s Disease Prediction Through Integration of Protein-Protein Interaction Data and Meta-Learning by Hansa J. Thattil, M. N. Arunkumar, Francis Antony

“…Our model achieved an accuracy of 96% and with precision, recall, and F1 scores also outperforming individual models like random forest, support vector machine, AdaBoost, and CatBoost. These results demonstrate the effectiveness of the proposed meta-learning model in predicting protein-disease associations for Alzheimer’s disease.…”
Get full text

Research on computer multi feature fusion SVM model based on remote sensing image recognition and low energy system by Yangming Wu, Hao Wu, Xin Tang, Jianwei Lv, Rufei Zhang

“…Therefore, this paper aims to explore a low-energy multi-feature fusion support vector machine (SVM) model based on remote sensing image recognition. …”
Get full text

Detection of DRFM Deception Jamming Based on Diagonal Integral Bispectrum by Dianxing Sun, Ao Li, Hao Ding, Jifeng Wei

“…A joint detection framework combining the DIBRP-DIBAE dual-feature space and a polynomial kernel support vector machine (SVM) is constructed to achieve active deception jamming detection. …”
Get full text

Prediction Approaches for Smart Cultivation: A Comparative Study by Amitabha Chakrabarty, Nafees Mansoor, Muhammad Irfan Uddin, Mosleh Hmoud Al-adaileh, Nizar Alsharif, Fawaz Waselallah Alsaade

“…It is observed that the neural network outperforms the other methods by maintaining an average prediction accuracy of 96.06% for six different crops. Other contemporary machine learning algorithms, namely, support vector machine, random forest, and logistic regression, have average prediction accuracy of around 68.9%, 91.2%, and 62.39%, respectively.…”
Get full text

A Novel Approach of Optimal Signal Streaming Analysis Implicated Supervised Feedforward Neural Networks by Farhan Ali, He Yigang

“…In addition, the signals were subjected to train using a real-time simulator, employing feedforward neural network and support vector machine (SVM) to validate the proposed methodology. …”
Get full text

TradeWise: Towards Context-Aware Stock Market Predictions with Sentiment and Political Insights by Andreas Marpaung, David Masterson

“…This enriched dataset is employed to train four different machine learning algorithms: a Hybrid, a Random Forest Model (RFM), a Support Vector Machine (SVM), and a K-Nearest Neighbors (KNN) model. …”
Get full text

SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks by Yao Wang, Zhongzhao Zhang, Lin Ma, Jiamei Chen

“…In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. …”
Get full text