Emmanuel Lule^1,2,*, Chomora Mikeka³, Alexander Ngenzi⁴, Didacienne Mukanyiligira⁵

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

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 into MATLAB 2018a is used… More >

Ahmed Zohair Ibrahim^1,*, 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

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 (DBN). Images are stored in… More >