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  1. 15361

    Catalyzing early ovarian cancer detection: Platelet RNA-based precision screening by Eunyong Ahn, Se Ik Kim, Sungmin Park, Sarah Kim, Hyejin Lee, Yeochan Kim, Sangick Park, Suyeon Lee, Dong Won Hwang, Heeyeon Kim, HyunA Jo, Untack Cho, Juwon Lee, Cheol Lee, TaeJin Ahn, Yong-Sang Song

    Published 2025-06-01
    “…If integrated with current screening methods, our algorithm holds promise for identifying ovarian or endometrial cancer in its early stages.…”
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  2. 15362
  3. 15363

    Extraction of typical oyster pile columns in the Maowei Sea, Beibu Gulf, based on unmanned aerial vehicle laser point cloud orthophotos by Jinze Du, Jinze Du, Meiqin Huang, Zhenjun Kang, Zhenjun Kang, Zhenjun Kang, Yichao Tian, Yichao Tian, Yichao Tian, Jin Tao, Jin Tao, Qiang Zhang, Qiang Zhang, Yutong Xie, Yutong Xie, Yutong Xie, Jinying Mo, Jinying Mo, LiYan Huang, LiYan Huang, Yusheng Feng, Yusheng Feng

    Published 2025-03-01
    “…The results demonstrate that: 1) By comparing three machine learning models and integrating the LiDAR point cloud oyster pile column height model (OPCHM) into the S3 scenario, the convolutional neural network (CNN) attains an impressive overall classification accuracy (OA) of 96.54% and a Kappa coefficient of 0.9593, significantly enhancing and optimizing the CNN’s predictive accuracy for classification tasks; 2) In comparison with conventional machine learning algorithms, deep learning exhibits remarkable feature extraction capability.…”
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  4. 15364

    Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity by Xiaochen Tang, Yanyan Wei, Jiaoyan Pang, Lihua Xu, Huiru Cui, Xu Liu, Yegang Hu, Mingliang Ju, Yingying Tang, Bin Long, Wei Liu, Min Su, Tianhong Zhang, Jijun Wang

    Published 2025-02-01
    “…Functional connectivity (FC) features that were correlated with symptom severity were subjected to the single-cell interpretation through multikernel learning (SIMLR) algorithm in order to identify latent homogeneous subgroups. …”
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  5. 15365

    Design, Fabrication, and Application of Large-Area Flexible Pressure and Strain Sensor Arrays: A Review by Xikuan Zhang, Jin Chai, Yongfu Zhan, Danfeng Cui, Xin Wang, Libo Gao

    Published 2025-03-01
    “…Real-time data processing requires innovative solutions such as edge computing and machine learning algorithms, ensuring low-latency, high-accuracy data interpretation while preserving the flexibility of sensor arrays. …”
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  6. 15366

    Impact of monitor unit optimization in volumetric modulated arc therapy planning for nasopharyngeal carcinoma. by Huaqu Zeng, Zhen Li, Zongyou Chen, Shukui Tang, Qifu Lin, Minzhi Zhong, Zunbei Wen

    Published 2025-01-01
    “…Dual-arc VMAT plan were designed using photon optimization algorithms without the monitor unit objective (MUO) tool, denoted as the base plan. …”
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  7. 15367

    Distribution of University Admission Quotas: Problems of Competitive Selection Process by L. M. Nurieva, S. G. Kiselev

    Published 2019-07-01
    “…The main reason for the inability of the competitive mechanism to carry out the correct ranking of participants in terms of success is the major methodological and mathematical errors inherent in the algorithms for determining the effectiveness of educational organisations. …”
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  8. 15368

    Comparison of Surface Current Measurement Between Compact and Square-Array Ocean Radar by Yu-Hsuan Huang, Chia-Yan Cheng

    Published 2025-04-01
    “…In Taiwan, the Taiwan Ocean Research Institute (TORI) has established a data quality management mechanism based on international standards for data filtering, noise reduction, and outlier detection, improving the accuracy and stability of radar-derived velocity measurements.To eliminate the effects caused by different center frequencies between MABT and KNTN, this study used the same algorithms and parameter settings as much as possible in all steps, from Doppler spectra processing to radial velocity calculation, ensuring the comparability of the data. …”
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  9. 15369

    Synthesis of a reversible quantum Vedic multiplier on IBM quantum computers by Mojtaba Noorallahzadeh, Mohammad Mosleh

    Published 2025-05-01
    “…Abstract Quantum computers provide considerable potential to enhance computing technology, anticipated to surpass conventional computers by resolving intricate challenges that existing systems cannot tackle. They use quantum algorithms for improved performance and depend on reversible computations based on quantum physics and linear algebra. …”
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  10. 15370

    INTEGRATED GRAPH-BASED TESTING PIPELINE FOR MODERN SINGLE-PAGE APPLICATIONS by Nataliia Golian, Varvara Tisheninova

    Published 2025-07-01
    “…Empirical evaluation on two large-scale SPAs demonstrates a 40 % reduction in manual test authoring effort and a 25 % increase in overall coverage metrics compared to traditional approaches.…”
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  11. 15371

    SkelETT—Skeleton-to-Emotion Transfer Transformer by Pedro Victor Vieira Paiva, Josue Junior Guimaraes Ramos, Marina Gavrilova, Marco Antonio Garcia de Carvalho

