An overview: Big data analysis by deep learning and image processing
Abstract
Big Data Analysis and Deep Learning are two fields of data science that are getting a proportion of interest nowadays. The relevance of big data has increased as a result of the massive amounts of domain-specific data that many public and private firms have gathered. This data may be useful for research on topics like national security, fraud, and medical informatics and detection. Huge volumes of data are analyzed by businesses like Google and Microsoft for business analysis and decision-making that affects both present and future technology. By using deep learning algorithms, high-level information is extracted. Multifaceted intellections are articulated as data illustrations using a tiered erudition approach. On the basis of comparably simpler abstractions created at the level before, sophisticated abstractions are learnt at a given level. The field of image processing is still in its infancy. Agriculture, textiles, transportation, and other fields have all made use of image alteration, image coding, compression, picture segmentation, and other technologies. Traditional image processing techniques, on the other hand, are unable to handle the huge quantity of image mockups available today. As a result, to raise the level and efficiency of image processing, a novel methodical area has emerged that focuses on exploring big data-based image processing technology and developing an image processing archetypal. The image processing prototypical based on big data offers recompenses including strong repeatability, high accuracy, extensive applicability, good flexibility, and a high potential for information reduction, according to existing big data research results. In this review, potential of Deep Learning and images processing to address some of the most challenging problems in Big Data Analytics, including the extraction of multifaceted designs from gigantic data sets, semantic indexing, data tagging, rapid facts repossession, and simplification of discriminative tasks will be explored. The integration and interaction of the three main topic is image processing, deep learning, and big data will be explained in this review. All three of these areas hold great promise for a variety of industries. The research issues highlighted in the integration and interplay of these broad domains are examined, as are some potential study avenues.
