Showing 1 - 20 results of 5,605 for search '(feature OR features) detection analysis', query time: 0.26s Refine Results
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    Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection by Alymzhan Toleu, Gulmira Tolegen, Rustam Mussabayev, Alexander Krassovitskiy, Bagashar Zhumazhanov

    Published 2024-12-01
    “…This study examines how ten different audio features, including MFCC, mel-spectrogram, chroma, and spectral contrast etc., influence speaker change detection (SCD) performance. …”
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    A novel method for optimizing epilepsy detection features through multi-domain feature fusion and selection by Guanqing Kong, Guanqing Kong, Shuang Ma, Shuang Ma, Wei Zhao, Wei Zhao, Haifeng Wang, Haifeng Wang, Qingxi Fu, Qingxi Fu, Jiuru Wang

    Published 2024-11-01
    “…To extract features strongly correlated with epileptic classification detection, an improved particle swarm optimization (PSO) algorithm and Pearson correlation analysis are combined. …”
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    Farsi License Plate Detection and Recognition Based on Characters Features by Sedigheh Ghofrani, Mehran Rasouli

    Published 2024-02-01
    “…The region elements analysis includes binarization, character analysis, character continuity analysis and character parallelism analysis. …”
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    Group feature calibration for sound event detection by Yanzhen Ren, Wuyang Liu, Chenyu Liu, Tingting Zhu

    Published 2025-06-01
    “…Through class-wise analysis of the results, the consistent improvement of the GFC module in detecting sound events with diverse time-frequency characteristics is proven. …”
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    Glaucoma Detection and Feature Identification via GPT-4V Fundus Image Analysis by Jalil Jalili, PhD, Anuwat Jiravarnsirikul, MD, Christopher Bowd, PhD, Benton Chuter, MD, Akram Belghith, PhD, Michael H. Goldbaum, MD, Sally L. Baxter, MD, Robert N. Weinreb, MD, Linda M. Zangwill, PhD, Mark Christopher, PhD

    Published 2025-03-01
    “…Conclusions: GPT-4V shows promise as a tool in glaucoma screening and detection through fundus image analysis, demonstrating generally high agreement with expert evaluations of key diagnostic features, although agreement did vary substantially across datasets. …”
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    Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue Detection by Zhen Gao, Xiaowen Chen, Jingning Xu, Rongjie Yu, Heng Zhang, Jinqiu Yang

    Published 2024-12-01
    “…This paper pioneers the use of the CLIP (Contrastive Language-Image Pre-training) model for fatigue detection. And by harnessing the power of a Transformer architecture, sophisticated and long-term temporal features are adeptly extracted from video sequences, paving the way for more nuanced and accurate fatigue analysis. …”
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