Optimized wavelength selection for eggplant seed vitality classification using information acquisition techniques by Bing Yang, Xuyang Liu, Dongfang Zhang, Dongfang Zhang, Xiaofei Fan, Xiaofei Fan, Bo Peng, Jun Zhang, Jun Zhang

“…Seed vigor classification models were developed using Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Machine (SVM).The optimal classification accuracies achieved were 90.0% for ELM, 91.45% for RF, and 90.5% for SVM. …”
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Height of Hydraulic Fracture Zone Based on PSO_LSSVM Model by Hebin Zhang, Tingting Wang, Bin Wu, Haijun Feng

“…At the same time, this study develops a particle swarm optimization algorithm based on adaptive inertia weight and a least squares support vector machine model to achieve height prediction of water conducting fracture zones. …”
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Elevating Accuracy: Enhanced Feature Selection Methods for Type 2 Diabetes Prediction by Ghazaleh Kakavand Teimoory, MohammadReza Keyvanpour

“…Previous research has utilized various algorithms like Naïve Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and decision trees for patient classification. …”
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Surrounding Object Material Detection and Identification Method for Robots Based on Ultrasonic Echo Signals by Bo Zhu, Tao Geng, Gedong Jiang, Zheng Guan, Yang Li, Xialun Yun

“…This method primarily adopts the 16-dimensional feature vector extracted from intrinsic mode functions that we gain from empirical mode decomposition as inputs of machine learning algorithms to recognize different materials, and we use K-nearest neighbor, decision tree, and support vector machine algorithms on the feature vector set to decide the best classifier, and its acoustic theoretical model is established additionally. …”
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Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model by Yang Hui, Xuesong Mei, Gedong Jiang, Tao Tao, Changyu Pei, Ziwei Ma

“…In this study, vibration signals collected during the milling process are analyzed through the time domain, frequency domain, and time-frequency domain to extract signal features. The support vector machine recursive feature elimination (SVM-RFE) algorithm is used to select the main features which are most relevant to tool wear states. …”
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A stacked ensemble model for traffic conflict prediction using emerging sensor data by Bowen Cai, Léah Camarcat, Nicolette Formosa, Mohammed Quddus

“…This model integrates a Random Forest (RF), three-layer Deep Neural Networks (DNN), Support Vector Machine Radial (SVM-R), and a Gradient Boosting Model (GBM) meta layer to enhance prediction accuracy. …”
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Polarimetric Identification of 3D-Printed Nano Particle Encoded Optical Codes by Kavan Ahmadi, Pedro Latorre-Carmona, Bahram Javidi, Artur Carnicer

“…Each sample is characterized by analyzing the polarization state of the emerging light. Using the one class-support vector machine algorithm we found high accuracy figures in the recognition of the true class codes. …”
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Deep Learning-Based Detection of Aflatoxin B1 Contamination in Almonds Using Hyperspectral Imaging: A Focus on Optimized 3D Inception–ResNet Model by Md. Ahasan Kabir, Ivan Lee, Sang-Heon Lee

“…The proposed model achieved higher classification accuracy than traditional methods, such as support vector machine (SVM), random forest (RF), quadratic discriminant analysis (QDA), and decision tree (DT), for classifying aflatoxin B1 contaminated almonds. …”
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Color and Grey-Level Co-Occurrence Matrix Analysis for Predicting Sensory and Biochemical Traits in Sweet Potato and Potato by Judith Ssali Nantongo, Edwin Serunkuma, Gabriela Burgos, Mariam Nakitto, Joseph Kitalikyawe, Thiago Mendes, Fabrice Davrieux, Reuben Ssali

“…Overall, the performance of the eXtreme Gradient Boosting (XGboost) was comparable to the radial-based support vector machine (NL-SVM) algorithm, and these could be used for the initial selection of genotypes for aromas and flavors (r2=0.64–0.72) and texture attributes like moisture or mealiness (r2>50). …”
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Non-Destructive Detection of Soybean Storage Quality Using Hyperspectral Imaging Technology by Yurong Zhang, Wenliang Wu, Xianqing Zhou, Jun-Hu Cheng

“…The feature variables were extracted by a variable iterative space shrinkage approach (VISSA), competitive adaptive reweighted sampling (CARS), and a successive projections algorithm (SPA). Partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models were developed to predict crude fatty acid values of soybeans. …”
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CO2 Gas Layer Recognition Method Based on Long Short-Term Memory Network by HE Lina, WU Wensheng, WANG Xiannan, ZHANG Wei, ZHANG Chuanju, SONG Xiaoyu

