Machine Learning for Community-Acquired Pneumonia Diagnosis Using Routine Clinical and Laboratory Data by Sung Yoon Lim, Eunhye Cho, Bokhee Jung, Jaeyeon Lee, Miyoung Kim, Sooyoung Yoo, Seyoung Jung, Joon Yhup Lee, Sejin Nam, Hyunju Lee, Eu Suk Kim

“…This study aims to develop a machine learning model to diagnose CAP using only clinical and laboratory data. …”
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From Coarse to Crisp: Enhancing Tree Species Maps with Deep Learning and Satellite Imagery by Taebin Choe, Seungpyo Jeon, Byeongcheol Kim, Seonyoung Park

“…Applying the proposed methodology to Sobaeksan and Jirisan National Parks in South Korea, the performance of various machine learning (ML) and deep learning (DL) models was compared, including traditional ML (linear regression, random forest) and DL architectures (multilayer perceptron (MLP), spectral encoder block (SEB)—linear, and SEB-transformer). …”
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Scalability analysis of heavy-duty gas turbines using data-driven machine learning by Shubhasmita Pati, Julian D. Osorio, Mayank Panwar, Rob Hovsapian

“…In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. …”
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A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments by Kim Bjerge, Henrik Karstoft, Hjalte M.R. Mann, Toke T. Høye

“…The cameras monitor arthropods, including insect visits, on a specific mix of Sedum plant species with white, yellow and red/pink colored of flowers. The proposed deep-learning pipeline estimates flower cover and detects and classifies arthropod taxa from time-lapse recordings. …”
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High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning by Yicheng Zhou, Lingbo Yang, Lin Yuan, Xin Li, Yihu Mao, Jiancong Dong, Zhenyu Lin, Xianfeng Zhou

“…Accurate mapping of tea plantations is crucial for agricultural management and economic planning, yet it poses a significant challenge due to the complex and variable nature of tea cultivation landscapes. This study presents a high-precision approach to mapping tea plantations in Anji County, Zhejiang Province, China, utilizing multi-source remote sensing data and advanced deep learning models. …”
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Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces by Bo Jiang, Xuefei Wang, Naidi Yang, Yuhan Liu, Xi Chen, Qiwen Wu

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Analysis With Deep Learning of Robust UAV-Mounted Active IRS NOMA Networks With Imperfections by Chandan Kumar Singh, Deepak Kumar, Janne J. Lehtomaki, Zaheer Khan, Matti Latva-Aho, Prabhat K. Upadhyay

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Advancing blood cell detection and classification: performance evaluation of modern deep learning models by Shilpa Choudhary, Sandeep Kumar, Pammi Sri Siddhaarth, Guntu Charitasri, Monali Gulhane, Nitin Rakesh, Feslin Anish Mon, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…A diverse set of blood cell images with fine-grained annotations is contained in this dataset to make it useful for deep learning models training and evaluation. Because the present dataset will be an important resource for researchers and developers working on automatic blood cell detection and classification systems, we will make it publicly available under the open-access nature in order to accelerate the collaboration and progress in this field.…”
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Optimizing corn yield prediction: Integrating multi-temporal UAS data and machine learning by Huihui Zhang, Yuting Zhou, Shengfang Ma, Kevin Yemoto

“…Across all growth stages, combining LWIR data with VNIR data enhanced the accuracy of corn yield predictions in the deficit irrigated field with RF, especially during the reproductive stages, highlighting the importance of thermal sensors in water-stressed fields. This study presents a novel UAS-based machine learning approach to estimate corn yield in both well-watered and water-stressed fields using multisource and multi-temporal data. …”
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Image-based honey bee larval viral and bacterial diagnosis using machine learning by Duan C. Copeland, Brendon M. Mott, Oliver L. Kortenkamp, Robert J. Erickson, Nathan O. Allen, Kirk E. Anderson

“…We leveraged transfer learning techniques, fine-tuning deep convolutional neural networks (ResNet-50v2, ResNet-101v2, InceptionResNet-v2) pre-trained on ImageNet to discriminate between EFB and viral infections. …”
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Preliminary analysis of salivary microbiota in catathrenia (nocturnal groaning) using machine learning algorithms by Min Yu, Yujia Lu, Wanxin Zhang, Xu Gong, Zeliang Hao, Liyue Xu, Yongfei Wen, Xiaosong Dong, Fang Han, Xuemei Gao

