Integrated analysis unraveling the immunologic and clinical prognostic values of synaptotagmin like 4 in pan-cancer by Yuehan Ren, Xiangbin Wu, Jinlei Li, Zhenhua Zhou, Shichang Ni, Jianhui Cai

“…We use multiple immune infiltration algorithms in TIMER2.0 and TISCH database to cross-verify the associations between SYTL4 expression and tumor immune microenvironment. …”
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ASAD: A Meta Learning-Based Auto-Selective Approach and Tool for Anomaly Detection by Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Saad Alqahtany, Juan M. Corchado

“…ASAD trains an ML model to predict the best candidate from a large pool of models by considering the specific characteristics and requirements of the dataset. …”
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Identification of markers correlating with mitochondrial function in myocardial infarction by bioinformatics. by Wenlong Kuang, Jianwu Huang, Yulu Yang, Yuhua Liao, Zihua Zhou, Qian Liu, Hailang Wu

“…The 10 MI-related hub MitoDEGs were then obtained by eight different algorithms. Immunoassays showed a significant increase in monocyte macrophage and T cell infiltration. …”
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Automated pipeline for leaf spot severity scoring in peanuts using segmentation neural networks by Joshua Larsen, Jeffrey Dunne, Robert Austin, Cassondra Newman, Michael Kudenov

“…Results The pipeline incorporated a neural network that accurately determined the infected leaf surface area and identified dead leaves from plot-level cellphone imagery. Image processing algorithms then convert these labels into quality metrics that can efficiently score these images based on infected versus non-infected area. …”
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Informational Approaches in Modelling Social and Economic Relations: Study on Migration and Access to Services in the European Union by Florentina-Loredana Dragomir-Constantin, Camelia Madalina Beldiman, Monica Laura Zlati

“…The applied methodology includes attribute distribution analysis, identification of hidden patterns through clustering algorithms (K-Means and Expectation-Maximisation) and training of classifiers using regression decision trees with linear leaf models (M5P) corresponding to interdependent data processing and integration modules, exploratory analysis module, machine learning and decision-making modules, oriented to support public policies through explainable scenarios and predictive-evaluative structures. …”
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MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection by Jun Liu, Haolin Li, Hao Liu, Jiuzhen Liang

“…Ultimately, AGNet’s true positive rate (TPR), positive predictive value (PPV), and f-measure exceed those of the state-of-the-art (SOTA) algorithms by 1.88%, 0.05%, and 0.77%, respectively, and with a consistent model architecture, its parameter quantity is reduced by 56%.…”
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Online Learning to Cache and Recommend in the Next Generation Cellular Networks by Krishnendu S. Tharakan, B. N. Bharath, Vimal Bhatia

“…Finally, simulation results confirm the superiority of the proposed algorithms in terms of average cache hit rate, delay and throughput.…”
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Data augmentation of time-series data in human movement biomechanics: A scoping review. by Christina Halmich, Lucas Höschler, Christoph Schranz, Christian Borgelt

“…However, the field faces challenges such as limited large-scale data sets, high data acquisition costs, and restricted participant access that hinder the development of robust algorithms. Additional issues include variability in sensor placement, soft tissue artifacts, and low diversity in movement patterns. …”
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BiLSTM-Based Parallel CNN Models With Attention and Ensemble Mechanism for Twitter Sentiment Analysis by Anas W. Abulfaraj

“…Our methodology incorporates four classifiers to produce text class predictions. Among them, five algorithms are selected for evaluation: Ridge Classifier (RC), Linear Discriminant Analysis (LDA), Extra Trees (ET), and Light Gradient Boosting Machine (LightGBM). …”
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Approach to Enhancing Panoramic Segmentation in Indoor Construction Sites Based on a Perspective Image Segmentation Foundation Model by Juho Han, Sebeen Yoon, Mingyun Kang, Taehoon Kim

“…The proposed method iteratively executes SAM with adjusted input parameters to extract objects of varying sizes and subsequently applies filtering algorithms to retain valid objects. Then, label assignment and merging processes are performed based on the predictions from the target model to improve segmentation accuracy. …”
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Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems by Natalya Denissova, Serik Nurakynov, Olga Petrova, Daniker Chepashev, Gulzhan Daumova, Alena Yelisseyeva

