A survey of cryptographic data protection and machine learning V. R. Kanagavalli, A. Meenakshi Machine Learning and Cryptographic Solutions for Data Protection and Network Security, 2024 In an era of information flooding, a data breach not only results in economic loss, but also a loss of goodwill for the business applications. Cryptographic algorithms have been studied and applied for the protection of sensitive data for a very long time. Machine learning applications deal with an enormous amount of data where the data may be critical and sensitive. There are a plethora of machine learning applications where cryptography is applied for the protection of data. In the same way, machine learning algorithms can be used for implementing cryptographic algorithms. It is used for analyzing and finding hidden patterns to improve the credibility of the security algorithms. This chapter analyzes the contribution of cryptography to machine learning algorithms and vice versa. It also describes the challenges and opportunities in application and interaction between these two fields.
Machine learning and cryptographic solutions for data protection and network security Meenakshi, A., Uma, R., Visalakshi, P., Mahesh, Vijayalakshmi G. V. 1978-, Ruth, J. Anitha Machine Learning and Cryptographic Solutions for Data Protection and Network Security, 2024 "In the relentless battle against escalating cyber threats, data security faces a critical challenge - the need for innovative solutions to fortify encryption and decryption processes. The increasing frequency and complexity of cyber-attacks demand a dynamic approach, and this is where the intersection of cryptography and machine learning emerges as a powerful ally. As hackers become more adept at exploiting vulnerabilities, the book stands as a beacon of insight, addressing the urgent need to leverage machine learning techniques in cryptography.Machine Learning and Cryptographic Solutions for Data Protection and Network Security unveil the intricate relationship between data security and machine learning and provide a roadmap for implementing these cutting-edge techniques in the field. The book equips specialists, academics, and students in cryptography, machine learning, and network security with the tools to enhance encryption and decryption procedures by offering theoretical frameworks and the latest empirical research findings. Its pages unfold a narrative of collaboration and cross-pollination of ideas, showcasing how machine learning can be harnessed to sift through vast datasets, identify network weak points, and predict future cyber threats."--
Preface Machine Learning and Cryptographic Solutions for Data Protection and Network Security, 2024
Breast Cancer Detection using Convolutional Autoencoder with Hybrid Deep Learning Model S. Ranjana, A. Meenakshi International Journal of Computational and Experimental Science and Engineering, 2024 The most deadly cancer among women in world is Breast cancer (BC). The early identification of malignancy helps in the disease diagnosis and it can help strongly to enhance the survival rate. With the rapid development of modern medical science and technology, medical image classification has become a more and more challenging problem. However, in most traditional classification methods, image feature extraction is difficult, and the accuracy of classifier needs to be improved. Therefore, this paper proposes a high-accuracy medical image classification method based on Deep Learning (DL) which is called Convolutional Neural Network (CNN). This research focused to create a hybrid DL model with a single test that subjected at inference and even adopted VGG16 as Autoencoder for Transfer Learning (TL) that performs an image analysis task such as segmentation and even set as an adaptor for pre training the model. The VGG16 is used to train from the source dataset and perform as the adaptors that have been optimized at the testing stage using a single test subject for effective computation. Therefore, this study has been used CNN with Bi-Long Short Term Memory (Bi-LSTM) method to extract features from Ultrasound Images of Breast for cancer detection database that involves images to benign as well as malignant breast tumors for performing analysis of the unsupervised images. The evaluated results showed that accuracy of VGG16 with CNN-Bi-LSTM has high accuracy as 98.24% indicates hybrid DL with VGG16 models have appropriate in detection and classification of the breast cancers precisely.
Sentiment Analysis and Homophobia detection of YouTube comments in Code-Mixed Dravidian Languages using machine learning and Transformer models Ceur Workshop Proceedings, 2022
