Artificial Intelligence Based Predictive Analysis of Customer Churn

Abstract

Deep learning has been evidenced to be a cutting-edge technology for big data scrutiny with a huge figure of effective cases in image processing, speech recognition, object detection, and so on. Lately, it has also been acquainted with in food science and business. In this paper, a fleeting overview of deep learning and detailly labelled the structure of some prevalent constructions of deep neural networks and the method for training a model is provided. Various techniques that used deep learning as the data analysis tool are analyzed to answer the complications and challenges in food sphere together with quality detection of fruits & vegetables. The precise difficulties, the datasets, the pre-processing approaches, the networks and frameworks used, the performance attained, and the evaluation with other prevalent explanations of each research are examined. We also analyzed the potential of deep learning to be used as a cutting-edge data mining tool in food sensory and consume explores. The outcome of our review specifies that deep learning outclasses other approaches such as physical feature extractors, orthodox machine learning algorithms, and deep learning as a capable tool in food quality and safety inspection. The cheering outcomes in classification and regression problems attained by deep learning will fascinate more research exertions to apply deep learning into the arena of food in the forthcoming. The main aim of this work is to facilitate our learning and implement that in real life. Food quality and food security are always issues which are always overlooked. In modern times, this has morphed into more significant concerns relating to optimization of on- demand supply chains and profitability of agri-businesses. But now with the advanced systems and technology, it is possible to resolve this issue efficiently using the power of AI.