Special Issues

Cognitive Granular Computing Methods for Big Data Analysis

Submission Deadline: 30 April 2023 (closed) View: 1

Guest Editors

Dr. Wentao Li, Southwest University, China.
Dr. Chao Zhang, Shanxi University, China.
Dr. Tao Zhan, Southwest University, China.
Dr. Hanyu E, University of Alberta, Canada.


Traditional data analysis usually focuses on employing intelligent methods to analyze and discover useful data hidden in a batch of raw datasets, so as to maximize the value of data. This plays a vital role in making developmental plans for a country, understanding commercial values for organizations, forecasting social demands for individuals, etc. Big data analysis is a special kind of data analysis with more massive volumes of data, which is becoming a driving force for changes in almost all walks of life. Therefore, plenty of traditional methods in data analysis still work in big data analysis, and cognitive granular computing methods are among the representative ones.


As a newly emerged computing paradigm in the field of artificial intelligence, granular computing methods primarily address complicated problems via formulating, processing and communicating information granules for enhancing the validity and efficiency of problem-solving procedures. Since the birth of granular computing, numerous cognitive-systems-inspired tools have been developed under the umbrella of granular computing in both theoretical and application areas, such as three-way decisions that formulate the natural thinking mode of human, multi-granularity structures that appear in complex social networks, cloud models that depict linguistic expressions, etc. During the past decade, big data analysis has become a focal point of scholars and practitioners, and the study on new methods in the context of big data analysis is conducive to understanding and digging massive values from the facet of countries, industries, organizations and individuals. Therefore, exploring new cognitive granular computing methods for big data analysis owns big scientific advances and significant application values.


The goal of this special issue is to collect recent developments in the area of cognitive granular computing methods for big data analysis and how can be applied to real-world issues. Original research work, significantly extended versions of conference papers, and review papers are welcome. Topics of interest include, but are not limited to, the following:


1. Cognitive granular computing methods for the creation, sharing and reuse of big data;

2. Cognitive granular computing tools and technologies for deploying and managing big data;

3. Cognitive granular computing methods to ensure the security and privacy of big data;

4. Cognitive granular computing methods to integrate multiple research methods on big data;

5. New big data analysis techniques by using cognitive granular computing methods;

6. New cognitive granular computing methods emerged from recent big data innovations.


• Big data modeling and analysis
• Cognitive granular computing
• The security and privacy of big data
• Uncertainty information analysis in engineering
• Intelligent decision-making techniques
• Engineering data-driven cognitive computing

Published Papers

  • Open Access


    LC-NPLA: Label and Community Information-Based Network Presentation Learning Algorithm

    Shihu Liu, Chunsheng Yang, Yingjie Liu
    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 203-223, 2023, DOI:10.32604/iasc.2023.040818
    (This article belongs to the Special Issue: Cognitive Granular Computing Methods for Big Data Analysis)
    Abstract Many network presentation learning algorithms (NPLA) have originated from the process of the random walk between nodes in recent years. Despite these algorithms can obtain great embedding results, there may be also some limitations. For instance, only the structural information of nodes is considered when these kinds of algorithms are constructed. Aiming at this issue, a label and community information-based network presentation learning algorithm (LC-NPLA) is proposed in this paper. First of all, by using the community information and the label information of nodes, the first-order neighbors of nodes are reconstructed. In the next, the More >

  • Open Access


    Pancreas Segmentation Optimization Based on Coarse-to-Fine Scheme

    Xu Yao, Chengjian Qiu, Yuqing Song, Zhe Liu
    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2583-2594, 2023, DOI:10.32604/iasc.2023.037205
    (This article belongs to the Special Issue: Cognitive Granular Computing Methods for Big Data Analysis)
    Abstract As the pancreas only occupies a small region in the whole abdominal computed tomography (CT) scans and has high variability in shape, location and size, deep neural networks in automatic pancreas segmentation task can be easily confused by the complex and variable background. To alleviate these issues, this paper proposes a novel pancreas segmentation optimization based on the coarse-to-fine structure, in which the coarse stage is responsible for increasing the proportion of the target region in the input image through the minimum bounding box, and the fine is for improving the accuracy of pancreas segmentation More >

  • Open Access


    SC-Net: A New U-Net Network for Hippocampus Segmentation

    Xinyi Xiao, Dongbo Pan, Jianjun Yuan
    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3179-3191, 2023, DOI:10.32604/iasc.2023.041208
    (This article belongs to the Special Issue: Cognitive Granular Computing Methods for Big Data Analysis)
    Abstract Neurological disorders like Alzheimer’s disease have a significant impact on the lives and health of the elderly as the aging population continues to grow. Doctors can achieve effective prevention and treatment of Alzheimer’s disease according to the morphological volume of hippocampus. General segmentation techniques frequently fail to produce satisfactory results due to hippocampus’s small size, complex structure, and fuzzy edges. We develop a new SC-Net model using complete brain MRI images to achieve high-precision segmentation of hippocampal structures. The proposed network improves the accuracy of hippocampal structural segmentation by retaining the original location information of More >

  • Open Access


    An Update Method of Decision Implication Canonical Basis on Attribute Granulating

    Yanhui Zhai, Rujie Chen, Deyu Li
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1833-1851, 2023, DOI:10.32604/iasc.2023.039553
    (This article belongs to the Special Issue: Cognitive Granular Computing Methods for Big Data Analysis)
    Abstract Decision implication is a form of decision knowledge representation, which is able to avoid generating attribute implications that occur between condition attributes and between decision attributes. Compared with other forms of decision knowledge representation, decision implication has a stronger knowledge representation capability. Attribute granularization may facilitate the knowledge extraction of different attribute granularity layers and thus is of application significance. Decision implication canonical basis (DICB) is the most compact set of decision implications, which can efficiently represent all knowledge in the decision context. In order to mine all decision information on decision context under attribute More >

  • Open Access


    Two-Layer Information Granulation: Mapping-Equivalence Neighborhood Rough Set and Its Attribute Reduction

    Changshun Liu, Yan Liu, Jingjing Song, Taihua Xu
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2059-2075, 2023, DOI:10.32604/iasc.2023.039592
    (This article belongs to the Special Issue: Cognitive Granular Computing Methods for Big Data Analysis)
    Abstract Attribute reduction, as one of the essential applications of the rough set, has attracted extensive attention from scholars. Information granulation is a key step of attribute reduction, and its efficiency has a significant impact on the overall efficiency of attribute reduction. The information granulation of the existing neighborhood rough set models is usually a single layer, and the construction of each information granule needs to search all the samples in the universe, which is inefficient. To fill such gap, a new neighborhood rough set model is proposed, which aims to improve the efficiency of attribute… More >

  • Open Access


    A PERT-BiLSTM-Att Model for Online Public Opinion Text Sentiment Analysis

    Mingyong Li, Zheng Jiang, Zongwei Zhao, Longfei Ma
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2387-2406, 2023, DOI:10.32604/iasc.2023.037900
    (This article belongs to the Special Issue: Cognitive Granular Computing Methods for Big Data Analysis)
    Abstract As an essential category of public event management and control, sentiment analysis of online public opinion text plays a vital role in public opinion early warning, network rumor management, and netizens’ personality portraits under massive public opinion data. The traditional sentiment analysis model is not sensitive to the location information of words, it is difficult to solve the problem of polysemy, and the learning representation ability of long and short sentences is very different, which leads to the low accuracy of sentiment classification. This paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text… More >

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