International Journal of Intelligent Engineering Informatics (5 papers in press)

Regular Issues

• Real-Time Indoor Positioning and Route Guidance System by using Beacons  
  by Sajjad Memon, Muhammad Ali, Faheem Abassir, Fayaz Ahmed Memon, Majid Hussain Memon, Muhammad Ali Nizamani 
  Abstract: Real-time tracking of objects in indoor environment has become important in numerous industries, including shopping, logistics, advertising, and healthcare. This paper represents a smartphone-based real time indoor positioning and route guidance system that employs BLE beacons. The system's main goal is to track a target's real-time location, such as a smartphone user, and guide them in a specific direction. The system combines location data and some contextual of pedestrians' movement directions to assist pedestrians in navigating to their destinations, such as the building's exit. The system is consisting of three components: beacon devices, a user's smartphone app that communicates with beacons and gives navigation guidance to users, and the building's floor plan database. Localisation approach based on the received signal strength indicator (RSSI) is used. In addition, a new building's floor plan based on database approach is proposed. The experimental results has shown that even with only two placed beacons, the proposed system could achieve satisfactory accuracy.
  Keywords: beacons; Bluetooth low energy; BLE; received signal strength indicator; RSSI; indoor positioning; internet of things; IoT.
  DOI: 10.1504/IJIEI.2023.10055905
   
  
• Utilizing Recurrent Neural Network Technique for Predicting Strand Settlement on Brittle Sand and Geocell  
  by Jeyanthi S. B. Saravanan, R. Venkatakrishnaiah, K.V.B. Raju 
  Abstract: In this study, geocell was used as a ground enhancement technology to enhance the tensile qualities of poor soil. Investigations have been done on the variations in settlement brought on by employing different combinations of depths of reinforcement, different layers of soil reinforcement using geocell, and varied relative densities of geocell as reinforcing material. Settlement predictions at various geocell placements in poor sand have been depicted using the recommended recurrent neural network (RNN) method. When comparing the two models, it was found that the RNN model performed better with geocell than the ANN-EHO model, the JSA model, and the MOA model. Independent variables for relative density, reinforcement depth, number of layers, and geocell height were used to provide simulation data for use in creating RNN models.
  Keywords: mayfly optimisation algorithm; MOA; artificial neural network; recurrent neural network; RNN; geocell; settlement; soil reinforcement; jellyfish search algorithm; JSA.
  DOI: 10.1504/IJIEI.2023.10056043
   
  
• Secure Task Scheduling in Cloud Computing with Enhanced ordinal Optimisation and integrated DNA, Morse Code Techniques  
  by Monika Yadav, Atul Mishra 
  Abstract: This paper presents two innovative approaches to address the challenges of task scheduling, efficient resource utilisation, and security in cloud computing environments. The first approach proposes an optimal task-scheduling strategy for virtual machines in dynamic cloud scenarios, utilising an enhanced ordinal optimisation technique and linear regression to predict future dynamic workload. The second approach utilises Morse code encoding of DNA sequences to optimise scheduling outcomes while ensuring secure scheduling in the cloud environment, integrating ordinal optimisation techniques and the unique properties of DNA sequences. The effectiveness of both proposed approaches is evaluated by comparing them to conventional task-scheduling techniques. The results demonstrate that the proposed approaches achieve better performance in terms of scheduling efficiency and security. These approaches offer a comprehensive solution to address the critical aspects of task scheduling, efficient resource utilisation, and security in cloud computing environments.
  Keywords: cloud computing; ordinal optimisation; makespan; CloudSim; schedules; DNA; Morse code; cloud security.
  DOI: 10.1504/IJIEI.2023.10056279
   
  
• Predicting the Settlement of Geogrid- Reinforced Soil Foundations in Railway Track Using Cuttlefish Algorithm  
  by BALASUBRAMANI M. A, R. Venkatakrishnaiah, K.V.B. Raju 
  Abstract: Geogrid-reinforced soil foundations have lately become vital to consider the possibilities of recycling and reusing these materials after years of employing geosynthetics in developing infrastructure and civil engineering. This study uses a suggested cuttlefish algorithm on the MATLAB platform to examine how recycled geogrid affects soil carrying capacity (CFA). A polyester geosynthetic was chosen for testing owing to its strong biodegradation resistance and widespread use. In a series of numerical analyses, two distinct kinds of road and railroad subgrade were combined with geosynthetic cuttings at two different weight concentrations. The study aimed to show if recycled materials from previous demolition geosynthetics might be strengthened for road and rail subgrade use. Additionally, taken into account was the effect of the geosynthetic cutting form. Under this numerical analysis, recycled geogrid can boost pavement subgrade carrying capacity. Adding 2.0% sand compacts the earth enough to support the subgrade-improved railway track. As expected, soil type and geogrid cutting shape effect this enhancement.
  Keywords: geosynthetics; geogrid; polyester; improved subgrade; cuttlefish algorithm; sustainable development.
  DOI: 10.1504/IJIEI.2023.10056606
   
  
• Efficient De-Noising Brain MRI Images Using Various Filtering Techniques  
  by Anand A. Selvakumar, Thangaraju P 
  Abstract: Brain cancer affects millions today. MRI images must be separated, identified, and extracted to locate a brain tumour. This process is complicated and error-prone. In order to minimise the limitations, segmentation and categorisation are currently done utilising automatic and semiautomatic techniques. The initial step of image processing is de-noising. The image may become hazy if the noise-reduction technique is not carefully followed. The image may become hazy if the noise-reduction technique is not carefully followed by salt and pepper sounds. Gaussian and speckle noise alter the MRI image. Therefore, getting exact photographs of the brain is a tough undertaking. Different de-noising techniques are performed on MRI scans; each has unique properties. The noise from the provided images is removed in this research effort using a variety of filters, including mean filter (MF), Gaussian filter (GF), Kalman filter (KF) and alpha-trimmed mean filter (ATMF). The outcomes of these techniques are evaluated based on various criteria, including peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and mean square error (MSE). The outcomes demonstrate how the suggested alpha-trimmed mean filter (ATMF) works better and used for MATLAB execution.
  Keywords: de-noising; MRI brain images; segmentation; filter; mean filter; Gaussian filter; Kalman filter; alpha-trimmed mean filter; ATMF.
  DOI: 10.1504/IJIEI.2023.10056757