Artificial intelligence driven mental health diagnosis based on physiological signals by P.R. Naregalkar, A.A. Shinde, M.V. Patil

“…The physiological signals used in this project are ECG, EMG, HR, RESP, Foot GSR, and Hand GSR. The machine learning algorithms, like Decision tree and kernel support vector machine, are employed for dope classification tasks. …”
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Evaluating anti-VEGF responses in diabetic macular edema: A systematic review with AI-powered treatment insights by S Tamilselvi, M Suchetha, Dhanashree Ratra, Janani Surya, S Preethi, Rajiv Raman

“…The article measures the effectiveness of different machine learning and deep learning algorithms, including linear discriminant analysis (LDA), ResNet-50, CNN with attention, quadratic discriminant analysis (QDA), random forest (RF), and support vector machines (SVM), in analyzing eyes that could tolerate extended interval dosing. …”
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Development and validation of a pathological model predicting the efficacy of neoadjuvant therapy for breast cancer based on RCB scoring by Huan Li, Xianli Ju, Chuanfei Zeng, Zhengzhuo Chen, LinXin Yu, Ge Ke, Ziyin Huang, Youping Wang, Jingping Yuan, Mingkai Chen

“…Based on clinical and pathological characteristics along with the Residual Cancer Burden (RCB) score, we utilized a support vector machine (SVM) algorithm to construct a Pathomics Breast Cancer Signature (PBCS) prediction model. …”
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Explainable Supervised Learning Models for Aviation Predictions in Australia by Aziida Nanyonga, Hassan Wasswa, Keith Joiner, Ugur Turhan, Graham Wild

“…A comparative evaluation of four machine learning algorithms is conducted for a three-class classification task:—Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and a deep neural network (DNN) comprising five hidden layers. …”
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Nitrogen content estimation of apple trees based on simulated satellite remote sensing data by Meixuan Li, Xicun Zhu, Xicun Zhu, Xinyang Yu, Cheng Li, Dongyun Xu, Ling Wang, Dong Lv, Yuyang Ma

“…Correlation coefficient method and partial least squares regression were used to screen sensitive bands for apple tree nitrogen content. Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN) algorithms were used to construct and screen the optimal models for apple tree nitrogen content estimation.ResultsResults showed that visible light, red edge, near-infrared, and yellow edge bands were sensitive bands for estimating apple tree nitrogen content. …”
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Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM by Han Peiqing

“…In response to the shortcomings of the traditional methods for evaluating the mental health status of college students in terms of computational complexity and low accuracy, a method for evaluating the mental health status of college students based on data reduction and support vector machines was proposed. A model experiment containing internal and external personality tendency classification, anxiety, and depression dichotomy was designed using logistic regression analysis, information entropy, and SVM algorithm to construct the feature dimensions of the network behavior data, combined with the labeled data of mental state to derive the sample data set for model experiments. …”
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Robust Tracking Method for Small and Weak Multiple Targets Under Dynamic Interference Based on Q-IMM-MHT by Ziqian Yang, Hongbin Nie, Yuxuan Liu, Chunjiang Bian

“…Furthermore, the algorithm utilizes Support Vector Machines (SVMs) for anomaly detection and trajectory recovery, thereby enhancing the accuracy of data association and the overall robustness of the system. …”
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Liquid-liquid equilibrium data prediction using large margin nearest neighbor by mohsen pirdashti, kamyar movagharnejad, silvia Curteanu, Florin Leon, Farshad Rahimpour

“…The results of our method are quite promising: they were clearly better than those obtained by well-established methods such as Support Vector Machines, k-Nearest Neighbour and Random Forest. …”
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An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection by C. Manzano, C. Meneses, P. Leger, H. Fukuda

“…This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural network, decision tree, random forest, and K-nearest neighbors. …”
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Research and analysis of differential gene expression in CD34 hematopoietic stem cells in myelodysplastic syndromes. by Min-Xiao Wang, Chang-Sheng Liao, Xue-Qin Wei, Yu-Qin Xie, Peng-Fei Han, Yan-Hui Yu

