Functional Diagnostic System for Multichannel Mine Lifting Machine Working in Factor Cluster Analysis Mode by Zimovets V. I., Shamatrin S. V., Olada D. E., Kalashnykova N. I.

“…Therefore, the creation of the basics of information synthesis of a functional diagnosis system (FDS) based on machine learning and pattern recognition is a topical task. …”
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Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation by Han Chen, Han Chen, Enguang Chen, Enguang Chen, Ting Cao, Feifan Feng, Min Lin, Xuan Wang, Yu Xu

“…Differentially expressed genes (DEGs) were identified using the DESeq2 package, followed by functional enrichment analysis through DAVID and Metascape tools. Three machine learning algorithms—LASSO regression, Random Forest, and SVM-RFE—were employed to identify hub genes. …”
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MLRec: A Machine Learning-Based Recommendation System for High School Students Context of Bangladesh by Momotaz Begum, Mehedi Hasan Shuvo, Jia Uddin

“…Social media and mobile devices, commonly referred to as socimedevices, have become integral to students’ daily lives, influencing both their academic performance and overall well-being. Depending on usage patterns, these technologies can positively or negatively impact students’ education. …”
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Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures by Tingting Liu, Lifan Zhong, Xizhe Sun, Zhijiang He, Witiao Lv, Liyun Deng, Yanfei Chen

“…The ABF-CatBoost integration facilitates a multi-targeted therapeutic approach, addressing drug resistance by analyzing mutation patterns, adaptive resistance mechanisms, and conserved binding sites. …”
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Machine learning for clustering and classification of early knee osteoarthritis using single-leg standing kinematics by Ui-Jae Hwang, Kyu Sung Chung, Sung-Min Ha

“…This study investigated the application of machine learning techniques to single-leg standing (SLS) kinematics to classify and predict EOA. (1) To identify distinct groups based on SLS kinematic patterns using unsupervised learning algorithms, (2) to develop supervised learning models to predict EOA status, and (3) to identify the most influential kinematic variables associated with EOA. …”
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Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation by Juan Yang, Xiang Li, Li Ma, Jun Zhang

“…The CIBERSORT algorithm was utilized to evaluate immune cell infiltration patterns. …”
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Machine learning and multi-omics analysis reveal key regulators of proneural–mesenchymal transition in glioblastoma by Can Xu, Jin Yang, Huan Xiong, Xiaoteng Cui, Yuhao Zhang, Mingjun Gao, Lei He, Qiuyue Fang, Changxi Han, Wei Liu, Yangyang Wang, Jin Zhang, Ying Yuan, Zhaomu Zeng, Ruxiang Xu

“…CIBERSORT, TIMER, MCPCOUNTER, and XCELL algorithms were used to analyze immune cell infiltration patterns. …”
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A machine learning approach to identifying key predictors of Peruvian school principals' job satisfaction by Luis Alberto Holgado-Apaza, Dany Dorian Isuiza-Perez, Nelly Jacqueline Ulloa-Gallardo, Yban Vilchez-Navarro, Ruth Nataly Aragon-Navarrete, Wilian Quispe Layme, Marleny Quispe-Layme, Danger David Castellon-Apaza, Remo Choquejahua-Acero, Jaime Cesar Prieto-Luna

“…Despite the significance of this issue, there is limited research on satisfaction predictors for these professionals, particularly using machine learning approaches. This study identified key predictors of job satisfaction among Peruvian school principals by applying an ensemble of feature selection methods and evaluating five machine learning algorithms (Random Forest, Decision Trees-CART, Histogram-Based Gradient Boosting, XGBoost, and LightGBM) with data from the 2018 National Survey of Directors. …”
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Transcriptomic analysis and machine learning modeling identifies novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy by Feng Zhang, Chunrui Li, Lulu Zhang

“…A predictive model for HCM was developed through systematic evaluation of 113 combinations of 12 machine-learning algorithms, employing 10-fold cross-validation on training datasets and external validation using an independent cohort (GSE180313).ResultsA total of 271 DEGs were identified, primarily enriched in multiple biological pathways. …”
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In-Process Monitoring of Inhomogeneous Material Characteristics Based on Machine Learning for Future Application in Additive Manufacturing by André Jaquemod, Marijana Palalić, Kamil Güzel, Hans-Christian Möhring

