Neural Network-based Framework for understanding Machine deep learning systems' open issues and future trends: A systematic literature review

Abstract

Nowadays, we live in the fourth industrial revolution era, where artificial intelligence, big data with machine learning engineering, and its subfield, the deep learning approach, uses a massive amount of data. This enormous amount of data must be analysed and computed efficiently. In this study, we present BiMDLs (Big machine deep learning systems), which contains state-of-the-art interfaces, frameworks, and libraries. To the best of our knowledge, significant limitations exist in several open aspects of BiMDLs and interfaces, and their ability to analyse, compute, and efficiently develop enormous data. Each of these aspects represents a framework issue that is interlinked in one way or another. This paper's goal is to summarize, organize and examine current BiMDLs and their technologies via a comprehensive review of recent research papers, to provide a synthesis and discuss observations of current open issues and future trends. Therefore, a systematic literature review (SLR) was developed, and 284 solid studies were conducted, analysed and discussed. Furthermore, we highlight several significant challenges and missing requirements of existing big machine deep learning engines and future extension directions. We believe that this SLR could benefit big machine deep learning researchers, developers, and specialists for further improvement; especially in parallel computing environments.