Special lssues
Table of Content

Transfroming from Data to Knowledge and Applications in Intelligent Systems

Submission Deadline: 01 December 2023 (closed)

Guest Editors

Dr. Hien D. Nguyen, University of Information Technology, VNU-HCM, Vietnam.
Prof. Yucong Duan, Hainan University, China.
Prof. Enrique-Herrera Viedma, University of Granada, Spain.

Summary

Nowadays there have been many research and applications in data science and knowledge engineering for modern daily lives. With the development of new technologies, smart systems have become more useful and be applied in a wide range of areas, including industry, education, healthcare, computer vision, software engineering, fintech, and administrator management.


Intelligent systems have been created to better serve the increasing needs of people. Those systems require a knowledge base to become more intelligent and acceptable in the real-world. Knowledge Engineering studies the methods for Knowledge Representation and Reasoning, which are exciting and well-established fields of research in designing knowledge bases for intelligent systems. Knowledge engineering has derived challenges from new and emerging fields including the semantic web, computational biology, and the development of software agents. It is also the foundation for building potential technologies of intelligent software.


The main objective of this Special Issue is to highlight the technologies in knowledge engineering anddata science. Those techniques tend to apply in the real-world, especially intelligent systems for real-world applications. It also discusses and exchanges recent innovations, developments and challenges in knowledge representation, automated reasoning and hybrid intelligent systems, such as, using knowledge base, big data, machine learning, etc. for application in industry, engineering, science, industry, automation & robotics, business & finance.


Keywords

We welcome authors to present new techniques, methodologies, mixed method approaches and research directions unsolved issues. Topics of interest include, but are not limited to:
Data Mining and Knowledge Discovery
Domain Analysis and Knowledge Modeling
Data Engineering
Database technology for AI
Spatial Databases and Temporal Databases
Cloud data management
Knowledge Management
Knowledge Representation
Ontology Engineering
Domain Ontologies
Ontology Matching and Alignment
Ontology Sharing and Reuse
Enterprise Ontology
Semantic Web
Intelligent tutoring system
Intelligent Problem Solver
Intelligent Information Systems
Expert systems
Decision Support Systems
Document Retrieval Systems
Human-Machine Cooperation
Social network and Information Diffusion
Intelligent software in education, healthcare, business, etc.

Published Papers


  • Open Access

    ARTICLE

    RoBGP: A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

    Xiaohui Cui, Chao Song, Dongmei Li, Xiaolong Qu, Jiao Long, Yu Yang, Hanchao Zhang
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3603-3618, 2024, DOI:10.32604/cmc.2024.047321
    (This article belongs to the Special Issue: Transfroming from Data to Knowledge and Applications in Intelligent Systems)
    Abstract Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer… More >

  • Open Access

    ARTICLE

    Personality Trait Detection via Transfer Learning

    Bashar Alshouha, Jesus Serrano-Guerrero, Francisco Chiclana, Francisco P. Romero, Jose A. Olivas
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1933-1956, 2024, DOI:10.32604/cmc.2023.046711
    (This article belongs to the Special Issue: Transfroming from Data to Knowledge and Applications in Intelligent Systems)
    Abstract Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains, including education, e-commerce, or human resources. Traditional machine learning techniques have been broadly employed for personality trait identification; nevertheless, the development of new technologies based on deep learning has led to new opportunities to improve their performance. This study focuses on the capabilities of pre-trained language models such as BERT, RoBERTa, ALBERT, ELECTRA, ERNIE, or XLNet, to deal with the task of personality recognition. These models are able to capture structural features from textual… More >

  • Open Access

    ARTICLE

    Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

    Shiyan Zhang, Ruohan Xu, Zhangbo Xu, Cenhua Yu, Yuyang Jiang, Yuting Zhao
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2287-2308, 2024, DOI:10.32604/cmc.2023.046550
    (This article belongs to the Special Issue: Transfroming from Data to Knowledge and Applications in Intelligent Systems)
    Abstract The 6th generation mobile networks (6G) network is a kind of multi-network interconnection and multi-scenario coexistence network, where multiple network domains break the original fixed boundaries to form connections and convergence. In this paper, with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness, this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain collaboration. Due to the conflict between the utility of different flows, the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward… More >

  • Open Access

    ARTICLE

    A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

    Haibo Li, Yongbo Yu, Zhenbo Zhao, Xiaokang Tang
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 653-676, 2024, DOI:10.32604/cmc.2023.046424
    (This article belongs to the Special Issue: Transfroming from Data to Knowledge and Applications in Intelligent Systems)
    Abstract Accurate forecasting of time series is crucial across various domains. Many prediction tasks rely on effectively segmenting, matching, and time series data alignment. For instance, regardless of time series with the same granularity, segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy. However, these events of varying granularity frequently intersect with each other, which may possess unequal durations. Even minor differences can result in significant errors when matching time series with future trends. Besides, directly using matched events but unaligned events as state vectors in machine… More >

  • Open Access

    ARTICLE

    Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

    Shuqin Zhang, Xinyu Su, Peiyu Shi, Tianhui Du, Yunfei Han
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 349-377, 2023, DOI:10.32604/cmc.2023.040964
    (This article belongs to the Special Issue: Transfroming from Data to Knowledge and Applications in Intelligent Systems)
    Abstract Cyber Threat Intelligence (CTI) is a valuable resource for cybersecurity defense, but it also poses challenges due to its multi-source and heterogeneous nature. Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly. To address these challenges, we propose a novel approach that consists of three steps. First, we construct the attack and defense analysis of the cybersecurity ontology (ADACO) model by integrating multiple cybersecurity databases. Second, we develop the threat evolution prediction algorithm (TEPA), which can automatically detect threats at device nodes, correlate More >

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