Using Machine Learning Approaches to Enhance Heatwave Measurement for Vulnerability Assessment and Timely Management of Heat-related Health Services by Le Jian, Dimpal Patel, Jing Guo, Jianguo Xiao, Janis Jansz, Grace Yun, Ting Lin, Laura Kirkland, Tim Landrigan, Andrew Robertson

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A Scalable Framework for Real-Time Network Security Traffic Analysis and Attack Detection Using Machine and Deep Learning by Zineb Maasaoui, Mheni Merzouki, Abdella Battou, Ahmed Lbath

“…By integrating supervised machine learning models trained on the UNSW-NB15 dataset, we evaluate Random Forest (RF), Decision Trees (DT), and Support Vector Machines (SVM), with the Random Forest classifier achieving a notable accuracy of 99.32%. …”
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The Visualization of the Importance of Covariance Importance in a Machine Learning Model for Advanced Liver Fibrosis in a Nationally Representative Sample by Alexander A. Huang, Samuel Y. Huang

“…Model gain, cover, and frequency measured feature importance, enabling transparent, and interpretable analysis. …”
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The Role of Sports in Building Resilience: A Machine Learning Approach to the Psychological Effects of the COVID-19 Pandemic on Children and Adolescents by Giulia Di Martino, Carlo della Valle, Alessandra di Cagno, Giovanni Fiorilli, Giuseppe Calcagno, Daniele Conte

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Landslide Hazard Assessment Under Record-Breaking Extreme Rainfall: Integration of SBAS-InSAR and Machine Learning Models by Wenbo Zheng, Wen Fan, Yanbo Cao, Yalin Nan, Pengxu Jing

“…Global climate change has led to a marked increase in the frequency of record-breaking extreme rainfall events, which often surpass historical benchmarks and pose significant challenges to conventional geological hazard risk assessment methods. …”
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Fine-grained analysis and mapping of urban flood susceptibility with interpretable machine learning: A case study of Hefei, China by Ziyao Xing, Guijia Lyu, Yu Yao, Zhe Liu, Xiaodong Zhang

“…Existing methods often treat cities as uniform entities, making it challenging to capture the complexity of these localized characteristics. …”
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Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish. by Andrea Garcia-Lopez, Juliana Cuervo-Rojas, Juan Garcia-Lopez, Fernando Giron-Luque

“…<h4>Methods</h4>We conducted a retrospective cohort of 2712 patients transplanted between July 2008 and January 2023, analyzing 117,566 unstructured medical records. …”
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Improving machine learning models through explainable AI for predicting the level of dietary diversity among Ethiopian preschool children by Gizachew Mulu Setegn, Belayneh Endalamaw Dejene

“…This research aims to enhance machine learning models by integrating Explainable AI (XAI) methods to more accurately predict the level of dietary diversity in Ethiopian preschool children. …”
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Estimation of Reference Crop Evapotranspiration in the Yellow River Basin Based on Machine Learning and Its Regional and Drought Adaptability Analysis by Jun Zhao, Huayu Zhong, Congfeng Wang

“…The main findings are as follows: machine learning models, particularly nonlinear approaches, significantly outperformed the PT and HG methods across all climatic regions. …”
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Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates. by Jun Kong, Lee A D Cooper, Fusheng Wang, Jingjing Gao, George Teodoro, Lisa Scarpace, Tom Mikkelsen, Matthew J Schniederjan, Carlos S Moreno, Joel H Saltz, Daniel J Brat

“…Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human review has limitations that can result in low reproducibility and inter-observer agreement. …”
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Noise Reduction Methods in the Vehicle Industry: Using Vibroacoustic Simulation for Sustainability by Krisztián Horváth, Ambrus Zelei

“…This approach integrates the Finite Element Method and Multibody Dynamics simulations with vibroacoustics to identify and redesign noisy components even during the conceptual design stage. …”
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Brain Magnetic Resonance Imaging Radiomic Signature and Machine Learning Model Prediction of Hepatic Encephalopathy in Adult Cirrhotic Patients by Gianvincenzo Sparacia, Giulia Colelli, Giuseppe Parla, Giuseppe Mamone, Luigi Maruzzelli, Vincenzina Lo Re, Federica Avorio, Roberto Miraglia, Anna Pichiecchio

“…The grade of hepatic encephalopathy (HE) was determined by considering factors such as the presence of underlying liver disease, the severity of clinical symptoms, and the frequency of encephalopathic episodes. Radiomic texture analysis based on five machine learning algorithms was applied to axial T1-weighted MR images of bilateral lentiform nuclei. …”
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Eel and Grouper Optimization-Based Fuzzy FOPI-TID^μ-PIDA Controller for Frequency Management of Smart Microgrids Under the Impact of Communication Delays and Cyberattacks... by Kareem M. AboRas, Mohammed Hamdan Alshehri, Ashraf Ibrahim Megahed

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Machine learning-based diagnostic model for neonatal intestinal diseases in multiple centres: a cross-sectional study protocol by Xiang Guo, Jing Shi, Yue Han, Qi Zhao, Jun Tang, Tao Xiong, Yi Yang, Jin Zhang, Qian Gao, Ling He

“…This study aims to develop an machine learning-based diagnostic model for neonatal intestinal diseases using BS analysis and to compare its diagnostic accuracy with that of manual clinical assessment.Methods and analysis This diagnostic study employs a cross-sectional design. …”
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Machine learning-based prognostic model of lactylation-related genes for predicting prognosis and immune infiltration in patients with lung adenocarcinoma by Mingjun Gao, Mengmeng Wang, Siding Zhou, Jiaqi Hou, Wenbo He, Yusheng Shu, Xiaolin Wang

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Fault Diagnosis Method of Gear based on VMD and Multi-feature Fusion by Wang Jianguo, Chen Shuai, Zhang Chao

“…Aiming at the problem that working condition is complex in fact so that it is difficult to extract the gear fault feature frequency,a method of gear fault diagnosis based on variational mode decomposition（ VMD） and multi- feature fusion is proposed. …”
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Impact of Sliding Window Overlap Ratio on EEG-Based ASD Diagnosis Using Brain Hemisphere Energy and Machine Learning by Bashar S. Falih, Mohannad K. Sabir, Ahmet Aydın

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Effects of Ultrasonic Oscillation on Shrink-Fitted Assembly by Loc Huu Nguyen, Phong Vi Lam

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Early detection of emerging SARS-CoV-2 Variants from wastewater through genome sequencing and machine learning by Xiaowei Zhuang, Van Vo, Michael A. Moshi, Ketan Dhede, Nabih Ghani, Shahraiz Akbar, Ching-Lan Chang, Angelia K. Young, Erin Buttery, William Bendik, Hong Zhang, Salman Afzal, Duane Moser, Dietmar Cordes, Cassius Lockett, Daniel Gerrity, Horng-Yuan Kan, Edwin C. Oh

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An Analysis Method of Principal Components of Power Quality Based on Fast S-transform by MAN Weishi, ZHANG Zhiyu, XI Lei

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