Predicting climate-driven shift of the East Mediterranean endemic Cynara cornigera Lindl by Heba Bedair, Heba Bedair, Yehia Hazzazi, Asmaa Abo Hatab, Marwa Waseem A. Halmy, Mohammed A. Dakhil, Mohammed A. Dakhil, Mubaraka S. Alghariani, Mubaraka S. Alghariani, Mari Sumayli, A. El-Shabasy, Mohamed M. El-Khalafy

“…Our analysis involved inclusion of bioclimatic variables, in the SDM modeling process that incorporated five algorithms: generalized linear model (GLM), Random Forest (RF), Boosted Regression Trees (BRT), Support Vector Machines (SVM), and Generalized Additive Model (GAM).Results and discussionThe ensemble model obtained high accuracy and performance model outcomes with a mean AUC of 0.95 and TSS of 0.85 for the overall model. …”
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Automated differentiation of wide QRS complex tachycardia using QRS complex polarity by Adam M. May, Bhavesh B. Katbamna, Preet A. Shaikh, Sarah LoCoco, Elena Deych, Ruiwen Zhou, Lei Liu, Krasimira M. Mikhova, Rugheed Ghadban, Phillip S. Cuculich, Daniel H. Cooper, Thomas M. Maddox, Peter A. Noseworthy, Anthony Kashou

“…Methods In a three-part study, we derive and validate machine learning (ML) models—logistic regression (LR), artificial neural network (ANN), Random Forests (RF), support vector machine (SVM), and ensemble learning (EL)—using engineered (WCT-PC and QRS-PS) and previously established WCT differentiation features. …”
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UHVDC Transmission Line Fault Identification Method Based on Generalized Regression Neural Network by XIE Jia, LIU Feng, KE Yanguo, YIN Zhen, RUAN Wei, YAO Jinming

“…Compared to traditional convolutional neural networks, generalized regression neural networks, support vector machines, and other methods, the fault recognition accuracy of the proposed method in this paper has been improved by 6. 6% , 0. 65% , and 7. 69% , respectively, meeting the requirements of protection speed and reliability.…”
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FragMangro: A cross-domain zero-shot model for monitoring fragmented mangrove ecosystems by Ruoxin Zhang, Angela An, Mingcan Cen, Chong Zhang, Aimee Huangfu, Sunny Vinnakota, Shuxiang Song

“…The results demonstrate that FragMangro achieves over 98% accuracy, an average F1 Score exceeding 85%, and the Intersection over Union(IoU) surpassing 74%, significantly outperforming conventional methods such as support vector machines (SVM), random forests (RF), and k-nearest neighbors (KNN). …”
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Enhancing Fake Review Detection Using Linguistic Exaggeration, BERT Embeddings, and Fuzzy Logic by Mohammed Ennaouri, Ahmed Zellou

“…We evaluated our approach against traditional classifiers such as Support Vector Machines (SVM), Logistic Regression. …”
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Application of Graph-Theoretic Methods Using ERP Components and Wavelet Coherence on Emotional and Cognitive EEG Data by Sencer Melih Deniz, Ahmet Ademoglu, Adil Deniz Duru, Tamer Demiralp

“…Global and local graph metrics such as energy efficiency, strength, transitivity, characteristic path length, and clustering coefficient were used as features for classification using support vector machines (SVMs), k-nearest neighbor(K-NN), and linear discriminant analysis (LDA). …”
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Multimodal Classification of Alzheimer’s Disease Using Longitudinal Data Analysis and Hypergraph Regularized Multi-Task Feature Selection by Shuaiqun Wang, Huan Zhang, Wei Kong

“…The selected features are subsequently integrated via multi-kernel support vector machines for final classification. …”
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Cryptographic hardness assumptions identification based on discrete wavelet transform by Ke Yuan, Yu Du, Yizheng Liu, Rongjin Feng, Bowen Xu, Gaojuan Fan, Chunfu Jia

“…To address the challenges posed by high-dimensionality, complex data distributions, and difficulty fitting ciphertext features, an ensemble learning model called MHERF is constructed, which combines four classifiers: Decision Tree, Adaptive Boosting, Support Vector Machines, and Gradient Boosting. The experiment involved conducting three-category classification tasks on the integer factorization problem, discrete logarithm problem, and elliptic curve discrete logarithm problem, using 9,000 mixed sample files generated by 15 cryptosystems based on various cryptographic hardness assumptions. …”
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TELEPROM Psoriasis: Enhancing patient-centered care and health-related quality of life (HRQoL) in moderate-to-severe plaque psoriasis by Gabriel Mercadal-Orfila, Gabriel Mercadal-Orfila, Piedad López Sánchez, Aranzazu Pou Alonso, Olatz Ibarra-Barrueta, Emilio Monte-Boquet, Joaquin Borrás Blasco, Nuria Padullés Zamora, Patricia Sanmartin-Fenollera, Cristina Capilla Montes, M. Ángeles Bernabéu Martínez, Salvador Herrera-Pérez

“…Machine learning models, particularly Random Forest (AUC = 0.98) and Support Vector Machine (AUC = 0.96), effectively predicted patient engagement. …”
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Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification by Santhakumar D, Gnanajeyaraman Rajaram, Elankavi R, Viswanath J, Govindharaj I, Raja J

