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  • Open Access

    ARTICLE

    Cyclic Autoencoder for Multimodal Data Alignment Using Custom Datasets

    Zhenyu Tang1, Jin Liu1,*, Chao Yu1, Y. Ken Wang2

    Computer Systems Science and Engineering, Vol.39, No.1, pp. 37-54, 2021, DOI:10.32604/csse.2021.017230 - 10 June 2021

    Abstract The subtitle recognition under multimodal data fusion in this paper aims to recognize text lines from image and audio data. Most existing multimodal fusion methods tend to be associated with pre-fusion as well as post-fusion, which is not reasonable and difficult to interpret. We believe that fusing images and audio before the decision layer, i.e., intermediate fusion, to take advantage of the complementary multimodal data, will benefit text line recognition. To this end, we propose: (i) a novel cyclic autoencoder based on convolutional neural network. The feature dimensions of the two modal data are aligned… More >

  • Open Access

    ARTICLE

    Algorithm of Helmet Wearing Detection Based on AT-YOLO Deep Mode

    Qingyang Zhou1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Neal N. Xiong2

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 159-174, 2021, DOI:10.32604/cmc.2021.017480 - 04 June 2021

    Abstract The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements, but they can’t accurately detect small objects and objects with obstructions. Therefore, we propose a helmet detection algorithm based on the attention mechanism (AT-YOLO). First of all, a channel attention module is added to the YOLOv3 backbone network, which can adaptively calibrate the channel features of the direction to improve the feature utilization, and a spatial attention module is added to the neck of the YOLOv3 network to capture the correlation… More >

  • Open Access

    ARTICLE

    An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

    Ruichao Zhang1,*, Liqiang Wang1, Dechun Chen2

    Energy Engineering, Vol.118, No.4, pp. 1069-1082, 2021, DOI:10.32604/EE.2021.014961 - 31 May 2021

    Abstract In recent years, deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields. In the diagnosis of sucker-rod pump working conditions, due to the lack of a large-scale dynamometer card data set, the advantages of a deep convolutional neural network are not well reflected, and its application is limited. Therefore, this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning, which is used to solve the problem of too few samples in a… More >

  • Open Access

    ARTICLE

    PotholeEye+: Deep-Learning Based Pavement Distress Detection System toward Smart Maintenance

    Juyoung Park1,*, Jung Hee Lee1, Junseong Bang2,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 965-976, 2021, DOI:10.32604/cmes.2021.014669 - 24 May 2021

    Abstract

    We propose a mobile system, called PotholeEye+, for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video. PotholeEye+ pre-processes the images, extracts features, and classifies the distress into a variety of types, while the road manager is driving. Every day for a year, we have tested PotholeEye+ on real highway involving real settings, a camera, a mini computer, a GPS receiver, and so on. Consequently, PotholeEye+ detected the pavement distress with accuracy of 92%, precision of 87% and recall 74% averagely during driving at an average speed of

    More >

  • Open Access

    ARTICLE

    Leveraging Convolutional Neural Network for COVID-19 Disease Detection Using CT Scan Images

    Mehedi Masud*, Mohammad Dahman Alshehri, Roobaea Alroobaea, Mohammad Shorfuzzaman

    Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 1-13, 2021, DOI:10.32604/iasc.2021.016800 - 12 May 2021

    Abstract In 2020, the world faced an unprecedented pandemic outbreak of coronavirus disease (COVID-19), which causes severe threats to patients suffering from diabetes, kidney problems, and heart problems. A rapid testing mechanism is a primary obstacle to controlling the spread of COVID-19. Current tests focus on the reverse transcription-polymerase chain reaction (RT-PCR). The PCR test takes around 4–6 h to identify COVID-19 patients. Various research has recommended AI-based models leveraging machine learning, deep learning, and neural networks to classify COVID-19 and non-COVID patients from chest X-ray and computerized tomography (CT) scan images. However, no model can… More >

  • Open Access

    ARTICLE

    Hybrid Efficient Convolution Operators for Visual Tracking

    Yu Wang*

    Journal on Artificial Intelligence, Vol.3, No.2, pp. 63-72, 2021, DOI:10.32604/jai.2021.010455 - 08 May 2021

    Abstract Visual tracking is a classical computer vision problem with many applications. Efficient convolution operators (ECO) is one of the most outstanding visual tracking algorithms in recent years, it has shown great performance using discriminative correlation filter (DCF) together with HOG, color maps and VGGNet features. Inspired by new deep learning models, this paper propose a hybrid efficient convolution operators integrating fully convolution network (FCN) and residual network (ResNet) for visual tracking, where FCN and ResNet are introduced in our proposed method to segment the objects from backgrounds and extract hierarchical feature maps of objects, respectively. More >

  • Open Access

    ARTICLE

    Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network

    Sajid Habib Gill1, Noor Ahmed Sheikh1, Samina Rajpar1, Zain ul Abidin2, N. Z. Jhanjhi3,*, Muneer Ahmad4, Mirza Abdur Razzaq1, Sultan S. Alshamrani5, Yasir Malik6, Fehmi Jaafar7

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3773-3787, 2021, DOI:10.32604/cmc.2021.016001 - 06 May 2021

    Abstract Medical data tampering has become one of the main challenges in the field of secure-aware medical data processing. Forgery of normal patients’ medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways recently. Therefore, the integrity of these data can be questionable. Forgery detection is a method of detecting an anomaly in manipulated forged data. An appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original data. Convolutional neural… More >

  • Open Access

    ARTICLE

    Deep Learning for Object Detection: A Survey

    Jun Wang1, Tingjuan Zhang2,*, Yong Cheng3, Najla Al-Nabhan4

    Computer Systems Science and Engineering, Vol.38, No.2, pp. 165-182, 2021, DOI:10.32604/csse.2021.017016 - 23 April 2021

    Abstract Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people s life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning algorithms for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of target detection, a comprehensive literature review of target detection and an overall discussion of the works closely related to it are More >

  • Open Access

    ARTICLE

    Classification of Domestic Refuse in Medical Institutions Based on Transfer Learning and Convolutional Neural Network

    Dequan Guo1, Qiao Yang2, Yu-Dong Zhang3, Tao Jiang1, Hanbing Yan1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 599-620, 2021, DOI:10.32604/cmes.2021.014119 - 19 April 2021

    Abstract The problem of domestic refuse is becoming more and more serious with the use of all kinds of equipment in medical institutions. This matter arouses people’s attention. Traditional artificial waste classification is subjective and cannot be put accurately; moreover, the working environment of sorting is poor and the efficiency is low. Therefore, automated and effective sorting is needed. In view of the current development of deep learning, it can provide a good auxiliary role for classification and realize automatic classification. In this paper, the ResNet-50 convolutional neural network based on the transfer learning method is More >

  • Open Access

    ARTICLE

    A Novel Technique for Early Detection of COVID-19

    Mohammad Yamin1,*, Adnan Ahmed Abi Sen2, Zenah Mahmoud AlKubaisy1, Rahaf Almarzouki1

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2283-2298, 2021, DOI:10.32604/cmc.2021.017433 - 13 April 2021

    Abstract COVID-19 is a global pandemic disease, which results from a dangerous coronavirus attack, and spreads aggressively through close contacts with infected people and artifacts. So far, there is not any prescribed line of treatment for COVID-19 patients. Measures to control the disease are very limited, partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease. Early detection of positive cases is critical in preventing further spread, achieving the herd immunity, and saving lives. Unfortunately, so far we do not have effective toolkits to diagnose… More >

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