Forecasting High‐Speed Solar Wind Streams From Solar Images by Daniel Collin, Yuri Shprits, Stefan J. Hofmeister, Stefano Bianco, Guillermo Gallego

“…The study shows that a small number of physical features explains most of the solar wind variation, and that focusing on these features with simple machine learning algorithms even outperforms current approaches based on deep neural networks and MHD simulations. …”
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Modern approaches to the treatment of joint pathology: the value of hyaluronic acid by M. M. Shamuilova, Yu. V. Sedyakina, G. Yu. Knorring

“…The article offers brief algorithms for the diagnosis and treatment of osteoarthritis for use at the outpatient stage of medical care. …”
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Facilitating real-time LED-based photoacoustic imaging with DenP2P: An optimized conditional generative adversarial deep learning solution by Avijit Paul, Srivalleesha Mallidi

“…Signal quality can be improved by traditional noise removal algorithms, but deep learning models outperform non-learning methods. …”
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Use of satellite data for detecting icebergs and evaluating the iceberg threats by I. A. Bychkova, V. G. Smirnov

“…Te «status» state of the iceberg is also taken into account when assessing the degree of danger of the detected object. …”
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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

“…However, packet-based and flow-based IDSs proposed in existing studies are vulnerable to attacks such as DoS and DDoS, which involve numerous packet or flow combination patterns. These methods also face challenges related to computational resource burdens caused by the increased volume of input data. …”
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A Study on an Improved Generative Adversarial Network Model for Super-Resolution Image Reconstruction by Ximin Qu, Cheng Peng

“…Experimental results show that the optimized model SRGAN-GBDB outperforms the traditional Bicubic interpolation method, SRGAN, and several other existing super-resolution algorithms on the Set5, Set14, BSD100, Urban100, and DIV2K datasets. …”
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Predicting temporomandibular disorders in adults using interpretable machine learning methods: a model development and validation study by Yuchen Cui, Fujia Kang, Xinpeng Li, Xinning Shi, Han Zhang, Xianchun Zhu

“…The model not only has good predictive performance, but also enhances the clinical application value of the model through the SHAP method. …”
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Comparison of sequence- and structure-based antibody clustering approaches on simulated repertoire sequencing data. by Katharina Waury, Stefan Lelieveld, Sanne Abeln, Henk-Jan van den Ham

“…Here, we aim to explore the promise of structure-based clustering algorithms to replace or augment the standard sequence-based approach, specifically by identifying low-sequence identity groups. …”
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COSMIC-Based Early Software Size Estimation Using Deep Learning and Domain-Specific BERT by Yohannes Sefane Molla, Esubalew Alemneh, Samuel Temesgen Yimer

“…This shows that domain-specific pre-trained models have a good effect on the performance of deep learning models. This research also ensures that BERT is good in regressions tasks like its counterpart classification.…”
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Advances in subsurface defect detection techniques for fused silica optical components: A literature review by Hongbing Cao, Xing Peng, Feng Shi, Ye Tian, Lingbao Kong, Menglu Chen, Qun Hao

“…The potential of deep learning algorithms for SSD detection is also examined. Finally, future directions for research and development are proposed to provide a valuable reference for researchers in the field.…”
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Mapping urban green structures using object-based analysis of satellite imagery: A review by Shivesh Kishore Karan, Bjørn Tobias Borchsenius, Misganu Debella-Gilo, Jonathan Rizzi

“…Future directions for UGS mapping include the integration of deep learning algorithms, advancements in satellite data technologies, and the development of standardized classification frameworks. …”
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Classification of Heart Failure Using Machine Learning: A Comparative Study by Bryan Chulde-Fernández, Denisse Enríquez-Ortega, Cesar Guevara, Paulo Navas, Andrés Tirado-Espín, Paulina Vizcaíno-Imacaña, Fernando Villalba-Meneses, Carolina Cadena-Morejon, Diego Almeida-Galarraga, Patricia Acosta-Vargas

“…Several machine learning classification algorithms were evaluated using a dataset focused on heart failure. …”
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Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers by Jawad Ashraf, Ghulam Musa Raza, Byung-Seo Kim, Abdul Wahid, Hye-Young Kim

“…After rigorous preprocessing, we used these data to train our algorithms. Our trained model, INIDS, is not only up to date and real-time but also capable of accurately identifying multiple categories of attacks specifically related to IoT networks. …”
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Acute Bronchiolitis in Children. Current Approaches to Diagnosis and Therapy by A. A. Baranov, L. S. Namazova-Baranova, V. K. Tatochenko, I. V. Davydova, M. D. Bakradze, E. A. Vishnyova, L. R. Selimzyanova, A. S. Polyakova

“…The most common etiologic factor is the virus — respiratory syncytial (the vast majority of cases — 90%); rhinovirus and influenza viruses A and B, parainfluenza, adenovirus, coronavirus, metapneumovirus, and human bocavirus are also important agents. A number of factors can cause the evolution of bronchiolitis in 0–2-years-old children. …”
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Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis by Siqi Li, Di Miao, Qiming Wu, Chuan Hong, Danny D’Agostino, Xin Li, Yilin Ning, Yuqing Shang, Ziwen Wang, Molei Liu, Huazhu Fu, Marcus Eng Hock Ong, Hamed Haddadi, Nan Liu

“…While the term “FL” was originally coined by the engineering community, the statistical field has also developed privacy-preserving algorithms, though these are less recognized. …”
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Early Diagnosis of Bloodstream Infections Using Serum Metabolomic Analysis by Shuang Han, Ruihua Li, Hao Wang, Lin Wang, Yiming Gao, Yaolin Wen, Tianyang Gong, Shiyu Ruan, Hui Li, Peng Gao

“…., Gram-positive and Gram-negative bacteria) could be differentiated using our optimized statistical algorithms. In addition, by using different machine-learning algorithms, the isolated pathogens could also be classified at the species levels (e.g., <i>Escherichia coli</i> and <i>Klebsiella pneumoniae</i>) or according to their specific antibiotic-resistant phenotypes (e.g., extended-spectrum β-lactamase-producing and non-producing phenotypes) if needed. …”
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Interleukin-4 and specific IgE to oranges levels study in persons with allergic anamnesis by A. S. Prilutskiy, N. B. Abylgazinova, K. Y. Tkachenko

“…The data obtained from the study might be useful both in public health and in further research in order to improve diagnostic and treatment algorithms in orange sensitized patients.…”
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The Emergence of AI-Driven Virtual Hospitals: Redefining Patient Care Beyond Physical Boundaries by Ifrah Hameed, Hafiz Muhammad Haseeb Khaliq

“…This functionality not only increases the clinical intervention accuracy but also shortens the timeline necessary for the identification and treatment of health issues. …”
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Fast and lightweight automatic lithology recognition based on efficient vision transformer network by Yan Guo, Zhuowu Li, Fujiang Liu, Weihua Lin, Hongchen Liu, Quansen Shao, Dexiong Zhang, Weichao Liang, Junshun Su, Qiankai Gao

“…To overcome these limitations, researchers have explored the use of rock images and intelligent algorithms to automatically identify rocks. However, models developed for automatic rock properties identification often require high-performance equipment that cannot be readily deployed on lightweight edge devices. …”
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Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic by Wei-Hua Hu, Hui-Min Sun, Yong-Yue Wei, Yuan-Tao Hao

“…These models not only rely on conventional surveillance data but also incorporate information from various sources. …”
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