A Comparative Study on Cyber security Technology in Big data Cloud Computing Environment

Handwritten character recognition is a challenging task in the field of image processing and pattern recognition. The success of character recognition systems depends heavily on the feature extraction methods used to represent the character images. In this chapter, the authors propose a novel feature extraction method called progressive stochastic learning (PSL) algorithm. The proposed work is based on the texture and structural features of the character image and is designed to extract discriminative features that capture the essential information of the characters. The PSL algorithm is used to classify the extracted features into their respective character classes. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 92.6% for correct characters predicted and 91.3% for correct words predicted. Moreover, the proposed method outperforms several state-of-the-art methods in terms of recognition accuracy, computation time, and memory requirements.

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Fine-Grained Deep Feature Expansion Framework for Fashion Apparel Classification Using Transfer Learning

Regin,

Sharma,

Singh

et al. 2023

Advances in Computational Intelligence and Robotics

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The chapter focuses on developing a deep learning-based image classification model for fashion and apparel. With the rise of online retail services, there is a growing need for accurate and efficient apps to categorize fashion garments based on their attributes from image data. The study proposes a fine-grained deep feature expansion framework using transfer learning to address this need. The dataset consists of approximately 44,000 images of fashion apparel with six categories, including gender, subcategory, article type, base color, season, and usage. The images are preprocessed to remove corrupted images and resized to 256 by 256 pixels. The proposed framework employs pre-trained CNN models such as ResNet50 or Vgg19 for feature extraction, fine-tuning, and transfer learning. The CNN architecture consists of several layers: convolutional layers, residual blocks, max-pooling layers, and dense layers.

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A Comparative Study on Cyber security Technology in Big data Cloud Computing Environment

Cited by 5 publications

References 15 publications

Comprehensive Review: Security Challenges and Countermeasures for Big Data Security in Cloud Computing

Comprehensive Review: Security Challenges and Countermeasures for Big Data Security in Cloud Computing

Light Weight Structure Texture Feature Analysis for Character Recognition Using Progressive Stochastic Learning Algorithm

Fine-Grained Deep Feature Expansion Framework for Fashion Apparel Classification Using Transfer Learning

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