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  • Open Access

    ARTICLE

    Interval Type-2 Fuzzy Model for Intelligent Fire Intensity Detection Algorithm with Decision Making in Low-Power Devices

    Emmanuel Lule1,2,*, Chomora Mikeka3, Alexander Ngenzi4, Didacienne Mukanyiligira5

    Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 57-81, 2023, DOI:10.32604/iasc.2023.037988 - 26 January 2024

    Abstract Local markets in East Africa have been destroyed by raging fires, leading to the loss of life and property in the nearby communities. Electrical circuits, arson, and neglected charcoal stoves are the major causes of these fires. Previous methods, i.e., satellites, are expensive to maintain and cause unnecessary delays. Also, unit-smoke detectors are highly prone to false alerts. In this paper, an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach. A free open–source MATLAB/Simulink fuzzy toolbox integrated… More >

  • Open Access

    ARTICLE

    Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

    Salah-ud-din Khokhar1,2,*, QinKe Peng1, Muhammad Yasir Noor3

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2413-2427, 2023, DOI:10.32604/cmc.2023.036148 - 31 March 2023

    Abstract For fuzzy systems to be implemented effectively, the fuzzy membership function (MF) is essential. A fuzzy system (FS) that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output (SISO) FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output. Utilizing a variety of non-linear techniques, a SISO FS is simulated. The results of FS experiments conducted in comparable conditions are then compared. The simulated results and the results of the experimental setup agree fairly well. The findings of the suggested More >

  • Open Access

    ARTICLE

    Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence

    Ahmed Zohair Ibrahim1,*, P. Prakash2, V. Sakthivel2, P. Prabu3

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2447-2460, 2023, DOI:10.32604/csse.2023.030134 - 21 December 2022

    Abstract In order to research brain problems using MRI, PET, and CT neuroimaging, a correct understanding of brainfunction is required. This has been considered in earlier times with the support of traditional algorithms. Deep learning process has also been widely considered in these genomics data processing system. In this research, brain disorder illness incliding Alzheimer’s disease, Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods. Moeover, deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks More >

  • Open Access

    ARTICLE

    Damage Detection of Cyclically Loaded Concrete Shear Wall using EMI Technique

    A. Likhith Reddy1, Shirleen Charles1, C. Bharathi Priya2, G.V. Rama Rao2, N. Gopalakrishnan3,4 , A. Rama Mohan Rao3

    Structural Durability & Health Monitoring, Vol.9, No.4, pp. 325-347, 2013, DOI:10.32604/sdhm.2013.009.325

    Abstract Details of the investigations on an unexplored application of Electro Mechanical Impedance (EMI) technique using smart piezoelectric (PZT) sensors for damage detection of concrete shear wall structures under crack opening and closing is presented in this paper. The behavior and the ability of this method to detect damages, in a heterogeneous quasi-brittle material is studied for its effective utilization in structural health monitoring. The paper discusses the experimental investigations conducted on a concrete shear wall using PZT patches. Conductance data is acquired at different applied lateral displacements of shear wall. Damage index is calculated using More >

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