Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (547)
  • Open Access

    ARTICLE

    Spatial-Resolution Independent Object Detection Framework for Aerial Imagery

    Sidharth Samanta1, Mrutyunjaya Panda1, Somula Ramasubbareddy2, S. Sankar3, Daniel Burgos4,*

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1937-1948, 2021, DOI:10.32604/cmc.2021.014406 - 13 April 2021

    Abstract Earth surveillance through aerial images allows more accurate identification and characterization of objects present on the surface from space and airborne platforms. The progression of deep learning and computer vision methods and the availability of heterogeneous multispectral remote sensing data make the field more fertile for research. With the evolution of optical sensors, aerial images are becoming more precise and larger, which leads to a new kind of problem for object detection algorithms. This paper proposes the “Sliding Region-based Convolutional Neural Network (SRCNN),” which is an extension of the Faster Region-based Convolutional Neural Network (RCNN) More >

  • Open Access

    ARTICLE

    Tomato Leaf Disease Identification and Detection Based on Deep Convolutional Neural Network

    Yang Wu1, Lihong Xu1,*, Erik D. Goodman2

    Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 561-576, 2021, DOI:10.32604/iasc.2021.016415 - 01 April 2021

    Abstract Deep convolutional neural network (DCNN) requires a lot of data for training, but there has always been data vacuum in agriculture, making it difficult to label all existing data accurately. Therefore, a lightweight tomato leaf disease identification network supported by Variational auto-Encoder (VAE) is proposed to improve the accuracy of crop leaf disease identification. In the lightweight network, multi-scale convolution can expand the network width, enrich the extracted features, and reduce model parameters such as deep separable convolution. VAE makes full use of a large amount of unlabeled data to achieve unsupervised learning, and then… More >

  • Open Access

    ARTICLE

    An Enhanced Convolutional Neural Network for COVID-19 Detection

    Sameer I. Ali Al-Janabi1, Belal Al-Khateeb2,*, Maha Mahmood2, Begonya Garcia-Zapirain3

    Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 293-303, 2021, DOI:10.32604/iasc.2021.014419 - 01 April 2021

    Abstract The recent novel coronavirus (COVID-19, as the World Health Organization has called it) has proven to be a source of risk for global public health. The virus, which causes an acute respiratory disease in persons, spreads rapidly and is now threatening more than 150 countries around the world. One of the essential procedures that patients with COVID-19 need is an accurate and rapid screening process. In this research, utilizing the features of deep learning methods, we present a method for detecting COVID-19 and a screening model that uses pulmonary computed tomography images to differentiate COVID-19 More >

  • Open Access

    ARTICLE

    Computer Decision Support System for Skin Cancer Localization and Classification

    Muhammad Attique Khan1, Tallha Akram2, Muhammad Sharif1, Seifedine Kadry3, Yunyoung Nam4,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1041-1064, 2021, DOI:10.32604/cmc.2021.016307 - 22 March 2021

    Abstract In this work, we propose a new, fully automated system for multiclass skin lesion localization and classification using deep learning. The main challenge is to address the problem of imbalanced data classes, found in HAM10000, ISBI2018, and ISBI2019 datasets. Initially, we consider a pre-trained deep neural network model, DarkeNet19, and fine-tune the parameters of third convolutional layer to generate the image gradients. All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network (HFaFFNN). The resultant image is further enhanced by employing a log-opening based activation function to generate a… More >

  • Open Access

    ARTICLE

    Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network Algorithm

    Fawaz Waselallah Alsaade1, Theyazn H. H. Aldhyani2,*, Mosleh Hmoud Al-Adhaileh3

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 805-819, 2021, DOI:10.32604/cmc.2021.016264 - 22 March 2021

    Abstract The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity, and developing a system to identify COVID-19 in its early stages will save millions of lives. This study applied support vector machine (SVM), k-nearest neighbor (K-NN) and deep learning convolutional neural network (CNN) algorithms to classify and detect COVID-19 using chest X-ray radiographs. To test the proposed system, chest X-ray radiographs and CT images were collected from different standard databases, which contained 95 normal images, 140 COVID-19 images and 10 SARS images. Two scenarios were considered to… More >

