Special Issue "Soft Computing Methods for Intelligent Automation Systems"

Submission Deadline: 31 October 2021
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Guest Editors
Dr. Prakash Mohan, Karpagam College of Engineering, Coimbatore, India.
Dr. Nithiyananthan Kannan, King Abdulaziz University, KSA.


In recent decades, a need has arisen for predicting approaches and techniques to provide real-time solutions to some problems by integrating the modes from the rapidly developing fields of Artificial Intelligence, Machine Learning, Blockchain, Cyber Security, Data Analytics, Big Data, IoT and Cloud Computing. Intelligent systems will not only improve productivity and efficiency, but can also cope with unpredictable and imprecise issues such as variability, downtime and human factors. Soft computing methodologies use a combination of heuristics, approximation models, stochastic and non-deterministic algorithmic behavior to address the imprecision, uncertainty and partial truths that are often present in complex manufacturing systems. This special issue of Soft Computing Methods for Intelligent Automation Systems will showcase the application of soft computing methods for intelligent automation systems.

Artificial Intelligence
Big Data
Cloud Computing
Cyber Security
Data Analytics
Deep Learning
Digital Image Processing
Fuzzy Logics
Internet of Things
Machine Learning
Optimization Algorithm

Published Papers
  • Big Data Analytics with OENN Based Clinical Decision Support System
  • Abstract In recent times, big data analytics using Machine Learning (ML) possesses several merits for assimilation and validation of massive quantity of complicated healthcare data. ML models are found to be scalable and flexible over conventional statistical tools, which makes them suitable for risk stratification, diagnosis, classification and survival prediction. In spite of these benefits, the utilization of ML in healthcare sector faces challenges which necessitate massive training data, data preprocessing, model training and parameter optimization based on the clinical problem. To resolve these issues, this paper presents new Big Data Analytics with Optimal Elman Neural network (BDA-OENN) for clinical decision… More
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  • Design and Analysis of a Novel Antenna for THz Wireless Communication
  • Abstract The frequency range of the terahertz (THz) band is usually defined as 0.3~3.0 THz, and some scholars have also extended it to 0.1~10 THz. THz technology has the characteristics of low photon radiation energy and rich spectrum information, and the THz band contains the vibration and rotation resonance frequencies of many material macromolecules, which can realize fingerprint detection. Therefore, THz technology has great academic value and a wide range of applications in basic research and applied science. Application prospects, such as THz spectroscopy technology provides a new means for studying the interaction between electromagnetic waves and matter, and its application… More
  •   Views:325       Downloads:191        Download PDF