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  • Open Access


    A New Framework for Employing Responsive End-Users Using FAHP and PSO Algorithm

    Reza Etemad1, Mohammad Sadegh Ghazizadeh1, Mehrdad Ahmadi Kamarposhti2,*, Ilhami Colak3, Kei Eguchi4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 951-964, 2023, DOI:10.32604/cmc.2023.032631

    Abstract The capacitor bank and synchronous condenser have been the only available sources of reactive power. Nowadays, most of the appliances use a power electronic interface for their connection. Applying a power electronic interface adds many features to these appliances. One of the promising features is their capability to interact with Volt-VAR programs. In this paper was investigated the reactive power interaction of the end-user appliances. For this purpose, the distribution network buses are ranked based on their effectiveness, followed by studying their interaction in the Volt-VAR program. To be able to consider the uncertainties, Probability Density Function (PDF) curve was… More >

  • Open Access


    Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

    S. Rama Sree1, Inderjeet Kaur2, Alexey Tikhonov3, E. Laxmi Lydia4, Ahmed A. Thabit5, Zahraa H. Kareem6, Yousif Kerrar Yousif7, Ahmed Alkhayyat8,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2195-2209, 2023, DOI:10.32604/cmc.2023.032586

    Abstract Autism spectrum disorder (ASD) is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills, recurrent conduct, and communication. Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD. Recognition of ASD related to objective pathogenic mutation screening is the initial step against prior intervention and efficient treatment of children who were affected. Nowadays, healthcare and machine learning (ML) industries are combined for determining the existence of various diseases. This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and… More >

  • Open Access


    Characteristic of Line-of-Sight in Infrastructure-to-Vehicle Visible Light Communication Using MIMO Technique

    Adisorn Kaewpukdee, Peerapong Uthansakul*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1025-1048, 2023, DOI:10.32604/cmc.2023.032569

    Abstract Visible Light Communication (VLC) technology is aggressive research for the next generation of communication. Currently, Radio Frequency (RF) communication has crowed spectrum. An Intelligent Transportation System (ITS) has been improved in the communication network for Vehicle-to-Vehicle (V2 V), Vehicle-to-Infrastructure (V2I), and Infrastructure-to-Vehicle (I2 V) by using the visible light spectrum instead of the RF spectrum. This article studies the characterization of Line-of-Sight (LOS) optical performance in an Outdoor Wireless Visible Light Communication (OWVLC) system employing a Multiple-Input Multiple-Output (MIMO) technique for I2 V communications in ITS regulations. We design the new configuration of the OWVLC-I2 V system, which is an alternative approach to communication… More >

  • Open Access


    Airstacknet: A Stacking Ensemble-Based Approach for Air Quality Prediction

    Amel Ksibi1, Amina Salhi1, Ala Saleh Alluhaidan1,*, Sahar A. El-Rahman2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2073-2096, 2023, DOI:10.32604/cmc.2023.032566

    Abstract The quality of the air we breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals. Unfortunately, personal air quality measurement remains challenging. In this study, we investigate the use of first-person photos for the prediction of air quality. The main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution prediction. AirStackNet consists of two layers and four regression models, where the first layer generates meta-data from… More >

  • Open Access


    Analysis of LDPC Code in Hybrid Communication Systems

    Hasnain Kashif1,*, Muhammad Nasir Khan1, Zubair Nawaz2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 769-781, 2023, DOI:10.32604/cmc.2023.032565

    Abstract Free-space optical (FSO) communication is of supreme importance for designing next-generation networks. Over the past decades, the radio frequency (RF) spectrum has been the main topic of interest for wireless technology. The RF spectrum is becoming denser and more employed, making its availability tough for additional channels. Optical communication, exploited for messages or indications in historical times, is now becoming famous and useful in combination with error-correcting codes (ECC) to mitigate the effects of fading caused by atmospheric turbulence. A free-space communication system (FSCS) in which the hybrid technology is based on FSO and RF. FSCS is a capable solution… More >

  • Open Access


    The Fusion Model of Catalytic Combustion and Thermal Conductivity

    Bin Lin1, Zhengyu Li2,*, Dong Wen2, Jianchao Liu2, Shan Yang3, Yong Zhou2, Chao Lu4, Qian Qiu4,5

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1509-1521, 2023, DOI:10.32604/cmc.2023.032557

    Abstract The further development of catalytic elements has been plagued by activation and binary problems. The automatic shift model that has emerged in recent years helps components achieve full range. However, the detection data still remains unstable in the shift area (7%~13%). This paper proposes a Catalytic Combustion and Thermal Conductivity (CCTC) model for the specified range, which can be explained from two aspects based on the existing methods. On the one hand, it uses iterative location search to process heterogeneous data, judges the prediction position of data points, and then gives weight evaluation. On the other hand, it corrects the… More >

  • Open Access


    Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

    Farah Mohammad*, Saad Al-Ahmadi

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 623-639, 2023, DOI:10.32604/cmc.2023.032552


    There exists various neurological disorder based diseases like tumor, sleep disorder, headache, dementia and Epilepsy. Among these, epilepsy is the most common neurological illness in humans, comparable to stroke. Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging (MRI). Neurons are intricately coupled in order to communicate and generate signals from human organs. Due to the complex nature of electroencephalogram (EEG) signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task. Computer based techniques and machine learning models… More >

  • Open Access


    Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

    M. V. Rajesh1, T. Archana Acharya2, Hafis Hajiyev3, E. Laxmi Lydia4, Haya Mesfer Alshahrani5, Mohamed K Nour6, Abdullah Mohamed7, Mesfer Al Duhayyim8,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 933-949, 2023, DOI:10.32604/cmc.2023.032549

    Abstract Recently, Internet of Things (IoT) has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications. Wireless sensor network (WSN) is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management, intelligent transportation, home automation, smart buildings, and other such IoT-based application. The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is, for balancing the energy amongst sensor nodes. The clustering and routing procedures… More >

  • Open Access


    Wind Power Prediction Based on Machine Learning and Deep Learning Models

    Zahraa Tarek1, Mahmoud Y. Shams2,*, Ahmed M. Elshewey3, El-Sayed M. El-kenawy4,5, Abdelhameed Ibrahim6, Abdelaziz A. Abdelhamid7,8, Mohamed A. El-dosuky1,9

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 715-732, 2023, DOI:10.32604/cmc.2023.032533

    Abstract Wind power is one of the sustainable ways to generate renewable energy. In recent years, some countries have set renewables to meet future energy needs, with the primary goal of reducing emissions and promoting sustainable growth, primarily the use of wind and solar power. To achieve the prediction of wind power generation, several deep and machine learning models are constructed in this article as base models. These regression models are Deep neural network (DNN), k-nearest neighbor (KNN) regressor, long short-term memory (LSTM), averaging model, random forest (RF) regressor, bagging regressor, and gradient boosting (GB) regressor. In addition, data cleaning and… More >

  • Open Access


    Power Prediction of VLSI Circuits Using Machine Learning

    E. Poovannan*, S. Karthik

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2161-2177, 2023, DOI:10.32604/cmc.2023.032512

    Abstract The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit. A big database is needed to undertake important analytical work like statistical method, heat research, and IR-drop research that results in extended running times. This unit focuses on the assessment of test strength. Because of the enormous number of successful designs for current models and the unnecessary time required for every test, maximum energy ratings with all tests cannot be achieved. Nevertheless, test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip. Generally, effective… More >

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