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  • Open Access


    Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

    Ashit Kumar Dutta1,*, Yasser Albagory2, Majed Alsanea3, Hamdan I. Almohammed4, Abdul Rahaman Wahab Sait5

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1643-1655, 2023, DOI:10.32604/iasc.2023.027865

    Abstract Eye state classification acts as a vital part of the biomedical sector, for instance, smart home device control, drowsy driving recognition, and so on. The modifications in the cognitive levels can be reflected via transforming the electroencephalogram (EEG) signals. The deep learning (DL) models automated extract the features and often showcased improved outcomes over the conventional classification model in the recognition processes. This paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classification (EDLCOA-ESC). The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing step. Besides, wavelet packet decomposition (WPD) technique is employed for… More >

  • Open Access


    Depression Detection on COVID 19 Tweets Using Chimp Optimization Algorithm

    R. Meena1,*, V. Thulasi Bai2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1643-1658, 2022, DOI:10.32604/iasc.2022.025305

    Abstract The Covid-19 outbreak has an unprecedented effects on people's daily lives throughout the world, causing immense stress amongst individuals owing to enhanced psychological disorders like depression, stress, and anxiety. Researchers have used social media data to detect behaviour changes in individuals with depression, postpartum changes and stress detection since it reveals critical aspects of mental and emotional diseases. Considerable efforts have been made to examine the psychological health of people which have limited performance in accuracy and demand increased training time. To conquer such issues, this paper proposes an efficient depression detection framework named Improved Chimp Optimization Algorithm based Convolution… More >

  • Open Access


    Efficient Key Management System Based Lightweight Devices in IoT

    T. Chindrella Priyadharshini1,*, D. Mohana Geetha2

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1793-1808, 2022, DOI:10.32604/iasc.2022.020422

    Abstract The Internet of Things (IoT) has changed our lives significantly. Although IoT provides new opportunities, security remains a key concern while providing various services. Existing research methodologies try to solve the security and time-consuming problem also exists. To solve those problems, this paper proposed a Hashed Advanced Encryption Standard (HAES) algorithm based efficient key management system for internet-based lightweight devices in IoT networks. The proposed method is mainly divided into two phases namely Data Owner (DO) and Data User (DU) phase. The DO phase consists of two processes namely authentication and secure data uploading. In authentication, the registration process consists… More >

  • Open Access


    Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

    A. Sheryl Oliver1, P. Suresh2, A. Mohanarathinam3, Seifedine Kadry4, Orawit Thinnukool5,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 2031-2047, 2022, DOI:10.32604/cmc.2022.019876

    Abstract Early diagnosis and detection are important tasks in controlling the spread of COVID-19. A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays. However, these methods suffer from biased results and inaccurate detection of the disease. So, the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network (OCOA-DDCNN) for COVID-19 prediction using CT images in IoT environment. The proposed methodology works on the basis of two stages such as pre-processing and prediction. Initially, CT scan images generated from prospective COVID-19 are collected… More >

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