Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine learning by Changjin Wei, Yongfeng Zhu, Caiming Chen, Feipeng Li, Li Zheng

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Enhanced Blade Fault Diagnosis Using Hybrid Deep Learning: A Comparative Analysis of Traditional Machine Learning and 1D Convolutional Transformer Architecture by Syed Asad Imam, Meng Hee Lim, Ahmed Mohammed Abdelrhman, Iftikhar Ahmad, Mohd Salman Leong

“…By investigating blade fault patterns and using appropriate diagnostic techniques, it becomes possible to predict potential failures and schedule maintenance proactively. …”
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Pengujian Rule-Based pada Dataset Log Server Menggunakan Support Vector Machine Berbasis Linear Discriminat Analysis untuk Deteksi Malicious Activity by Kurnia Adi Cahyanto, Muhammad Anis Al Hilmi, Muhamad Mustamiin

“…In addition, if there is a file uploaded by a user, it can also be linked in server log analysis in recognizing activity patterns and malicious files. The log dataset that has been obtained is processed using rule-based labeling which will later be tested with a Linear Discriminant Analysis-based Support Vector Machine modeling. …”
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Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Effic... by Shadi Jacob Khoury, Yazeed Zoabi, Mickey Scheinowitz, Noam Shomron

“…In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. …”
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ARTIFICIAL LEARNING BASED ON KERNEL SVM FOR THE PREDICTION OF CARDIOVASCULAR DISEASE HYPERTENSION by Patient MUSUBAO SWAMBI, Albert Ntumba Nkongolo, Pierre Kafunda Katalay, Rostin Mabela Matendo Makengo, Eugène Mbuyi Mukendi

“…This study examines the application of kernel-based Support Vector Machines (SVM) for predicting hypertension, utilizing advanced machine learning techniques to address the complex, non-linear relationships inherent in healthcare data. …”
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Introducing HeliEns: A Novel Hybrid Ensemble Learning Algorithm for Early Diagnosis of <i>Helicobacter pylori</i> Infection by Sultan Noman Qasem

“…Recent advancements in machine learning (ML) and quantum machine learning (QML) offer promising non-invasive alternatives capable of analyzing complex datasets to identify patterns not easily discernible by human analysis. …”
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Efficient IDS for IoT Networks Using Host-Based Data Aggregation and Multi-Entropy Analysis by Yusei Katsura, Arata Endo, Ismail Arai, Kazutoshi Fujikawa

“…Additionally, the method captures host-level communication behaviors by leveraging multiple entropies, focusing on characteristic patterns of IoT devices, such as periodic communication with specific servers during normal operation. …”
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Intra-tumor microbiome-based tumor survival indices predict immune interaction and drug sensitivity on pan-cancer scale by Yan Gao, Haohong Zhang, Dongliang Chu, Kang Ning

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Computational Estimation of Football Player Wages by Yaldo L., Shamir L.

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Reassembling Agency by Francis Lee

“…By discussing three ideal types of agencing, the article argues that AI should not be regarded as a rupture in the tooling and practices of science, but rather as a continuation of long-standing patterns of practice. That is, agency, and the space for action and judgement, is organised differently in the AI-driven laboratory; however, this is not a new configuration of epistemic agency. …”
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Explainable deep learning for stratified medicine in inflammatory bowel disease by Nora Verplaetse, Piero Fariselli, Yves Moreau, Daniele Raimondi

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PARENTING SELF-EFFICACY BASED ON STIFIN AS INTELLEGENCE MECHINE OF LEARNING by Eka Diana, Siti Wardatus Sholehah

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Developing a molecular diagnostic model for heatstroke-induced coagulopathy: a proteomics and metabolomics approach by Qingbo Zeng, Qingwei Lin, Longping He, Lincui Zhong, Ye Zhou, Xingping Deng, Nianqing Zhang, Qing Song, Qing Song, Jingchun Song, Jingchun Song

