An overview for assessing a number of systems for estimating age and gender of speakers by Aalaa Ahmed Mohammed, Yusra Faisal Al-Irhayim

“…This paper aims to conduct a comparative study of age and gender classification algorithms applied to the speech signal. Comparison of experimental results of different sources of voices for speakers of different languages and methods of miscellaneous classification such as Bayes classifier, neural network, support vector machines, K-nearest neighbor, gaussien mixture model and hybrid method based on weighted analysis of a directed non-negative matrix and a neural network with a general recession as well as some deep learning methods, is done in order to show different results  to classify the age and gender of the speaker when processing the speech signal. …”
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Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning by Bo Wang, Jun Wei, Le Zhang, Hui Jiang, Cheng Jin, Shaowen Huang

“…Finally, based on the source and target domain data after substructure domain adaptation, the least squares support vector machine algorithm is used to establish the prediction model. …”
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Identification of fermented soy sauce and blended soy sauce based on dielectric spectra by Yingman Xie, Jiayao Zhao, Chao Mao, Huiyun Pang, Pengfei Ye, Xiangwei Chen, Hongfei Fu, Yequn Wang, Yunyang Wang

“…The sample set was divided into a correction set and a prediction set using the joint x-y distances (SPXY) algorithm, and the partial least squares (PLS) and support vector machine (SVM) models were adopted to distinguish the different samples. …”
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Distributed robust scheduling of distribution-microgrid based on deep learning method integration by WANG Yihong, LIU Jichun, QIU Gao, ZHOU Hao, HE Peixin

“…The uncertainty probability set of renewable energy and load of microgrid is constructed based on probabilistic output support vector machine, Bayesian neural network and deep belief network. …”
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Hyperparameters optimization of evolving spiking neural network using artificial bee colony for unsupervised anomaly detection by Rehan Rabie, Sahran Shahnorbanun, Alyasseri Zaid Abdi Alkareem, Sani Nor Samsiah, Al-Betar Mohammed Azmi

“…Further validation was provided by comparing the proposed OeSNN-ABC against five well-known optimization algorithms: particle swarm optimization, grey wolf optimization, flower pollination algorithm, whale optimization algorithm, and grid search, alongside other classifiers such as random forest, support vector machine, and k-nearest neighbor. …”
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Out-of-hospital multimodal seizure detection: a pilot study by Martin Ballegaard, Troels Wesenberg Kjær, Ivan Chrilles Zibrandtsen, Jonas Munch Nielsen, Ástrós Eir Kristinsdóttir, Paolo Masulli, Tobias Søren Andersen

“…Furthermore, we examined the signal quality of out-of-hospital EEG recordings.Methods Seventeen patients were monitored for up to 5 days. A support vector machine based seizure detection algorithm was applied using both in-patient seizures and out-of-hospital electrographic seizures in one patient. …”
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Automated Tomato Leaf Disease Detection Using Image Processing: An SVM-Based Approach with GLCM and SIFT Features by Rashid Khan, Nasir Ud Din, Asim Zaman, Bingding Huang

“…Therefore, we proposed an approach that employs robust feature extraction methods, including the gray level co-occurrence matrix (GLCM) and scale-invariant feature transform (SIFT), coupled with a support vector machine (SVM) for adequate classification. …”
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Pavement condition detection using acceleration data collected by smartphones based on 1D convolutional neural network by Yudong Han, Zhaobo Li, Jiaqi Li

“…Five types of 1D-CNN with different activation functions and network structures were designed to classify the data and were compared with machine learning algorithms, including support vector machine (SVM) and radial basis function (RBF) neural networks. …”
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Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography by Ruiting Jia, Baozhi Liu, Mohsin Ali

“…Nodule detection was done using the Retina-UNet model, while the features were classified using a Support Vector Machine (SVM). Performance measures, including accreditation, sensitivity, specificity, and the AUROC, were used to evaluate the model’s performance during training and validation. …”
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Evaluation of Withering Quality of Black Tea Based on Multi-Information Fusion Strategy by Ting An, Yongwen Jiang, Hanting Zou, Xuan Xuan, Jian Zhang, Haibo Yuan

“…Subsequently, the different fused features were combined with a support vector regression (SVR) algorithm to establish the moisture perception models of withering leaves. …”
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Detection of asbestos-based cement rooftops in conflict-affected settings using EnMAP hyperspectral data: a research article by Jonti Evan Shepherd, Elad Sagi, Gal Zagron, Eyal Ben-Dor