“…Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. The review found that remote sensing significantly improves avalanche monitoring by providing continuous, large-scale coverage of snowpack stability and terrain features. …”
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Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas by Yue Hu, Xin Cao, Hongyi Chen, Daoying Geng, Kun Lv

“…The logical regression, random forest, support vector machine (SVM) and adaptive boosting algorithms were used to establish models. The model performance was evaluated using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. …”
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The intelligent fault identification method based on multi-source information fusion and deep learning by Dashu Guo, Xiaoshuang Yang, Peng Peng, Lei Zhu, Handong He

“…By training samples and applying fusion algorithms, the spectral, topographic, geomorphic, and structural features are integrated to enhance the morphological features information of faults. …”
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Advanced strategies for the efficient optimization and control of industrial compressed air systems by D. Salomone-González

“…Real-time data transmission, powered by big data algorithms, enables continuous analysis to optimize the overall performance of the compressor plant. …”
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Identification and optimization of relevant factors for chronic kidney disease in abdominal obesity patients by machine learning methods: insights from NHANES 2005–2018 by Xiangling Deng, Lifei Ma, Pin Li, Mengyang He, Ruyue Jin, Yuandong Tao, Hualin Cao, Hengyu Gao, Wenquan Zhou, Kuan Lu, Xiaoye Chen, Wenchao Li, Huixia Zhou

“…Furthermore, an optimal predictive model was developed for CKD using ten machine learning algorithms and enhanced model interpretability with the Shapley Additive Explanations (SHAP) method. …”
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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens by Paul English, Muir J. Morrison, Blaine Mathison, Elizabeth Enrico, Ryan Shean, Brendan O'Fallon, Deven Rupp, Katie Knight, Alexandra Rangel, Jeffrey Gilivary, Amanda Vance, Haleina Hatch, Leo Lin, David P. Ng, Salika M. Shakir

“…A machine learning computer vision model was trained using 11,411 annotated organisms across 109 WSI. Model predictions were correlated with final culture-confirmed results. …”
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Breast tumors from ATM pathogenic variant carriers display a specific genome-wide DNA methylation profile by Nicolas M. Viart, Anne-Laure Renault, Séverine Eon-Marchais, Yue Jiao, Laetitia Fuhrmann, Sophia Murat El Houdigui, Dorothée Le Gal, Eve Cavaciuti, Marie-Gabrielle Dondon, Juana Beauvallet, Virginie Raynal, Dominique Stoppa-Lyonnet, Anne Vincent-Salomon, Nadine Andrieu, Melissa C. Southey, Fabienne Lesueur

“…Moreover, using three different deep learning algorithms (logistic regression, random forest and XGBoost), we identified a set of 27 additional biomarkers predictive of ATM status, which could be used in the future to provide evidence for or against pathogenicity in ATM variant classification strategies. …”
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Machine Learning and Experimental Validation Reveal MYH11 as a Novel Prognostic Biomarker and Therapeutic Target in Bladder Cancer by Tan Z, Chen X, Fu S, Huang Y, Li H, Gong C, Lv D, Yang C, Wang J, Ding M, Wang H

“…Finally, single-cell analysis identified key cells involved in BCa pathogenesis, and in vitro experiments validated the expression and function of key genes.Results: The risk model constructed by 8 prognostic genes identified using 101 algorithms effectively predicted the survival outcomes of BCa patients. …”
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Machine learning aids in the discovery of efficient corrosion inhibitor molecules by Haiyan GONG, Lingwei MA, Dawei ZHANG

“…Specifically, ML models can extract key information and construct predictive models through feature extraction and pattern recognition using existing data. …”
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Metabolic dysfunction-associated steatotic liver disease (MASLD) biomarkers and progression of lower limb arterial calcification in patients with type 2 diabetes: a prospective coh... by Damien Denimal, Maharajah Ponnaiah, Franck Phan, Anne-Caroline Jeannin, Alban Redheuil, Joe-Elie Salem, Samia Boussouar, Pauline Paulstephenraj, Suzanne Laroche, Chloé Amouyal, Agnès Hartemann, Fabienne Foufelle, Olivier Bourron

“…We also measured the serum biomarkers included in the FibroMax® panels (SteatoTest®, FibroTest®, NashTest®, ActiTest®). The predictive ability of these biomarkers of MASLD on LLACS progression was assessed through univariate and multivariate linear regression models, principal component regression analysis, as well as machine learning algorithms. …”
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