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
  • 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
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