Table of Content

Big Data Analytics and Artificial Intelligence Techniques for Complex Systems

Submission Deadline: 30 May 2021 (closed)

Guest Editors

Dr. Omar Tarawneh, Al Zahra College for Women, Oman.
Prof. Dharm Singh Jat, Namibia University of Science and Technology (NUST), Namibia.
Dr. Heena Rathore, University of Texas, San Antonio, USA.
Dr. Jamaiah Yahaya, The National University of Malaysia (UKM), Malaysia.


Complex systems are nonlinear systems composed of agents that can act with local environmental information. As the agents are usually of a high degree of complexity, such systems require a huge amount of data to extract appropriate insights for their decision-making. To support huge-volume data sensing, collection, storage, transmission, management, and analytics, cloud/edge computing and Internet of Things (IoT) have been leveraged as the supporting computation infrastructure, making the big data technology a recent disruptive revolution in the IT industry. The enormous commercial benefits, scientific advances, management efficiency, and analytical accuracy brought by big data have been recognized and further developed for a wide range of applications including complex systems.

Due to the complexity of a complex system, the engaged agents and the data they work on are often geographically distributed across a suite of computation resources. Traditionally, this is supported by cloud computing where an enormous server farm with thousands of computing servers are used to provide computation capability. Although the cloud service providers have placed multiple cloud centers across the whole world, the data transmission delay between the data sources and the cloud centers is still problematic for many complex systems where responses are usually required to be time-critical or real-time. Instead, a recently emerging computation paradigm, edge computing, is promising to cater to these requirements, as edge computing resources are deployed data sources that support time-critical or real-time data processing and analysis. Together with cloud computing as the computation backend, edge computing has been adopted in complex systems. However, it is still a challenge to conduct big data management and analytics in complex systems that are supported by artificial intelligent, given the complexity of complex systems and the unique features of artificial intelligent techniques.

The collaborative methodology has been gradually recognized as an effective way to handle the complexity in a complex system. For instance, computational intelligence-based approaches enable agents in a complex system to learn a particular task of the system from big data to further facilitate complicated problem-solving, and these play an increasing important role in complex systems. Given that these approaches are usually not inherently designed for big volumes of data, it is quite interesting to investigate research problems such as how they handle big data for complex systems and design more scalable computational intelligence methods accordingly. Also, many emerging collaborative data analysis paradigms such as federated learning from distributed data have been put forth for real applications.

Therefore, the special issue is dedicated to addressing the existing and emerging problems in the theories and practices of collaborative innovation for big data management and analytics in complex systems with advanced computing infrastructure which highly demands great research efforts from researchers and practitioners. This Special Issue intends to provide a timely chance to researchers, experts, engineers, and practitioners, who have been engaging in the recent advances of big data analytics and artificial intelligence techniques in complex systems, to publish their latest findings and inspire new solutions. The contributors are encouraged to share with the public their experimental data, making the Special Issue more practical and meaningful. All submissions will be reviewed strictly for originality, significance, relevance, and clarity of presentation.


Potential topics include but are not limited to the following:
• Complex system modeling in the cloud/edge/IoT environment
• Knowledge-based collaboration in complex systems
• Decision-making collaboration over big data for complex systems
• Computational intelligence and Mathematical Modelling in big data-driven complex systems
• Collaborative machine learning models for complex systems
• IoT and Big Data Analytic Optimization and Machine Learning
• Collaborative recommendation methods over big data for complex systems
• Security, privacy, and trust issues in a big data-driven complex system
• Modelling and analysis of complex systems
• Optimization and control of complex systems
• Evolutionary analysis of complex systems
• Data-driven modeling and simulation of complex systems
• Machine learning techniques in model and simulation of complex systems

Published Papers

  • Open Access


    Multi-Level Knowledge Engineering Approach for Mapping Implicit Aspects to Explicit Aspects

