Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction by Licheng Qu, Minghao Zhang, Zhaolu Li, Wei Li

“…Besides, the performances were compared between three variants of RNNs (LSTM, GRU, and BiLSTM) and 6 frequently used models, which are decision tree (DT), support vector machine (SVM), k-nearest neighbour (KNN), random forest (RF), gradient boosting decision tree (GBDT), and stacked autoencoder (SAE). …”
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Speech emotion recognition based on a stacked autoencoders optimized by PSO based grass fibrous root optimization by Chi Zeng, Jialing Li, Abbas Habibi

“…The model’s performance is evaluated on a standard emotion recognition dataset, comparing with some state-of-the-art models, including Convolutional Neural Network (CNN), Support Vector Machine (SVM), Deep Learning (DL), CNN and Iterative Neighborhood Component Analysis (CNN/INCA), VGG-16 achieving high accuracy in identifying various emotional states.…”
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Bird Call Identification Using Ensemble Empirical Mode Decomposition by Jingxuan Liu, Hailan Chen

“…These ratios, in conjunction with the correlation coefficients are used as the call features. Finally, applying a support vector machine classification and recognition algorithm enables a comparative analysis of various calls. …”
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Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images by Vahid Faghih Dinevari, Ghader Karimian Khosroshahi, Mina Zolfy Lighvan

“…In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. …”
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Optimized CNN-LSTM with hybrid metaheuristic approaches for solar radiation forecasting by İrem Fatma Şener, İhsan Tuğal

“…The performance of several machine learning and deep learning models, including Long Short-Term Memory, Autoregressive Integrated Moving Average, Multilayer Perceptron, Random Forest, XGBoost, Support Vector Regression, and a hybrid CNN-LSTM model, is evaluated for daily solar radiation forecasting. …”
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Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios. by Naser Shiri, Jalal Shiri, Zaher Mundher Yaseen, Sungwon Kim, Il-Moon Chung, Vahid Nourani, Mohammad Zounemat-Kermani

“…Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. …”
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Short-Term Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory by Qi Wang, Shunxiang Ji, Minqiang Hu, Wei Li, Fusuo Liu, Ling Zhu

“…Then, the least square support vector machine (LSSVM), autoregressive and moving average (ARMA), and back propagation (BP) neural network are used to forecast PV power, respectively. …”
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Self-Supervised Sensor Learning and Its Application: Building 3D Semantic Maps Using Terrain Classification by Chuho Yi, Donghui Song, Jungwon Cho

“…It learns about the surrounding environment using a support vector machine with the stored data, which is divided into terrains where people or vehicles have moved and other regions. …”
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Wetland landscape based on Sentinel-2 images and geo-tagged photographs in Centla, Tabasco by Alejandra Aurelia López-Caloca, Amilcar Morales Gamas, María Gabriela López Aguilar

“…The central map of this article presents a land use and land cover study, obtained from Sentinel-2 MSI data for the Centla wetland zones. The support vector machine algorithm is used to classify Sentinel-2 images. …”
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An optimised scattering power decomposition method oriented to ship detection in polarimetric synthetic aperture radar imagery by Lu Fang, Wenxing Mu, Ning Wang, Tao Liu

“…Furthermore, pocket algorithm and support vector machine are adopted to solve linear non‐separable problems under complex experimental conditions in this study. …”
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Facial expression using Histogram of Oriented Gradients and Ensemble Classifier by Maher Kh. Hussien, Fawziya Mahmood Ramo

“…We have proposed a group classifier consisting of three basic classifiers: support vector machines, knn-algorithm closest to neighbors, and Naive Bayes in the classification stage. …”
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Recognition of PQ stego images based on identifiable statistical feature by Ji-cang LU, Fen-lin LIU, Xiang-yang LUO, Yi ZHANG