  • Open Access

    ARTICLE

    Deep Learning Multimodal for Unstructured and Semi-Structured Textual Documents Classification

    Nany Katamesh, Osama Abu-Elnasr*, Samir Elmougy

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 589-606, 2021, DOI:10.32604/cmc.2021.015761 - 22 March 2021

    Abstract Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information, the document classification task becomes an interesting area for controlling data behavior. This paper presents a document classification multimodal for categorizing textual semi-structured and unstructured documents. The multimodal implements several individual deep learning models such as Deep Neural Networks (DNN), Recurrent Convolutional Neural Networks (RCNN) and Bidirectional-LSTM (Bi-LSTM). The Stacked Ensemble based meta-model technique is used to combine the results of the individual classifiers to produce better results, compared to those reached by… More >

  • Open Access

    ARTICLE

    An Optimized Deep Residual Network with a Depth Concatenated Block for Handwritten Characters Classification

    Gibrael Abosamra*, Hadi Oqaibi

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1-28, 2021, DOI:10.32604/cmc.2021.015318 - 22 March 2021

    Abstract Even though much advancements have been achieved with regards to the recognition of handwritten characters, researchers still face difficulties with the handwritten character recognition problem, especially with the advent of new datasets like the Extended Modified National Institute of Standards and Technology dataset (EMNIST). The EMNIST dataset represents a challenge for both machine-learning and deep-learning techniques due to inter-class similarity and intra-class variability. Inter-class similarity exists because of the similarity between the shapes of certain characters in the dataset. The presence of intra-class variability is mainly due to different shapes written by different writers for… More >

  • Open Access

    ARTICLE

    Automatic BIM Indoor Modelling from Unstructured Point Clouds Using a Convolutional Neural Network

    Uuganbayar Gankhuyag, Ji-Hyeong Han*

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 133-152, 2021, DOI:10.32604/iasc.2021.015227 - 17 March 2021

    Abstract The automated reconstruction of building information modeling (BIM) objects from unstructured point cloud data for indoor as-built modeling is still a challenging task and the subject of much ongoing research. The most important part of the process is to detect the wall geometry clearly. A popular method is first to segment and classify point clouds, after which the identified segments should be clustered according to their corresponding objects, such as walls and clutter. To perform this process, a major problem is low-quality point clouds that are noisy, cluttered and that contain missing parts in the… More >

  • Open Access

    ARTICLE

    Design of Network Cascade Structure for Image Super-Resolution

    Jianwei Zhang, Zhenxing Wang, Yuhui Zheng, Guoqing Zhang*

    Journal of New Media, Vol.3, No.1, pp. 29-39, 2021, DOI:10.32604/jnm.2021.018383 - 15 March 2021

    Abstract Image super resolution is an important field of computer research. The current mainstream image super-resolution technology is to use deep learning to mine the deeper features of the image, and then use it for image restoration. However, most of these models mentioned above only trained the images in a specific scale and do not consider the relationships between different scales of images. In order to utilize the information of images at different scales, we design a cascade network structure and cascaded super-resolution convolutional neural networks. This network contains three cascaded FSRCNNs. Due to each sub More >

  • Open Access

    ARTICLE

    Weed Recognition for Depthwise Separable Network Based on Transfer Learning

    Yanlei Xu1, Yuting Zhai1, Bin Zhao1, Yubin Jiao2, ShuoLin Kong1, Yang Zhou1,*, Zongmei Gao3

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 669-682, 2021, DOI:10.32604/iasc.2021.015225 - 01 March 2021

    Abstract For improving the accuracy of weed recognition under complex field conditions, a weed recognition method using depthwise separable convolutional neural network based on deep transfer learning was proposed in this study. To improve the model classification accuracy, the Xception model was refined by using model transferring and fine-tuning. Specifically, the weight parameters trained by ImageNet data set were transferred to the Xception model. Then a global average pooling layer replaced the full connection layer of the Xception model. Finally, the XGBoost classifier was added to the top layer of the model to output results. The… More >

Displaying 461-470 on page 47 of 547. Per Page