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RISS 인기검색어
- 검색키워드
- 저자 : Yagya Raj Pandeya (검색결과 3 건)
- 무료
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Domestic Cat Sound Classification Using Transfer Learning
Yagya Raj Pandeya,Joonwhoan Lee 한국지능시스템학회 2018 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.18 No.2
The domestic cat or house cats (Felis catus) are an ancient human pet animal that can deliver various alert message to human on environmental changes by its mysterious kinds of sounds generation capability. Cat sound classification using deep neural network had scarcity of labeled data, that impelled us to make CatSound dataset across 10 categories of sound. The dataset was even not enough to select data driven approach for end to end learning, so we choose transfer learning for feature extraction. Extracted feature are input to six various classifiers and ensemble techniques applied with predicted probabilities of all classifier results. The ensemble and data augmentation perform better in this research. Finally, various results are evaluated using confusion matrix and receiver operating characteristic curve.
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Domestic Cat Sound Classification Using Transfer Learning
Pandeya, Yagya Raj,Lee, Joonwhoan Korean Institute of Intelligent Systems 2018 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.18 No.2
The domestic cat or house cats (Felis catus) are an ancient human pet animal that can deliver various alert message to human on environmental changes by its mysterious kinds of sounds generation capability. Cat sound classification using deep neural network had scarcity of labeled data, that impelled us to make CatSound dataset across 10 categories of sound. The dataset was even not enough to select data driven approach for end to end learning, so we choose transfer learning for feature extraction. Extracted feature are input to six various classifiers and ensemble techniques applied with predicted probabilities of all classifier results. The ensemble and data augmentation perform better in this research. Finally, various results are evaluated using confusion matrix and receiver operating characteristic curve.
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Machine Learning Techniques for Web Page Classification with Search Engine Optimization
Priya Pandey,Yagya Raj Pandeya 한국디지털융합학회 2023 IJICTDC Vol.8 No.2
Automated Search Engine Optimization (SEO) is crucial for streamlining processes, ensuring consistency, and adapting to changes, thereby enhancing a website's overall success and visibility in the competitive online landscape. This research introduces a dataset and a baseline method for classifying website SEO ranks into three categories. Using 26 keywords, data was collected from 780 web pages across various Google rankings, and 36 ranking factors were employed to predict their rank. Key considerations for webpage preparation include anchor text, backlinks, Ref Domain, unique visits, and text length. The Random Forest model exhibited superior performance, achieving an average accuracy of 72% in predicting actual search rankings. The significance of this automated approach lies in identifying web pages requiring SEO improvements, leading to enhanced search engine rankings.
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