@article{MAKHILLJEAS201914217335,
    title = {Deep Intelligent System for Human Recognition in Complex Domain},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {2},
    pages = {373-385},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.373.385},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.373.385},
    author = {Swati and},
    keywords = {Complex neuron structure,C-TROIKA,fused fuzzy distribution,complex neural classifier,effectiveness,intelligent system},
    abstract = {This study aims to develop a deep computational model which is a novel aggregation of fuzzy
clustering fused with evolutionary searching and a neural network based on a proposed artificial neuron
structure in complex domain. In our Complex Deep Intelligent System (CDIS), we propose a complex neural
classifier built upon a new complex neuron structure ‘C-TROIKA’. The proposed deep model which is an
amalgamation of Fused Fuzzy Distribution (FFD) and Complex Neural Classifier (CNC) capitulates an efficient
tool for human recognition. The functional aptitudes of conventional neurons have been explored with
complex-valued non-linear aggregation functions. This aggregation has the ability to confine higher-order
correlations among input patterns. The proposed neuron structure based on these aggregation functions
enables the system to provide faster convergence, better learning and recognition accuracy. The effectiveness
and strengths of proposed complex neuron structure ‘C-TROIKA’ based deep intelligent system have been
demonstrated over three benchmark biometric datasets, CASIA iris, Yale face and Indian face to realize the
motivation.}
    }