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  • Open Access

    ARTICLE

    Interleaved Boost Integrated Flyback Converter for Power Factor Correction in Brushless DC Motor Drive

    S. Benisha1,*, J. Anitha Roseline2

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1363-1378, 2022, DOI:10.32604/iasc.2022.023012 - 24 March 2022

    Abstract The scope of this research is to manage the speed of Permanent Magnet Brushless DC Motor Drive (PMBLDCMD) for various less capacity applications. In the circuit, a 1ϕ AC power is given to Diode Rectifier and the converted DC supply is given to condenser, which leads to abnormal pulsating current. Because of this pulsating current, the power quality disturbances arise at the supply point. Hence, the PMBLDCMD requires Power Factor Correction (PFC) converter for many household and profitable applications. The rotors of PMBLDCMD are driven by 3ϕ voltage source inverter (VSI), which performs electronic commutation.… More >

  • Open Access

    ARTICLE

    Numerical Computational Heuristic Through Morlet Wavelet Neural Network for Solving the Dynamics of Nonlinear SITR COVID-19

    Zulqurnain Sabir1, Abeer S. Alnahdi2,*, Mdi Begum Jeelani2, Mohamed A. Abdelkawy2,3,*, Muhammad Asif Zahoor Raja4, Dumitru Baleanu5,6, Muhammad Mubashar Hussain7

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 763-785, 2022, DOI:10.32604/cmes.2022.018496 - 14 March 2022

    Abstract The present investigations are associated with designing Morlet wavelet neural network (MWNN) for solving a class of susceptible, infected, treatment and recovered (SITR) fractal systems of COVID-19 propagation and control. The structure of an error function is accessible using the SITR differential form and its initial conditions. The optimization is performed using the MWNN together with the global as well as local search heuristics of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The detail of each class of the SITR nonlinear COVID-19 system is also discussed. The obtained outcomes of the SITR system are More >

  • Open Access

    ARTICLE

    Machine Learning Empowered Electricity Consumption Prediction

    Maissa A. Al Metrik*, Dhiaa A. Musleh

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1427-1444, 2022, DOI:10.32604/cmc.2022.025722 - 24 February 2022

    Abstract Electricity, being the most efficient secondary energy, contributes for a larger proportion of overall energy usage. Due to a lack of storage for energy resources, over supply will result in energy dissipation and substantial investment waste. Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as: smart distributed grids, assessing the degree of socioeconomic growth, distributed system design, tariff plans, demand-side management, power generation planning, and providing electricity supply stability by balancing the amount of electricity produced… More >

  • Open Access

    ARTICLE

    Artificial Monitoring of Eccentric Synchronous Reluctance Motors Using Neural Networks

    Shuguang Wei, Jiaqi Li*, Zixu Zhao, Dong Yuan

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1035-1052, 2022, DOI:10.32604/cmc.2022.024201 - 24 February 2022

    Abstract This paper proposes an artificial neural network for monitoring and detecting the eccentric error of synchronous reluctance motors. Firstly, a 15 kW synchronous reluctance motor is introduced and took as a case study to investigate the effects of eccentric rotor. Then, the equivalent magnetic circuits of the studied motor are analyzed and developed, in cases of dynamic eccentric rotor and static eccentric rotor condition, respectively. After that, the analytical equations of the studied motor are derived, in terms of its air-gap flux density, electromagnetic torque, and electromagnetic force, followed by the electromagnetic finite element analyses. Then,… More >

  • Open Access

    ARTICLE

    Detection and Classification of Diabetic Retinopathy Using DCNN and BSN Models

    S. Sudha*, A. Srinivasan, T. Gayathri Devi

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 597-609, 2022, DOI:10.32604/cmc.2022.024065 - 24 February 2022

    Abstract Diabetes is associated with many complications that could lead to death. Diabetic retinopathy, a complication of diabetes, is difficult to diagnose and may lead to vision loss. Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians. Because clinical testing involves complex procedures and is time-consuming, an automated system would help ophthalmologists to detect DR and administer treatment in a timely manner so that blindness can be avoided. Previous research works have focused on image processing algorithms, or neural networks, or signal processing techniques… More >

