Intelligent algorithm-based model for predicting mass transfer performance in CO2 absorption within a rotating packed bed by Wei Zhang, Hao Chen, Ke Huang, Xing Shu, Cheng Fu, Bin Huang

“…Using dimensional analysis, key factors are transformed into dimensionless numbers, which are then input into models integrating least squares support vector machine (LSSVM) with genetic algorithm (GA), particle swarm optimization (PSO), and a hybrid GA-PSO (HGAPSO). …”
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Advancing patient care: Machine learning models for predicting grade 3+ toxicities in gynecologic cancer patients treated with HDR brachytherapy. by Andres Portocarrero-Bonifaz, Salman Syed, Maxwell Kassel, Grant W McKenzie, Vishwa M Shah, Bryce M Forry, Jeremy T Gaskins, Keith T Sowards, Thulasi Babitha Avula, Adrianna Masters, Jose G Schneider, Scott R Silva

“…Recent studies have applied models such as logistic regression, support vector machines, and deep learning networks to predict specific toxicities in patients who have undergone brachytherapy.…”
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A novel prediction of the PV system output current based on integration of optimized hyperparameters of multi-layer neural networks and polynomial regression models by Hussein Mohammed Ridha, Hashim Hizam, Seyedali Mirjalili, Mohammad Lutfi Othman, Mohammad Effendy Ya’acob, Noor Izzri Bin Abdul Wahab, Masoud Ahmadipour

“…The proposed hybrid IMGOPMFFNN model proved its superior correlation evaluations, surpassing the performance of ant lion optimizer based on random forest (ALORF) model, two stages of ANN (ALO2ANN) model, long short-term memory (LSTM), gated recurrent unit (GRU), extreme learning machine (ELM), least square support vector machine (LSSVM), and convolutional neural network (CNN) models. …”
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Identification and Patient Benefit Evaluation of Machine Learning Models for Predicting 90-Day Mortality After Endovascular Thrombectomy Based on Routinely Ready Clinical Informati... by Andrew Tik Ho Ng, Lawrence Wing Chi Chan

“…This study identified support vector machine (SVM) using model II as the best algorithm among the various options. …”
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Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers by Nguyen Le BPharm, Sola Han PhD, Ahmed S. Kenawy MS, Yeijin Kim MS, Chanhyun Park PhD

“…MACE included acute myocardial infarction, ischemic heart disease, stroke, heart failure, revascularization, malignant arrhythmias, and cardiovascular-related death. Six ML algorithms (L2-Logistic regression, Support Vector Machine, Complement Naïve Bayes, Random Forest, XGBoost, and CatBoost) were trained on 2017-2018 data and tested on 2019 data. …”
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Inversion of SPAD Values of Pear Leaves at Different Growth Stages Based on Machine Learning and Sentinel-2 Remote Sensing Data by Ning Yan, Qu Xie, Yasen Qin, Qi Wang, Sumin Lv, Xuedong Zhang, Xu Li

“…First, spectral reflectance and representative vegetation indices were extracted and subjected to Pearson correlation analysis to construct three input feature schemes. Subsequently, four machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and an Optimized Integrated Algorithm (OIA)—were employed to develop SPAD retrieval models, and the performance differences across various input combinations and models were systematically evaluated. …”
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Analisis Perbandingan Algoritma Machine Learning dan Deep Learning untuk Klasifikasi Citra Sistem Isyarat Bahasa Indonesia (SIBI) by Mohammad Farid Naufal, Selvia Ferdiana Kusuma

“…K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional neural network (CNN) with transfer learning are three popular classification algorithms compared in this study. …”
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Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features by Haoru Wang MD, Ling He MD, Xin Chen MD, Shuang Ding MD, Mingye Xie MD, Jinhua Cai MD

“…A predictive model for bone marrow metastasis was then developed using the support vector machine algorithm based on the selected radiomics features. …”
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Detecting severe coronary artery stenosis in T2DM patients with NAFLD using cardiac fat radiomics-based machine learning by Mengjie Liang, Liting Fang, Xie Chen, Wendi Huang

“…The clinical model obtained an area under the curve (AUC) of 0.747 with the support vector machine (SVM), while the radiomics model reached an AUC of 0.838 with the extreme gradient boosting (XGBoost) algorithm. …”
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Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios by Arifur Rahman Rifath, Md Golam Muktadir, Mahmudul Hasan, Md Ashraful Islam

“…Earth observation data, field surveys, and past flood records were used to create a detailed flood inventory. Among the machine learning models tested, the random forest (RF) algorithm outperformed others, including support vector machine (SVC), logistic regression (LR), and extreme gradient boosting (XGBoost), and was subsequently used for flood susceptibility mapping based on future precipitation projections under two Sixth Coupled model intercomparison project (CMIP6) climate change scenarios: SSP1-2.6 and SSP5-8.5. …”
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A risk prediction model for poor joint function recovery after ankle fracture surgery based on interpretable machine learning by Congyang Li, Chenggang Wang, Jiru Zhang, Wenjun Zheng, Jing Shi, Li Li, Xuezhi Shi

