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  • Open Access

    ARTICLE

    Stock-Price Forecasting Based on XGBoost and LSTM

    Pham Hoang Vuong1, Trinh Tan Dat1, Tieu Khoi Mai1, Pham Hoang Uyen2, Pham The Bao1,*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 237-246, 2022, DOI:10.32604/csse.2022.017685 - 26 August 2021

    Abstract Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and… More >

  • Open Access

    ARTICLE

    Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

    A. Thasil Mohamed, Sundar Santhoshkumar*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 191-205, 2022, DOI:10.32604/csse.2022.016754 - 26 August 2021

    Abstract Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases… More >

  • Open Access

    ARTICLE

    Solving the Feature Diversity Problem Based on Multi-Model Scheme

    Guanghao Jin1, Na Zhao1, Chunmei Pei1, Hengguang Li2, Qingzeng Song3, Jing Yu1,*

    Journal on Artificial Intelligence, Vol.3, No.4, pp. 135-143, 2021, DOI:10.32604/jai.2021.027154 - 07 February 2022

    Abstract Generally, the performance of deep learning models is related to the captured features of training samples. When the training samples belong to different domains, the diverse features may increase the difficulty of training high performance models. In this paper, we built a new framework that generates multiple models on the organized samples to increase the accuracy of classification. Firstly, our framework selects some existing models and trains each of them on organized training sets to get multiple trained models. Secondly, we select some of them based on a validation set. Finally, we use some fusion More >

  • Open Access

    ARTICLE

    A Deep Learning Breast Cancer Prediction Framework

    Asmaa E. E. Ali*, Mofreh Mohamed Salem, Mahmoud Badway, Ali I. EL Desouky

    Journal on Artificial Intelligence, Vol.3, No.3, pp. 81-96, 2021, DOI:10.32604/jai.2021.022433 - 25 January 2022

    Abstract Breast cancer (BrC) is now the world’s leading cause of death for women. Early detection and effective treatment of this disease are the only rescues to reduce BrC mortality. The prediction of BrC diseases is very difficult because it is not an individual disease but a mixture of various diseases. Many researchers have used different techniques such as classification, Machine Learning (ML), and Deep Learning (DL) of the prediction of the breast tumor into Benign and Malignant. However, still there is a scope to introduce appropriate techniques for developing and implementing a more effective diagnosis… More >

  • Open Access

    ARTICLE

    Implementation of Art Pictures Style Conversion with GAN

    Xinlong Wu1, Desheng Zheng1,*, Kexin Zhang1, Yanling Lai1, Zhifeng Liu1, Zhihong Zhang2

    Journal of Quantum Computing, Vol.3, No.4, pp. 127-136, 2021, DOI:10.32604/jqc.2021.017251 - 10 January 2022

    Abstract Image conversion refers to converting an image from one style to another and ensuring that the content of the image remains unchanged. Using Generative Adversarial Networks (GAN) for image conversion can achieve good results. However, if there are enough samples, any image in the target domain can be mapped to the same set of inputs. On this basis, the Cycle Consistency Generative Adversarial Network (CycleGAN) was developed. This article verifies and discusses the advantages and disadvantages of the CycleGAN model in image style conversion. CycleGAN uses two generator networks and two discriminator networks. The purpose… More >

  • Open Access

    ARTICLE

    Incomplete Image Completion through GAN

    Biying Deng1 , Desheng Zheng1, *, Zhifeng Liu1 , Yanling Lai1, Zhihong Zhang2

    Journal of Quantum Computing, Vol.3, No.3, pp. 119-126, 2021, DOI:10.32604/jqc.2021.017250 - 21 December 2021

    Abstract There are two difficult in the existing image restoration methods. One is that the method is difficult to repair the image with a large damaged, the other is the result of image completion is not good and the speed is slow. With the development and application of deep learning, the image repair algorithm based on generative adversarial networks can repair images by simulating the distribution of data. In the process of image completion, the first step is trained the generator to simulate data distribution and generate samples. Then a large number of falsified images More >

  • Open Access

    ARTICLE

    A Hybrid Intrusion Detection Model Based on Spatiotemporal Features

    Linbei Wang1 , Zaoyu Tao1, Lina Wang2,*, Yongjun Ren3

    Journal of Quantum Computing, Vol.3, No.3, pp. 107-118, 2021, DOI:10.32604/jqc.2021.016857 - 21 December 2021

    Abstract With the accelerating process of social informatization, our personal information security and Internet sites, etc., have been facing a series of threats and challenges. Recently, well-developed neural network has seen great advancement in natural language processing and computer vision, which is also adopted in intrusion detection. In this research, a hybrid model integrating MultiScale Convolutional Neural Network and Long Short-term Memory Network (MSCNN-LSTM) is designed to conduct the intrusion detection. Multi-Scale Convolutional Neural Network (MSCNN) is used to extract the spatial characteristics of data sets. And Long Short-term Memory Network (LSTM) is responsible for processing More >

  • Open Access

    ARTICLE

    CTSF: An End-to-End Efficient Neural Network for Chinese Text with Skeleton Feature

    Hengyang Wang, Jin Liu*, Haoliang Ren

    Journal on Big Data, Vol.3, No.3, pp. 119-126, 2021, DOI:10.32604/jbd.2021.017184 - 22 November 2021

    Abstract The past decade has seen the rapid development of text detection based on deep learning. However, current methods of Chinese character detection and recognition have proven to be poor. The accuracy of segmenting text boxes in natural scenes is not impressive. The reasons for this strait can be summarized into two points: the complexity of natural scenes and numerous types of Chinese characters. In response to these problems, we proposed a lightweight neural network architecture named CTSF. It consists of two modules, one is a text detection network that combines CTPN and the image feature More >

  • Open Access

    REVIEW

    Review of Unsupervised Person Re-Identification

    Yang Dai*, Zhiyuan Luo

    Journal of New Media, Vol.3, No.4, pp. 129-136, 2021, DOI:10.32604/jnm.2021.023981 - 05 November 2021

    Abstract Person re-identification (re-ID) aims to match images of the same pedestrian across different cameras. It plays an important role in the field of security and surveillance. Although it has been studied for many years, it is still considered as an unsolved problem. Since the rise of deep learning, the accuracy of supervised person re-ID on public datasets has reached the highest level. However, these methods are difficult to apply to real-life scenarios because a large number of labeled training data is required in this situation. Pedestrian identity labeling, especially cross-camera pedestrian identity labeling, is heavy More >

  • Open Access

    ARTICLE

    Traffic Flow Statistics Method Based on Deep Learning and Multi-Feature Fusion

    Liang Mu, Hong Zhao*, Yan Li, Xiaotong Liu, Junzheng Qiu, Chuanlong Sun

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 465-483, 2021, DOI:10.32604/cmes.2021.017276 - 08 October 2021

    Abstract Traffic flow statistics have become a particularly important part of intelligent transportation. To solve the problems of low real-time robustness and accuracy in traffic flow statistics. In the DeepSort tracking algorithm, the Kalman filter (KF), which is only suitable for linear problems, is replaced by the extended Kalman filter (EKF), which can effectively solve nonlinear problems and integrate the Histogram of Oriented Gradient (HOG) of the target. The multi-target tracking framework was constructed with YOLO V5 target detection algorithm. An efficient and long-running Traffic Flow Statistical framework (TFSF) is established based on the tracking framework.… More >

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