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  • Open Access

    ARTICLE

    An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

    Shabana Habib1, Noreen Fayyaz Khan2,*

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3321-3335, 2021, DOI:10.32604/cmc.2021.015504 - 24 August 2021

    Abstract Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used… More >

  • Open Access

    ARTICLE

    Improving Stock Price Forecasting Using a Large Volume of News Headline Text

    Daxing Zhang1,*, Erguan Cai2

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3931-3943, 2021, DOI:10.32604/cmc.2021.012302 - 24 August 2021

    Abstract Previous research in the area of using deep learning algorithms to forecast stock prices was focused on news headlines, company reports, and a mix of daily stock fundamentals, but few studies achieved excellent results. This study uses a convolutional neural network (CNN) to predict stock prices by considering a great amount of data, consisting of financial news headlines. We call our model N-CNN to distinguish it from a CNN. The main concept is to narrow the diversity of specific stock prices as they are impacted by news headlines, then horizontally expand the news headline data… More >

  • Open Access

    ARTICLE

    Adversarial Examples Generation Algorithm through DCGAN

    Biying Deng1, Ziyong Ran1, Jixin Chen1, Desheng Zheng1,*, Qiao Yang2, Lulu Tian3

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 889-898, 2021, DOI:10.32604/iasc.2021.019727 - 20 August 2021

    Abstract In recent years, due to the popularization of deep learning technology, more and more attention has been paid to the security of deep neural networks. A wide variety of machine learning algorithms can attack neural networks and make its classification and judgement of target samples wrong. However, the previous attack algorithms are based on the calculation of the corresponding model to generate unique adversarial examples, and cannot extract attack features and generate corresponding samples in batches. In this paper, Generative Adversarial Networks (GAN) is used to learn the distribution of adversarial examples generated by FGSM More >

  • Open Access

    ARTICLE

    Performance Comparison of PoseNet Models on an AIoT Edge Device

    Min-Jun Kim1, Seng-Phil Hong2, Mingoo Kang1, Jeongwook Seo1,*

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 743-753, 2021, DOI:10.32604/iasc.2021.019329 - 20 August 2021

    Abstract In this paper, we present an oneM2M-compliant system including an artificial intelligence of things (AIoT) edge device whose principal function is to estimate human poses by using two PoseNet models built on MobileNet v1 and ResNet-50 backbone architectures. Although MobileNet v1 is generally known to be much faster but less accurate than ResNet50, it is necessary to analyze the performances of whole PoseNet models carefully and select one of them suitable for the AIoT edge device. For this reason, we first investigate the computational complexity of the models about their neural network layers and parameters… More >

  • Open Access

    ARTICLE

    Predicting the Breed of Dogs and Cats with Fine-Tuned Keras Applications

    I.-Hung Wang1, Mahardi2, Kuang-Chyi Lee2,*, Shinn-Liang Chang1

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 995-1005, 2021, DOI:10.32604/iasc.2021.019020 - 20 August 2021

    Abstract The images classification is one of the most common applications of deep learning. Images of dogs and cats are mostly used as examples for image classification models, as they are relatively easy for the human eyes to recognize. However, classifying the breed of a dog or a cat has its own complexity. In this paper, a fine-tuned pre-trained model of a Keras’ application was built with a new dataset of dogs and cats to predict the breed of identified dogs or cats. Keras applications are deep learning models, which have been previously trained with general More >

  • Open Access

    ARTICLE

    Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

    Jiaming Mao1,*, Mingming Zhang1, Mu Chen2, Lu Chen2, Fei Xia1, Lei Fan1, ZiXuan Wang3, Wenbing Zhao4

    Computer Systems Science and Engineering, Vol.39, No.3, pp. 373-390, 2021, DOI:10.32604/csse.2021.018086 - 12 August 2021

    Abstract The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning… More >

  • Open Access

    ARTICLE

    Morphological Feature Aware Multi-CNN Model for Multilingual Text Recognition

    Yujie Zhou1, Jin Liu1,*, Yurong Xie1, Y. Ken Wang2

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 715-733, 2021, DOI:10.32604/iasc.2021.020184 - 11 August 2021

    Abstract Text recognition is a crucial and challenging task, which aims at translating a cropped text instance image into a target string sequence. Recently, Convolutional neural networks (CNN) have been widely used in text recognition tasks as it can effectively capture semantic and structural information in text. However, most existing methods are usually based on contextual clues. If only recognize a single character, the accuracy of these approaches can be reduced. For example, it is difficult to distinguish 0 and O in the traditional CNN network because they are very similar in composition and structure. To… More >

  • Open Access

    ARTICLE

    Method of Bidirectional LSTM Modelling for the Atmospheric Temperature

    Shuo Liang1, Dingcheng Wang1,*, Jingrong Wu1, Rui Wang1, Ruiqi Wang2

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 701-714, 2021, DOI:10.32604/iasc.2021.020010 - 11 August 2021

    Abstract Atmospheric temperature forecast plays an important role in weather forecast and has a significant impact on human daily and economic life. However, due to the complexity and uncertainty of the atmospheric system, exploring advanced forecasting methods to improve the accuracy of meteorological prediction has always been a research topic for scientists. With the continuous improvement of computer performance and data acquisition technology, meteorological data has gained explosive growth, which creates the necessary hardware support conditions for more accurate weather forecast. The more accurate forecast results need advanced weather forecast methods suitable for hardware. Therefore, this… More >

  • Open Access

    ARTICLE

    CT Segmentation of Liver and Tumors Fused Multi-Scale Features

    Aihong Yu1, Zhe Liu1,*, Victor S. Sheng2, Yuqing Song1, Xuesheng Liu3, Chongya Ma4, Wenqiang Wang1, Cong Ma1

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 589-599, 2021, DOI:10.32604/iasc.2021.019513 - 11 August 2021

    Abstract Liver cancer is one of frequent causes of death from malignancy in the world. Owing to the outstanding advantages of computer-aided diagnosis and deep learning, fully automatic segmentation of computed tomography (CT) images turned into a research hotspot over the years. The liver has quite low contrast with the surrounding tissues, together with its lesion areas are thoroughly complex. To deal with these problems, we proposed effective methods for enhancing features and processed public datasets from Liver Tumor Segmentation Challenge (LITS) for the verification. In this experiment, data pre-processing based on the image enhancement and… More >

  • Open Access

    ARTICLE

    A Step-Based Deep Learning Approach for Network Intrusion Detection

    Yanyan Zhang1, Xiangjin Ran2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 1231-1245, 2021, DOI:10.32604/cmes.2021.016866 - 11 August 2021

    Abstract In the network security field, the network intrusion detection system (NIDS) is considered one of the critical issues in the detection accuracy and missed detection rate. In this paper, a method of two-step network intrusion detection on the basis of GoogLeNet Inception and deep convolutional neural networks (CNNs) models is proposed. The proposed method used the GoogLeNet Inception model to identify the network packets’ binary problem. Subsequently, the characteristics of the packets’ raw data and the traffic features are extracted. The CNNs model is also used to identify the multiclass intrusions by the network packets’ More >

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