    Jibran Mir, Azhar Mahmood, Shaheen Khatoon
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3491-3509, 2022, DOI:10.32604/cmc.2022.019952
    (This article belongs to this Special Issue: Big Data Analytics and Artificial Intelligence Techniques for Complex Systems)
    Abstract Aspect's extraction is a critical task in aspect-based sentiment analysis, including explicit and implicit aspects identification. While extensive research has identified explicit aspects, little effort has been put forward on implicit aspects extraction due to the complexity of the problem. Moreover, existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’ dependency problems. Therefore, in this paper, a multi-level knowledge engineering approach for identifying implicit movie aspects is proposed. The proposed method first identifies explicit aspects using a variant of BiLSTM and CRF (Bidirectional Long Short Memory-Conditional Random Field), which serve… More >

  • Open Access


    Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

    Mamdouh El Haj Assad, Ibrahim Mahariq, Raymond Ghandour, Mohammad Alhuyi Nazari, Thabet Abdeljawad
    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 361-374, 2022, DOI:10.32604/cmc.2022.019048
    (This article belongs to this Special Issue: Big Data Analytics and Artificial Intelligence Techniques for Complex Systems)
    Abstract Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their performance. Specific heat capacity of nanofluids, as one of the thermophysical properties, performs principal role in heat transfer of thermal mediums utilizing nanofluids. In this regard, different studies have been carried out to investigate the influential factors on nanofluids specific heat. Moreover, several regression models based on correlations or artificial intelligence have been developed for forecasting this property of nanofluids. In the current review paper, influential parameters on the specific heat capacity of nanofluids are introduced. Afterwards, the proposed models for their… More >

  • Open Access


    Modeling CO2 Emission of Middle Eastern Countries Using Intelligent Methods

    Mamdouh El Haj Assad, Ibrahim Mahariq, Zaher Al Barakeh, Mahmoud Khasawneh, Mohammad Ali Amooie
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3767-3781, 2021, DOI:10.32604/cmc.2021.018872
    (This article belongs to this Special Issue: Big Data Analytics and Artificial Intelligence Techniques for Complex Systems)
    Abstract CO2 emission is considerably dependent on energy consumption and on share of energy sources as well as on the extent of economic activities. Consequently, these factors must be considered for CO2 emission prediction for seven middle eastern countries including Iran, Kuwait, United Arab Emirates, Turkey, Saudi Arabia, Iraq and Qatar. In order to propose a predictive model, a Multilayer Perceptron Artificial Neural Network (MLP ANN) is applied. Three transfer functions including logsig, tansig and radial basis functions are utilized in the hidden layer of the network. Moreover, various numbers of neurons are applied in the structure of the models. It… More >

  • Open Access


    Automatic PV Grid Fault Detection System with IoT and LabVIEW as Data Logger

    Rohit Samkria, Mohammed Abd-Elnaby, Rajesh Singh, Anita Gehlot, Mamoon Rashid, Moustafa H. Aly, Walid El-Shafai
    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1709-1723, 2021, DOI:10.32604/cmc.2021.018525
    (This article belongs to this Special Issue: Big Data Analytics and Artificial Intelligence Techniques for Complex Systems)
    Abstract Fault detection of the photovoltaic (PV) grid is necessary to detect serious output power reduction to avoid PV modules’ damage. To identify the fault of the PV arrays, there is a necessity to implement an automatic system. In this IoT and LabVIEW-based automatic fault detection of 3 × 3 solar array, a PV system is proposed to control and monitor Internet connectivity remotely. Hardware component to automatically reconfigure the solar PV array from the series-parallel (SP) to the complete cross-linked array underneath partial shading conditions (PSC) is centered on the Atmega328 system to achieve maximum power. In the LabVIEW environment,… More >

  • Open Access


    Advanced Community Identification Model for Social Networks

    Farhan Amin, Jin-Ghoo Choi, Gyu Sang Choi
    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1687-1707, 2021, DOI:10.32604/cmc.2021.017870
    (This article belongs to this Special Issue: Big Data Analytics and Artificial Intelligence Techniques for Complex Systems)
    Abstract Community detection in social networks is a hard problem because of the size, and the need of a deep understanding of network structure and functions. While several methods with significant effort in this direction have been devised, an outstanding open problem is the unknown number of communities, it is generally believed that the role of influential nodes that are surrounded by neighbors is very important. In addition, the similarity among nodes inside the same cluster is greater than among nodes from other clusters. Lately, the global and local methods of community detection have been getting more attention. Therefore, in this… More >

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