Designing of AIML (Artificial Intelligence and Machine Learning) and Convolutional Neural Network (CNN) Based Architectures and Its Various Applications in the Field of Engineering

Dear Colleagues,

The Special Issue aims to present the latest research advancements and challenges in the field of Artificial Intelligence and Machine Learning (AIML). The focus is on exploring new AIML and Convolutional Neural Network (CNN) based architectures and techniques for improving their performance and efficiency, as well as case studies and practical applications in various domains such as computer vision, pattern recognition, speech recognition, natural language processing, and others. Deep learning models have become more robust to variations in input data, such as changes in lighting or viewpoint. Researchers have developed new architectures for deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) that have proven to be highly effective in a variety of AIML and Deep Learning-based tasks. Researchers have developed techniques to transfer the knowledge learned by deep learning models from one task to another task, which has enabled the development of models that can recognize patterns in a wide range of domains. Despite these advancements, there are still several key challenges that need to be addressed in the field of AIML and Deep Learning. Some of these challenges include generalization, interpretability, adversarial examples, privacy and security, and complexity. Overall, the field of AIML and Deep Learning is constantly evolving with new research and advancements, and these challenges will continue to be the focus of research in the years to come.

Topics of interest include, but are not limited to:

• Image recognition tasks such as object detection, image classification, and semantic segmentation using deep learning;
• Video recognition such as action recognition, activity recognition, and video captioning using deep learning;
• Audio recognition such as speech recognition, speaker identification, and music classification, using deep learning;
• Transfer learning techniques to transfer the knowledge learned by deep learning models from one task to another task, which can be used to improve performance or reduce the need for labeled data;
• Explainable AI techniques to make deep learning models more interpretable, such as feature visualization and attention mechanisms;
• Edge computing techniques to deploy deep learning models on resource-constrained devices, such as smartphones, IoT devices, and embedded systems;
• Designing of Geographical Information Systems using Machine Learning techniques;
• Design and implementation of CNN-based cloud network traffic estimation;
• Designing of CNN-based architectures for Disease Recognition for human body, flora, and fauna (vegetables, fruits, flowers, fishes, chicken);
• Freshness gradient design by AIML techniques for various fruits, vegetables, or any eatables.

Dr. Tanupriya Choudhury
Dr. Sachi Nandan Mohanty
Prof. Dr. Jung-Sup Um
Dr. Bappaditya Koley
Guest Editors

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• artificial intelligence
• machine learning
• CNN
• GIS
• design
• architecture