A Multilevel and Hierarchical Approach for Multilabel Classification Model in SDGs Research by Berliana Sugiarti Putri, Lya Hulliyyatus Suadaa, Efri Diah Utami

“…Problem transformation methods used were binary relevance, label powerset (LP), and classifier chains. Machine learning classification algorithms used were logistic regression (LR) and support vector machine (SVM). …”
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Explainable Artificial Intelligence to Predict the Water Status of Cotton (<i>Gossypium hirsutum</i> L., 1763) from Sentinel-2 Images in the Mediterranean Area by Simone Pietro Garofalo, Anna Francesca Modugno, Gabriele De Carolis, Nicola Sanitate, Mesele Negash Tesemma, Giuseppe Scarascia-Mugnozza, Yitagesu Tekle Tegegne, Pasquale Campi

“…The research was conducted in Rutigliano, Southern Italy, during the 2023 cotton growing season. Different machine learning algorithms, including random forest, support vector regression, and extreme gradient boosting, were evaluated using Sentinel-2 spectral bands as predictors. …”
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A XGBoost-Based Prediction Method for Meat Sheep Transport Stress Using Wearable Photoelectric Sensors and Infrared Thermometry by Ruiqin Ma, Runqing Chen, Buwen Liang, Xinxing Li

“…Subsequently, we built a qualitative stress assessment method based on external manifestations with reference to the Karolinska drowsiness scale to establish stage classification rules for monitoring data in the transportation environment of meat sheep. Finally, machine learning algorithms such as Gaussian Naive Bayes (GaussianNB), Passive-Aggressive Aggregative Classifier (PAC), Nearest Centroid (NC), K-Nearest Neighbor Classification (KNN), Random Forest (RF), Support Vector Classification (SVC), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGB) were established to predict the classification models of transportation stress in meat sheep. …”
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Comprehensive analysis of senescence-related genes identifies prognostic clusters with distinct characteristics in glioma by Wenyuan Wei, Ying Dang, Gang Chen, Chao Han, Siwei Zhang, Ziqiang Zhu, Xiaohua Bie, Jungang Xue

“…Various computational and experimental methods, including WGCNA (Weighted Gene Co-expression Network Analysis), ssGSEA (single-sample Gene Set Enrichment Analysis), and machine learning algorithms (lasso regression, support vector machines, random forests), were employed for analysis. …”
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A Robust Regression-Based Modeling to Predict Antiplasmodial Activity of Thiazolyl–Pyrimidine Hybrid Derivatives against <i>Plasmodium falciparum</i> by Kevin S. Umoette, Charles O. Nnadi, Wilfred O. Obonga

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Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study by Weiyue Chen, Guihan Lin, Ye Feng, Yongjun Chen, Yanjun Li, Jianbin Li, Weibo Mao, Yang Jing, Chunli Kong, Yumin Hu, Minjiang Chen, Shuiwei Xia, Chenying Lu, Jianfei Tu, Jiansong Ji

“…The GPTV3 radiomics model using a support vector machine algorithm achieved the best predictive performance, with the highest average AUC of 0.811 in the validation sets. …”
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Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry by Valeria Di Stefano, Martina D’Angelo, Francesco Monaco, Annarita Vignapiano, Vassilis Martiadis, Eugenia Barone, Michele Fornaro, Luca Steardo, Marco Solmi, Mirko Manchia, Luca Steardo

“…Algorithms such as support vector machines (SVMs) and Vision Transformers (ViTs) have proven particularly effective in identifying biomarkers and aiding early diagnosis. …”
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Predicting Hospitalization Length in Geriatric Patients Using Artificial Intelligence and Radiomics by Lorenzo Fantechi, Federico Barbarossa, Sara Cecchini, Lorenzo Zoppi, Giulio Amabili, Mirko Di Rosa, Enrico Paci, Daniela Fornarelli, Anna Rita Bonfigli, Fabrizia Lattanzio, Elvira Maranesi, Roberta Bevilacqua

“…Three ML classification algorithms, linear support vector machine (LSVM), medium neural network (MNN), and ensemble subspace discriminant (ESD), were trained and validated through a 5-fold cross-validation technique. …”
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Identification and analysis of the endoplasmic reticulum stress hub genes in sepsis-associated ARDS by Ling Gao, Tingting Liu, Xiaoyan Li

“…By intersecting DEGs, key WGCNA module genes, and ERS-related genes(ERGs), ERS-associated differential genes in sepsis-related ARDS were obtained. Subsequently, three machine learning algorithms—least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine (SVM)—were used to further screen for hub ERS hub genes. …”
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Predicting ICU mortality in heart failure patients based on blood tests and vital signs by Yeao Wang, Jianke Rong, Zhili Wei, Xiaoyu Bai, YunDan Deng

“…We utilized a variety of machine learning algorithms for modeling purposes, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Trees, Random Forests, Gradient Boosting Machines (GBM), XGBoost, and Neural Networks. …”
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Protecting Industrial Control Systems From Shodan Exploitation Through Advanced Traffic Analysis by Sayed Reza Ghazinour Naeini, Alireza Shameli-Sendi, Masoume Jabbarifar

“…Additionally, a dual-pronged approach combining customized honeypots with protocol analysis is proposed, enhancing detection capabilities by integrating decoy technologies with enriched traffic insights. Several machine learning algorithms were evaluated, including Random Forest, Support Vector Machine, Logistic Regression, and Gradient Boosting. …”
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Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens, Zamir Libohova

