Home / Journals / JAI / Vol.3, No.3, 2021
  • 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
    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 system. This paper proposes a… More >

  • Open Access

    ARTICLE

    A Study on Technological Dynamics in Structural Health Monitoring Using Intelligent Fault Diagnosis Techniques: A Patent-Based Approach

    Saqlain Abbas1,2,*, Zulkarnain Abbas3, Xiaotong Tu4, Yanping Zhu1
    Journal on Artificial Intelligence, Vol.3, No.3, pp. 97-113, 2021, DOI:10.32604/jai.2021.023020
    Abstract The performance and reliability of structural components are greatly influenced by the presence of any abnormality in them. To this purpose, structural health monitoring (SHM) is recognized as a necessary tool to ensure the safety precautions and efficiency of both mechanical and civil infrastructures. Till now, most of the previous work has emphasized the functioning of several SHM techniques and systematic changes in SHM execution. However, there exist insufficient data in the literature regarding the patent-based technological developments in the SHM research domain which might be a useful source of detailed information for worldwide research institutes. To address this research… More >

  • Open Access

    ARTICLE

    Churn Prediction Model of Telecom Users Based on XGBoost

    Hao Chen*, Qian Tang, Yifei Wei, Mei Song
    Journal on Artificial Intelligence, Vol.3, No.3, pp. 115-121, 2021, DOI:10.32604/jai.2021.026851
    Abstract As the cost of accessing a telecom operator’s network continues to decrease, user churn after arrears occurred repeatedly, which has brought huge economic losses to operators and reminded them that it is significant to identify users who are likely to churn in advance. Machine learning can form a series of judgment rules by summarizing a large amount of data, and telecom user data naturally has the advantage of user scale, which can provide data support for learning algorithms. XGBoost is an improved gradient boosting algorithm, and in this paper, we explore how to use the algorithm to train an efficient… More >

  • Open Access

    ARTICLE

    Facial Expression Recognition Based on the Fusion of Infrared and Visible Image

    Jiancheng Zou1, Jiaxin Li1,*, Juncun Wei1, Zhengzheng Li1, Xin Yang2
    Journal on Artificial Intelligence, Vol.3, No.3, pp. 123-134, 2021, DOI:10.32604/jai.2021.027069
    Abstract Facial expression recognition is a research hot spot in the fields of computer vision and pattern recognition. However, the existing facial expression recognition models are mainly concentrated in the visible light environment. They have insufficient generalization ability and low recognition accuracy, and are vulnerable to environmental changes such as illumination and distance. In order to solve these problems, we combine the advantages of the infrared and visible images captured simultaneously by array equipment our developed with two infrared and two visible lens, so that the fused image not only has the texture information of visible image, but also has the… More >

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