Special Issue "Big Data Security Using Artificial Intelligence-based Approaches"

Submission Deadline: 28 February 2022 (closed)
Guest Editors
Dr. Muhammad Ashfaq Khan, Incheon National University, Korea.
Dr. B.S. Chowdhry, Mehran University of Engineering & Technology (MUET) Jamshoro, Pakistan.
Dr. Hoon Kim, Incheon National University, Korea.


Every day we experience an unprecedented amount of data growth from numerous sources, which contribute to big data. As these data is growing rapidly in terms of volume, velocity, and variability, these impose great challenges on analytics framework and computational resources, making the overall analysis difficult to extract meaningful information in a timely manner. Thus, to harness such kind of challenges, developing an efficient big data analytics framework is a non-trivial research. Machine learning (ML) and deep learning (DL) algorithms are being in use in such an analytics framework to address these challenges by exploiting non-linear relationships from very high-dimensional datasets.

Dataset size is increasing rapidly, and it is essential to develop tools capable of controlling data size and extracting meaningful information. Big data is now used in many aspects of science, technology, and commerce. This data is generated from many disparate sources, including online transactions, emails, audios, videos, social networking sites, etc. Every corporation or organization that produces these data needs to manage and analyze it Big data can be defined as massive data collections (real-time, non-structured, streaming) that make it complicated to utilize conventional data management and analysis methods to manage, analyze, store, and obtain meaningful information . To evaluate big data, it is essential to employ suitable analysis tools. This pressing need to control the increasingly huge amount of data has led to a major interest in designing suitable big data frameworks. Significant research has investigated various big data domains, e.g. infrastructure, management, data searching, mining, security, etc. Big data infrastructure has been developed to identify analytics to utilize quick, reliable, and versatile computational design, providing efficient quality attributes including flexibility, accessibility, and resource pooling with on-demand and ease-of-use.

Big data, Artificial Intelligence; Deep learning; Machine learning, CNN, LSTM, Big data processing framework

Published Papers
  • Pseudo NLP Joint Spam Classification Technique for Big Data Cluster
  • Abstract Spam mail classification considered complex and error-prone task in the distributed computing environment. There are various available spam mail classification approaches such as the naive Bayesian classifier, logistic regression and support vector machine and decision tree, recursive neural network, and long short-term memory algorithms. However, they do not consider the document when analyzing spam mail content. These approaches use the bag-of-words method, which analyzes a large amount of text data and classifies features with the help of term frequency-inverse document frequency. Because there are many words in a document, these approaches consume a massive amount of resources and become infeasible… More
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  • Integrating Blockchain Technology into Healthcare Through an Intelligent Computing Technique
  • Abstract The blockchain technology plays a significant role in the present era of information technology. In the last few years, this technology has been used effectively in several domains. It has already made significant differences in human life, as well as is intended to have noticeable impact in many other domains in the forthcoming years. The rapid growth in blockchain technology has created numerous new possibilities for use, especially for healthcare applications. The digital healthcare services require highly effective security methodologies that can integrate data security with the available management strategies. To test and understand this goal of security management in… More
  •   Views:1323       Downloads:768       Cited by:1        Download PDF