“…The proposed model, which identifies the optimal feature set for classification using Support Vector Machine (SVM), has achieved an impressive prediction accuracy of 93.94 %. …”
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Automatic recognition and differentiation of pulmonary contusion and bacterial pneumonia based on deep learning and radiomics by Tie Deng, Junbang Feng, Xingyan Le, Yuwei Xia, Feng Shi, Fei Yu, Yiqiang Zhan, Xinghua Liu, Chuanming Li

“…PC and BP were automatically recognized, segmented using VB-net and radiomics features were automatically extracted. Four machine learning algorithms including Decision Trees, Logistic Regression, Random Forests and Support Vector Machines(SVM) were using to built the models. …”
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Multimodal MRI radiomics-based stacking ensemble learning model with automatic segmentation for prognostic prediction of HIFU ablation of uterine fibroids: a multicenter study by Bing Wen, Chengwei Li, Qiuyi Cai, Dan Shen, Xinyi Bu, Fuqiang Zhou

“…Feature selection was performed using t-test, Pearson correlation, and LASSO to identify the most predictive features for preoperative prognosis Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP) were employed as base learners to construct base predictive models. …”
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Identification and Validation of Aging Related Genes Signature in Chronic Obstructive Pulmonary Disease by Tian-Tian Li, Hong-Yan Bai, Jing-Hong Zhang, Xiu-He Kang, Yi-Qing Qu

“…The least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE) algorithms were chosen to identify the hub genes and the diagnostic ability. …”
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Development and validation of a radiomic prediction model for TACC3 expression and prognosis in non-small cell lung cancer using contrast-enhanced CT imaging by Weichao Bai, Xinhan Zhao, Qian Ning

“…The radiomics model was constructed using logistic regression (LR) and support vector machine (SVM) algorithms. We predicted TACC3 expression and evaluated its correlation with NSCLC prognosis using contrast-enhanced CT-based radiomics. …”
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Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants by Mohamed Farag Taha, Hanping Mao, Samar Mousa, Lei Zhou, Yafei Wang, Gamal Elmasry, Salim Al-Rejaie, Abdallah Elshawadfy Elwakeel, Yazhou Wei, Zhengjun Qiu

“…The results demonstrated that the LSTM outperformed the convolutional neural network (CNN) and multi-class support vector machine (MCSVM) approaches. Also, features selected by the DAE showed better performance compared to features extracted using both genetic algorithms (GAs) and sequential forward selection (SFS). …”
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Electronic Nose System Based on Metal Oxide Semiconductor Sensors for the Analysis of Volatile Organic Compounds in Exhaled Breath for the Discrimination of Liver Cirrhosis Patient... by Makhtar War, Benachir Bouchikhi, Omar Zaim, Naoual Lagdali, Fatima Zohra Ajana, Nezha El Bari

“…Sensor’s measurement data were analyzed using machine learning techniques, such as principal component analysis (PCA), discriminant function analysis (DFA), and support vector machines (SVMs) that were utilized to uncover meaningful patterns and facilitate accurate classification of sensor-derived information. …”
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A Systematic Literature Review of Concept Drift Mitigation in Time-Series Applications by Mujaheed Abdullahi, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Yahia Baashar, Abdussalam Ahmed Alashhab, Afroza Afrin

“…The findings show that Support Vector Machines (SVM) is the most effective learning algorithms for the detection and adaptation of CD in regression and classification tasks using time-series data. …”
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MSIMRS: Multi-Scale Superpixel Segmentation Integrating Multi-Source Remote Sensing Data for Lithology Identification in Semi-Arid Area by Jiaxin Lu, Liangzhi Li, Junfeng Wang, Ling Han, Zhaode Xia, Hongjie He, Zongfan Bai

“…In order to explore the application potential of superpixel segmentation in lithology classification, this study proposed the Multi-scale superpixel Segmentation Integrating Multi-source RS data (MSIMRS), and conducted a lithology classification study in Duolun County, Inner Mongolia Autonomous Region, China combining MSIMRS and the Support Vector Machine (MSIMRS-SVM). In addition, pixel-level K-Nearest Neighbor (KNN), Random Forest (RF) and SVM classification algorithms, as well as deep-learning models including Resnet50 (Res50), Efficientnet_B8 (Effi_B8), and Vision Transformer (ViT) were chosen for a comparative analysis. …”
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Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity by Yoonjae Lee, Minjae Kim, Jaehyun Noh, Gyoujin Cho, Changwoo Lee

“…The study employs the FCM method to enhance defect detection accuracy, using Support Vector Machine (SVM) as the classifier to consistently evaluate the effectiveness of selected feature sets in identifying and positioning eccentricity in R2R systems. …”
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Simulating the Deterioration Behavior of Tunnel Elements Using Amalgamation of Regression Trees and State-of-the-Art Metaheuristics by Eslam Mohammed Abdelkader, Abobakr Al-Sakkaf, Moaaz Elkabalawy, Abdelhady Omar, Ghasan Alfalah

“…Comparative analyses against conventional regression trees, artificial neural networks, and support vector machines demonstrated that the hybrid model consistently outperformed baseline techniques regarding predictive accuracy and generalizability. …”
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