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  • Open Access


    Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

    Joe Louis Paul Ignatius1,*, Sasirekha Selvakumar1, Kavin Gabriel Joe Louis Paul2, Aadhithya B. Kailash1, S. Keertivaas1, S. A. J. Akarvin Raja Prajan1

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 81-97, 2023, DOI:10.32604/csse.2023.025195

    Abstract Tuberculosis (TB) is a severe infection that mostly affects the lungs and kills millions of people’s lives every year. Tuberculosis can be diagnosed using chest X-rays (CXR) and data-driven deep learning (DL) approaches. Because of its better automated feature extraction capability, convolutional neural networks (CNNs) trained on natural images are particularly effective in image categorization. A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets. Ten different deep CNNs (Resnet50, Resnet101, Resnet152, InceptionV3, VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, MobileNet) are trained and tested for identifying TB and normal cases. This… More >

  • Open Access


    Convolutional Neural Networks Based Video Reconstruction and Computation in Digital Twins

    M. Kavitha1, B. Sankara Babu2, B. Sumathy3, T. Jackulin4, N. Ramkumar5, A. Manimaran6, Ranjan Walia7, S. Neelakandan8,*

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1571-1586, 2022, DOI:10.32604/iasc.2022.026385

    Abstract With the advancement of communication and computing technologies, multimedia technologies involving video and image applications have become an important part of the information society and have become inextricably linked to people's daily productivity and lives. Simultaneously, there is a growing interest in super-resolution (SR) video reconstruction techniques. At the moment, the design of digital twins in video computing and video reconstruction is based on a number of difficult issues. Although there are several SR reconstruction techniques available in the literature, most of the works have not considered the spatio-temporal relationship between the video frames. With this motivation in mind, this… More >

  • Open Access


    Automatic Localization and Segmentation of Vertebrae for Cobb Estimation and Curvature Deformity

    Joddat Fatima1,*, Amina Jameel2, Muhammad Usman Akram3, Adeel Muzaffar Syed1, Malaika Mushtaq3

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1489-1504, 2022, DOI:10.32604/iasc.2022.025935

    Abstract The long twisted fragile tube, termed as spinal cord, can be named as the second vital organ of Central Nervous System (CNS), after brain. In human anatomy, all crucial life activities are controlled by CNS. The spinal cord does not only control the flow of information from the brain to rest of the body, but also takes charge of our reflexes control and the mobility of body. It keeps the body upright and acts as the main support for the flesh and bones. Spine deformity can occur by birth, due to aging, injury or spine surgery. In this research article,… More >

  • Open Access


    Classification of Arrhythmia Based on Convolutional Neural Networks and Encoder-Decoder Model

    Jian Liu1,*, Xiaodong Xia1, Chunyang Han2, Jiao Hui3, Jim Feng4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 265-278, 2022, DOI:10.32604/cmc.2022.029227

    Abstract As a common and high-risk type of disease, heart disease seriously threatens people’s health. At the same time, in the era of the Internet of Thing (IoT), smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases. Therefore, the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases. In this paper, we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network (CNN) and Encoder-Decoder model. The model uses Long Short-Term Memory (LSTM) to consider the… More >

  • Open Access


    A Novel Convolutional Neural Networks Based Spinach Classification and Recognition System

    Sankar Sennan1, Digvijay Pandey2,*, Youseef Alotaibi3, Saleh Alghamdi4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 343-361, 2022, DOI:10.32604/cmc.2022.028334

    Abstract In the present scenario, Deep Learning (DL) is one of the most popular research algorithms to increase the accuracy of data analysis. Due to intra-class differences and inter-class variation, image classification is one of the most difficult jobs in image processing. Plant or spinach recognition or classification is one of the deep learning applications through its leaf. Spinach is more critical for human skin, bone, and hair, etc. It provides vitamins, iron, minerals, and protein. It is beneficial for diet and is readily available in people's surroundings. Many researchers have proposed various machine learning and deep learning algorithms to classify… More >

