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  • Open Access


    A Novel Machine Learning–Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

    Saurabh Adhikari1, Tushar Kanti Gangopadhayay1, Souvik Pal2,3, D. Akila4, Mamoona Humayun5, Majed Alfayad6, N. Z. Jhanjhi7,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2123-2140, 2023, DOI:10.32604/csse.2023.034431

    Abstract Machine learning is a technique for analyzing data that aids the construction of mathematical models. Because of the growth of the Internet of Things (IoT) and wearable sensor devices, gesture interfaces are becoming a more natural and expedient human-machine interaction method. This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns. The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition. Potential applications of hand gesture recognition research… More >

  • Open Access


    Convolutional Neural Network-Based Identity Recognition Using ECG at Different Water Temperatures During Bathing

    Jianbo Xu, Wenxi Chen*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1807-1819, 2022, DOI:10.32604/cmc.2022.021154

    Abstract This study proposes a convolutional neural network (CNN)-based identity recognition scheme using electrocardiogram (ECG) at different water temperatures (WTs) during bathing, aiming to explore the impact of ECG length on the recognition rate. ECG data was collected using non-contact electrodes at five different WTs during bathing. Ten young student subjects (seven men and three women) participated in data collection. Three ECG recordings were collected at each preset bathtub WT for each subject. Each recording is 18 min long, with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were… More >

  • Open Access


    Highly Accurate Recognition of Handwritten Arabic Decimal Numbers Based on a Self-Organizing Maps Approach

    Amin Alqudah1,2, Hussein R. Al-Zoubi2, Mahmood A. Al-Khassaweneh2,3, Mohammed Al-Qodah1

    Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 493-505, 2018, DOI:10.31209/2018.100000005

    Abstract Handwritten numeral recognition is one of the most popular fields of research in automation because it is used in many applications. Indeed, automation has continually received substantial attention from researchers. Therefore, great efforts have been made to devise accurate recognition methods with high recognition ratios. In this paper, we propose a method for integrating the correlation coefficient with a Self-Organizing Maps (SOM)-based technique to recognize offline handwritten Arabic decimal digits. The simulation results show very high recognition rates compared with the rates achieved by other existing methods. More >

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