“…Feature variables were selected using the Boruta algorithm, and five machine learning algorithms (logistic regression, random forest, extreme gradient boosting, support vector machine, and lasso-stacking) were employed to construct models. …”
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Machine learning approaches for identifying and predicting voltage conditions in power system networks using network topology behavior input formulation by Tolulope David Makanju, Oluwole John Famoriji, Ali N. Hasan, Thokozani Shongwe

“…This paper developed a predictive model for determining the condition of voltage in a power system based on the network topology behavior as input formulation. Different machine learning algorithms random forest, support vector machine and gradient boosting were used to evaluate the input formulation approach for voltage condition prediction in power system networks. …”
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Neurocognition as a major predictor of 8-week response to antipsychotics for drug-naïve first-episode schizophrenia using machine learning by Xianghe Wang, Tianqi Gao, Xiaodong Guo, Bingjie Huang, Yunfei Ji, Wanheng Hu, Xiaolin Yin, Yue Zheng, Chengcheng Pu, Xin Yu

“…The current study aimed to build a prediction model of eight-week antipsychotic response based on baseline clinical and demographic features. Six machine learning algorithms, including random forest, eXtreme gradient boosting (XGBoost), logistic regression, linear support vector machine (SVM), radial basis function SVM and poly SVM were applied and compared to draw the prediction model. …”
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Clinical prediction model by machine learning to determine the results of maternal dietary avoidance in food protein-induced allergic proctocolitis infants by Jing Li, Meng-yao Zhou, Yang Li, Xue Wu, Xin Li, Xiao-li Xie, Li-jing Xiong

“…Classification models were built utilizing various machine learning algorithms including XGB Classifier, Logistic Regression, Random Forest Classifier, Ada Boost Classifier, KNeighbors Classifier, LGBM Classifier, Decision Tree Classifier, Gradient Boosting Classifier, Support Vector Classifier. …”
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Forest cover and canopy health mapping in Australian subalpine landscape: supervised machine learning models for Sentinel-2 and Landsat images by Weerach Charerntantanakul, Marta Yebra, Hilary Rose Dawson, Adrienne Beth Nicotra, Saul Alan Cunningham, Matthew Theodore Brookhouse

“…We tested random-forest (RF), support vector machine (SVM), and multiple linear regression (MLR) to find the algorithm that provides the best accuracy. …”
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Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study by Min Yang, Huiqin Zhang, Minglan Yu, Yunxuan Xu, Bo Xiang, Xiaopeng Yao

“…Logistic regression (LR), support vector machine (SVM), random forest (RF) and eXtreme gradient boosting (XGBoost) algorithm models were used to construct risk prediction models in the training set, and the model performance was verified in the test set. …”
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Integration of multi-temporal SAR data and robust machine learning models for improvement of flood susceptibility assessment in the southwest coast of India by Pankaj Prasad, Sourav Mandal, Sahil Sandeep Naik, Victor Joseph Loveson, Simanku Borah, Priyankar Chandra, Karthik Sudheer

“…Six efficient machine learning models, namely support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (ANN), random forest (RF), partial least squares (PLS) and penalized discriminant analysis (PDA) have been applied to delineate the flood susceptible areas of the study region. …”
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Comparative study on risk prediction model of type 2 diabetes based on machine learning theory: a cross-sectional study by Shu Wang, Shuang Wang, Rong Chen, Ling Luo, Qiaoli Zhang, Danli Kong, Rudai Cao, Chunwen Lin, Jialu Huang, Haibing Yu, Yuan Lin Ding

“…The comparison results of the six models showed that back propagation neural network model has the best prediction effect with 93.7% accuracy, 94.6% accuracy, 92.8% recall and the AUC value of 0.977, followed by logistic model, support vector machine model, CART decision tree model and C4.5 decision tree model. …”
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Secure data collection method of WSN based on mobile Sink by Chunyu MIAO, Yuan FAN, Hui LI, Kaiqiang GE, Xiaomeng ZHANG

“…At present, WSN data collection method based on mobile Sink has some problems, such as low detection rate of network attack and large memory cost, which makes the network vulnerable to network attack and difficult to be applied in practice.To solve this problem, a secure data collection WSN method of WSN based on mobile Sink was proposed, which used convex hull algorithm of energy perception to identify data collection points, used elliptic encryption algorithm (ECC) to generate key for all nodes in the network, used ElGamal algorithm to realize node identity and message authentication, and used support vector machine (SVM) to identify network attack types.The simulation results show that the proposed secure data collection method has good performance in attack detection rate, memory overhead and packet delivery rate.…”
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A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data by Jordi Mahardika Puntu, Ping-Yu Chang, Haiyina Hasbia Amania, Ding-Jiun Lin, M. Syahdan Akbar Suryantara, Jui-Pin Tsai, Hwa-Lung Yu, Liang-Cheng Chang, Jun-Ru Zeng, Lingerew Nebere Kassie

“…Subsequently, this model was transformed into a 3D AGM using the SML technique. Four algorithms, namely, random forest (RF), decision tree (DT), support vector machine (SVM), and extreme gradient boosting (XGBoost) were implemented. …”
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