Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…Research investigations demonstrate that the ELNET-BDE model attains significantly lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) than contesting Machine Learning (ML) algorithms like Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM). …”
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An Efficient RTL Design for a Wearable Brain–Computer Interface by Tahereh Vasei, Mohammad Ali Saber, Alireza Nahvy, Zainalabedin Navabi

“…To achieve a suitable accuracy considering hardware constraints, we explore BCI transducer algorithms, among which Infinite impulse response (IIR) filter, common spatial pattern, and support vector machine are used to preprocess, extract features, and classify data, respectively. …”
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Development and validation of a radiomics-based nomogram for predicting pathological grade of upper urinary tract urothelial carcinoma by Yanghuang Zheng, Hongjin Shi, Shi Fu, Haifeng Wang, Xin Li, Zhi Li, Bing Hai, Jinsong Zhang

“…The maximum relevance minimum redundancy algorithm, least absolute shrinkage and selection operator, and various machine learning (ML) algorithms—including random forest, support vector machine, and eXtreme gradient boosting—were employed to select radiomics features and calculate radiomics scores. …”
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Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images by Veysel Yusuf Cambay, Prabal Datta Barua, Abdul Hafeez Baig, Sengul Dogan, Mehmet Baygin, Turker Tuncer, U. R. Acharya

“…Four iterative feature selectors are employed in the feature selection stage to obtain distinct feature vectors. The classifiers k-nearest neighbors (kNN) and support vector machine (SVM) are used to produce specific outcomes. …”
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Forecasting Urban Sprawl Dynamics in Islamabad: A Neural Network Approach by Saddam Sarwar, Hafiz Usman Ahmed Khan, Falin Wu, Sarah Hasan, Muhammad Zohaib, Mahzabin Abbasi, Tianyang Hu

“…Landsat imagery is categorized into four thematic classes using a supervised classification method called the support vector machine (SVM): built-up, bareland, vegetation, and water. …”
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Magnetic Resonance Imaging Texture Analysis Based on Intraosseous and Extraosseous Lesions to Predict Prognosis in Patients with Osteosarcoma by Yu Mori, Hainan Ren, Naoko Mori, Munenori Watanuki, Shin Hitachi, Mika Watanabe, Shunji Mugikura, Kei Takase

“…Manual segmentation was performed in intraosseous, extraosseous, and entire lesions on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted images to extract texture features and perform principal component analysis. A support vector machine algorithm with 3-fold cross-validation was used to construct and validate the models. …”
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Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis by Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram, Sungwon Kim, Kaywan Othman Ahmed, Salim Heddam

“…In this study, the predictive accuracy of six different machine learning models, including Natural Gradient-based Boosting (NGBoost), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Support vector regression (SVR), Gaussian Process Regression (GPR), and Extremely Randomized Tree (ERT) was evaluated for modelling the parameter of permeate flow as a key element in system efficiency, energy consumption, and water quality using six various input combinations of feed water salt concentration, condenser inlet temperature, feed flow rate, and evaporator inlet temperature. …”
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LYN and CYBB are pivotal immune and inflammatory genes as diagnostic biomarkers in recurrent spontaneous abortion by Zhuna Wu, Qiuya Lin, Zhimei Zhou, Yajing Xie, Li Huang, Liying Sheng, Qirong Shi, Yumin Ke

“…The candidate DIIRGs were analyzed by the least absolute shrinkage and selection operator (LASSO) and the multiple support vector machine recursive feature elimination (mSVM-RFE). …”
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Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches by Suleiman Ibrahim Mohammad, Hamza Abu Owida, Asokan Vasudevan, Suhas Ballal, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, Ahmad Abumalek

“…A range of sophisticated artificial intelligence methods, including One-Dimensional Convolutional Neural Network (1D-CNN), Artificial Neural Networks (ANN), Decision Tree (DT), Ensemble Learning (EL), Adaptive Boosting (AdaBoost), Random Forest (RF), and Least Squares Support Vector Machine (LSSVM), were utilized to model and predict pH variations with high accuracy. …”
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Design of upper limb muscle strength assessment system based on surface electromyography signals and joint motion by Siqi Wang, Wei Lai, Yipeng Zhang, Junyu Yao, Xingyue Gou, Hui Ye, Jun Yi, Dong Cao

