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  • Open Access


    A Novel Fractional Dengue Transmission Model in the Presence of Wolbachia Using Stochastic Based Artificial Neural Network

    Zeshan Faiz1, Iftikhar Ahmed1, Dumitru Baleanu2,3,4, Shumaila Javeed1,5,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1217-1238, 2024, DOI:10.32604/cmes.2023.029879

    Abstract The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model (FDTM) in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network (LM-NN) technique. The fractional dengue transmission model (FDTM) consists of 12 compartments. The human population is divided into four compartments; susceptible humans (Sh), exposed humans (Eh), infectious humans (Ih), and recovered humans (Rh). Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments: aquatic (eggs, larvae, pupae), susceptible, exposed, and infectious. We investigated three different cases of vertical transmission probability (η), namely when Wolbachia-free mosquitoes persist only (η =… More >

  • Open Access


    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

    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 >

  • Open Access


    Energy Efficient and Intelligent Mosquito Repellent Fuzzy Control System

    Aaqib Inam1, Zhu Li1,*, Salah-ud-din Khokhar2, Zubia Zafar3, Muhammad Imran4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 699-715, 2023, DOI:10.32604/cmc.2023.039707

    Abstract Mosquitoes are of great concern for occasionally carrying noxious diseases (dengue, malaria, zika, and yellow fever). To control mosquitoes, it is very crucial to effectively monitor their behavioral trends and presence. Traditional mosquito repellent works by heating small pads soaked in repellant, which then diffuses a protected area around you, a great alternative to spraying yourself with insecticide. But they have limitations, including the range, turning them on manually, and then waiting for the protection to kick in when the mosquitoes may find you. This research aims to design a fuzzy-based controller to solve the above issues by automatically determining… More >

  • Open Access


    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

    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 >

  • Open Access


    A Robust Asynchrophasor in PMU Using Second-Order Kalman Filter

    Nayef Alqahtani1,2,*, Ali Alqahtani3

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2557-2573, 2023, DOI:10.32604/cmc.2023.026316

    Abstract Phasor Measurement Units (PMUs) provide Global Positioning System (GPS) time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system. Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition. A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view. However, such ongoing development and improvement to PMUs’ principal work are essential to the network operators to enhance the grid… More >

  • Open Access


    Application of ANFIS Model for Thailand’s Electric Vehicle Consumption

    Narongkorn Uthathip1,*, Pornrapeepat Bhasaputra1, Woraratana Pattaraprakorn2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 69-86, 2022, DOI:10.32604/csse.2022.020120

    Abstract Generally, road transport is a major energy-consuming sector. Fuel consumption of each vehicle is an important factor that affects the overall energy consumption, driving behavior and vehicle characteristic are the main factors affecting the change of vehicle fuel consumption. It is difficult to analyze the influence of fuel consumption with multiple and complex factors. The Adaptive Neuro-Fuzzy Inference System (ANFIS) approach was employed to develop a vehicle fuel consumption model based on multivariate input. The ANFIS network was constructed by various experiments based on the ANFIS Parameter setting. The performance of the ANFIS network was validated using Root Mean Square… More >

  • Open Access


    Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

    I. Kalphana1,*, T. Kesavamurthy2

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 171-185, 2022, DOI:10.32604/csse.2022.019799

    Abstract Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from… More >

  • Open Access


    Joint Channel and Multi-User Detection Empowered with Machine Learning

    Mohammad Sh. Daoud1, Areej Fatima2, Waseem Ahmad Khan3, Muhammad Adnan Khan4,5,*, Sagheer Abbas3, Baha Ihnaini6, Munir Ahmad3, Muhammad Sheraz Javeid7, Shabib Aftab3

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 109-121, 2022, DOI:10.32604/cmc.2022.019295

    Abstract The numbers of multimedia applications and their users increase with each passing day. Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems. In this article, a fuzzy logic empowered adaptive backpropagation neural network (FLeABPNN) algorithm is proposed for joint channel and multi-user detection (CMD). FLeABPNN has two stages. The first stage estimates the channel parameters, and the second performs multi-user detection. The proposed approach capitalizes on a neuro-fuzzy hybrid system that combines the competencies of both fuzzy logic and neural networks. This study analyzes the… More >

  • Open Access


    AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States vs. Union Territories

    Meenu Gupta1, Rachna Jain2, Simrann Arora2, Akash Gupta2, Mazhar Javed Awan3, Gopal Chaudhary2,*, Haitham Nobanee4,5,6

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 933-950, 2021, DOI:10.32604/cmc.2021.014221

    Abstract The COVID-19 disease has already spread to more than 213 countries and territories with infected (confirmed) cases of more than 27 million people throughout the world so far, while the numbers keep increasing. In India, this deadly disease was first detected on January 30, 2020, in a student of Kerala who returned from Wuhan. Because of India’s high population density, different cultures, and diversity, it is a good idea to have a separate analysis of each state. Hence, this paper focuses on the comprehensive analysis of the effect of COVID-19 on Indian states and Union Territories and the development of… More >

  • Open Access


    Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

    Amer Ibrahim Al-Omari1, *, Ibrahim M. Almanjahie2, Amal S. Hassan3, Heba F. Nagy4

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 835-857, 2020, DOI:10.32604/cmc.2020.10944

    Abstract In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength… More >

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