“…Leveraging EnMAP Level 2A hyperspectral imagery (17 May 2024), we applied MNF noise reduction, precise co-registration, and cloud/shadow masking before executing eight supervised classifiers; Linear Spectral Unmixing, Support Vector Machine, Spectral Angle Mapper, Adaptive Coherence Estimator, Mahalanobis Distance, Maximum Likelihood, Spectral Information Divergence, and Matched Filtering, in an iterative filtering cascade. …”
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Usage of the dwarf mongoose optimization-based ANFIS on the static strength of seasonally frozen soils by Bowen Liu, Junbin Chen, Xiaoguang Zhang, Zhenwei Wang

“…To precisely characterize the diminishment of soil under various situations, a forecasting system for soil static strength (S s) has been developed using ML technology. In this study, two machine learning (ML) tactics were designed and validated to evaluate the S s of seasonally frozen soils, namely the adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR). …”
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Snow depth estimation in Northeast China based on space-borne scatterometer data and ML model with optimal features by Wenfei Chen, Lingjia Gu, Xiaofeng Li, Xintong Fan

“…This study explores its application to SD estimation in Northeast China. Multiple machine learning (ML) models, including support vector regression (SVR), k-nearest neighbors (KNN), XGBoost, and random forest (RF), were deployed and contrasted for SD estimation. …”
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A Deep Learning-Driven CAD for Breast Cancer Detection via Thermograms: A Compact Multi-Architecture Feature Strategy by Omneya Attallah

“…Ultimately, support vector machine (SVM) classifiers are employed for classification purposes. …”
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Radiation hematologic toxicity prediction in rectal cancer: a comparative radiomics-based study on CT image and dose map by Yingpeng Liu, Liping Guo, Yi Wang, Qingtao Xu, Jingfeng Zhang, Xianyun Meng

“…Then, the radiomic features of the clinical target volume (CTV) in the radiotherapy were extracted, and the least absolute shrinkage and selection operator (LASSO) algorithm was used for feature dimension deduction; three classifiers, that is, support vector machine (SVM) (rbf kernel), random forest, and CatBoost, were used to construct the HT classification model in rectal cancer patients. …”
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Extraction and analysis of alteration minerals from GF-5 hyperspectral data: a case study of the quartz-vein type tungsten deposit in Jiaoxi, Tibet by Ziqiong Guan, Xinxing Liu, Liqiang Wang, Meng Wang, Yingxue Wang, Yang Cheng

“…The study employed the spectral angle mapper (SAM) technique and the support vector machine (SVM) algorithm to successfully extract six alteration minerals: paragonite, muscovite, phengite, Fe-Mg chlorite, Fe chlorite, and siderite. …”
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Encoding local label correlations in multi-instance multi-label learning with an improved multi-objective particle swarm optimization by Xiang Bao, Fei Han, Qinghua Ling

“…Subsequently, the loss function of the framework is solved by an alternating optimization process where Support Vector Machine (SVM) classifiers are constructed for optimization. …”
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A Multi-Epiphysiological Indicator Dog Emotion Classification System Integrating Skin and Muscle Potential Signals by Wenqi Jia, Yanzhi Hu, Zimeng Wang, Kai Song, Boyan Huang

“…Comprehensive feature extraction (time-domain, frequency-domain, nonlinearity) was conducted for each signal modality, and inter-emotional variance was analyzed to establish discriminative patterns. Four machine learning algorithms—Neural Networks (NN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT), and XGBoost—were trained and evaluated, with XGBoost achieving the highest classification accuracy of 90.54%. …”
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An Effective and Fast Model for Characterization of Cardiac Arrhythmia and Congestive Heart Failure by Salim Lahmiri, Stelios Bekiros

“…The latter achieved highest accuracy under ten-fold cross-validation in comparison to Naïve Bayes (NB) and nonlinear support vector machine (SVM). The kNN yielded 97% accuracy, 99% sensitivity, 90% specificity and 0.63 s processing time when classifying ARR against NS, and it yielded 99% accuracy, 99.7% sensitivity, and 99.2% specificity, and 0.27 seconds processing time when classifying HCF against NS. …”
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Maize Seed Variety Classification Based on Hyperspectral Imaging and a CNN-LSTM Learning Framework by Shuxiang Fan, Quancheng Liu, Didi Ma, Yanqiu Zhu, Liyuan Zhang, Aichen Wang, Qingzhen Zhu

“…A CNN-LSTM model, built on the selected wavelengths, achieved an accuracy of 95.27% in maize variety classification, outperforming traditional chemometric models like partial least squares discriminant analysis, support vector machines, and extreme learning machines. …”
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