“…Additionally, three optimal predictive models (AUC &gt;0.9) were developed and validated for classifying HSIC from HS individuals based on proteomic patterns and machine learning, with the logistic regression model showing the best diagnostic performance (AUC = 0.979, sensitivity = 81.8%, specificity = 96.7%), highlighting lactate dehydrogenase A chain (LDHA), neutrophil gelatinase-associated lipocalin (NGAL), prothrombin and glucan-branching enzyme (GBE) as key predictors of HSIC.ConclusionThe study uncovered critical metabolic and protein changes linked to heatstroke, highlighting the involvement of energy regulation, lipid metabolism, and carbohydrate metabolism. …”
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Risk of autism spectrum disorder at 18 months of age is associated with prenatal level of polychlorinated biphenyls exposure in a Japanese birth cohort by Hirokazu Doi, Akira Furui, Rena Ueda, Koji Shimatani, Midori Yamamoto, Akifumi Eguchi, Naoya Sagara, Kenichi Sakurai, Chisato Mori, Toshio Tsuji

“…There was no reliable relationship between PCB PCs and problematic behaviors at 5 years of age. Furthermore, machine learning-based analysis showed the possibility that, when the information of the pattern of infants’ spontaneous bodily motion, a potential marker of ASD risk, was used as the predictors together, prenatal PCB exposure levels predict ASD risk at 18 months of age. …”
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Subdivision of Seismicity Beneath the Summit Region of Kilauea Volcano: Implications for the Preparation Process of the 2018 Eruption by Xin Cui, Zefeng Li, Hui Huang

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Predicting and Preventing School Dropout with Business Intelligence: Insights from a Systematic Review by Diana-Margarita Córdova-Esparza, Juan Terven, Julio-Alejandro Romero-González, Karen-Edith Córdova-Esparza, Rocio-Edith López-Martínez, Teresa García-Ramírez, Ricardo Chaparro-Sánchez

“…We collected literature from the Google Scholar and Scopus databases using a comprehensive search strategy, incorporating keywords such as “business intelligence”, “machine learning”, and “big data”. The results highlight a wide range of predictive tools and methodologies, notably data visualization platforms (e.g., Power BI) and algorithms like decision trees, Random Forest, and logistic regression, demonstrating effectiveness in identifying dropout patterns and at-risk students. …”
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Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases by Christoph J Blohmke, Julius Muller, Malick M Gibani, Hazel Dobinson, Sonu Shrestha, Soumya Perinparajah, Celina Jin, Harri Hughes, Luke Blackwell, Sabina Dongol, Abhilasha Karkey, Fernanda Schreiber, Derek Pickard, Buddha Basnyat, Gordon Dougan, Stephen Baker, Andrew J Pollard, Thomas C Darton

“…Our analysis highlights the power of data‐driven approaches to identify host response patterns for the diagnosis of febrile illnesses. Expression signatures were validated using qPCR, highlighting their utility as PCR‐based diagnostics for use in endemic settings.…”
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Innovación en sueño by Laura Vigil, Toni Zapata, Andrea Grau, Marta Bonet, Montserrat Montaña, María Piñar

“…In addition, techniques such as cluster analysis are used to identify symptomatic patterns and phenotypes, which improves understanding of OSA pathophysiology and optimizes CPAP treatment.However, implementation of AI in hospitals faces technological, ethical, and legal barriers. …”
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Beyond Linearity: Uncovering the Complex Spatiotemporal Drivers of New-Type Urbanization and Eco-Environmental Resilience Coupling in China’s Chengdu–Chongqing Economic Circle with... by Caoxin Chen, Shiyi Wang, Meixi Liu, Ke Huang, Qiuyi Guo, Wei Xie, Jiangjun Wan

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Predicting High-Cost Healthcare Utilization Using Machine Learning: A Multi-Service Risk Stratification Analysis in EU-Based Private Group Health Insurance by Eslam Abdelhakim Seyam

“…The prediction of high-cost utilization patterns is important for the sustainable management of healthcare and focused intervention measures. …”
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