Special Issue "Data Analytics in Industry 4.0"

Submission Deadline: 09 May 2021 (closed)
Guest Editors
Dr. Cherry Bhargava, Lovely Professional University, India.
Dr. Pardeep Kumar Sharma, Lovely Professional University, India.
Dr. Rajkumar Bhimgonda Patil, Annasaheb Dange College of Engineering and Technology, India.
Dr. Mohamed Arezki Mellal, M’Hamed Bougara University, Algeria.
Dr. Sameer Al-Dahidi, German Jordanian University, Jordan.


The fourth industrial revolution, known as Industry 4.0, has digitally transformed the traditional manufacturing and industry practices. To predict the equipment failures and streamline the production process, the data analytics has been identified as a significant component, that provides valuable insights to manage the operations of machines and processes well, which further requires data processing with advanced tools and technologies. The predictive & proactive maintenance, early warning detection and residual lifetime estimations optimizes the timing for intervention. The industrial processes from maintaining machines to managing supply chains, can be optimized and transformed smarter by capturing and analysing data more intelligently.

This special issue is seeking high-quality research articles as well as reviews about state-of-the-art technologies in industry 4.0. The main focus of this special session will also be to address the challenges and opportunities related to Industrial IOT, Smart manufacturing, Intelligent robotics, Big data analytics and Machine learning. The intelligent modelling for the data analysis and residual life prediction is also targeted in this special issue.

· Accelerated Life Testing
· Big Data Analytics
· Biomedical
· Cloud Computing
· Cognitive Computing
· Cyber Physical Systems
· Data Mining and Predictive Analysis
· Industrial Internet of Things (IIOT)
· Machine Learning
· Power Electronics
· Quality Estimation
· Reliability Analysis
· Robotics and Automation
· Smart Manufacturing
· VLSI Circuits and Systems

Published Papers
  • Stock-Price Forecasting Based on XGBoost and LSTM
  • Abstract Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we… More
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  • Efficient Process Monitoring Under General Weibull Distribution
  • Abstract Product testing is a key ingredient in maintaining the quality of a production process. The production process is considered an efficient process if it is capable of quick identification of faulty products. The items produced by any production process are usually packed and acceptance or rejection of the pack depends upon its conformity to some specified quality level. Generally, the specified quality level is based upon the number of defective items found in the inspected number of items. Such decisions are based upon some rules and usually acceptance of the pack is based upon a fewer number of defective items… More
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