“…The algorithms are trained to recognize patterns, anomalies, or deviations from expected behavior, which can aid in evaluating the effect of detected defects on the machining process and the resultant component quality. …”
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Machine Learning and Multilayer Perceptron-Based Customized Predictive Models for Individual Processes in Food Factories by Byunghyun Lim, Dongju Kim, Woojin Cho, Jae-Hoi Gu

“…Additionally, it proposes a customized predictive model employing four machine learning algorithms—linear regression, decision tree, random forest, and k-nearest neighbor—as well as two deep learning algorithms: long short-term memory and multi-layer perceptron. …”
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Enhancing Mobile App Recommendations With Crowdsourced Educational Data Using Machine Learning and Deep Learning by Naadiya Mirbahar, Kamlesh Kumar, Asif Ali Laghari

“…In the rapidly evolving digital landscape, personalized recommendations have become essential for enhancing user experience. Machine learning models analyze user behavior patterns to suggest relevant entertainment, education, or e-commerce content. …”
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The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure by Nima Kianfar, Benn Sartorius, Colleen L. Lau, Robert Bergquist, Behzad Kiani

“…Research in spatial epidemiology relies on both conventional approaches and Machine- Learning (ML) algorithms to explore geographic patterns of diseases and identify influential factors (Pfeiffer & Stevens, 2015). …”
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A Deep Learning Algorithm of Neural Network for the Parameterization of Typhoon‐Ocean Feedback in Typhoon Forecast Models by Guo‐Qing Jiang, Jing Xu, Jun Wei

“…It tends to produce an unstable SSTC distribution, for which any perturbations may lead to changes in both SSTC pattern and strength. The D‐L algorithm extends the neural network to a 4 × 5 neuron matrix with atmospheric and oceanic factors being separated in different layers of neurons, so that the machine learning can determine the roles of atmospheric and oceanic factors in shaping the SSTC. …”
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On Training Spiking Neural Networks by Means of a Novel Quantum Inspired Machine Learning Method by Jean Michel Sellier, Alexandre Martini

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A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization by Md. Saddam Hossain, Md. Parvez Khandocar, Farzana Akter Riti, Md. Yeakub Ali, Prithbey Raj Dey, S M Jahurul Haque, Amira Metouekel, Atrsaw Asrat Mengistie, Mohammed Bourhia, Farid Khallouki, Khalid S. Almaary

“…We trained ML-supervised algorithms like XG Boost, Logistic Regression, Random Forest Classifier, Ad- aBoost, and Support Vector Machine to help classify TB patients from large RNA-sequence count data. …”
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Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm by Fang Liu, Xiaoli Liu, Changyou Yin, Hongrong Wang

“…Feature extraction is done using local binary patterns (LBP). Classification is performed using a fuzzy support vector machine (FSVM) classifier. …”
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Enhancing DDoS Attack Classification through SDN and Machine Learning: A Feature Ranking Analysis by Aymen AlAwadi, Kawthar Rasoul ALesawi

“…Due to the growing dependence of digital services on the Internet, Distributed Denial of Service (DDoS) attacks are a common threat that can cause significant disruptions to online operations and financial losses. Machine learning (ML) offers a promising way for early DDoS attack detection due to its ability to analyze large datasets and identify patterns. …”
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Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation by A. Srinivaas, N. R. Sakthivel, Binoy B. Nair

“…This paper concludes with a review of the progress in fault identification in ICE components and prospects, highlighted by an experimental investigation using 16 machine learning algorithms with seven feature selection techniques under three load conditions to detect faults in a four-cylinder ICE. …”
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Novel application of unsupervised machine learning for characterization of subsurface seismicity, tectonic dynamics and stress distribution by Mohammad Salam, Muhammad Tahir Iqbal, Raja Adnan Habib, Amna Tahir, Aamir Sultan, Talat Iqbal

“…Our study pioneers an innovative use of unsupervised machine learning, a powerful tool for navigating unclassified data, to unravel the complexities of subsurface seismic activities and extract meaningful patterns. …”
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