    Published 2025-01-01
    “…Human skeletons, derived from depth sensors or pose estimation algorithms, offer an alternative for facial expression, including valuable spatial and temporal cues. …”
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  12. 15372

    Classification of intracranial tumors based on optical-spectral analysis by I. D. Romanishkin, T. A. Savelieva, A. Ospanov, K. G. Linkov, S. V. Shugai, S. A. Goryajnov, G. V. Pavlova, I. N. Pronin, V. B. Loschenov

    Published 2023-10-01
    “…In case the number of parameters exceeds a couple of dozens, it is necessary to use machine learning algorithms  to build a intraoperative decision support system for the surgeon. …”
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  13. 15373

    Revolutionizing Electric Vehicle Charging Stations with Efficient Deep Q Networks Powered by Multimodal Bioinspired Analysis for Improved Performance by Sugunakar Mamidala, Yellapragada Venkata Pavan Kumar, Rammohan Mallipeddi

    Published 2025-03-01
    “…This paper proposes a novel framework that integrates deep Q networks (DQNs) for real-time charging optimization, coupled with multimodal bioinspired algorithms like ant lion optimization (ALO) and moth flame optimization (MFO). …”
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  14. 15374

    Methodology сomparative statistical analysis of Russian industry based on cluster analysis by Sergey S. Shishulin

    Published 2017-07-01
    “…Thus, for example, in analyzing the structure of industrial production, the reduction was from fifteen industries to three basic, well-interpreted factors: the relatively extractive industries (with a low degree of processing), high-tech industries and consumer goods (medium-technology) sectors. …”
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  15. 15375

    Monitoring the antibiotic resistance in the intensive care unit of a multidisciplinary hospital by V. Yu. Zemko, V. K. Okulich, A. M. Dzyadz’ko

    Published 2018-12-01
    “…It is necessary to monitor the resistance of common microorganisms to certain antibiotics in order to develop algorithms for rational antibacterial treatment in each hospital.…”
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  16. 15376

    Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments by Billel Essaid, Hamza Kheddar, Noureddine Batel, Muhammad E. H. Chowdhury

    Published 2025-01-01
    “…These processes begin with capturing speech in analog form and applying signal processing algorithms to ensure compatibility with devices like cochlear implants (CIs). …”
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  17. 15377

    Critical load exceedances for North America and Europe using an ensemble of models and an investigation of causes of environmental impact estimate variability: an AQMEII4 study by P. A. Makar, P. Cheung, C. Hogrefe, A. Akingunola, U. Alyuz, J. O. Bash, M. D. Bell, R. Bellasio, R. Bianconi, T. Butler, H. Cathcart, O. E. Clifton, O. E. Clifton, A. Hodzic, I. Kioutsioukis, R. Kranenburg, A. Lupascu, A. Lupascu, J. A. Lynch, K. Momoh, J. L. Perez-Camanyo, J. Pleim, Y.-H. Ryu, R. San Jose, D. Schwede, D. Schwede, T. Scheuschner, M. W. Shephard, R. S. Sokhi, S. Galmarini

    Published 2025-03-01
    “…If model–measurement fusion is only applied to some but not all of the fields contributing to the total deposition of sulfur or nitrogen, the corrections may result in greater variability between models or less accurate results for an ensemble of models, for those cases where an unobserved or unused observed component contributes significantly to predicted total deposition.</p> <p>Based on these results, an increased process-research focus is therefore recommended for the following model processes and for observations which may assist in model evaluation and improvement: multiphase hydrometeor scavenging combined with updated particle dry-deposition, cuticle, and soil deposition pathway algorithms for acidifying gases, base cation chemistry and emissions, and NH<span class="inline-formula"><sub>3</sub></span> bidirectional fluxes. …”
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  18. 15378

    Digital Academic Leadership in Higher Education Institutions: A Bibliometric Review Based on CiteSpace by Olaniyi Joshua Olabiyi, Carl Jansen van Vuuren, Marieta Du Plessis, Yujie Xue, Chang Zhu

    Published 2025-07-01
    “…R1-64 bit), we analyzed 5837 documents, identifying 24 key publications that formed a network of 90 nodes and 256 links. The reduction to 24 publications occurred as part of a structured bibliometric analysis using CiteSpace, which employs algorithmic thresholds to identify the most influential and structurally significant publications within a large corpus. …”
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  19. 15379

    Air pollution and prostate cancer: Unraveling the connection through network toxicology and machine learning by Yuqi Li, Tao Zhou, Zhiyu Liu, Xinyao Zhu, Qilong Wu, Chunyang Meng, Qingfu Deng

    Published 2025-03-01
    “…Subsequently, the best predictive model was selected through a combination of 108 machine learning algorithms. …”
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  20. 15380

    The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

    Published 2025-03-01
    “…Dear Editor Diabetes remains a significant public health challenge, and the integration of artificial intelligence (AI) presents remarkable opportunities to enhance early diagnosis, personalized treatment, and effective prevention strategies.1 AI algorithms, including supervised learning and convolutional neural networks, can efficiently analyze large datasets to identify patterns and risk factors associated with diabetes, surpassing the capabilities of traditional methods.2 This advanced analysis enables healthcare providers to predict the likelihood of diabetes in individuals and populations, facilitating timely interventions and customized prevention strategies. …”
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