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  • Open Access


    Multifactorial Disease Detection Using Regressive Multi-Array Deep Neural Classifier

    D. Venugopal1, T. Jayasankar2,*, N. Krishnaraj3, S. Venkatraman4, N. B. Prakash5, G. R. Hemalakshmi5

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 27-38, 2021, DOI:10.32604/iasc.2021.015205

    Abstract Comprehensive evaluation of common complex diseases associated with common gene mutations is currently a hot area of human genome research into causative new developments. A multi-fractal analysis of the genome is performed by placing the entire DNA sequence into smaller fragments and using the chaotic game representation and systematic methods to calculate the general dimensional spectrum of each fragment. This is a time consuming process as it uses floating point to represent large data sets and requires processing time. The proposed Regressive Multi-Array Deep Neural Classifier (RMDNC) system is implemented to reduce the computation time,… More >

  • Open Access


    Underground Disease Detection Based on Cloud Computing and Attention Region Neural Network

    Pinjie Xu2, Ce Li1,2,*, Liguo Zhang3,4, Feng Yang1,2, Jing Zheng1,5, Jingwu Feng2

    Journal on Artificial Intelligence, Vol.1, No.1, pp. 9-18, 2019, DOI:10.32604/jai.2019.06157

    Abstract Detecting the underground disease is very crucial for the roadbed health monitoring and maintenance of transport facilities, since it is very closely related to the structural health and reliability with the rapid development of road traffic. Ground penetrating radar (GPR) is widely used to detect road and underground diseases. However, it is still a challenging task due to data access anywhere, transmission security and data processing on cloud. Cloud computing can provide scalable and powerful technologies for large-scale storage, processing and dissemination of GPR data. Combined with cloud computing and radar detection technology, it is More >

  • Open Access


    Parkinson’s Disease Detection Using Biogeography-Based Optimization

    Somayeh Hessam1, Shaghayegh Vahdat1, Irvan Masoudi Asl2,*, Mahnaz Kazemipoor3, Atefeh Aghaei4, Shahaboddin Shamshirband,5,6,*, Timon Rabczuk7

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 11-26, 2019, DOI:10.32604/cmc.2019.06472

    Abstract In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, More >

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