Forthcoming articles

International Journal of Hybrid Intelligence

International Journal of Hybrid Intelligence (IJHI)

These 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 Articles will not be published until the final proofs are validated by their authors.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.

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

Open AccessArticles marked with this Open Access icon are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.
We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Hybrid Intelligence (2 papers in press)

Regular Issues

  • A Brief Survey on Image Segmentation based on Quantum Inspired Neural Network   Order a copy of this article
    by Pankaj Pal, Siddhartha Bhattacharyya, Jan Platos, Vaclav Snasel 
    Abstract: Information retrieval is a rudimentary approach and is highly solicited and recognized from image analysis for soft computing research field. In the field of classical approach, image processing task is predictable to recover the objects from the noisy image. Quantum computation plays a vital role to recover the object from binary, gray, pure or true color images using quantum computation implementation endorsement from its superposition principle. In this review paper sigmoidal activation function and multilevel sigmoidal activation function is used to fulfil the objective for recovering the object using either denoising or segmentation technique. Neuro-biological network architecture comprises different nodes corresponding to the pixels are converted to the qubit neurons and has the ability for information retrieval capability from the objects by means of qubit neurons in phase manner. In this review paper authors present the brief survey on the image processing trend over image denoising as well as image segmentation scenario.
    Keywords: Classical approach; Image segmentation; Sigmoidal activation function; Multilevel sigmoidal activation function; Neuro-biological network.

  • Quantitative prognostic factor extraction of epidemic thrombosis using machine learning strategy   Order a copy of this article
    by Tianle Zhou, Danni Deng, Chaoyi Chu, Jie Cao 
    Abstract: In recent years, artificial intelligence and machine learning have become increasingly involved in the treatment of prevalent human diseases. Acute ischemic stroke (AIS) is an increasingly severe disease with a high risk of thrombosis resulting in loss of neurological function or death. MT with mechanical thrombectomy has become the mainstream treatment. Apart from the common factors such as blood glucose, NIHSS, and blood pressure level, etc., there are still unknown factors may have influence for prognosis after MT surgery. In this study, with the help of machine learning strategy, high-dimensional data of patients are mined, and the AIS prognostic prediction model is established in order to quantify the key influencing factors and determine the relationship between these parameters and the prognosis. This study is supposed to provide a set of methodology to evaluate the prognosis effectively.
    Keywords: acute ischemic stroke; AIS; mechanical thrombectomy; prognosis factor extraction; high-dimensional data; machine learning.
    DOI: 10.1504/IJHI.2020.10031627