“…Among the most important genera in catathrenia and control classification identified by machine learning algorithms, four genera, Alloprevotella, Peptostreptococcaceae_XI_G1, Actinomyces and Rothia, changed significantly with MAD treatment. …”
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ChromosomeNet: Deep Learning-Based Automated Chromosome Detection in Metaphase Cell Images by Chih-En Kuo, Jun-Zhou Li, Jenn-Jhy Tseng, Feng-Chu Lo, Ming-Jer Chen, Chien-Hsing Lu

“…<italic>Methods:</italic> In the present study, we proposed a deep learning&#x2013;based system for the automatic chromosome detection and recognition of metaphase cell images. …”
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Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer’s disease biomarkers by Christoffer Ivarsson Orrelid, Oscar Rosberg, Sophia Weiner, Fredrik D. Johansson, Johan Gobom, Henrik Zetterberg, Newton Mwai, Lena Stempfle

“…Results We present a machine learning workflow for working with high-dimensional TMT proteomics data that addresses their inherent data characteristics. …”
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Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity by Caden P. Chamberlain, Garrett W. Meigs, Derek J. Churchill, Jonathan T. Kane, Astrid Sanna, James S. Begley, Susan J. Prichard, Maureen C. Kennedy, Craig Bienz, Ryan D. Haugo, Annie C. Smith, Van R. Kane, C. Alina Cansler

“…Our framework used (1) machine learning to identify key bioclimatic, topographic, and fire weather drivers of burn severity in each fire, (2) standardized workflows to statistically sample untreated control units, and (3) spatial regression modeling to evaluate the effects of treatment type and time since treatment on burn severity. …”
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Multistakeholder Assessment of Project-Based Service-Learning in Medical Education: A Comparative Evaluation by Liao SC, Hung YN, Chang CR, Ting YX

“…Effective assessments should incorporate perspectives from multiple stakeholders. The present study developed a service-learning course assessment model that incorporates assessments from multiple stakeholders, compared assessments between stakeholder types, and explored the effects of evaluator–student relationship.Participants and Methods: The study recruited 126 students from a service-learning course at China Medical University in 2024. …”
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Convolutional neural networks with transfer learning for natural river flow prediction in ungauged basins by Henrique Echternacht, Luciana Campos, Alfeu Dias de Martinho, Danilo Pinto Moreira de Souza, Rodrigo Barbosa de Santis, Tiago Silveira Gontijo, Matteo Bodini, Angela Gorgoglione, Camila Martins Saporetti, Leonardo Goliatt

“…Effective river flow prediction holds significant relevance, particularly given the substantial societal implications of river usage, encompassing areas such as transportation, agriculture, and power generation. The present study introduces a novel approach to streamflow prediction involving the development of a Deep Learning (DL) model that combines a convolutional neural network with Transfer Learning (TL) techniques to predict streamflow in river systems. …”
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UAVs and Blockchain Synergy: Enabling Secure Reputation-Based Federated Learning in Smart Cities by Syed M. Aqleem Abbas, Muazzam A. Khan Khattak, Wadii Boulila, Anis Kouba, M. Shahbaz Khan, Jawad Ahmad

“…To tackle these challenges, we present a differentially private federated learning framework based on Accumulative Reputation-based Selection (ARS) for the edge-aided UAV network that utilizes blockchains to prevent single-point failures where we switched from central control to decentralized control, Interplanetary File System (IPFS) for off-chain model storage and their respective hash-keys on-chain to ensure model integrity. …”
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Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning by Umesh Bhati, Akanksha Sharma, Sagar Gupta, Anchit Kumar, Upendra Kumar Pradhan, Ravi Shankar

“…To decipher that the present study brings forward a novel computational framework designed to reason about the spatio-temporal dynamics of TF interaction. …”
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Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in trauma-induced coagulopathy by Qingsong Chen, Tao Li, Tao Zhang, Yue Zhou, Weifeng Huang, Hui Li, Li Shi, Jianxiao Li, Qi Zhang, Man Ma, Pan Wang, Hui Hu, Gongbin Wei, Jiangxia Xiang, Yuan Cheng, Jun Yang, Guangbin Huang, Yongming Li, Dingyuan Du

“…The multifactor regulation network provided insights into complex gene regulatory mechanisms. This study presents a detailed genetic and molecular profile of TIC. …”
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Effect of shear rate on early Shewanella oneidensis adhesion dynamics monitored by deep learning by Lucie Klopffer, Nicolas Louvet, Simon Becker, Jérémy Fix, Cédric Pradalier, Laurence Mathieu

“…Secondly, at the individual scale, by implementing an automated image processing method based on deep learning to track each individual pioneer bacterium on the wall. …”
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