Editorial: Special Issue on Recent Advances in Cognitive Learning and Data Analysis

Jinchang Ren, Amir Hussain, Jiangbin Zheng, Cheng-Lin Liu, Bin Luo
<span title="2020-06-15">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fyqcejpxl5gdvbw7yq7x2x5abu" style="color: black;">Cognitive Computation</a> </i> &nbsp;
# Springer Science+Business Media, LLC, part of Springer Nature 2020 With the rapid development of artificial intelligence (AI)-related techniques and the continuous explosion of multimodal data, there is a new trend in combining AI machine learning with multimodal big data analytics. This has enabled emerging new techniques and applications in a wide range of fields. Accordingly, these have brought in both challenges in effective big data analysis and opportunities in innovative applications.
more &raquo; ... o this end, cognitive modeling and cognitive systems have attracted increasing attention under the framework of big data enabled machine learning, especially the sparse representation and sparse learning, deep learning, and reinforcement learning. To address these challenges and opportunities, we have successfully organized the Brain Inspired Cognitive System (BICS) Conference series, including the 9 th in Xi'an, China in July 2018 [1] and 10 th in Guangzhou, China in July 2019 [2] . Selected papers are extended and included in this special issue. The special issue has solicited the state-of-the-art contributions in cognitive learning and data analysis, which has also provided a premier forum for both the academic and industrial research community to report progress, exchange findings, and facilitate future multidisciplinary research directions as detailed below. In total, there are seven papers included in this special issue. The selected papers have covered a wide range of relevant topics, showing both theoretical and applicable values. Therefore, these can be categorized into various groups under different criteria, where detailed introduction of the included papers is given as follows. From Conventional Machine Learning to Deep Learning In the seven contributed papers, three of them papers are deep learning based, including Style Neutralization Generative Adversarial Network (SN-GAN) upgraded U-Net [3], , and recurrent neural network (RNN) [5] . On the other hand, conventional machine learning approaches are also adopted in the other four papers, which include multi-scale mahalanobis kernel-based support vector machine [6], graph model based salient superpixel visual tracking [7] , an evolutionary safelevel synthetic minority over-sampling technique (ESLSMOTE) for balanced learning [8] , and Laplacian-regularized correlative sparse ranking enabed matching [9] . The transition from conventional approaches to deep learning has shown a changing trend in AI and cognitive computing. Various Tasks The seven contributed papers in the special issue cover several typical tasks of cognitive computation. These include classification/recognition [3, 4, 6, 9] , image captioning [5], and object segmentation for visual tracking [7] . The work in [8] is also tested on three classifiers, where the work in [9] for
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