Automatic Classification of Stegano files using the features of Natural Language Processing and hybrid Deep Learning Algorithms

Abstract

The corporate sector has adopted communication as its principal medium due to the improvements of the digital era. In the past, in-person meetings, purchases, settlements, business talks, and profile-taking were all done. However, everything has gone digital in the present period. Over the last several years, there has been a noticeable exponential rise in the amount of complicated texts and documents that require a deeper comprehension of machine learning techniques in order to recognize languages in a variety of applications. Outstanding results in the processing of natural languages have been obtained by a number of Artificial Intelligent techniques. A key component of machine learning and deep learning techniques' efficacy is their capacity to understand intricate models and non-linear connections in data. Choosing the right architecture, frameworks, and algorithms for classifying input, like text files, managing audio and video files, however, is a difficult undertaking. The proposed work's goal is to identify text, audio messages, chatbots, and smart records using natural language processing. Using a hybrid deep learning approach, text, voice, and video recordings are used as inputs to be classified. Problem Synopsis: With contact becoming more and more important to business, companies have created advanced NLP programs. Through chatbots, digital records, phone conversations, and messages, these NLP swiftly fulfil human desires. Customer preferences, desires, and demands have been found to be more strongly impacted by the ease of communication and connection. Today's service providers use chatbots, digital records, messaging apps, email, and phone conversations as their main methods of communication for almost all of their trade channels of preference, client queries, and transactions. Proposed Method: The study shows how input stegano file is processed automatically based on user reactions, text message replies, and audio record identification during communications using text content, voice messages, and audio using the features of Natural Language Processing and hybrid Deep Learning Algorithms