Special Issue "Soft Computing Methods for Intelligent Automation Systems"

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

Summary

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.


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

Published Papers
  • Smart Lamp Using Google Firebase as Realtime Database
  • Abstract Along with modernization in Indonesia, electricity users often do not realize that electrical energy is still used from electronic devices left on and unused. Much electrical energy is wasted due to unwise use. Modernization requires creative automation. This significantly minimizes the amount of human labor needed to complete the job. Energy efficiency is critical due to environmental concerns and limited research on alternative renewable energy sources. When evaluating the impact of technology on the environment, energy is an essential factor to consider. Most big cities and provinces in Indonesia still use conventional lighting systems where electricity users manually turn on… More
  •   Views:444       Downloads:447        Download PDF

  • Cancelable Multi-biometric Template Generation Based on Dual-Tree Complex Wavelet Transform
  • Abstract In this article, we introduce a new cancelable biometric template generation layout depending on selective encryption technology and Dual-Tree Complex Wavelet Transform (DT-CWT) fusion. The input face biometric is entered into the automatic face-segmentation (Viola-Jones) algorithm to detect the object in a short time. Viola-Jones algorithm can detect the left eye, right eye, nose, and mouth of the input biometric image. The encoder can choose the left or right eye to generate a cancelable biometric template. The selected eye image of size M × N is XORed with the created pseudo-random number (PRN) matrix CM × N to provide an… More
  •   Views:495       Downloads:327        Download PDF

  • Exact Run Length Evaluation on Extended EWMA Control Chart for Autoregressive Process
  • Abstract Extended Exponentially Weighted Moving Average (Extended EWMA or EEWMA) control chart is one of the control charts which can quickly detect a small shift. The average run length (ARL) measures the performance of control chart. Due to the derivation of the explicit formulas for ARL on the EEWMA control chart for the autoregressive AR(p) process has not previously been reported. The aim of the article is to derive explicit formulas of ARL using a Fredholm integral equation of the second kind on EEWMA control chart for Autoregressive process, as AR(2) and AR(3) processes with exponential white noise. The accuracy of… More
  •   Views:385       Downloads:313        Download PDF

  • Selective Cancellable Multi-Biometric Template Generation Scheme Based on Multi-Exposure Feature Fusion
  • Abstract This article introduces a new cancellable multi-biometric system based on the combination of a selective encryption method and a deep-learning-based fusion technology. The biometric face image is treated with an automatic face segmentation algorithm (Viola-Jones), and the image of the selected eye is XORed with a PRNG (Pseudo Random Number Generator) matrix. The output array is used to create a primary biometric template. This process changes the histogram of the selected eye image. Arnold’s Cat Map is used to superimpose the PRN pixels only on the pixels of the primary image. Arnold’s cat map deformed eyes are encrypted using the… More
  •   Views:573       Downloads:346        Download PDF

  • A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning
  • Abstract The indoor positioning system comprises portable wireless devices that aid in finding the location of people or objects within the buildings. Identification of the items is through the capacity level of the signal received from various access points (i.e., Wi-Fi routers). The positioning of the devices utilizing some algorithms has drawn more attention from the researchers. Yet, the designed algorithm still has problems for accurate floor planning. So, the accuracy of position estimation with minimum error is made possible by introducing Gaussian Distributive Feature Embedding based Deep Recurrent Perceptive Neural Learning (GDFE-DRPNL), a novel framework. Novel features from the dataset… More
  •   Views:385       Downloads:231        Download PDF

  • Light-Weight Present Block Cipher Model for IoT Security on FPGA
  • Abstract The Internet of Things (IoT) plays an essential role in connecting a small number of billion devices with people for diverse applications. The security and privacy with authentication are challenging work for IoT devices. A light-weight block cipher is designed and modeled with IoT security for real-time scenarios to overcome the above challenges. The light-weight PRESENT module with the integration of encryption (E)-decryption (D) is modeled and implemented on FPGA. The PRESENT module has 64-bit data input with 80/128/256-bit symmetric keys for IoT security. The PRESENT module performs16/32/64 round operations for state register and key updation. The design mainly uses… More
  •   Views:557       Downloads:401        Download PDF

