Forthcoming and Online First Articles

International Journal of Hybrid Intelligence

International Journal of Hybrid Intelligence (IJHI)

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International Journal of Hybrid Intelligence (4 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.

  • Image processing techniques for precision agriculture: a survey   Order a copy of this article
    by Rahul Agarwal, Narpat Singh Shekhawat 
    Abstract: Agriculture and its allied sector has influence in every aspect of life of an individual around the globe. Economy of most of the countries depends on agriculture. This sector directly or indirectly employ a significant part of population. Due to involvement of large population, it is essential to involve computer vision-based approaches to minimise the production cost of crop yields and to maximise profit of farmers. Precision agriculture has potential to achieve these goals. Precision agriculture includes optimum utilisation of available resources, use of technology and deployment of resources as and when required with aim to improve life of farmers and improvement in production. This paper aims to conduct an in-depth study of precision agriculture and use of image processing techniques in filed of agriculture.
    Keywords: precision agriculture; smart farming; image processing; feature selection; feature extraction; image classification; swarm intelligence.
    DOI: 10.1504/IJHI.2020.10033479
     
  • Flood areas prediction in Bangladesh using Apriori algorithm   Order a copy of this article
    by N. Narayanan Prasanth, Anushree Karajgi, Adiksha Sood, S.P. Raja 
    Abstract: Floods often turn out to be a major natural disaster in some parts of world due to overflow of water which submerges land that is usually dry. This leads to loss of life and vast damage to economy; therefore, it becomes extremely important to have systematic and dynamic prediction of flood areas so that people are more aware and better prepared for the impending disaster. The aim of the paper is to develop a comprehensive model for the prediction of flood area using Apriori algorithm. Our primary focus is to assess the spatial dataset of Bangladesh which provides hazard data of various risks including the risk of flooding. The various factors resulting in flooding such as water level and flood area have been analysed and the possible relations have been developed using the association rules
    Keywords: Apriori; association rules; Bangladesh; flood; data mining.
    DOI: 10.1504/IJHI.2021.10035127
     
  • Convolutional neural networks and support vector machines for hybrid number plate recognition model   Order a copy of this article
    by Peter Muthuri Kibaara, Edna C. Too, David Gitonga Mwathi 
    Abstract: Recognition accuracy is a determinant performance metric factor that greatly affects the optimal implementation of ANPRs. The recognition accuracy of the existing ANPRs can be improved by adopting a hybrid approach that leverages on the strengths of two machine learning algorithms, i.e., CNN and SVM that have previously been deployed independently in ANPRs. Two models were developed using a deep cascade framework; a pure CNN with a SoftMax classifier and a hybrid CNN with a SVM classifier. UFPR-ALPR dataset was used to train validate and test the models. The hybrid CNN-SVM model had a recognition accuracy of 91.25% against 89.07% from the pure CNN model. The weighted average precision, recall, and F1-score of the hybrid CNN-SVM was 92%, 91% and 91%, respectively, which was better compared to that of pure CNN. The hybrid model was tested for external validity using the SSIG dataset. The hybrid CNN-SVM model had a recognition accuracy of 91% against 89% from the pure CNN model. The weighted average precision, recall, and F1-score of the hybrid CNN-SVM was 91%, 91% and 91% respectively which was better compared to that of pure CNN.
    Keywords: hybrid; convolutional neural networks; support vector machines; automatic number plate recognition; ANPR; confusion matrix; accuracy; precision; recall; F1-score.
    DOI: 10.1504/IJHI.2022.10048614