“…This model is used to identify the CO2 gas layer of well L2 in Enping sag, the Pearl River Mouth basin, and is compared with the recognition results of support vector machine and K nearest neighbor algorithm. …”
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Research on Pedestrian and Cyclist Classification Method Based on Micro-Doppler Effect by Xinyu Chen, Xiao Luo, Zeyu Xie, Defang Zhao, Zhen Zheng, Xiaodong Sun

“…Then, for the heightened sensitivity to inter-class differences, a fractional polynomial is introduced into the extraction of micro-Doppler characteristics of VRU targets to enhance extraction precision. Subsequently, the support vector machine technique is embedded for precise feature classification. …”
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Detection of axonal synapses in 3D two-photon images. by Cher Bass, Pyry Helkkula, Vincenzo De Paola, Claudia Clopath, Anil Anthony Bharath

“…The final algorithm proposed has the following main steps: (1) a Laplacian of Gaussian (LoG) based feature enhancement module to accentuate the appearance of boutons; (2) a Speeded Up Robust Features (SURF) interest point detector to find candidate locations for feature extraction; (3) non-maximum suppression to eliminate candidates that were detected more than once in the same local region; (4) generation of feature descriptors based on Gabor filters; (5) a Support Vector Machine (SVM) classifier, trained on features from labelled data, and was used to distinguish between bouton and non-bouton candidates. …”
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Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L. by Mahboobe Hojati, Ruhollah Naderi, Mohsen Edalat, Hamid Reza Pourghasemi

“…To identify ecological factors affecting the distribution and amount of silymarin in S. marianum three machine learning algorithms including boosted regression trees (BRT), random forest (RF), and support vector machines (SVM) have been applied in Fars Province, Iran. …”
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Genome-wide association between branch point properties and alternative splicing. by André Corvelo, Martina Hallegger, Christopher W J Smith, Eduardo Eyras

“…Using sequence conservation and positional bias we obtained a set of motifs with good agreement with U2 snRNA binding stability. Using a Support Vector Machine algorithm, we created a model complemented with polypyrimidine tract features, which considerably improves the prediction accuracy over previously published methods. …”
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Intelligence model-driven multi-stress adaptive reliability enhancement testing technology by Shouqing Huang, Beichen He, Jing Wang, Xiaoyang Li, Rui Kang, Fangyong Li

“…The TSO-GPR model-driven IMD-MSARET is superior to GPR, TSO-SVM, support vector machine and Tuna Swarm Optimization–Backpropagation Neural Network (TSO-BPNN) in terms of accuracy, efficiency, and test item cost for constructing multi-stress limit envelopes.…”
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Android malware detection method based on deep neural network by Fan CHAO, Zhi YANG, Xuehui DU, Yan SUN

“…Android is increasingly facing the threat of malware attacks.It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine,method for Android malware detection and family classification based on deep neural network was proposed.Based on the comprehensive extraction of application components,Intent Filter,permissions,and data flow,the method performed an effective feature selection to reduce dimensions,and conducted a large-sample detection and multi-class classification for malware based on deep neural network.The experimental results show that the method can conduct an effective detection and classification.The accuracy of binary classification between benign and malicious Apps is 97.73%,and the accuracy of family multi-class classification can reach 93.54%,which is higher than other machine learning algorithms.…”
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A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM by Xin-Sheng Zhang

“…Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs). …”
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Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians. by Sazzli Kasim, Putri Nur Fatin Amir Rudin, Sorayya Malek, Firdaus Aziz, Wan Azman Wan Ahmad, Khairul Shafiq Ibrahim, Muhammad Hanis Muhmad Hamidi, Raja Ezman Raja Shariff, Alan Yean Yip Fong, Cheen Song

“…In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. …”
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A Deep Neural Network to Identify Vacuum Degrees in Vacuum Interrupter Based on Partial Discharge Diagnosis by Hong Nhung-Nguyen, Young-Woo Youn, Yong-Hwa Kim

“…The classification performance of the proposed method is significantly better than those of machine learning algorithms such as support vector machines and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbor algorithm and the proposed method achieves an 100&#x0025; classification accuracy.…”
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