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  • Open Access

    ARTICLE

    Text Extraction with Optimal Bi-LSTM

    Bahera H. Nayef1,*, Siti Norul Huda Sheikh Abdullah2, Rossilawati Sulaiman2, Ashwaq Mukred Saeed3

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3549-3567, 2023, DOI:10.32604/cmc.2023.039528

    Abstract Text extraction from images using the traditional techniques of image collecting, and pattern recognition using machine learning consume time due to the amount of extracted features from the images. Deep Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant results. This study proposes using Dual Maxpooling and concatenating convolution Neural Networks (CNN) layers with the activation functions Relu and the Optimized Leaky Relu (OLRelu). The proposed method works by dividing the word image into slices that contain characters.… More >

  • Open Access

    ARTICLE

    Performance Analysis of Intelligent Neural-Based Deep Learning System on Rank Images Classification

    Muhammad Hameed Siddiqi1,*, Asfandyar Khan2, Muhammad Bilal Khan2, Abdullah Khan2, Madallah Alruwaili1, Saad Alanazi1

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2219-2239, 2023, DOI:10.32604/csse.2023.040212

    Abstract The use of the internet is increasing all over the world on a daily basis in the last two decades. The increase in the internet causes many sexual crimes, such as sexual misuse, domestic violence, and child pornography. Various research has been done for pornographic image detection and classification. Most of the used models used machine learning techniques and deep learning models which show less accuracy, while the deep learning model ware used for classification and detection performed better as compared to machine learning. Therefore, this research evaluates the performance analysis of intelligent neural-based deep… More >

  • Open Access

    ARTICLE

    Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

    Fawad Ali Shah1, Habib Akbar1, Abid Ali2,3, Parveen Amna4, Maha Aljohani5, Eman A. Aldhahri6, Harun Jamil7,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1385-1413, 2023, DOI:10.32604/csse.2023.036144

    Abstract The detection of rice leaf disease is significant because, as an agricultural and rice exporter country, Pakistan needs to advance in production and lower the risk of diseases. In this rapid globalization era, information technology has increased. A sensing system is mandatory to detect rice diseases using Artificial Intelligence (AI). It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases. Deep Neural Network (DNN) is a novel technique that will help detect disease present on a rice leave… More >

  • Open Access

    ARTICLE

    Railway Passenger Flow Forecasting by Integrating Passenger Flow Relationship and Spatiotemporal Similarity

    Song Yu*, Aiping Luo, Xiang Wang

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1877-1893, 2023, DOI:10.32604/iasc.2023.039132

    Abstract Railway passenger flow forecasting can help to develop sensible railway schedules, make full use of railway resources, and meet the travel demand of passengers. The structure of passenger flow in railway networks and the spatiotemporal relationship of passenger flow among stations are two distinctive features of railway passenger flow. Most of the previous studies used only a single feature for prediction and lacked correlations, resulting in suboptimal performance. To address the above-mentioned problem, we proposed the railway passenger flow prediction model called Flow-Similarity Attention Graph Convolutional Network (F-SAGCN). First, we constructed the passenger flow relations… More >

  • Open Access

    ARTICLE

    Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

    Aiping Xu1,2, Xuan Zou3, Chao Wang2,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1043-1059, 2023, DOI:10.32604/csse.2023.039645

    Abstract To protect the environment, the discharged sewage’s quality must meet the state’s discharge standards. There are many water quality indicators, and the pH (Potential of Hydrogen) value is one of them. The natural water’s pH value is 6.0–8.5. The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard. This paper aims to study the deep learning prediction model of wastewater’s pH. Firstly, the research uses the random forest method to select the data features and then, based on the sliding window,… More >

  • Open Access

    ARTICLE

    Identifying Severity of COVID-19 Medical Images by Categorizing Using HSDC Model

    K. Ravishankar*, C. Jothikumar

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 613-635, 2023, DOI:10.32604/csse.2023.038343

    Abstract Since COVID-19 infections are increasing all over the world, there is a need for developing solutions for its early and accurate diagnosis is a must. Detection methods for COVID-19 include screening methods like Chest X-rays and Computed Tomography (CT) scans. More work must be done on preprocessing the datasets, such as eliminating the diaphragm portions, enhancing the image intensity, and minimizing noise. In addition to the detection of COVID-19, the severity of the infection needs to be estimated. The HSDC model is proposed to solve these problems, which will detect and classify the severity of… More >

  • Open Access

    ARTICLE

    CNN-LSTM: A Novel Hybrid Deep Neural Network Model for Brain Tumor Classification

    R. D. Dhaniya1, K. M. Umamaheswari2,*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1129-1143, 2023, DOI:10.32604/iasc.2023.035905

    Abstract Current revelations in medical imaging have seen a slew of computer-aided diagnostic (CAD) tools for radiologists developed. Brain tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging (MRI). In this work, we reported on new observations based on binary brain tumor categorization using HYBRID CNN-LSTM. Initially, the collected image is pre-processed and augmented using the following steps such as rotation, cropping, zooming, CLAHE (Contrast Limited Adaptive Histogram Equalization), and Random Rotation with panoramic stitching (RRPS). Then, a method called particle swarm optimization (PSO) is used to segment tumor regions More >

  • Open Access

    ARTICLE

    A Novel Detection Method for Pavement Crack with Encoder-Decoder Architecture

    Yalong Yang1,2,3, Wenjing Xu1,2,3, Yinfeng Zhu4, Liangliang Su1,2,3,*, Gongquan Zhang1,2,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 761-773, 2023, DOI:10.32604/cmes.2023.027010

    Abstract As a current popular method, intelligent detection of cracks is of great significance to road safety, so deep learning has gradually attracted attention in the field of crack image detection. The nonlinear structure, low contrast and discontinuity of cracks bring great challenges to existing crack detection methods based on deep learning. Therefore, an end-to-end deep convolutional neural network (AttentionCrack) is proposed for automatic crack detection to overcome the inaccuracy of boundary location between crack and non-crack pixels. The AttentionCrack network is built on U-Net based encoder-decoder architecture, and an attention mechanism is incorporated into the… More >

  • Open Access

    ARTICLE

    Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

    Amarendra Reddy Panyala1,2, M. Baskar3,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3883-3899, 2023, DOI:10.32604/csse.2023.037050

    Abstract The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey… More >

  • Open Access

    ARTICLE

    Identification of Rice Leaf Disease Using Improved ShuffleNet V2

    Yang Zhou, Chunjiao Fu, Yuting Zhai, Jian Li, Ziqi Jin, Yanlei Xu*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4501-4517, 2023, DOI:10.32604/cmc.2023.038446

    Abstract Accurate identification of rice diseases is crucial for controlling diseases and improving rice yield. To improve the classification accuracy of rice diseases, this paper proposed a classification and identification method based on an improved ShuffleNet V2 (GE-ShuffleNet) model. Firstly, the Ghost module is used to replace the convolution in the two basic unit modules of ShuffleNet V2, and the unimportant convolution is deleted from the two basic unit modules of ShuffleNet V2. The Hardswish activation function is applied to replace the ReLU activation function to improve the identification accuracy of the model. Secondly, an effective… More >

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