Special Issue "Soft Computing and Machine Learning for Predictive Data Analytics"

Submission Deadline: 30 November 2021 (closed)
Guest Editors
Dr. Mohammad Tabrez Quasim, University of Bisha, Saudi Arabia.
Dr. Surbhi Bhatia, King Faisal University, Saudi Arabia.
Prof. Kavita Khanna, The NorthCap University, India.
Dr. Pankaj Dadheech, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), India.
Dr. Deepsubhra Guha Roy, University of Tartu, Estonia.


Predictive data analytics is a promising and innovative research field that comprises a huge number of statistical techniques from soft computing, machine learning, statistics, and data mining that analyze current and historical data to make predictions about unknown future events. Soft Computing and Machine Learning are focused approaches used in predictive data analytics.

Soft computing is the collection of various computational method that comprises of fuzzy logic, neural network, neuro-fuzzy, probabilistic and evolutionary computing.These techniques are specially designed to deals with the imprecise, uncertain and difficult problems. Machine Learning is totally based on Artificial Neural Networks.

Machine Learning techniques have become increasingly popular in conducting predictive analytics due to their outstanding performances in handling large data sets It can be successfully applied in several domains likes health care, cloud computing, software quality, fault and defect prediction etc.

Soft Computing and Machine learning will fit the predictive analytics using various techniques in a very efficient way by replacing all the other methods and produce forecasts as accurate as or better than those available from other statistical methods. Predictive analytics machine learning and soft computing go hand-in-hand, as predictive models typically include a machine learning algorithm and neural networks. Neural networks are a specific set of algorithms that have revolutionized machine learning. Predictive modeling largely overlaps with the field of machine learning and soft computing.

Software Quality
Health Care Applications
Recommender Systems
Expert Systems
Decision Support System
Clustering and Classification
Evolutionary Computing
Image Processing
Cyber Security
Decision Support System
Artificial Neural Networks
Pattern Recognition

Published Papers
  • Constructing Collective Signature Schemes Using Problem of Finding Roots Modulo
  • Abstract Digital signature schemes are often built based on the difficulty of the discrete logarithm problems, of the problem of factor analysis, of the problem of finding the roots modulo of large primes or a combination of the difficult problems mentioned above. In this paper, we use the new difficult problem, which is to find the root in the finite ground field to build representative collective signature schemes, but the chosen modulo p has a special structure distinct , where is an even number and are prime numbers of equal magnitude, about . The characteristics of the proposed scheme are: i)… More
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  • IoT and Blockchain-Based Mask Surveillance System for COVID-19 Prevention Using Deep Learning
  • Abstract On the edge of the worldwide public health crisis, the COVID-19 disease has become a serious headache for its destructive nature on humanity worldwide. Wearing a facial mask can be an effective possible solution to mitigate the spreading of the virus and reduce the death rate. Thus, wearing a face mask in public places such as shopping malls, hotels, restaurants, homes, and offices needs to be enforced. This research work comes up with a solution of mask surveillance system utilizing the mechanism of modern computations like Deep Learning (DL), Internet of things (IoT), and Blockchain. The absence or displacement of… More
  •   Views:578       Downloads:399        Download PDF

  • An Evolutionary Normalization Algorithm for Signed Floating-Point Multiply-Accumulate Operation
  • Abstract In the era of digital signal processing, like graphics and computation systems, multiplication-accumulation is one of the prime operations. A MAC unit is a vital component of a digital system, like different Fast Fourier Transform (FFT) algorithms, convolution, image processing algorithms, etcetera. In the domain of digital signal processing, the use of normalization architecture is very vast. The main objective of using normalization is to perform comparison and shift operations. In this research paper, an evolutionary approach for designing an optimized normalization algorithm is proposed using basic logical blocks such as Multiplexer, Adder etc. The proposed normalization algorithm is further… More
  •   Views:491       Downloads:340        Download PDF

  • Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language
  • Abstract The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based Attention Module (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study has created 2071 videos for 19 dynamic signs. Two different experiments were conducted for dynamic signs, using CBAM-3DResNet implementing ‘Within Blocks’ and ‘Before Classifier’ methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time were recorded to evaluate the models’ efficiency. Results showed that CBAM-ResNet models had good performances in videos… More
  •   Views:791       Downloads:708        Download PDF