“…After comprehensive evaluation, we ultimately selected three algorithms-Lasso regression, random forest, and support vector machine (SVM)-as our core predictive models. …”
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Morphometric and radiomics analysis toward the prediction of epilepsy associated with supratentorial low-grade glioma in children by Min-Lan Tsai, Kevin Li-Chun Hsieh, Yen-Lin Liu, Yi-Shan Yang, Hsi Chang, Tai-Tong Wong, Syu-Jyun Peng

“…The most important predictor was temporal lobe involvement, followed by high dependence high grey level emphasis, elongation, area density, information correlation 1, midbrain and intensity range. The Linear Support Vector Machine (SVM) model yielded the best prediction performance, when implemented with a combination of radiomics features and tumor location features, as evidenced by the following metrics: precision (0.955), recall (0.913), specificity (0.960), accuracy (0.938), F-1 score (0.933), and area under curve (AUC) (0.950). …”
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SMART HYBRID MODELS FOR IMPROVED BREAST CANCER DETECTION by Nageswara Rao Gali, Panduranga Vital Terlapu, Yasaswini Mandavakuriti, Sai Manoj Somu, Madhavi Varanasi, Vijay Telugu, Maheswara Rao V V R

“…We explored various methodologies, including CNN, CNN in conjunction with Support Vector Machine (SVM), CNN with Random Forest, and VGG-16 combined with XGBOOST. …”
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C-reactive protein-triglyceride glucose index predicts stroke incidence in a hypertensive population: a national cohort study by Songyuan Tang, Han Wang, Kunwei Li, Yaqing Chen, Qiaoqi Zheng, Jingjing Meng, Xin Chen

“…The Support Vector Machine (SVM) survival model exhibited the best predictive performance for stroke risk in hypertensive patients, with an area under the curve (AUC) of 0.956. …”
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Cardiometabolic risk factors in predicting obstructive coronary artery disease in patients with non-ST-segment elevation acute coronary syndrome by B. I. Geltser, M. M. Tsivanyuk, K. I. Shakhgeldyan, E. D. Emtseva, A. A. Vishnevskiy

“…In addition, for the development of predictive models, we used multivariate LR (MLR), support vector machine (SVM) and random forest (RF). …”
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Optimizing Bi-LSTM networks for improved lung cancer detection accuracy. by Su Diao, Yajie Wan, Danyi Huang, Shijia Huang, Touseef Sadiq, Mohammad Shahbaz Khan, Lal Hussain, Badr S Alkahtani, Tehseen Mazhar

“…The results revealed that the highest performance using hand-crafted features was achieved by extracting GLCM features and utilizing Support Vector Machine (SVM) with different kernels, reaching an accuracy of 99.78% and an AUC of 0.999. …”
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A Novel Fractional Filter Design and Cross-Term Elimination in Wigner Distribution by Jiexiao Yu, Kaihua Liu, Liang Zhang, Peng Luo

“…A time-frequency binary image is obtained from the time-frequency distribution of the observed signal and the optimal separating lines are determined by the support vector machine (SVM) classifier where the image boundary extraction algorithms are used to construct the training set of SVM. …”
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Wineinformatics: Wine Score Prediction with Wine Price and Reviews by Yuka Nagayoshi, Bernard Chen

“…To explore the relationship between wine price and wine score, naive Bayes classifier and support vector machine (SVM) classifier are employed to predict the scores as either equal to or above 90 or below 90. …”
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Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches by Chunhua Gao, Hui Wang

“…Finally, the proposed method is compared with commonly used machine learning-based classification algorithms such as decision tree, random forest, support vector machine, and artificial neural networks. …”
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Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering by Bo Xu, Chunjiang Zhao, Guijun Yang, Yuan Zhang, Changbin Liu, Haikuan Feng, Xiaodong Yang, Hao Yang

“…Finally, we compared the GFC (Gaussian Fuzzy Clustering algorithm) used in this study with commonly used algorithms, such as RF (Random Forest), SVM (Support Vector Machine), and BPNN (BP Neural Network), as well as k-Means, HCM (Hierarchical), and FCM (Fuzzy C-Means). …”
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Assessing Climate and Land-Use Change Scenarios on Future Desertification in Northeast Iran: A Data Mining and Google Earth Engine-Based Approach by Weibo Yin, Qingfeng Hu, Jinping Liu, Peipei He, Dantong Zhu, Abdolhossein Boali

“…Six remote sensing indices were selected to model desertification using four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Generalized Linear Models (GLM). …”
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