Identification of factors associated with acute malnutrition in children under 5 years and forecasting future prevalence: assessing the potential of statistical and machine learnin... by Christopher Coffey, Meike Reusken, Frans Cruijssen, Bertrand Melenberg, Cascha van Wanrooij

“…Among the forecasting methods tested, random forest machine learning proves to be the most effective and was found to accurately forecast the direction of GAM prevalence in test data for many of the districts in Somalia.…”
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False data injection attack dataset for classification, identification, and detection for IIoT in Industry 5.0Zenodo by A K M Ahasan Habib, Mohammad Kamrul Hasan, Rosilah Hassan, Shayla Islam, Huda Saleh Abbas

“…The dataset offers an accurate characterization of IoT environments by covering many different kinds of network configurations and scenarios. …”
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VMDU-net: a dual encoder multi-scale fusion network for polyp segmentation with Vision Mamba and Cross-Shape Transformer integration by Peng Li, Jianhua Ding, Chia S. Lim

“…Additionally, capturing long-range dependencies remains difficult, with many existing algorithms struggling to converge effectively, limiting their practical application.MethodsTo address these challenges, we propose a novel Dual Encoder Multi-Scale Feature Fusion Network, termed VMDU-Net. …”
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Inductive Power Transfer Coil Misalignment Perception and Correction for Wirelessly Recharging Underground Sensors by John Sanchez, Juan Arteaga, Cody Zesiger, Paul Mitcheson, Darrin Young, Shad Roundy

“…Field implementations of fully underground sensor networks face many practical challenges that have limited their overall adoption. …”
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Research on implementation of interventions in tuberculosis control in low- and middle-income countries: a systematic review. by Frank Cobelens, Sanne van Kampen, Eleanor Ochodo, Rifat Atun, Christian Lienhardt

“…Evaluations of diagnostic and screening algorithms were more frequent (n = 19) but geographically clustered and mainly of non-comparative design. …”
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Artificial Intelligence in cancer epigenomics: a review on advances in pan-cancer detection and precision medicine by Karishma Sahoo, Prakash Lingasamy, Masuma Khatun, Sajitha Lulu Sudhakaran, Andres Salumets, Vino Sundararajan, Vijayachitra Modhukur

“…Despite their promise, challenges remain, including limited sensitivity for early-stage cancers, the black-box nature of many AI algorithms, and the need for validation across diverse populations to ensure equitable implementation. …”
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Adaptive Cut Selection in Mixed-Integer Linear Programming by Turner, Mark, Koch, Thorsten, Serrano, Felipe, Winkler, Michael

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Use of deep learning-based NLP models for full-text data elements extraction for systematic literature review tasks by Jingcheng Du, Dong Wang, Bin Lin, Long He, Liang-Chin Huang, Jingqi Wang, Frank J. Manion, Yeran Li, Nicole Cossrow, Lixia Yao

“…The annotation corpora contain 4,498, 579, and 252 annotated entity mentions for HPV Prevalence, Pneumococcal Epidemiology, and Pneumococcal Economic Burden tasks respectively. Deep learning algorithms achieved superior performance in recognizing the targeted SLR data elements, compared to conventional machine learning algorithms. …”
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Social Media Suicide Watch by Katherine Prothro

“…However, the Samaritan’s Radar app did not notify users that an algorithm was analyzing their content for signs of distress. …”
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The clinical utility and safety of biomarker-guided immunosuppression withdrawal in liver transplantation: the LIFT prospective RCT by Julien Vionnet, Rosa Miquel, Juan G Abraldes, Juan-Jose Lozano, Pablo Ruiz, Miquel Navasa, Aileen Marshall, Frederik Nevens, William Gelson, Joanna Leithead, Steven Masson, Elmar Jaeckel, Richard Taubert, Phaedra Tachtatzis, Dennis Eurich, Kenneth Simpson, Eliano Bonaccorsi-Riani, James Ferguson, Alberto Quaglia, Maria Elstad, Marc Delord, Abdel Douiri, Alberto Sánchez-Fueyo

“…A previous clinical trial showed that a logistic regression algorithm including the transcript levels of a set of five genes in a liver biopsy could predict the success of immunosuppression withdrawal with high sensitivity and specificity. …”
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Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics by Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun

