Forthcoming and Online First Articles

International Journal of Applied Pattern Recognition

International Journal of Applied Pattern Recognition (IJAPR)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Applied Pattern Recognition (2 papers in press)

Regular Issues

  • A Novel Approach to Detect Phone Usage of Motor-vehicle Drivers by balancing Image Quality on Roads   Order a copy of this article
    by Mallikarjun Anandhalli, Pavana Baligar, Vishwanath P. Baligar, Srijan Bhattacharya 
    Abstract: Mobile phone usage during driving identified as one of the major causes of accident in traffic system as it distracted the driver, mainly uring driving the motor cycle. In this article authors are focused on detection of mobile phone usage during motor cycle driving. It has been studied that limited research work done in this domain due to the lack of ready datasets, occlusion of object (mobile phone), rotation and difficulty in extracting the features object. The authors collected the data in ifferent Indian traffic conditions and applied convolutional neural network (CNN), deep learning-based YOLOv4 architecture with CSPDarknet-54 as backbone of YOLOv4 algorithm. The results show the detection of mobile phone usage in traffic with precision of 94%.
    Keywords: mobile detection; YOLOv4; SERB-GIT dataset.
    DOI: 10.1504/IJAPR.2022.10048018
     
  • A new Artificial Neural Network (ANN) based approach for recognition of handwritten digits   Order a copy of this article
    by Anil Agrawal, Susheel Yadav, Amit Ambar Gupta, Vishnu Pandey 
    Abstract: A new artificial neural network (ANN) based approach has been proposed in this paper to recognise handwritten digits. Handwritten digit recognition finds its applications in many areas of computer vision and artificial intelligence. The proposed ANN has a logical framework of five levels. Three hidden layers independently capture the features of a digit; then associative relationship among the features followed by the possible forms of a handwritten digit. The performance of the neural network is analysed by varying the number of nodes in these three layers. It is further suggested to pre-process the data to avoid the problem of overfitting in which case the noise is incorporated into the model instead of the signal. The data are pre-processed for removing white spaces outside the boundary of a digit’s image, considering them as noise. In addition, the dropout strategy of Srivastava et al. (2014) has also been implemented, resulting in a better accuracy at a cost of about 18% of extra CPU time. Finally, the optimised size of the neural network with the proposed architecture is also determined to yield the best performance. The performance of the proposed architecture was found to be very close to that of Srivastava et al. (2014), but comparatively very small in size and requiring much less CPU time.
    Keywords: machine learning; pattern recognition; artificial neural network; ANN; handwritten digits; hidden layers.
    DOI: 10.1504/IJAPR.2022.10048395