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  • Open Access

    ARTICLE

    Modeling of Hyperparameter Tuned Hybrid CNN and LSTM for Prediction Model

    J. Faritha Banu1,*, S. B. Rajeshwari2, Jagadish S. Kallimani2, S. Vasanthi3, Ahmed Mateen Buttar4, M. Sangeetha5, Sanjay Bhargava6

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1393-1405, 2022, DOI:10.32604/iasc.2022.024176 - 24 March 2022

    Abstract The stock market is an important domain in which the investors are focused to, therefore accurate prediction of stock market trends remains a hot research area among business-people and researchers. Because of the non-stationary features of the stock market, the stock price prediction is considered a challenging task and is affected by several factors. Anticipating stock market trends is a difficult endeavor that requires a lot of attention, because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, noisy, and chaotic data, stock market prediction is… More >

  • Open Access

    ARTICLE

    Attention-Based Deep Learning Model for Early Detection of Parkinson's Disease

    Mohd Sadiq1, Mohd Tauheed Khan2,*, Sarfaraz Masood3

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5183-5200, 2022, DOI:10.32604/cmc.2022.020531 - 14 January 2022

    Abstract Parkinson's disease (PD), classified under the category of a neurological syndrome, affects the brain of a person which leads to the motor and non-motor symptoms. Among motor symptoms, one of the major disabling symptom is Freezing of Gait (FoG) that affects the daily standard of living of PD patients. Available treatments target to improve the symptoms of PD. Detection of PD at the early stages is an arduous task due to being indistinguishable from a healthy individual. This work proposed a novel attention-based model for the detection of FoG events and PD, and measuring the… More >

  • Open Access

    ARTICLE

    Automatic Human Detection Using Reinforced Faster-RCNN for Electricity Conservation System

    S. Ushasukhanya*, M. Karthikeyan

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1261-1275, 2022, DOI:10.32604/iasc.2022.022654 - 17 November 2021

    Abstract Electricity conservation systems are designed to conserve electricity to manage the bridge between the high raising demand and the production. Such systems have been so far using sensors to detect the necessity which adds an additional cost to the setup. Closed-circuit Television (CCTV) has been installed in almost everywhere around us especially in commercial places. Interpretation of these CCTV images is being carried out for various reasons to elicit the information from it. Hence a framework for electricity conservation that enables the electricity supply only when required, using existing resources would be a cost effective… More >

  • Open Access

    ARTICLE

    Optimized Convolutional Neural Network for Automatic Detection of COVID-19

    K. Muthumayil1, M. Buvana2, K. R. Sekar3, Adnen El Amraoui4,*, Issam Nouaouri4, Romany F. Mansour5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1159-1175, 2022, DOI:10.32604/cmc.2022.017178 - 07 September 2021

    Abstract The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe. Radiologists use X-Rays or Computed Tomography (CT) images to confirm the presence of COVID-19. So, image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times. The current research work introduces Multi-objective Black Widow Optimization (MBWO)-based Convolutional Neural Network i.e., MBWO-CNN technique for diagnosis and classification of COVID-19. MBWO-CNN model involves four steps such as preprocessing, feature extraction, parameter tuning, and classification. In the beginning, the input images undergo preprocessing… More >

  • Open Access

    ARTICLE

    An Early Stopping-Based Artificial Neural Network Model for Atmospheric Corrosion Prediction of Carbon Steel

    Phyu Hnin Thike1, 2, Zhaoyang Zhao1, Peng Liu1, Feihu Bao1, Ying Jin1, Peng Shi1, *

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2091-2109, 2020, DOI:10.32604/cmc.2020.011608 - 16 September 2020

    Abstract The optimization of network topologies to retain the generalization ability by deciding when to stop overtraining an artificial neural network (ANN) is an existing vital challenge in ANN prediction works. The larger the dataset the ANN is trained with, the better generalization the prediction can give. In this paper, a large dataset of atmospheric corrosion data of carbon steel compiled from several resources is used to train and test a multilayer backpropagation ANN model as well as two conventional corrosion prediction models (linear and Klinesmith models). Unlike previous related works, a grid searchbased hyperparameter tuning… More >

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