“…Existing studies often use either machine learning classification or clustering algorithms. Additionally, several studies have used clustering algorithms to extract features related to brain states trajectories that can be used to train interpretable classifiers. …”
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What happens between first symptoms and first acute exacerbation of COPD – observational study of routine data and patient survey by Alex Bottle, Alex Adamson, Xiubin Zhang, Benedict Hayhoe, Jennifer K Quint

“…The first AECOPD was identified using our group’s published algorithm and was restricted to hospital admissions to reliably capture the most serious ones. …”
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Rapid and accurate multi-phenotype imputation for millions of individuals by Lin-Lin Gu, Hong-Shan Wu, Tian-Yi Liu, Yong-Jie Zhang, Jing-Cheng He, Xiao-Lei Liu, Zhi-Yong Wang, Guo-Bo Chen, Dan Jiang, Ming Fang

“…In the present study, we have developed a multi-phenotype imputation method based on mixed fast random forest (PIXANT) by leveraging efficient machine learning (ML)-based algorithms. We demonstrate by extensive simulations that PIXANT is reliable, robust and highly resource-efficient. …”
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An ensemble machine learning framework with explainable artificial intelligence for predicting haemoglobin anaemia considering haematological markers by Dhruva Darshan B S, Punit Sharma, Krishnaraj Chadaga, Niranjana Sampathila, G. Muralidhar Bairy, Sushma Belurkar, Srikanth Prabhu, Swathi K S

“…The algorithms are further ensembled using a customized stacking approach. …”
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Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures by Yudong Shi, Yinggan Zhang, Jiansen Wen, Zhou Cui, Jianhui Chen, Xiaochun Huang, Cuilian Wen, Baisheng Sa, Zhimei Sun

“…Abstract Machine learning (ML) techniques have made enormous progress in the field of materials science. However, many conventional ML algorithms operate as “black‐boxes”, lacking transparency in revealing explicit relationships between material features and target properties. …”
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Comparison of Machine Learning Models for Classification of Breast Cancer Risk Based on Clinical Data by Haniyeh Rafiepoor, Alireza Ghorbankhanloo, Kazem Zendehdel, Zahra Zangeneh Madar, Sepideh Hajivalizadeh, Zeinab Hasani, Ali Sarmadi, Behzad Amanpour‐Gharaei, Mohammad Amin Barati, Mozafar Saadat, Seyed‐Ali Sadegh‐Zadeh, Saeid Amanpour

“…Ten classification algorithms were applied to the dataset. The accuracy, sensitivity, precision, and feature importance in the machine learning algorithms were assessed and compared to previous studies for evaluation. …”
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Comparative Effectiveness of Risankizumab vs Other Biologics in Crohn’s Disease: Long-Term and Quality-of-Life Outcomes after Inadequate Response to Prior Therapy by Monika Babczyńska, Agnieszka Buliszak, Piotr Marcjasz, Anna Bioły, Agnieszka Borończyk, Piotr Zając

“…Despite the availability of biologics, many patients fail to achieve sustained remission. …”
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Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment by Ebrahimpourboura Zahra, Mosalpuri Manish, Jonas Baltrusaitis, Dubey Pallavi, Mba Wright Mark

“…TEA and LCA methods are enhanced through ML’s predictive modeling, optimization algorithms, and data analysis capabilities, providing more precise and efficient evaluations of SES. …”
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A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery Classification by Neha Sharma, Manoj Sharma, Amit Singhal, Nuzhat Fatema, Vinay Kumar Jadoun, Hasmat Malik, Asyraf Afthanorhan

“…The core challenge addressed herein is the development of robust algorithms that enable the accurate identification of MI tasks, thereby empowering individuals with mobility impairments to control devices and interfaces through cognitive commands. …”
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Exploring Feature Selection with Deep Learning for Kidney Tissue Microarray Classification Using Infrared Spectral Imaging by Zachary Caterer, Jordan Langlois, Connor McKeown, Mikayla Hady, Samuel Stumo, Suman Setty, Michael Walsh, Rahul Gomes

“…Chromophobe renal cell carcinoma is a rarer form of RCC that poses significant challenges to accurate diagnosis, as it shares many histologic features with Oncocytoma, a benign renal tumor. …”
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