“…The extracted features from the sEMG and joint motion data were analyzed using three algorithms: Random Forest (RF), Backpropagation Neural Network (BPNN), and Support Vector Machines (SVM), to predict muscle strength through regression models. …”
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Predicting cardiotoxicity in drug development: A deep learning approach by Kaifeng Liu, Huizi Cui, Xiangyu Yu, Wannan Li, Weiwei Han

“…We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms, including Gaussian naive Bayes (NB), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), eXtreme gradient boosting (XGBoost), and Transformer models, to build predictive models. …”
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Using Wireless Sensor Networks to Achieve Intelligent Monitoring for High-Temperature Gas-Cooled Reactor by Jianghai Li, Jia Meng, Xiaojing Kang, Zhenhai Long, Xiaojin Huang

“…To process nonlinear and non-Gaussian data obtained by WSN for fault diagnosis, novel algorithms combining Kernel Entropy Component Analysis (KECA) and support vector machine (SVM) are developed.…”
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Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis by Pierre Heudel, Mashal Ahmed, Felix Renard, Arnaud Attye

“…Manifold learning and machine learning algorithms were applied to uncover complex data relationships and develop predictive models. …”
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Development of a non-contrast CT-based radiomics nomogram for early prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage by Lingxu Chen, Xiaochen Wang, Sihui Wang, Xuening Zhao, Ying Yan, Mengyuan Yuan, Shengjun Sun

“…Results The nomogram integrated the radscore and three clinically significant parameters (aneurysm and aneurysm treatment and admission Hunt-Hess score), with the Support Vector Machine model yielding the highest performance in the validation set. …”
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The Analytical System for Determining the Attitude of Students to the University by Violeta Tretynyk, Mariia Pinda

“…Existing software solutions use methods for processing and analyzing text tone based on machine learning methods and algorithms (naive Bayesian classifier, support vector machine, logistic regression), as well as deep learning (recurrent neural networks). …”
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Data-driven predictive models for sustainable smart buildings by Prabhu Rajaram, Gnana Swathika O․V․

“…It highlights the critical role of energy efficiency and the importance of lowering carbon footprints through the implementation of advanced algorithms, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest, XGBOOST, AdaBoost, and Naive Bayes classifiers. …”
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Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model by Jie Ren, Delong Tian, Hexiang Zheng, Guoshuai Wang, Zekun Li

“…We propose a multimodal regression prediction model utilizing the TCLA framework—comprising the Transient Trigonometric Harris Hawks Optimizer (TTHHO), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), and Adaptive Bandwidth Kernel Density Estimation (ABKDE)—with the Hetao Irrigation District, a vast irrigation basin in China, serving as the study area. …”
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Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu, Guangyu Bin

“…Significance: Our research identifies key electroencephalographic (EEG) biomarkers, including low-frequency connectivity and burst suppression thresholds, to improve early and objective prognosis assessments. By integrating machine learning (ML) algorithms, such as Gradient Boosting Models and Support Vector Machines, with SHAP-based feature visualization, robust screening methods were applied to ensure the reliability of predictions. …”
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A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure by Sardar Muhammad Ali, Abdul Razzaque, Muhammad Yousaf, Sardar Sadaqat Ali

“…We trained three ML classifiers: Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbors (KNN), along with three DL models: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). …”
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Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform by S Ramana Kumar Joga, Chidurala SaiPrakash, Srikanth Velpula, Alivarani Mohapatra, Theophilus A. T. Kambo Jr.

“…SBCT in combination with Support Vector Machine, Decision Tree, Random Forest, and ANN classifiers are able to detect faults in PV array with 99%, 98.5%, 99.2%, and 99.5% accuracies in no shading condition and 88%, 85%, 89%, and 89.5% accuracies in severe shading condition. …”
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