  • Open Access


    HDAM: Heuristic Difference Attention Module for Convolutional Neural Networks

    Yu Xue*, Ziming Yuan

    Journal on Internet of Things, Vol.4, No.1, pp. 57-67, 2022, DOI:10.32604/jiot.2022.025327

    Abstract The attention mechanism is one of the most important priori knowledge to enhance convolutional neural networks. Most attention mechanisms are bound to the convolutional layer and use local or global contextual information to recalibrate the input. This is a popular attention strategy design method. Global contextual information helps the network to consider the overall distribution, while local contextual information is more general. The contextual information makes the network pay attention to the mean or maximum value of a particular receptive field. Different from the most attention mechanism, this article proposes a novel attention mechanism with the heuristic difference attention module… More >

  • Open Access


    Hybrid Optimized Learning for Lung Cancer Classification

    R. Vidhya1,*, T. T. Mirnalinee2

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 911-925, 2022, DOI:10.32604/iasc.2022.025060

    Abstract Computer tomography (CT) scan images can provide more helpful diagnosis information regarding the lung cancers. Many machine learning and deep learning algorithms are formulated using CT input scan images for the improvisation in diagnosis and treatment process. But, designing an accurate and intelligent system still remains in darker side of the research side. This paper proposes the novel classification model which works on the principle of fused features and optimized learning network. The proposed framework incorporates the principle of saliency maps as a first tier segmentation, which is then fused with deep convolutional neural networks to improve the classification maps… More >

  • Open Access


    Arabic Music Genre Classification Using Deep Convolutional Neural Networks (CNNs)

    Laiali Almazaydeh1,*, Saleh Atiewi2, Arar Al Tawil3, Khaled Elleithy4

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5443-5458, 2022, DOI:10.32604/cmc.2022.025526

    Abstract Genres are one of the key features that categorize music based on specific series of patterns. However, the Arabic music content on the web is poorly defined into its genres, making the automatic classification of Arabic audio genres challenging. For this reason, in this research, our objective is first to construct a well-annotated dataset of five of the most well-known Arabic music genres, which are: Eastern Takht, Rai, Muwashshah, the poem, and Mawwal, and finally present a comprehensive empirical comparison of deep Convolutional Neural Networks (CNNs) architectures on Arabic music genres classification. In this work, to utilize CNNs to develop… More >

  • Open Access


    End-to-end Handwritten Chinese Paragraph Text Recognition Using Residual Attention Networks

    Yintong Wang1,2,*, Yingjie Yang2, Haiyan Chen3, Hao Zheng1, Heyou Chang1

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 371-388, 2022, DOI:10.32604/iasc.2022.027146

    Abstract Handwritten Chinese recognition which involves variant writing style, thousands of character categories and monotonous data mark process is a long-term focus in the field of pattern recognition research. The existing methods are facing huge challenges including the complex structure of character/line-touching, the discriminate ability of similar characters and the labeling of training datasets. To deal with these challenges, an end-to-end residual attention handwritten Chinese paragraph text recognition method is proposed, which uses fully convolutional neural networks as the main structure of feature extraction and employs connectionist temporal classification as a loss function. The novel residual attention gate block is more… More >

  • Open Access


    Classification of Transmission Line Ground Short Circuit Fault Based on Convolutional Neural Network

    Tao Guo, Gang Tian, Zhimin Ao*, Xi Fang, Lili Wei, Fei Li

    Energy Engineering, Vol.119, No.3, pp. 985-996, 2022, DOI:10.32604/ee.2022.018185

    Abstract Ground short circuit faults in current transmission lines are common in the power systems. In order to prevent the power system from aggravating the accident caused by short-circuit faults of transmission lines, a novel convolutional neural network (CNN) model is constructed to identify the short-circuit fault of the transmission line in the power system. The CNN model is mainly consisted of five convolutional layers, three max-pooling layers, one concatenate layer, one dropout layer, one fully connected layer, and a Softmax classifier. This method uses a fixed time window to intercept system short-circuit fault data, extracts the deep features of these… More >

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