AN EXPLORATORY STUDY TOWARDS APPLYING AND DEMYSTIFYING DEEP LEARNING CLASSIFICATION ON BEHAVIORAL BIG DATA - Sudhir Allam (International Journal of Innovations in Engineering Research and Technology, 2021)
The superior performance of deep learning (DL) in natural language processing and machine vision and has ignited renewed interest in applying these technologies more broadly in research and practice. This study looks at how deep learning approaches can be used in the classification of large volumes of sparse behavioral data, which in the age of big data is becoming increasingly common. The excellent performance of deep learning algorithms in tasks such as classification and natural language processing has attracted the interest of both academics and professionals of these algorithms [1]. Since then, many researchers have tried, in the expectation of achieving comparable superior outcomes, to extend these algorithms to other machine learning situations with various data types. The above-mentioned study is based on inspiration and correlates the predictive performance of deep learning classification techniques with several behavioral examples. In addition to the usage of new data categories and a thorough comparison of its outputs with widespread classifications, this study seeks to highlight where and why the approaches to deep learning perform best. It demonstrates that an uncontrolled pre-training process does not improve classification efficiency and that tanh nonlinearity delivers the best predictive results by utilizing profound knowledge about this specific data sort. Deep learning achieves results that are equal or equivalent to traditional low classifiers as it is applied to massive behavioral data sets [1]. However, the findings have not changed much. Looking at how well deep learning performs; it finds that data sets with low signal-to-noise separation had the worse effects. We examine the significance of the clustered, hierarchical characteristic of the learning process to learn why deep learning usually works well on this kind of data. In contrast to superficial classifiers, neurons in the distributed model are more complex in certain behavioral functions [1]. Deep learning classification is also referred to as a black-box technique, and this article addresses how to determine whether and when these strategies perform well. Keywords: Deep learning, Behavioral big data, Neural Convolutional Networks, Feed-Forward Neural Networks