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    AttentionEP: Predicting essential proteins via fusion of multiscale features by attention mechanisms by Chuanyan Wu, Bentao Lin, Jialin Zhang, Rui Gao, Rui Song, Zhi-Ping Liu

    Published 2024-12-01
    “…Following this, mechanisms involving self-attention and cross-attention are employed to enhance the interaction between diverse information sources. To identify essential proteins, a classifier based on the ResNet architecture is developed. …”
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    A Hybrid Strategy for Forward Kinematics of the Stewart Platform Based on Dual Quaternion Neural Network and ARMA Time Series Prediction by Jie Tao, Huicheng Zhou, Wei Fan

    Published 2025-03-01
    “…In DQ-BPNN, a residual network (ResNet) is employed, endowing DQ-BPNN with the capacity to capture deeper-level system characteristics and enabling DQ-BPNN to achieve a better fitting effect. …”
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    Integrating the Prior Shape Knowledge Into Deep Model and Feature Fusion for Topologically Effective Brain Tumor Segmentation by Salma Asif, Ahmad Raza Shahid, Kiran Aftab, Syed Ather Enam

    Published 2025-01-01
    “…To address these shortcomings, we proposed a novel technique termed TDAConvAttentionNet that captures the local, global and topological features and integrates the prior shape, captured using persistent homology, into the deep model to enhance segmentation results and reduce topological errors. …”
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    FGBNet: A Bio-Subspecies Classification Network with Multi-Level Feature Interaction by Yang Yuan, Danping Huang, Bingbin Cai, Yang Shen, Jingdan Wang, Jiale Xv, Siyu Chen

    Published 2025-03-01
    “…Therefore, this study proposes FineGrained-BioNet (FGBNet), a deep learning network model specifically constructed for fine-grained bio-subspecies image classification. …”
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  13. 5613

    Enhancing UAS-Based Multispectral Semantic Segmentation Through Feature Engineering by Elena Vollmer, Mishal Benz, James Kahn, Leon Klug, Rebekka Volk, Frank Schultmann, Markus Gotz

    Published 2025-01-01
    “…This article investigates how feature engineering (FE), the process of adapting raw data to serve as DL training data, can impact performance when transferring prevalent model architectures to combined red, green, blue (RGB) thermal imagery. The popular U-Net is utilized for the common task of multiclass semantic segmentation in remote sensing. …”
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    SARS-CoV-2 infection heightens the risk of developing HPV-related carcinoma in situ and cancer by Yu-Hsiang Shih, Chiao-Yu Yang, Chia-Chi Lung

    Published 2025-08-01
    “…Method We utilized data from TriNetX, a database encompassing 106 healthcare organizations spanning 15 countries and comprising information from over 124 million participants. …”
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  16. 5616

    Mobil Uyumlu PREDIABE-TR Web Sayfasının Oluşturulması, Kapsamı, Kalitesi ve Kullanılabilirliğinin Değerlendirilmesi by Güven Barış Cansu, İbrahim Topuz, Sebahat Gözüm, Yusuf Güver

    Published 2024-12-01
    “…Amaç: Çalışmanın amacı prediyabetli bireylerin bilgilenmeleri amacıyla bir web sayfası oluşturulması, kapsamı, kalitesi ve kullanılabilirliğinin değerlendirilmesidir.Yöntemler: Metodolojik tipte yürütülen bu çalışmada, Prediabe-TR web sayfası (prediabe-tr.net) araştırmacılar tarafından WordPress içerik yönetim sistemi kullanılarak hazırlanmıştır. …”
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    An automatic laryngoscopic image segmentation system based on SAM prompt engineering: from glottis annotation to vocal fold segmentation by Yucong Zhang, Yucong Zhang, Yuchen Song, Juan Liu, Juan Liu, Ming Li, Ming Li

    Published 2025-07-01
    “…Specifically, vocal fold-related features are extracted from U-Net-generated glottis masks, which are enhanced via brightness contrast adjustment and morphological closing. …”
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  20. 5620

    Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device by Kei Suzuki, Tipporn Laohakangvalvit, Midori Sugaya

    Published 2024-10-01
    “…The feature selection methods were Light Gradient Boosting Machine (LightGBM) feature importance, mutual information, ReliefF and ElasticNet coefficients. The selected EEG indices were learned by the LightGBM model, which is reported to be as accurate as the latest deep learning models. …”
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