Titlebook: Recent Trends in Image Processing and Pattern Recognition; Second International K. C. Santosh,Ravindra S. Hegadi Conference proceedings 201

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書目名稱Recent Trends in Image Processing and Pattern Recognition影響因子(影響力)

書目名稱Recent Trends in Image Processing and Pattern Recognition影響因子(影響力)學(xué)科排名

書目名稱Recent Trends in Image Processing and Pattern Recognition網(wǎng)絡(luò)公開度

書目名稱Recent Trends in Image Processing and Pattern Recognition網(wǎng)絡(luò)公開度學(xué)科排名

書目名稱Recent Trends in Image Processing and Pattern Recognition被引頻次

書目名稱Recent Trends in Image Processing and Pattern Recognition被引頻次學(xué)科排名

書目名稱Recent Trends in Image Processing and Pattern Recognition年度引用

書目名稱Recent Trends in Image Processing and Pattern Recognition年度引用學(xué)科排名

書目名稱Recent Trends in Image Processing and Pattern Recognition讀者反饋

書目名稱Recent Trends in Image Processing and Pattern Recognition讀者反饋學(xué)科排名

只看該作者 · 發(fā)表于 2025-3-21 21:14:05

A Filter Based Feature Selection for Imbalanced Text Classificationf interval valued data. For classification purpose, a suitable symbolic classifier is recommended. The experimentation is done with two standard benchmarking datasets viz., Reuters 21578 and TDT2. The experimental results obtained from the proposed model are better in terms of f-measure when compared to the available models.

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Conference proceedings 2019lications; and image processing. Part II:?healthcare and medical imaging; biometrics and applications. Part III: document image analysis; image analysis in agriculture; and data mining, information retrieval and applications..

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1865-0929 ng and applications; and image processing. Part II:?healthcare and medical imaging; biometrics and applications. Part III: document image analysis; image analysis in agriculture; and data mining, information retrieval and applications..978-981-13-9186-6978-981-13-9187-3Series ISSN 1865-0929 Series E-ISSN 1865-0937

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只看該作者 · 發(fā)表于 2025-3-22 13:15:32

Recognition of Meitei Mayek Using Statistical Texture and Histogram Featuresern (ULBP) and Projection Histogram (PH) have been considered to evaluate and validate the developed dataset. Finally, recognition is performed using the K-nearest neighbor (KNN) classifier. The combination of ULBP and PH has given the recognition rate of 97.85%.

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Symbolic Approach for Word-Level Script Classification in Video Framesymbolic classifier is employed for classification. For experimentation, we have extracted 600 word images from each script and total of 3000 word images from video frames. Further, we have made comparative study to show the robustness of symbolic representation and classifier with SVM and ANN classifiers.

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