“…Covariates included remotely sensed data collected from Sentinel-2 MSI, Sentinel-1A, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and topographic attributes were calculated from a 20 m digital elevation model derived from hydrologic data (HC-DEM). The machine learning algorithms (Generalized Linear Models with Elastic Net Regularization (GLMNET), Nearest Neighbors (KNN), Neural Network (NNET), Random Forest (RF) and Support Vector Machine (SVMRadial) were used in combination with covariates and measured elements at point locations to spatially map the concentrations of these chemical elements. …”
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Disease activity and treatment response in early rheumatoid arthritis: an exploratory metabolomic profiling in the NORD-STAR cohort by Tahzeeb Fatima, Yuan Zhang, Georgios K. Vasileiadis, Araz Rawshani, Ronald van Vollenhoven, Jon Lampa, Bjorn Gudbjornsson, Espen A. Haavardsholm, Dan Nordström, Gerdur Gröndal, Kim Hørslev-Petersen, Kristina Lend, Marte S. Heiberg, Merete Lund Hetland, Michael Nurmohamed, Mikkel Østergaard, Till Uhlig, Tuulikki Sokka-Isler, Anna Rudin, Cristina Maglio

“…Participants achieving clinical disease activity index remission at 24 weeks were defined as responders. Machine learning models for treatment response were constructed using random forest, logistic regression, support vector machine and extreme gradient boosting algorithms based on selected features. …”
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The diagnostic and prognostic value of C1orf174 in colorectal cancer by Elham Nazari, Ghazaleh Khalili-Tanha, Ghazaleh Pourali, Fatemeh Khojasteh-Leylakoohi, Hanieh Azari, Mohammad Dashtiahangar, Hamid Fiuji, Zahra Yousefli, Alireza Asadnia, Mina Maftooh, Hamed Akbarzade, Mohammadreza Nassiri, Seyed Mahdi Hassanian, Gordon A Ferns, Godefridus J Peters, Elisa Giovannetti, Jyotsna Batra, Majid Khazaei, Amir Avan

“…Predictive biomarkers were determined by machine learning algorithms such as Deep learning, Decision Tree, and Support Vector Machine. …”
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Study of high-speed malicious Web page detection system based on two-step classifier by Zheng-qi WANG, Xiao-bing FENG, Chi ZHANG

“…In view of the increasing number of new Web pages and the increasing pressure of traditional detection methods,the naive Bayesian algorithm and the support vector machine algorithm were used to design and implement a malicious Web detection system with both efficiency and function,TSMWD ,two-step malicious Web page detection.The first step of detection system was mainly used to filter a large number of normal Web pages,which was characterized by high efficiency,speed,update iteration easy,real rate priority.After the former filter,due to the limited number of samples,the main pursuit of the second step was the detection rate.The experimental results show that the proposed scheme can improve the detection speed of the system under the condition that the overall detection accuracy is basically the same,and can accept more detection requests in certain time.…”
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Unsupervised learning analysis on the proteomes of Zika virus by Edgar E. Lara-Ramírez, Gildardo Rivera, Amanda Alejandra Oliva-Hernández, Virgilio Bocanegra-Garcia, Jesús Adrián López, Xianwu Guo

“…Unsupervised learning (UL), a form of machine learning algorithm, can be applied on the datasets without the need of known information for training. …”
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FEATURE EXTRACTION AND ESTABLISHMENT BASED ON PUMPING UNIT WORKING CONDITIONS AND GLOBAL FAULT IDENTIFICATION by REN Taizhu, FAN Jun, JIANG Xiaxin

“…Then, two methods of obtaining valve opening and closing points and load variation characteristics were proposed, and 54 new features of global faults of pumping units were extracted, and the characteristic database of working conditions of the pumping unit was established.Finally, the algorithm of decision tree, logistic regression and support vector machine was used to verify that the feature database has good classification effect under different working conditions. …”
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Cellular senescence defining the disease characteristics of Crohn’s disease by Wenyu Zhang, Xianzong Ma, Xianzong Ma, Wenqing Tian, Yongsheng Teng, Meihua Ji

“…However, the role of CS in the pathogenesis and diagnosis prediction of CD are still unknown.MethodsWe utilized CD-related datasets from the GEO database for differential gene expression analysis, and CS related differentially expressed genes (CSRDEGs) in CD by a comprehensive bioinformatics analysis encompassing GSEA, WGCNA, and various interaction networks. The support vector machine (SVM) algorithm, random forest algorithm and LASSO regression analysis was used to construct a diagnostic model. …”
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IoT-enabled real-time health monitoring system for adolescent physical rehabilitation by Jie Yang, Juanjuan Hu, Wenrui Chen

“…A particle swarm optimization support vector machine (PSO-SVM) algorithm is utilized to classify motion patterns. …”
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Curing simulation and data-driven curing curve prediction of thermoset composites by Chenchen Wu, Ruming Zhang, Pengyuan Zhao, Liang Li, Dingguo Zhang

“…Then, the temperature–time and the resulting degree-of-cure-time curves obtained from finite element simulations were created for training the prediction models using machine learning approaches of support vector regression (SVR), back propagation (BP) neural network and BP neural network optimized by genetic algorithm (GA-BP). …”
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