ABSTRACT
Artificial Intelligence with Machine Learning and Neural Network approaches has explored major application areas from marketing to research. AI is being used in a variety of business areas (medical care, Information Technology, power, agribusiness, clothing, engineering, intelligent cities, touristy, and transportation), as well as administration and marketing operations (Human Resources, client services, reverse engineering, sound body and security, project administration, recommendations, organizational monitoring, and automation implementation)[2]. Nowadays, the Textile Industry is highly automated with different AI algorithms. In textile fabric inspection, artificial intelligence techniques such as neural networks and artificial vision are utilised to improve the performance of productive systems[3]. This study presents in detail how various Intelligent Learning Techniques such as Support Vector Machine, Bayesian Classification, Decision Tree, and K-Nearest Neighbour are implemented in the textile industry to overcome the problems with the traditional methods they are implementing. It also illustrates clearly the various Neural Network approaches such as Artificial Neural Networks, and Convolutional Neural Networks to provide better performance in textile applications. We conclude this work with comparisons of different Machine Learning and Neural Network Approaches, their advantages and how they can be used to overcome the current challenges in the field of the Textile Industry. The detailed survey represents the optimal strategy that can be implemented in the textile industry as neural network approaches.
