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

Regular Issues

• Deep Learning based Concrete Compressive Strength Prediction with Modified Resilient Backpropagation Training  
  by M. Adams Joe, J. Sahaya Ruben, M.Prem Anand, M. Anand 
  Abstract: This article proposes a novel approach for predicting concrete compressive strength using deep learning techniques. It overcomes limitations of traditional methods like memory footprint, training time, and computational requirements for predicting concrete compressive strength. Currently various filter pruning techniques are used to compress models by removing irrelevant information, but they cannot decrease memory consumption due to their large parameters. So, the entropy-based filter pruning is suggested to reduce the complexity of the model by decreasing the parameters. Then for training the CNN model, the modified resilient backpropagation technique (MRPROP) is suggested, because the previous backpropagation techniques take more time for training and also it loss the accuracy. This MRPROP improve the efficiency and convergence of CNN training and also it updates the models weight. The proposed approach demonstrated superior performance in mean squared error, root mean squared error, loss function, and regression analysis, as per the experimental results.
  Keywords: machine learning; deep learning; convolutional neural network; CNN; pruning technique; backpropagation.
  DOI: 10.1504/IJIEI.2024.10063960
   
  
• Coati Optimization Algorithm based hyperparameter tuned attention B-BiLTF model for Spectrum Prediction  
  by Avani Vithalani  
  Abstract: The burgeoning demand for spectrum in the 5G era and Internet of Things underscores the critical need for accurate spectrum prediction models. Existing methods grapple with challenges, particularly the inability to capture frequency band features at specific times. This research introduces the Coati Optimization Algorithm-based attention B-BiLTF model, addressing the pervasive issue of gradient disappearance in spectrum prediction. Combining BiLSTM and BP neural networks, the B-BiLTF algorithm achieves enhanced convergence speed and overall prediction accuracy. The attention B-BiLTF mechanism mitigates the impact of sequence length changes on performance. Leveraging the Coati Optimization Algorithm ensures systematic hyperparameter optimisation, outperforming existing approaches across diverse Signal-to-Noise Ratio conditions and sequence lengths. Experimental results on RML2016.10a dataset demonstrate superior accuracy, RMSE, MAE, and Haversine distance, affirming the model's reliability and robustness modulation modes and SNR levels. This research contributes an efficient approach to spectrum prediction, advancing cognitive radio systems, and optimizing spectrum utilisation.
  Keywords: Spectrum prediction; Coati Optimization Algorithm; attention-B-BiLTF; deep learning.
  DOI: 10.1504/IJIEI.2024.10064201
   
  
• "Cloud Gaming: The Future of Gaming Infrastructure"  
  by Shrikant Harle, Pradeep Bhaduria, Amol Bhagat, Shrikant Bhuskade, RAJAN Wankhade, Milind Mohod 
  Abstract: This paper delves into the transformative impact of cloud gaming on the gaming industry. The problem statement outlined in the paper revolves around the changing landscape of gaming infrastructure, specifically focusing on the shift towards cloud-based platforms. The study emphasises the need to understand how cloud gaming affects various aspects of the gaming ecosystem, including accessibility, gameplay experience, and game development processes. The findings of the study highlight several key aspects of cloud gaming. Firstly, the paper identifies the significant benefit of reduced entry barriers for gamers, as they no longer need to invest in expensive gaming hardware. This increased accessibility has led to a broader gaming community and improved access to high-quality gaming experiences. Additionally, the study emphasises the advantages of cross-platform compatibility, allowing gamers to seamlessly switch between devices without losing progress.
  Keywords: cloud gaming; gaming industry; bandwidth; virtual reality; VR.
  DOI: 10.1504/IJIEI.2024.10064799
   
  
• Efficient Authentication Framework with Blake2s and a Hash-based Signature Scheme for Industry 4.0 Applications  
  by Purvi Tandel, Jitendra Nasriwala 
  Abstract: Industry 4.0, the new production standard, integrates small to medium-level devices for efficient automation. Secure communication among these devices requires protection from external attacks in IoT applications. The imminent threat of quantum attacks to prevailing public-key approaches necessitates a secure authentication architecture tailored for IoT devices. Furthermore, IoT devices' limited computing, storage, and energy resources demand a lightweight authentication mechanism. In response, hash-based signatures have been proposed as a post-quantum solution for Industry 4.0. Presented approach involves an in-depth analysis of collision-resistant hash functions for faster, memory-optimised authentication. Implementing a hash-based signature scheme through experiments, a 27.18% improvement has been achieved in key generation speed, particularly with Blake2s over popularly used SHA-256. These results affirm the efficiency of the proposed hash-based signature scheme, offering superior performance in time and memory utilisation for Industry 4.0 applications.
  Keywords: authentication mechanism; hash-based signature scheme; IoT applications; hash functions; SHA-256; Blake2s; SHA-3; collision resistant hash-function.
  DOI: 10.1504/IJIEI.2024.10064828
   
  
• OHON4D: Optimized Histogram of 4D Normals for Human Behaviour Recognition in Depth Sequences  
  by Mourad Bouzegza, Ammar Belatreche, Ahmed Bouridane, Mohamed Elarbi-Boudihir 
  Abstract: Understanding human behaviour in video streams is one of the most active areas in computer vision research. Its purpose is to automatically detect, track and describe human activities in a sequence of image frames. The challenges that researchers have to face are numerous and complex so that building a faithful feature vector that describes and identifies the human behaviour remains a crucial aspect. This paper presents a geometry-based descriptor whose features are extracted from data acquired by depth sensors. It uses a heuristic approach to optimise the histogram of oriented 4D normals (HON4D) descriptor proposed by O. Oreifej and Z. Liu. The latter used a histogram to describe the depth sequence by extracting the normal orientation of the surface distribution in the 4D space of time, depth, and spatial coordinates. The proposed approach in this paper, called optimised histogram of 4D normals (OHON4D), enhances the HON4D method by considering only four projectors to represent a 4D normal instead of 120. We obtained a similar accuracy while saving approximately half of the computational time.
  Keywords: computer vision; optimised histrogram; 4D normals; human behaviour recognition; HAR; video streams; geometry based descriptor; Kinect depth sensors.
  DOI: 10.1504/IJIEI.2024.10064833