“…Then, the SVM (support vector machines) classifier is trained to recognize PQ stego images. …”
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Detection of Tomato Leaf Pesticide Residues Based on Fluorescence Spectrum and Hyper-Spectrum by Jiayu Gao, Xuhui Yang, Simo Liu, Yufeng Liu, Xiaofeng Ning

“…The data in the spectral raw bands were optimized using convolutional smoothing (S-G), standard normal variable transformation (SNV), multiplicative scatter correction (MSC), and baseline calibration (baseline) algorithms, respectively. In order to improve the operating rate of discrimination, a continuous projection algorithm (SPA) was used to extract the characteristic wavelengths of the fluorescence spectra and hyperspectral data of pesticide residues, and algorithms such as the least-squares support vector machine (LSSVM) algorithm and least partial squares regression (PLSR) were used to build a quantitative model, while algorithms such as the convolutional neural network (BPNN) algorithm and decision tree algorithm (CART) were used to build a qualitative model. …”
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Investigating the Triple Code Model in numerical cognition using stereotactic electroencephalography. by Alexander P Rockhill, Hao Tan, Christian G Lopez Ramos, Caleb Nerison, Beck Shafie, Maryam N Shahin, Adeline Fecker, Mostafa Ismail, Daniel R Cleary, Kelly L Collins, Ahmed M Raslan

“…Time-frequency spectrograms were dimensionally reduced with principal component analysis and passed into a linear support vector machine classification algorithm to identify regions associated with number perception compared to inter-trial periods. …”
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Optimisation Study of Investment Decision-Making in Distribution Networks of New Power Systems—Based on a Three-Level Decision-Making Model by Wanru Zhao, Ziteng Liu, Rui Zhang, Mai Lu, Wenhui Zhao

“…Next, the Pearson correlation coefficient is employed to screen key influencing factors, and in conjunction with the grey MG(1,1) model and the support vector machine algorithm, precise forecasting of the investment scale is achieved. …”
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Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform by Jianhui Wu, Lu Zhang, Sufeng Yin, Haidong Wang, Guoli Wang, Juxiang Yuan

“…Then, the logistic regression model, decision tree model, BP neural network model, and support vector machine (SVM) model of hypo-MDS and AA were established. …”
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An Early Detection of Asthma Using BOMLA Detector by Md. Abdul Awal, Md. Shahadat Hossain, Kumar Debjit, Nafiz Ahmed, Rajan Dev Nath, G. M. Monsur Habib, Md. Salauddin Khan, Md. Akhtarul Islam, M. A. Parvez Mahmud

“…Ten classifiers have been utilized in the BOMLA detector, where Support Vector Classifier (SVC), Random Forest (RF), Gradient Boosting Classifier (GBC), eXtreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) are state-of-the-art classifiers. …”
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Stochastic differential equation modeling approach for grading astrocytomas on brain MRI images by Mahsa Raisi-Nafchi, Mahnoosh Tajmirriahi, Hossein Rabbani, Zahra Amini

“…Three classification algorithms were evaluated: support vector machine, K-nearest neighbor, and random forest. …”
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The predictive value of radiomics and deep learning for synchronous distant metastasis in clear cell renal cell carcinoma by Wan-Bin He, Chuan Zhou, Zhi-Jun Yang, Yun-Feng Zhang, Wen-Bo Zhang, Han He, Jia Wang, Feng-Hai Zhou

“…With these 15 features, the support vector machine (SVM) model emerged as the most effective, demonstrating areas under the curve (AUC) of 0.860 and 0.813 in the training and validation cohort, respectively. …”
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Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach by Rahul Kumar Gupta, Asmaul Hassan, Samir Kumar Majhi, Nikhat Parveen, Abu Taha Zamani, Raju Anitha, Binayak Ojha, Abhinav Kumar Singh, Debendra Muduli

“…A stacking ensemble model is developed with support vector machine (SVM), K-nearest neighbors (KNN), and extreme learning machine (ELM) to enhance forecast accuracy. …”
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