Forthcoming and Online First Articles

International Journal of Intelligent Engineering Informatics

International Journal of Intelligent Engineering Informatics (IJIEI)

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

Regular Issues

  • On the Performance of Modified Generalised Quadrature Spatial Modulation under Correlated Weibull Fading   Order a copy of this article
    by Kiran Gunde, Anuradha Sundru 
    Abstract: To improve the data rate of ``quadrature spatial modulation (QSM)'', a ``modified generalized quadrature spatial modulation (mGQSM)'' scheme is developed. This scheme can activate more antennas to transmit the data by utilising multiple radio frequency chains. The mGQSM employs two sets of active antenna patterns (AAP), one for transmitting the real part of a selected complex data symbol and the other for transmitting the imaginary part of the same symbol. In mGQSM, the number of AAP vectors are more when compared to the generalised QSM (GQSM) for the same system configuration. This paper presents the mGQSM and ``reduced codebook mGQSM (RC-mGQSM)'' system performances over correlated Weibull fading channels under two different fading environments. For the computer simulations, we consider Weibull non-fading and deep-fading environments with the Weibull parameter values are equal to 5 and 0.5, respectively. Using maximum likelihood (ML) detector, the mGQSM and RC-mGQSM systems are compared to that of QSM and GQSM systems over uncorrelated and correlated Weibull fading channels. Also, presents the mGQSM system performance in the existence of imperfect channel knowledge and compared to that of QSM.
    Keywords: spatial modulation (SM); modified GQSM (mGQSM); correlated; Weibull fading; imperfect channel; maximum likelihood (ML) detection.

  • Harmonics Estimator Design with Trigonometric function inspired Grey Wolf Optimizer   Order a copy of this article
    by Aishwarya Mehta, Jitesh Jangid, Akash Saxena, Shalini Shekhawat, Rajesh Kumar 
    Abstract: Modern power systems are vulnerable to power quality issues due to competitive business environment, stressed grid conditions and usage of renewable technology in generation. Power quality issues emerge as potential threats sometimes in stressed grid conditions. Addition to that, presence of harmonics in fundamental waves consider as a root cause of deterioration of equipment\'s life and performance. Moreover, the ill effect of these harmonics associated with the quality of life, mitigation of these required accurate estimation of harmonic components. More recently, some efficient approaches regarding design of mitigation technologies are with the help of metaheuristics. From taking inspiration of these, the work presented in the manuscript is a proposal of harmonic estimator design based on proposed Trigonometric Function Inspired Grey Wolf Optimizer (T-GWO). To validate performance of T-GWO we tested proposed variant on conventional bench and then a harmonic estimator design is executed. We observe and witness the satisfactory performance of T-GWO as compared with the other state of the art approaches existed in literature.
    Keywords: Wavelet Transform; Auto-Regressive Model; Grey Wolf Optimization; Chirp-Z Transform.

  • Detection of Coronavirus Disease using Texture Analysis and Machine Learning Methods   Order a copy of this article
    by Sami Bourouis 
    Abstract: The recent outbreak of the novel SARS-CoV-2 virus (COVID-19) has caused serious problems across the world. Patients with such disease can have severe symptoms and may die. The early diagnosis of COVID-19 may reduce the death rate. Chest X-ray technology is one of the good low-cost diagnostic tools in analyzing such disease. However, its accurate detection is becoming prone to serious errors caused by the low radiographic contrast. In this paper, we address the problem of data classification using texture features and machine learning along with artificial intelligence algorithms. The aim is to show that it is possible to take advantage of some well-known AI algorithms to accurately diagnose COVID-19. An evaluation process was conducted on real datasets showing the merits of these algorithms. The other aim is to show the robustness of texture features in solving the current problem. Experiments show promising results with high accuracy for most models.
    Keywords: COVID-19; X-ray images; Texture features; Image classification; Machine learning; Comparative study.
    DOI: 10.1504/IJIEI.2022.10047137
     
  • Machine learning of irreducible error floor in space-time trellis code   Order a copy of this article
    by Ungku Azmi Ungku Chulan, Mardina Abdullah, Nor Fadzilah Abdullah, Abdullah Ramli 
    Abstract: The phenomenon of irreducible error floor in space-time trellis code is not fully understood. This comes from the fact that the connection between the trellis structure of the generator matrix G and the instigation of irreducible error floor is uncertain. Previous approach utilises error- prone substructure to predict irreducible error floor. However, it does not establish any clarification from the aspect of the trellis structure. Given this difficulty, the present study attempts to gain a better insight into the ordeal via a data-driven approach. The classification and regression trees (CART) machine learning model is employed to predict the occurrence of irreducible error floor from the trellis structure. Further analysis of the combinatorial characterisation of the trellis structure unveils a series of dominant patterns that consistently instigate irreducible error floor. Furthermore, simulation also reveals that the codewords within the initial state of the trellis structure are primal in the occurrence of irreducible error floor. On average, CART can achieve approximately 0.92 accuracy in predicting irreducible error floor when the train test ratio is set at 70-30. Implemented with scikit-learn, the average prediction time of CART when using two attributes is 0.3833
    Keywords: Space time trellis code; irreducible error floor; combinatorial characterization; machine learning.

Special Issue on: MIDAS-2020 Machine Learning Algorithms and Applications in Industry 4.0

  • Integration of three color channel with four diagonal of GLCM approach for similar image retrieval   Order a copy of this article
    by JOHN BOSCO P 
    Abstract: Feature extractions are very perceptive problems due to differences of opinion in image assets and contents. Feature extractions signify that different features are obtained. Generally, images have very strong texture and color channel i.e. GLCM, RGB, LBP and etc., while others are more sensitive to image features. In this paper, we propose combined color and GLCM significant image features, which are used for image retrieval and reduced retrieved time. In these feature extraction methods are obtained best possible features and the computation of image retrieval is shortened. We propose a combination of color channel and four directions of GLCM features. There are four steps: 1) Extract the color channel which is RGB, YCbCr, and L* a* b*, 2) Exactly pick up the G channel, Y channel, and L* channel, 3) Apply the GLCM method in G, Y and L* channel, 4) , We proposed algorithm for Integration of three colors with Diagonal Approach Method (ITDM) then image features are based on the classification of features by the C-means algorithm and finally, apply for re-ranking methods. To develop an image retrieval rate and simplify the computation of image retrieval methods, sequential forward texture feature extraction is adopted for texture techniques. A series of analyses and comparisons are performed in our experiment. We use five different image databases to carry out feature extraction. It is also proved to enhance the retrieval rate.
    Keywords: Color; RGB; YCbCr; GLCM; Re-ranking; color channel;.