Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis by Jingtai Ma, Jingtai Ma, Yiting Fang, Yiting Fang, Shiqi Li, Shiqi Li, Lilian Zeng, Siyi Chen, Zhifeng Li, Guiyuan Ji, Xingfen Yang, Wei Wu, Wei Wu

“…Therefore, we have constructed an interpretable machine learning framework to quantitatively screen key gut flora.MethodsThe 16S rRNA dataset, after applying the centered log-ratio transformation, was analyzed using five different machine learning models: random forest, light gradient boosting machine, extreme gradient boosting, support vector machine with radial kernel, and logistic regression. …”
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Federated Learning-Driven IoT Request Scheduling for Fault Tolerance in Cloud Data Centers by Sheeja Rani S, Raafat Aburukba

“…At first, radial kernelized support vector regression is applied in the local training model to identify resource-efficient virtual machines. …”
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SHM System for Composite Material Based on Lamb Waves and Using Machine Learning on Hardware by Gracieth Cavalcanti Batista, Carl-Mikael Zetterling, Johnny Öberg, Osamu Saotome

“…The system employs machine learning (ML), specifically support vector machines (SVM), to classify damage while addressing outlier challenges with the Mahalanobis distance during the classification phase. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and k-Nearest Neighbors (KNN). …”
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Sentiment Analysis from Face Expressions Based on Image Processing Using Deep Learning Methods by Orhan Emre Aksoy, Selda Güney

“…With the application written in Python programming language, classical machine learning methods such as k-Nearest Neighborhood and Support Vector Machines and deep learning methods such as AlexNet, ResNet, DenseNet, Inception architectures were applied to FER2013, JAFFE and CK+ datasets. …”
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Machine learning algorithms for manufacturing quality assurance: A systematic review of performance metrics and applications by Ashfakul Karim Kausik, Adib Bin Rashid, Ramisha Fariha Baki, Md Mifthahul Jannat Maktum

“…Adopting Machine Learning (ML) in manufacturing quality assurance (QA) has accelerated with Industry 4.0, enabling automated defect detection, predictive maintenance, and real-time process optimization. …”
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Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour by Yuling Wang, Chen Zhang, Xinhua Li, Longzhu Xing, Mengchao Lv, Hongju He, Leiqing Pan, Xingqi Ou

“…This research implemented a miniaturized near-infrared spectroscopy (NIRS) system integrated with machine learning approaches for the quantitative evaluation of dry gluten content (DGC), wet gluten content (WGC), and the gluten index (GI) in wheat flour in a noninvasive manner. …”
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Identification of neutrophil extracellular trap-related biomarkers in ulcerative colitis based on bioinformatics and machine learning by Jiao Li, Yupei Liu, Zhiyi Sun, Suqi Zeng, Caisong Zheng

“…To identify potential diagnostic biomarkers, we applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE) model, and Random Forest (RF) algorithm, and constructed Receiver Operating Characteristic (ROC) curves to evaluate accuracy. …”
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A supervised machine-learning analysis of doxorubicin-loaded electrospun nanofibers and their anticancer activity capabilities by Mohammadreza Rostami, Mohammadreza Rostami, Maliheh Gharibshahian, Maliheh Gharibshahian, Mehrnaz Mostafavi, Ali Sufali, Mahsa Golmohammadi, Mohammad Reza Barati, Reza Maleki, Nima Beheshtizadeh, Nima Beheshtizadeh

“…This study employed a supervised machine-learning analysis to extract the influencing parameters of the input from quantitative data for doxorubicin-loaded electrospun nanofibers. …”
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Advancing Real-Time Remote Learning: A Novel Paradigm for Cognitive Enhancement Using EEG and Eye-Tracking Analytics by Nuraini Jamil, Abdelkader Nasreddine Belkacem

“…In this study, various machine learning models were employed to identify cognitive states using eye-tracking and electroencephalogram data, which can provide quantitative indicators of cognitive activity. …”
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Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form. by Annarita Fanizzi, Samantha Bove, Maria Colomba Comes, Erika Francesca Di Benedetto, Agnese Latorre, Francesco Giotta, Annalisa Nardone, Alessandro Rizzo, Clara Soranno, Alfredo Zito, Raffaella Massafra

“…After transforming each patient, represented by a vector of clinical information, to an image form, we extracted low-level quantitative imaging features by means of a pre-trained Convolutional Neural Network, namely, AlexNET. …”
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A Deep Learning-Driven Black-Box Benchmark Generation Method via Exploratory Landscape Analysis by Haoming Liang, Fuqing Zhao, Tianpeng Xu, Jianlin Zhang

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Deterministic and Stochastic Machine Learning Classification Models: A Comparative Study Applied to Companies’ Capital Structures by Joseph F. Hair, Luiz Paulo Fávero, Wilson Tarantin Junior, Alexandre Duarte

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Radiomics and machine learning for osteoporosis detection using abdominal computed tomography: a retrospective multicenter study by Zhai Liu, Yongjun Li, Chenguang Zhang, Hui Xu, Junlu Zhao, Chencui Huang, Xingzhi Chen, Qingyun Ren

“…Seven radiomic-based ML models, including logistic regression (LR), Bernoulli, Gaussian NB, SGD, decision tree, support vector machine (SVM), and K-nearest neighbor (KNN) models, were constructed. …”
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Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review by Shailesh S. Nayak, Saikiran Pendem, Girish R. Menon, Niranjana Sampathila, Prakashini Koteshwar

“…Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. …”
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Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy by Jing Zhang, Lulu Cheng, Shan Jiang, Duosheng Zhu

“…After that, receiver operating characteristic (ROC) analysis was employed to assess the diagnostic accuracy of each gene and the real-time quantitative polymerase chain reaction (RT-qPCR) was adopted to quantitatively detect the expression of biomarkers in DN cell models. …”
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Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells by Tianxiang Zhang, Chunhui Yuan, Mo Chen, Jinjiang Liu, Wei Shao, Ning Cheng

“…Gene ontology and Kyoto encyclopedia of genes and genomes analyses were performed to explore the functions of common FR-related DEGs (FRDEGs). Two machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were used to screen for overlapping FRDEGs in CS and AS. …”
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Single-cell and machine learning approaches uncover intrinsic immune-evasion genes in the prognosis of hepatocellular carcinoma by Jiani Wang, Xiaopeng Chen, Donghao Wu, Changchang Jia, Qinghai Lian, Yuhang Pan, Jiumei Yang

“…To address these issues, single-cell technology and machine learning methods have emerged as a promising approach to identify genes associated with immune escape in HCC. …”
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Drivers of academic achievement in high school: Assessing the impact of COVID-19 using machine learning techniques by Ana Beatriz-Afonso, Frederico Cruz-Jesus, Catarina Nunes, Mauro Castelli, Tiago Oliveira, Luísa Canto e Castro

“…Estimates suggest that students’ learning decreased by up to 50% compared to a typical year, though the full impact remains unclear. …”
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On the Readability of Kernel-based Deep Learning Models in Semantic Role Labeling Tasks over Multiple Languages by Daniele Rossini, Danilo Croce, Roberto Basili

“…Sentence embeddings are effective input vectors for the neural learning of a number of inferences about content and meaning. …”
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