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  • Open Access

    ARTICLE

    Task-Oriented Battlefield Situation Information Hybrid Recommendation Model

    Chunhua Zhou*, Jianjing Shen, Xiaofeng Guo, Zhenyu Zhou

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 127-141, 2021, DOI:10.32604/iasc.2021.012532

    Abstract In the process of interaction between users and battlefield situation information, combat tasks are the key factors that affect users’ information selection. In order to solve the problems of battlefield situation information recommendation (BSIR) for combat tasks, we propose a task-oriented battlefield situation information hybrid recommendation model (TBSI-HRM) based on tensor factorization and deep learning. In the model, in order to achieve high-precision personalized recommendations, we use Tensor Factorization (TF) to extract correlation relations and features from historical interaction data, and use Deep Neural Network (DNN) to learn hidden feature vectors of users, battlefield situation information and combat tasks from… More >

  • Open Access

    ARTICLE

    A Quantum Spatial Graph Convolutional Network for Text Classification

    Syed Mustajar Ahmad Shah1, Hongwei Ge1,*, Sami Ahmed Haider2, Muhammad Irshad3, Sohail M. Noman4, Jehangir Arshad5, Asfandeyar Ahmad6, Talha Younas7

    Computer Systems Science and Engineering, Vol.36, No.2, pp. 369-382, 2021, DOI:10.32604/csse.2021.014234

    Abstract The data generated from non-Euclidean domains and its graphical representation (with complex-relationship object interdependence) applications has observed an exponential growth. The sophistication of graph data has posed consequential obstacles to the existing machine learning algorithms. In this study, we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph data. Additionally, the quantum information theory has been applied through Graph Neural Networks (GNNs) to generate Riemannian metrics in closed-form of several graph layers. In further, to pre-process the adjacency matrix of graphs, a new… More >

  • Open Access

    ARTICLE

    Vehicle License Plate Recognition System Based on Deep Learning in Natural Scene

    Ze Chen, Leiming Yan*, Siran Yin, Yuanmin Shi

    Journal on Artificial Intelligence, Vol.2, No.4, pp. 167-175, 2020, DOI:10.32604/jai.2020.012716

    Abstract With the popularity of intelligent transportation system, license plate recognition system has been widely used in the management of vehicles in and out of closed communities. But in the natural environment such as video monitoring, the performance and accuracy of recognition are not ideal. In this paper, the improved Alex net convolution neural network is used to remove the false license plate in a large range of suspected license plate areas, and then the projection transformation and Hough transformation are used to correct the inclined license plate, so as to build an efficient license plate recognition system in natural environment.… More >

  • Open Access

    ARTICLE

    A Survey of Knowledge Based Question Answering with Deep Learning

    Chaoyu Deng, Guangfu Zeng, Zhiping Cai, Xiaoqiang Xiao*

    Journal on Artificial Intelligence, Vol.2, No.4, pp. 157-166, 2020, DOI:10.32604/jai.2020.011541

    Abstract The purpose of automated question answering is to let the machine understand natural language questions and give accurate answers in the form of natural language. This technology requires the machine to store a large amount of background knowledge. In recent years, the rapid development of knowledge graph has made the knowledge based question answering (KBQA) more and more popular. Traditional styles of KBQA methods mainly include semantic parsing, information extraction and vector modeling. With the development of deep learning, KBQA with deep learning has gradually become the mainstream method. This paper introduces the application of deep learning in KBQA mainly… More >

  • Open Access

    ARTICLE

    Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

    Hyun Kyu Shin1, Si Woon Lee2, Goo Pyo Hong3, Lee Sael2, Sang Hyo Lee4, Ha Young Kim5,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2493-2507, 2021, DOI:10.32604/cmc.2021.014170

    Abstract The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures. However, conventional manpower-based inspection methods not only incur considerable cost and time, but also cause frequent disputes regarding defects owing to poor inspections. Therefore, the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent, as the reduction in maintenance costs is significant from a long-term perspective. Thus, this paper proposes a deep learning-based image object-identification method to detect the defects of paint peeling, leakage peeling, and leakage traces that… More >

  • Open Access

    ARTICLE

    An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection

    Sitara Afzal1, Muazzam Maqsood1,*, Irfan Mehmood2, Muhammad Tabish Niaz3, Sanghyun Seo4

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2301-2315, 2021, DOI:10.32604/cmc.2021.013966

    Abstract Cerebral Microbleeds (CMBs) are microhemorrhages caused by certain abnormalities of brain vessels. CMBs can be found in people with Traumatic Brain Injury (TBI), Alzheimer’s disease, and in old individuals having a brain injury. Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke. The CMBs seriously impact individuals’ life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life. The existing work report good results but often ignores false-positive’s perspective for this research area. In this paper, an efficient approach is… More >

  • Open Access

    ARTICLE

    Classification of Positive COVID-19 CT Scans Using Deep Learning

    Muhammad Attique Khan1, Nazar Hussain1, Abdul Majid1, Majed Alhaisoni2, Syed Ahmad Chan Bukhari3, Seifedine Kadry4, Yunyoung Nam5,*, Yu-Dong Zhang6

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2923-2938, 2021, DOI:10.32604/cmc.2021.013191

    Abstract In medical imaging, computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis. In response to the coronavirus 2019 (COVID-19) pandemic, new testing procedures, medical treatments, and vaccines are being developed rapidly. One potential diagnostic tool is a reverse-transcription polymerase chain reaction (RT-PCR). RT-PCR, typically a time-consuming process, was less sensitive to COVID-19 recognition in the disease’s early stages. Here we introduce an optimized deep learning (DL) scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography (CT) scans. In the proposed method, contrast enhancement is used to… More >

  • Open Access

    ARTICLE

    Deep Learning in DXA Image Segmentation

    Dildar Hussain1, Rizwan Ali Naqvi2, Woong-Kee Loh3, Jooyoung Lee1,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2587-2598, 2021, DOI:10.32604/cmc.2021.013031

    Abstract Many existing techniques to acquire dual-energy X-ray absorptiometry (DXA) images are unable to accurately distinguish between bone and soft tissue. For the most part, this failure stems from bone shape variability, noise and low contrast in DXA images, inconsistent X-ray beam penetration producing shadowing effects, and person-to-person variations. This work explores the feasibility of using state-of-the-art deep learning semantic segmentation models, fully convolutional networks (FCNs), SegNet, and U-Net to distinguish femur bone from soft tissue. We investigated the performance of deep learning algorithms with reference to some of our previously applied conventional image segmentation techniques (i.e., a decision-tree-based method using… More >

  • Open Access

    ARTICLE

    Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data

    Phong Thanh Nguyen1, Vy Dang Bich Huynh2, Khoa Dang Vo1, Phuong Thanh Phan1, Mohamed Elhoseny3, Dac-Nhuong Le4,5,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2555-2571, 2021, DOI:10.32604/cmc.2021.012941

    Abstract Data fusion is a multidisciplinary research area that involves different domains. It is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare sources. The generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become essential. Securing medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems (IDS). In this regard, since singular-modality is not adequate to attain high detection rate, there is a need exists to merge diverse techniques using… More >

  • Open Access

    ARTICLE

    An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy

    Phong Thanh Nguyen1, Vy Dang Bich Huynh2, Khoa Dang Vo1, Phuong Thanh Phan1, Eunmok Yang3,*, Gyanendra Prasad Joshi4

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2815-2830, 2021, DOI:10.32604/cmc.2021.012315

    Abstract Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification. The proposed model initially involves… More >

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