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

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Synergistic Non-Intrusive Load Monitoring: Dual-Model Training and Inference for Improved Load Disaggregation Prediction by Mazen Bouchur, Andreas Reinhardt

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Reconciling Global Terrestrial Evapotranspiration Estimates From Multi‐Product Intercomparison and Evaluation by Yaoting Cai, Qingchen Xu, Fan Bai, Xueqi Cao, Zhongwang Wei, Xingjie Lu, Nan Wei, Hua Yuan, Shupeng Zhang, Shaofeng Liu, Yonggen Zhang, Xueyan Li, Yongjiu Dai

“…It is shown that global ET magnitudes of categories differ considerably, with averages ranging from 518.4 to 706.3 mm yr−1. Spatial patterns are generally consistent but with significant divergence in tropical rainforests. …”
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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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Advancing EEG-based biometric identification through multi-modal data fusion and deep learning techniques by Touseef Ur Rehman, Madallah Alruwaili, Muhammad Hameed Siddiqi, Yousef Alhwaiti, Sajid Anwar, Zahid Halim, Maaz Alam

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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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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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Intelligent prediction of thyroid cancer in China based on GBD data and hospital electronic medical records: disease burden analysis combined with multiple machine learning models by Lina Yang, Shixia Zhang, Xinguo Wang, Jianjun Yang, Mengya Chen

“…This study aims to conduct an in-depth analysis of the disease burden pattern and future trends of thyroid cancer in China, and constructed an intelligent prediction model in combination with hospital electronic medical record data. …”
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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

“…Against this backdrop, research on Intrusion Detection Systems (IDSs) leveraging machine learning in IoT environments has been actively conducted. …”
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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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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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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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A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite by Prashant Anerao, Atul Kulkarni, Yashwant Munde, Namrate Kharate

“…Four distinct machine learning algorithms have been selected for predictive modeling: Linear Regression, Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost). …”
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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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