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Search Results (7)
  • Open Access


    Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

    Sarah M. Alhammad1, Doaa Sami Khafaga1,*, Aya Y. Hamed2, Osama El-Koumy3, Ehab R. Mohamed3, Khalid M. Hosny3

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2939-2952, 2023, DOI:10.32604/csse.2023.041011

    Abstract Face mask detection has several applications, including real-time surveillance, biometrics, etc. Identifying face masks is also helpful for crowd control and ensuring people wear them publicly. With monitoring personnel, it is impossible to ensure that people wear face masks; automated systems are a much superior option for face mask detection and monitoring. This paper introduces a simple and efficient approach for masked face detection. The architecture of the proposed approach is very straightforward; it combines deep learning and local binary patterns to extract features and classify them as masked or unmasked. The proposed system requires hardware with minimal power consumption… More >

  • Open Access


    Real-Time CNN-Based Driver Distraction & Drowsiness Detection System

    Abdulwahab Ali Almazroi1,*, Mohammed A. Alqarni2, Nida Aslam3, Rizwan Ali Shah4

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2153-2174, 2023, DOI:10.32604/iasc.2023.039732

    Abstract Nowadays days, the chief grounds of automobile accidents are driver fatigue and distractions. With the development of computer vision technology, a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them, reducing accidents. This paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor vehicle. Employing the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network (CNN) any changes by focusing on the eyes and mouth zone, precision is achieved. One of the… More >

  • Open Access


    DeepQ Based Automated Irrigation Systems Using Deep Belief WSN

    E. Gokulakannan*

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3415-3427, 2023, DOI:10.32604/iasc.2023.030965

    Abstract Deep learning is the subset of artificial intelligence and it is used for effective decision making. Wireless Sensor based automated irrigation system is proposed to monitor and cultivate crop. Our system consists of Distributed wireless sensor environment to handle the moisture of the soil and temperature levels. It is automated process and useful for minimizing the usage of resources such as water level, quality of the soil, fertilizer values and controlling the whole system. The mobile app based smart control system is designed using deep belief network. This system has multiple sensors placed in agricultural field and collect the data.… More >

  • Open Access


    Deep Sentiment Learning for Measuring Similarity Recommendations in Twitter Data

    S. Manikandan1,*, P. Dhanalakshmi2, K. C. Rajeswari3, A. Delphin Carolina Rani4

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 183-192, 2022, DOI:10.32604/iasc.2022.023469

    Abstract The similarity recommendation of twitter data is evaluated by using sentiment analysis method. In this paper, the deep learning processes such as classification, clustering and prediction are used to measure the data. Convolutional neural network is applied for analyzing multimedia contents which is received from various sources. Recurrent neural network is used for handling the natural language data. The content based recommendation system is proposed for selecting similarity index in twitter data using deep sentiment learning method. In this paper, sentiment analysis technique is used for finding similar images, contents, texts, etc. The content is selected based on repetitive comments… More >

  • Open Access


    Deep Learning Based Modeling of Groundwater Storage Change

    Mohd Anul Haq1,*, Abdul Khadar Jilani1, P. Prabu2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4599-4617, 2022, DOI:10.32604/cmc.2022.020495

    Abstract The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 2003–2025 for five basins of Saudi Arabia. An attempt has been made… More >

  • Open Access


    Handwritten Character Recognition Based on Improved Convolutional Neural Network

    Yu Xue1,2,*, Yiling Tong1, Ziming Yuan1, Shoubao Su2, Adam Slowik3, Sam Toglaw4

    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 497-509, 2021, DOI:10.32604/iasc.2021.016884

    Abstract Because of the characteristics of high redundancy, high parallelism and nonlinearity in the handwritten character recognition model, the convolutional neural networks (CNNs) are becoming the first choice to solve these complex problems. The complexity, the types of characters, the character similarity of the handwritten character dataset, and the choice of optimizers all have a great impact on the network model, resulting in low accuracy, high loss, and other problems. In view of the existence of these problems, an improved LeNet-5 model is proposed. Through increasing its convolutional layers and fully connected layers, higher quality features can be extracted. Secondly, a… More >

  • Open Access


    A Novel Approach for the Numerical Simulation of Fluid-Structure Interaction Problems in the Presence of Debris

    Miaomiao Ren*, Xiaobin Shu

    FDMP-Fluid Dynamics & Materials Processing, Vol.16, No.5, pp. 979-991, 2020, DOI:10.32604/fdmp.2020.09563

    Abstract A novel algorithm is proposed for the simulation of fluid-structure interaction problems. In particular, much attention is paid to natural phenomena such as debris flow. The fluid part (debris flow fluid) is simulated in the framework of the smoothed particle hydrodynamics (SPH) approach, while the solid part (downstream obstacles) is treated using the finite element method (FEM). Fluid-structure coupling is implemented through dynamic boundary conditions. In particular, the software “TensorFlow” and an algorithm based on Python are combined to conduct the required calculations. The simulation results show that the dynamics of viscous and non-viscous debris flows can be extremely different… More >

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