Special Issues

Soft Computing and Big Data Mining

Submission Deadline: 30 December 2021 (closed) View: 8

Guest Editors

Dr. Shah Nazir, University of Swabi, Pakistan.
Dr. Iván García-Magariño, Complutense University of Madrid, Spain.
Dr. Sara Shahzad, University of Peshawar, Pakistan.


With the rise of smart devices, digital, and cloud technology every device produces enormous quantities of digital data. On daily basis, receiving email, visiting websites, carrying out online transactions, using resources such as Google docs, uploading images, making calls, utilizing Google search for various purposes, chatting with friends, paying bills online, and so on produce hug amount of data. Such data are tracked and recorded by various means as it contain useful information for various purposes like decision making. Organizations can collect and store data of various types from virtually available sources, but collecting and storing data adds value only when it serves a useful purpose. If data is to generate real value for organizations, it must be used to provide input to analytics and decision support capabilities. The aim of big data is to integrate structure and un-structure data together in order to gain insight into user preferences and discover underlying routines that could lead to even more wise decisions. Big data is a buzz term used for collecting big data, generating useful information. Big data is an umbrella term used for various magnitudes of data such as data volume, data diversity, data velocity, data variability, and data complexity. The ability to effectively process massive datasets has become integral part to a wide range of scientific and other academic disciplines in the world of information technology. Big data appears in a variety of contexts in different disciplines including Meteorology, mineral prospecting, computer-aided design, bioinformatics, computational steering, genomics, complex physics simulations, biological and environmental research, finance business, healthcare, and many others.

Data volume is increasing at an exponential rate and the researchers in industries are developing new models and distributed tools to manage big data. Researchers and practitioners must use certain methods and tools effectively to integrate the insights into their business processes in order to gain competitive advantages. Businesses organizations must convert data into timely and useful knowledge for decision making and process of optimization. In today's dynamic market settings, companies must process high-speed data and incorporate useful information into production processes. Rapid advances in high-performance computing and data acquisition tools in a wide range of scientific domains have occurred over the last two decades. When combined with the availability of massive storage systems and fast networking technology to manage and assimilate data, these have provided a significant impetus to data mining in the scientific domain. To be able to manage extremely large transaction volumes, often in a distributed environment, and to support versatile, complex data structures data processing is required that is more difficult and complex than simply finding, recognizing, comprehending, and citing data. All of this must be done entirely automatically in order for large-scale research to be accurate. This necessitates the expression of variations in data structure and semantics in machine understandable and "robotically" resolvable ways. Big Data allows scientists to solve problems associated with small data samples by relaxing theoretical model assumptions, preventing curse of dimensionality models to train data, better handling noisy train data, and having enough test data to validate models.

This Special Issue invites original research articles and review articles that discover the incorporation of big data according to scientific programming and its applications. Research that considers technological and computational barriers to big data management is particularly welcome.


• Data mining and management of big data
• Data analytics and visualization
• Smart system data management
• City data collection and visualization
• Smart environment sensing and forecasting data
• Intelligent smart systems data
• Data mining, and big data modeling for computation
• Data warehouse and data mining
• Data science and its analytics

Published Papers

  • Open Access


    An Intelligent Recommendation System for Real Estate Commodity

    Tsung-Yin Ou, Guan-Yu Lin, Hsin-Pin Fu, Shih-Chia Wei, Wen-Lung Tsai
    Computer Systems Science and Engineering, Vol.42, No.3, pp. 881-897, 2022, DOI:10.32604/csse.2022.022637
    (This article belongs to the Special Issue: Soft Computing and Big Data Mining)
    Abstract Most real estate agents develop new objects by visiting unfamiliar clients, distributing leaflets, or browsing other real estate trading website platforms, whereas consumers often rely on websites to search and compare prices when purchasing real property. In addition to being time consuming, this search process renders it difficult for agents and consumers to understand the status changes of objects. In this study, Python is used to write web crawler and image recognition programs to capture object information from the web pages of real estate agents; perform data screening, arranging, and cleaning; compare the text of… More >

  • Open Access


    CNN Based Automated Weed Detection System Using UAV Imagery

    Mohd Anul Haq
    Computer Systems Science and Engineering, Vol.42, No.2, pp. 837-849, 2022, DOI:10.32604/csse.2022.023016
    (This article belongs to the Special Issue: Soft Computing and Big Data Mining)
    Abstract The problem of weeds in crops is a natural problem for farmers. Machine Learning (ML), Deep Learning (DL), and Unmanned Aerial Vehicles (UAV) are among the advanced technologies that should be used in order to reduce the use of pesticides while also protecting the environment and ensuring the safety of crops. Deep Learning-based crop and weed identification systems have the potential to save money while also reducing environmental stress. The accuracy of ML/DL models has been proven to be restricted in the past due to a variety of factors, including the selection of an efficient… More >

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