This book constitutes the refereed proceedings of the 5th International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2023, held in Hamirpur, India, during December 21–22, 2023.

The 29 full papers included in this book were carefully reviewed and selected from 173 submissions. They were organized in topical sections as follows: Machine Learning; Image Processing; Network Security; and Data Sciences.

This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.

This book constitutes the refereed proceedings of the 5th International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2023, held in Hamirpur, India, during December 21–22, 2023.

The 29 full papers included in this book were carefully reviewed and selected from 173 submissions. They were organized in topical sections as follows: Machine Learning; Image Processing; Network Security; and Data Sciences.

This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.