  • Open Access

    ARTICLE

    Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques

    Rohit Raja1, Chetan Swarup2, Abhishek Kumar3,*, Kamred Udham Singh4, Teekam Singh5, Dinesh Gupta6, Neeraj Varshney7, Swati Jain8

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2015-2031, 2022, DOI:10.32604/cmc.2022.022904 - 24 February 2022

    Abstract As far as the present state is concerned in detecting the behavioral pattern of humans (subject) using morphological image processing, a considerable portion of the study has been conducted utilizing frontal vision data of human faces. The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach. In this example, hybridization includes an artificial neural network (ANN) with a genetic algorithm (GA). We researched the geometrical properties extracted from side-vision human-face data. An additional study was conducted to determine the ideal number of… More >

  • Open Access

    ARTICLE

    ANN Based Reduced Switch Multilevel Inverter in UPQC for Power Quality Improvement

    Y. Alexander Jeevanantham1,*, S. Srinath2

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 909-921, 2022, DOI:10.32604/iasc.2022.022907 - 08 February 2022

    Abstract A unified power quality conditioner (UPQC) plays a crucial role in the Power quality improvement of a power system. In this paper, a reduced switch multilevel inverter is with artificial neural network, soft computing technique control is proposed for UPQC. This proposed topology is employed for the mitigation of various power quality issues such as voltage sag, voltage swell, power factor, harmonics, and restoration time of voltage compensation. To show the enriched performance of the proposed topology comparative analysis is made with other two topologies of UPQC such as Conventional UPQC and UPQC using cascaded More >

  • Open Access

    ARTICLE

    E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks

    Sanaa A. A. Ghaleb1,3,4, Mumtazimah Mohamad1, Syed Abdullah Fadzli1, Waheed A.H.M. Ghanem2,3,4,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4749-4766, 2022, DOI:10.32604/cmc.2022.020472 - 14 January 2022

    Abstract Spam has turned into a big predicament these days, due to the increase in the number of spam emails, as the recipient regularly receives piles of emails. Not only is spam wasting users’ time and bandwidth. In addition, it limits the storage space of the email box as well as the disk space. Thus, spam detection is a challenge for individuals and organizations alike. To advance spam email detection, this work proposes a new spam detection approach, using the grasshopper optimization algorithm (GOA) in training a multilayer perceptron (MLP) classifier for categorizing emails as ham More >

  • Open Access

    ARTICLE

    Predicting Mobile Cross-Platform Adaptation Using a Hybrid Sem–ANN Approach

    Ali Alkhalifah*

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 639-658, 2022, DOI:10.32604/csse.2022.022519 - 04 January 2022

    Abstract Owing to constant changes in user needs, new technologies have been introduced to keep pace by building sustainable applications. Researchers and practitioners are keen to understand the factors that create an attractive user interface. Although the use of cross-platform applications and services is increasing, limited research has examined and evaluated cross-platforms for developing mobile applications for different operating systems. This study evaluates cross-platform features, identifying the main factors that help to create an attractive user adaptation when building sustainable applications for both Android and iOS. Flutter and React Native were selected so end-users could test… More >

  • Open Access

    ARTICLE

    Predicting the Reflection Coefficient of a Viscoelastic Coating Containing a Cylindrical Cavity Based on an Artificial Neural Network Model

    Yiping Sun1,2, Qiang Bai1, Xuefeng Zhao1, Meng Tao1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1149-1170, 2022, DOI:10.32604/cmes.2022.017760 - 13 December 2021

    Abstract A cavity viscoelastic structure has a good sound absorption performance and is often used as a reflective baffle or sound absorption cover in underwater acoustic structures. The acoustic performance field has become a key research direction worldwide. Because of the time-consuming shortcomings of the traditional numerical analysis method and the high cost of the experimental method for measuring the reflection coefficient to evaluate the acoustic performance of coatings, this innovative study predicted the reflection coefficient of a viscoelastic coating containing a cylindrical cavity based on an artificial neural network (ANN). First, the mapping relationship between… More >

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