  • Bacterial Foraging Based Algorithm Front-end to Solve Global Optimization Problems
  • Abstract The Bacterial Foraging Algorithm (BFOA) is a well-known swarm collective intelligence algorithm used to solve a variety of constraint optimization problems with wide success. Despite its universality, implementing the BFOA may be complex due to the calibration of multiple parameters. Moreover, the Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA) is a state-of-the-art modification of the BFOA which may lead to solutions close to the optimal but with more parameters than the original BFOA. That is why in this paper we present the design using the Unified Modeling Language (UML) and the implementation in the MATLAB platform of a front-end for… More
  •   Views:567       Downloads:484        Download PDF

  • Mobile Robots’ Collision Prediction Based on Virtual Cocoons
  • Abstract The research work presents a collision prediction method of mobile robots. The authors of the work use so-called, virtual cocoons to evaluate the collision criteria of two robots. The idea, mathematical representation of the calculations and experimental simulations are presented in the paper work. A virtual model of the industrial process with moving mobile robots was created. Obstacle avoidance was not solved here. The authors of the article were working on collision avoidance problem solving between moving robots. Theoretical approach presents mathematical calculations and dependences of path angles of mobile robots. Experimental simulations, using the software Centaurus CPN, based on… More
  •   Views:456       Downloads:330        Download PDF

  • Autonomous Exploration Based on Multi-Criteria Decision-Making and Using D* Lite Algorithm
  • Abstract An autonomous robot is often in a situation to perform tasks or missions in an initially unknown environment. A logical approach to doing this implies discovering the environment by the incremental principle defined by the applied exploration strategy. A large number of exploration strategies apply the technique of selecting the next robot position between candidate locations on the frontier between the unknown and the known parts of the environment using the function that combines different criteria. The exploration strategies based on Multi-Criteria Decision-Making (MCDM) using the standard SAW, COPRAS and TOPSIS methods are presented in the paper. Their performances are… More
  •   Views:514       Downloads:339       Cited by:1        Download PDF

  • Machine Learning for Modeling and Control of Industrial Clarifier Process
  • Abstract In sugar production, model parameter estimation and controller tuning of the nonlinear clarification process are major concerns. Because the sugar industry’s clarification process is difficult and nonlinear, obtaining the exact model using identification methods is critical. For regulating the clarification process and identifying the model parameters, this work presents a state transition algorithm (STA). First, the model parameters for the clarifier are estimated using the normal system identification process. The STA is then utilized to improve the accuracy of the system parameters that have been identified. Metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and State Transition… More
  •   Views:512       Downloads:321       Cited by:10        Download PDF

  • Applying t-SNE to Estimate Image Sharpness of Low-cost Nailfold Capillaroscopy
  • Abstract Machine learning can classify the image clarity of low-cost nailfold capillaroscopy (NC) and can be applied to the design verification for other medical devices. The method can be beneficial for systems that require a large number of image datasets. This investigation covers the design, integration, image sharpness estimation, and deconvolution sharpening of the NC. The study applies this device to record two videos and extract 600 photos, including blurry and sharp images. It then uses the Laplace operator method for blur detection of the pictures. Statistics are recorded for each image’s Laplace score and the distribution of clear photos in… More
  •   Views:507       Downloads:313        Download PDF

  • IoT and Machine Learning Based Stem Borer Pest Prediction
  • Abstract Global climatic changes have severe impacts on agricultural productivity. Enhanced pest attacks on crops are one of the major impacts on sustainable developments in agriculture to come up with the needs of the ever-increasing human population. Early warning of a pest attack is important for Integrated Pest Management (IPM) activities to be effective. Early warning of pest attacks is also important for judicious use of pesticides for efficient use of resources for minimal impacts on the environment. Sugarcane is the major cash crop and is also severely affected by different types of pests. This study proposed stem borer attack prediction… More
  •   Views:576       Downloads:570        Download PDF

  • 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
  •   Views:671       Downloads:585        Download PDF

  • 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:971       Downloads:634       Cited by:1        Download PDF