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    Artificial intelligence, generative artificial intelligence and research integrity: a hybrid systemic review by Khalid H. Arar, Hamit Özen, Gülşah Polat, Selahattin Turan

    Published 2025-07-01
    “…Abstract Current advances in academic research stem from two main sources: artificial intelligence technologies and the specific field of generative artificial intelligence. …”
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    Article
  3. 563

    Collaborative swarm robotics for sustainable environment monitoring and exploration: Emerging trends and research progress by Belkacem Khaldi, Fouzi Harrou, Ying Sun

    Published 2025-03-01
    “…The second collection, consisting of around 198 papers, examines the different swarm and evolutionary computing algorithms employed in this niche area, identifying ten significant research clusters. …”
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    Article
  4. 564

    Algorithmic management and human-centered task design: a conceptual synthesis from the perspective of action regulation and sociomaterial systems theory by Carsten Röttgen, Britta Herbig, Tobias Weinmann, Andreas Müller

    Published 2024-09-01
    “…For this, we link research on algorithmic management (AM) with Sociomaterial System Theory and Action Regulation Theory (ART). …”
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    Implementation of the Support Vector Machine (SVM) Algorithm on Sentiment Analysis of Public Opinion on The Prohibition of the use of Syrupy Drugs for Kidney Health by Galih Purnomo, Rumini Rumini, Tri Susanto

    Published 2024-11-01
    “…While the K-Fold Cross Validation test with a value of k = 10, produced a value of 74.64% for the linear kernel. This research concludes that the Support Vector Machine (SVM) algorithm with a linear kernel achieves the highest accuracy in sentiment analysis.…”
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    A Bionic-Based Multi-Objective Optimization for a Compact HVAC System with Integrated Air Conditioning, Purification, and Humidification by He Li, Bozhi Yang, Xinyu Gu, Wen Xu, Xuan Liu

    Published 2025-03-01
    “…This study is dedicated to the development of a multifunctional device that integrates air conditioning, humidification, and air purification functions, aimed at meeting the demands for energy efficiency, space-saving, and comfortable indoor environments in modern residential and commercial settings. The research focuses on achieving a balance between performance, energy consumption, and noise levels by combining bionic design principles with advanced optimization algorithms to propose innovative design and optimization methods. …”
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    Do Deep Learning Algorithms Accurately Segment Intracerebral Hemorrhages on Noncontrast Computed Tomography? A Systematic Review and Meta‐Analysis by Diana Zarei, Mahbod Issaiy, Shahriar Kolahi, David S. Liebeskind

    Published 2024-07-01
    “…This study conducts a comprehensive systematic review and meta‐analysis to evaluate the precision of deep learning algorithms in delineating ICH on noncontrast computed tomography. …”
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  17. 577

    Predicting CO<sub>2</sub> Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm by Amel Ali Alhussan, Marwa Metwally, S. K. Towfek

    Published 2025-04-01
    “…Transitioning to recurrent neural network architectures, mainly BIGRU, enabled the modeling of sequential dependence on emissions data. …”
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  18. 578

    Architectural design visual information mining system based on image processing technology by Pan Junyu

    Published 2025-03-01
    “…Image processing technology mainly uses computer algorithms to collect images, thereby converting these images into digital signals that can be recognized by a computer, and then displaying them on a computer display. …”
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    Research on a Panoramic Image Stitching Method for Images of Corn Ears, Based on Video Streaming by Yi Huangfu, Hongming Chen, Zhonghao Huang, Wenfeng Li, Jie Shi, Linlin Yang

    Published 2024-12-01
    “…<b>Discussions:</b> Although the experimental results demonstrated the significant advantages of the panoramic splicing method for corn ear images proposed in this paper in terms of improving information collection efficiency and automating corn assessment, the method still faces certain challenges. Future research will focus on the following points: 1. addressing the issue of environmental interference caused by diseases, pests, and plant nutritional status on the measurement of corn ear parameters in order to enhance the stability and accuracy of the algorithm; 2. expanding the dataset for the U-Net model to include a wider range of corn ears with complex backgrounds, different growth stages, and various environmental conditions to improve the model’s segmentation recognition rate and precision. …”
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