Applications of Artificial Intelligent and Machine Learning Techniques in Image Processing

Abstract

This chapter explores the role of AI and machine learning (ML) in image processing, focusing on their applications. It covers AI techniques like supervised learning, unsupervised learning, reinforcement learning, and deep learning. AI techniques include rule-based systems, expert systems, fuzzy logic, and genetic algorithms. Machine learning techniques include SVM, decision trees, random forests, K-means clustering, and PCA. Deep learning techniques like CNN, RNN, and GANs are used in tasks like object recognition, classification, and segmentation. The chapter emphasizes the impact of AI and ML on accuracy, efficiency, and decision-making. It also discusses evaluation metrics and performance analysis, emphasizing the importance of selecting appropriate metrics and techniques. The chapter also addresses ethical considerations, such as fairness, privacy, transparency, and human-AI collaboration.

An efficient tomato diseases detection and classification methodology using CNN Deep Learning Network

“…This ability to train directly from data allows for continuous adaptation and improvement of machine performance, emphasizing the importance of the quantity and quality of data during the training phase [4]. All artificial intelligence approaches and methods can be categorized into supervised, unsupervised, and reinforcement methods, providing different strategies for data delivery and knowledge extraction [5] [6] [7].…”