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 (9 papers in press)

Regular Issues

  • A Novel Feature Extraction Method for Identifying Quality Seed Selection   Order a copy of this article
    by Suganthi M, J.G.R. Sathiaseelan 
    Abstract: Nowadays, research works in the agriculture field have been widely incorporated and showing promising growth. Digital image mining techniques were used in this paper to test different seeds. Analysis of physical purity tells us the proportion of pure seed in many seeds. The software that allows seed images to be predicted on seed lots is developed with digital image mining techniques. As seeds are the main part of any cultivation, healthy seeds yield healthy crops. So, it becomes necessary to provide the farmers with healthy seeds. The seed disease, which is only classified into healthy and unhealthy seeds, is difficult for most farmers to describe. The seed’s spatial, colour, texture, shape and statistical properties are connected to feature extraction. In order to get the best results, this study utilises a brand-new feature extraction technique for classifying high-quality seeds. It was concluded that Bresenham’s Line Technique plus a few textural qualities might be utilised to compare the DDA (Digital Differential Analyzer) line drawing algorithm and determine the seed type.
    Keywords: Image mining; Feature Extraction; seeds; Mean Square Error (MSE); Bresenham’s Line Algorithm; Structural Similarity Index Metric (SSIM).
    DOI: 10.1504/IJIEI.2022.10053490
     
  • An Intelligent Fuzzy and IoT aware Air Quality Prediction and Monitoring System using CRF and Bi-LSTM   Order a copy of this article
    by A.N.U. PRIYA S, V. Khanna 
    Abstract: Recently, air pollution has been increasing drastically in the majority of metropolitan cities of the world. This is necessary to reduce air pollution, and we propose a new air quality prediction system to predict air quality and pollution levels in different seasons in Beijing, China. The proposed air quality model applies a preliminary data preprocess to get exact data, a newly proposed Conditional Random Field (CRF), and a Fuzzy Rule-based Data Grouping Algorithm (CRF-FRDGA) to group the data according to the different seasonal data by applying the necessary rules, the standard Bidirectional LSTM (Bi-LSTM) for performing effective classification and prediction process. The PM2.5 concentration in Beijing, China, is forecasted season-wise for the next five years. Various experiments have been done to prove the capability of the proposed air quality prediction system and proved better than the existing works in prediction accuracy.
    Keywords: Conditional Random Field (CRF); Fuzzy Rules; LSTM; Bi-LSTM and Data Grouping.
    DOI: 10.1504/IJIEI.2022.10053546
     
  • Utilization of Audible Steganography to Organise And Analyse the Text Within WAV Files   Order a copy of this article
    by R. Ramyadevi, V. Poornima 
    Abstract: This project seeks to encrypt audio cover files and Create temporal-domain audio steganography. An audio file with hidden options and text. MSE, MAE, SNR, and cross-correlation analysis identify audio stream text data. Comparing 8-bit and 16-bit PCM audio. This study estimates how many characters may be added to an audio file without changing its structure. MP3's bit rate is audible. Audio steganography is a secure, cost-effective approach to encrypting network data. It's useful for steganography due to less noise distortion. Undetectable embedding is preferable. The suggested technique improves accuracy at low embed levels, according to testing. The suggested strategy delivers the highest PSNR with hidden information in the first, second, and third LSBs. Three LSB had 98% accuracy and the lowest false alarm rate (less than 5 percent). Experiments reveal that this study's method extracts audio. Mathematica and Excel produce results. The recommended method is hard and capable. Novel audio file steganography is imperceptible and recovers messages, and text message length affects robustness.
    Keywords: Audio Steganography; PCM; Information Security; WAV file format; Least-Significant Bit (LSB) method; Audiovisual Systems; SNR; Cycle Coding; Asymmetric Coding;.
    DOI: 10.1504/IJIEI.2022.10053658
     
  • Deceptive Web-Reviews Detection Strategies: A Survey   Order a copy of this article
    by Rajdavinder Singh Boparai, Rekha Bhatia 
    Abstract: Deceptive reviews on internet platforms are a harsh reality in today's world. Specific services, businesses, and products are praised or vilified in these reviews. In an online or web society, users get help from reviews before making a decision and in a similar way, web reviews are also very helpful for organizations to keep them updated as per customer needs. A lot of effort has gone into detecting this sort of reviews. Apart from evaluating state-of-the-art research papers on fraudulent review identification, a taxonomy of machine learning algorithms for detecting deceptive or non-genuine web reviews is provided in this article. It begins with a broad introduction to sentiment analysis, web reviews, deceptive reviews, and machine learning. Thereafter, providing crucial information regarding available deceptive review detection approaches along with datasets, methodologies, and their performance. It critically summarizes existing techniques to find out research gaps w.r.t. supervised, unsupervised, and semi-supervised methods and also quantitively with the help of specific datasets such as Yelp and AWS. The paper also looks at research gaps and future recommendations for detecting deceptive reviews.
    Keywords: Deceptive; Review; Machine learning; Lexicon; Web review; Survey; Classifier.
    DOI: 10.1504/IJIEI.2022.10053698
     
