Semantic Context and Attention-driven Framework for Predicting Visual Description Utilizing a Deep Neural Network and Natural Language Processing

Abstract

Background/Purpose: This literature review's goal is to inspect various machine learning algorithms for visual description and their applications to prediction. Examining the numerous approaches mentioned in this area brings up a fresh avenue for expanding the current research methods. Design/Methodology/Approach: The study results that are provided in different scholarly works are acquired from secondary sources, such as scholarly journal publications. This review study analyses these articles and highlights their interpretations. Findings/Result: This research focuses on several cataloguing methods for isolated identifying images and visions. When developing research topics in the idea of inaccessible detecting geographic information systems, the gaps discovered during analysis using various methodologies have made things simpler. Research limitations/implications: This study examined a range of AI tool uses. The scope of this work is rivetted to a assessment of the many machine-learning implementation strategies for analysis and prediction. More research might be done on the many deep learning constructions for image and video classification. Originality/Value: The articles chosen for this study's review are from academic journals and are cited by other authors in their works. The articles that were selected for the examination have a connection to the investigation and research plan described in the paper. Paper Type: Literature review paper.