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Journal of Artificial Intelligence and Capsule Networks

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Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
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Deniable Authentication Encryption for Privacy Protection using Blockchain
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Real Time Anomaly Detection Techniques Using PySpark Frame Work
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Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
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Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
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Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
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Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis
Volume-3 | Issue-1

An Efficient Machine Learning based Model for Classification of Wearable Clothing
Volume-3 | Issue-4

Home / Archives / Volume-3 / Issue-4 / Article-4

Volume - 3 | Issue - 4 | december 2021

An Efficient Machine Learning based Model for Classification of Wearable Clothing
Judy Simon   233  240
Pages: 317-329
Cite this article
Simon, J. (2021). An Efficient Machine Learning based Model for Classification of Wearable Clothing. Journal of Artificial Intelligence and Capsule Networks, 3(4), 317-329. doi:10.36548/jaicn.2021.4.004
Published
02 December, 2021
Abstract

Computer vision research and its applications in the fashion industry have grown popular due to the rapid growth of information technology. Fashion detection is increasingly popular because most fashion goods need detection before they could be worn. Early detection of the human body component of the input picture is necessary to determine where the garment area is and then synthesize it. For this reason, detection is the starting point for most of the in-depth research. The cloth detection of landmarks is retrieved through many feature items that emphasis on fashionate things. The feature extraction can be done for better accuracy, pose and scale transmission. These convolution filters extract the features through many epochs and max-pooling layers in the neural networks. The optimized classification has been done using SVM in this study, for attaining overall high efficiency. This proposed CNN approach fashionate things prediction is combined with SVM for better classification. Furthermore, the classification error is minimized through the evaluation procedure for obtaining better accuracy. Finally, this research work has attained good accuracy and other performance metrics than the different traditional approaches. The benchmark datasets, current methodologies, and performance comparisons are all reorganized for each piece.

Keywords

Machine learning back-propagation classification technique activation functions fashion image detection and CNN

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