  • Segmentation Free Text Recognition for Overlapping Characters using Spectral Features and Bidirectional Recurrent Wavelet Neural Network   Order a copy of this article
    by Neha Tripathi, Pushpinder Singh Patheja 
    Abstract: This paper addresses the problem of text recognition with overlapping and touching characters which is very challenging task due to its inability to be segmented effectively. The abrupt feature variations generated due to the overlapping, noise removal and smoothing are also some of the major challenges in this field of research. A novel framework based on word level recognition has been presented in this work which doesn’t require the character level segmentation. Dynamic window selection technique for different aspect ratios of input images is presented to overcome limitation of existing techniques whose performance is subject to the normalized window size and uniform aspect ratio prior to the feature extraction. The combination of spectral and structural features through DWT and HOG have been used to represent the characteristics of overlapping and touching characters. Also, the robustness and accuracy in the recognition phase is enhanced through bidirectional adaptive recurrent Wavelet Neural Network (BRWNN). The word accuracy in the proposed work is achieved to be 83.14% which is better than the conventional techniques over the dataset IAM-DB.
    Keywords: Overlapped Text recognition; image processing; Wavelets; Deep Neural Network; Feature extraction.
    DOI: 10.1504/IJIEI.2022.10053817
     
  • Machine learning of irreducible error floor in the space-time trellis code   Order a copy of this article
    by Ungku Azmi Iskandar Ungku Chulan, Mardina Abdullah, Nor Fadzillah Abdullah, Abdullah Ramli 
    Abstract: The phenomenon of irreducible error floor in the space-time trellis code (STTC) 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 an irreducible error floor is uncertain. 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 the 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 the irreducible error floor. Furthermore, simulation also reveals that the codewords within the 'initial state' of the trellis structure are primal in the occurrence of the irreducible error floor. CART can achieve approximately 0.92 accuracy in predicting the irreducible error floor, with an average prediction time of 0.3833 μs.
    Keywords: STTC; space-time trellis code; irreducible error floor; combinatorial characterisation; machine learning; low density parity check code; coding scheme; trellis structure.
    DOI: 10.1504/IJIEI.2022.10053802
     
  • A new feature selection algorithm for evolutionary analysis of Aramaic and Arabic script variants   Order a copy of this article
    by Osama A. Salman, Gábor Hosszú, Ferenc Kovács 
    Abstract: This paper deals with applying phylogenetic modelling to the evolution of scripts (writing systems) as taxa. Aramaic and Arabic script variants are studied in the present cladistic analysis. The selection of the most suitable features of taxa for accurate modelling as part of the feature engineering step could improve the result of the cladistic analysis. The main objective is to filter out features of the taxa under study that could potentially cause homoplasy. The effect of feature filtering is investigated using some widely used phylogenetic software products for biological databases. Studies have consistently shown that the phylogenetic tree (cladogram) generated after filtering out the most variable features is more optimal for less homoplasy than the tree obtained without feature filtering. Hence, the proposed algorithm effectively pre-filters the features that may cause homoplasy. Furthermore, the results also demonstrated that different cladistic methods investigated gave similar results for the dataset under study.
    Keywords: Arabic script; Aramaic script; cladistics; evolutionary analysis; feature selection; maximum likelihood; maximum parsimony; pattern evolution; pattern system; scriptinformatics.
    DOI: 10.1504/IJIEI.2022.10053548
     
  • An automated approach for electroencephalography-based seizure detection using machine learning algorithms   Order a copy of this article
    by Vibha Patel, Dharmendra Bhatti, Amit Ganatra, Jaishree Tailor 
    Abstract: Epilepsy is a chronic neurological disease that causes recurrent life-threatening seizures due to irregular brain activity. The purpose of seizure detection algorithms is to detect seizures from electroencephalography (EEG) recordings accurately. The main goal of our work is to facilitate an automated seizure detection system using machine learning algorithms. We proposed a model that evaluates eight machine learning algorithms on the Bonn University dataset. Two classifiers, Random Forest and Gaussian Naive Bayes, achieve the highest accuracy of 100% with 100% sensitivity, 100% specificity, 0.01 FPR, and 0.99 AUC with feature extraction. These two algorithms also work better without using feature extraction. This performance is superior to existing seizure detection approaches and comparable to deep learning approaches. Our work comprehensively compares the traditional machine learning algorithms and reinforces the effectiveness of feature extraction. Our work contributes to aiding neurologists in making faster and more precise decisions for epilepsy treatment.
    Keywords: epileptic seizure detection; EEG; electroencephalography; machine learning; deep learning; random forest; Gaussian Naïve Bayes.
    DOI: 10.1504/IJIEI.2022.10052919
     

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

  • Integration of a three-colour channel with the four diagonals of the GLCM approach for similar image retrieval   Order a copy of this article
    by P. John Bosco 
    Abstract: Feature extractions are perceptive problems due to differences of opinion in image assets and contents. An image has strong texture and colour channels, i.e., a gray-level cooccurrence matrix (GLCM) and a Red, Green and Blue (RGB) channel. We proposed an algorithm for the integration of three colours with diagonal approach method (ITDM) in combined colour and GLCM significant image features, which are used for image retrieval and reduced time. It has four steps: 1) Extract the colour channel, which is RGB, YCbCr, and L* a* b*; 2) Extract the colour channel of G, Y, and L*; 3) Apply the GLCM method in the G, Y, and L* channels and 4) Image features are based on the classification of features by the C-means algorithm and re-ranking methods. To develop an image, simplify the computation of image retrieval methods. A series of analyses and comparisons are performed in the experiment and proven to enhance the retrieval rate.
    Keywords: content based image retrieval (CBIR); colour feature: texture feature; integration of three colours with diagonal approach method (ITDM); GLCM; RGB.
    DOI: 10.1504/IJIEI.2022.10053801