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Machine Learning (ML) and Deep Learning (DL) have achieved high success in many textual, auditory, medical imaging, and visual recognition patterns. Concerning the importance of ML/DL in recognizing patterns due to its high accuracy, many researchers argued for many solutions for improving pattern recognition performance using ML/DL methods. Due to the importance of the required intelligent pattern recognition of machines needed in image processing and the outstanding role of big data in generating state-of-the-art modern and classical approaches to pattern recognition, we conducted a thorough Systematic Literature Review (SLR) about DL approaches for big data pattern recognition. Therefore, we have discussed different research issues and possible paths in which the abovementioned techniques might help materialize the pattern recognition notion. Similarly, we have classified 60 of the most cutting-edge articles put forward pattern recognition issues into ten categories based on the DL/ML method used: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Generative Adversarial Network (GAN), Autoencoder (AE), Ensemble Learning (EL), Reinforcement Learning (RL), Random Forest (RF), Multilayer Perception (MLP), Long-Short Term Memory (LSTM), and hybrid methods. SLR method has been used to investigate each one in terms of influential properties such as the main idea, advantages, disadvantages, strategies, simulation environment, datasets, and security issues. The results indicate most of the articles were published in 2021. Moreover, some important parameters such as accuracy, adaptability, fault tolerance, security, scalability, and flexibility were involved in these investigations. [ABSTRACT FROM AUTHOR]

The morphology algorithms are an exciting field that provides a powerful and unified method applied to image and video processing applications. Also, they have many advantages to give manageable hardware implementations and are widely employed as parts of image processing techniques. On the other hand, Quantum-dot Cellular Automata (QCA) is a promising and novel nanotechnology that contains notable benefits against common technologies in diverse features like extremely small size, low energy loss, and high operation frequency. Besides, schematizing circuits with no info loss or fault-tolerant might be beneficial in reducing power wastage. In the present paper, we suggest a novel fault-tolerant procedure to execute the morphology operations in digital image processing based on QCA. The mentioned fault-tolerant schematization for executing the morphology operations in digital image processing is easy to build. It contains notably lower elements than Complementary Metal Oxide Semiconductor (CMOS) design. Implementation and testing of all circuits are achieved using the QCADesigner tool. The suggested fault-tolerant circuit for morphological dilation/erosion operation attains high fault-tolerant in all cases of defects. [ABSTRACT FROM AUTHOR]

Massive multimedia data are being created due to the rising amount of the Internet and user-generated content, low-cost commodity devices with cameras (like cellphones, surveillance systems, and so on), and the proliferation of social networks, forming a unique type of big data. Several studies have been conducted in this research area using a survey and event analysis approach; however, none has been conducted to investigate the status of knowledge, its features, evolution, and emerging trend of multimedia big data. Therefore, in this paper, a bibliometric study using VOSviewer software is carried out with 1,865 documents from 2008 to 2020. Based on the result, 2013 is the starting year where the total publication excess of 100 articles and the configuration of leading countries, productive organizations, and authors are investigated. The most cited journals, popular publications venues, and hot research topics are also included in the investigations. Our investigation uncovered useful information, such as annual publishing patterns, the hottest research topic, the top 10 important authors and articles, and the most helpful funding organizations and venues. [ABSTRACT FROM AUTHOR]