JAIOpen Access

Journal on Artificial Intelligence

ISSN:2579-0021(print)
ISSN:2579-003X(online)
Publication Frequency:Quarterly

  • Online
    Articles

    57

  • on board
    editors

    12


About the Journal

Artificial Intelligence (AI) techniques have been attracted increasing attention around the world and are now being widely used to solve a whole range of hitherto intractable problems. This journal welcomes foundational and applied papers describing mature work involving AI methods.

  • Open Access

    ARTICLE

    Online First

    Experimental Study on Permeability Characteristics of Geotubes for Seepage Analysis on Safety Assessment of Dams

    Xiaolei Man1,*, Ganggang Sha2, Shuigen Hu1, Hui Bao1, Guangying Liu1
    Journal on Artificial Intelligence, DOI:10.32604/sdhm.2020.013001
    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Online First

    Research on Concrete Beam Crack Recognition Algorithm Based on Block Threshold Value Image Processing

    Wenting Qiao1,2, Xiaoguang Wu1,*, Wen Sun3, Qiande Wu4,*
    Journal on Artificial Intelligence, DOI:10.32604/sdhm.2020.011479
    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Online First

    A Deep Learning Based Approach for Response Prediction of Beam-like Structures

    Tianyu Wang1, Wael A. Altabey1,2, Mohammad Noori3,*, Ramin Ghiasi1
    Journal on Artificial Intelligence, DOI:10.32604/sdhm.2020.011083
    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Online First

    Damage Detection for CFRP Based on Planar Electrical Capacitance Tomography

    Wenru Fan, Chi Wang*
    Journal on Artificial Intelligence, DOI:10.32604/sdhm.2020.011009
    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Online First

    Ambient Vibration Testings and Field Investigations of Two Historical Buildings in Europe

    Ehsan Noroozinejad Farsangi1,*, Aleksandra Bogdanovic2, Zoran Rakicevic2, Angela Poposka2, Marta Stojmanovska2
    Journal on Artificial Intelligence, DOI:10.32604/sdhm.2020.010564
    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Applications Classification of VPN Encryption Tunnel Based on SAE-2dCNN Model

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 133-142, 2022, DOI:10.32604/jai.2022.031800
    Abstract How to quickly and accurately identify applications in VPN encrypted tunnels is a difficult technique. Traditional technologies such as DPI can no longer identify applications in VPN encrypted tunnel. Various VPN protocols make the feature engineering of machine learning extremely difficult. Deep learning has the advantages that feature extraction does not rely on manual labor and has a good early application in classification. This article uses deep learning technology to classify the applications of VPN encryption tunnel based on the SAE-2dCNN model. SAE can effectively reduce the dimensionality of the data, which not only improves the training efficiency of 2dCNN,… More >

  • Open Access

    ARTICLE

    Research on Early Warning of Customer Churn Based on Random Forest

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 143-154, 2022, DOI:10.32604/jai.2022.031843
    Abstract With the rapid development of interest rate market and big data, the banking industry has shown the obvious phenomenon of “two or eight law”, 20% of the high quality customers occupy most of the bank’s assets, how to prevent the loss of bank credit card customers has become a growing concern for banks. Therefore, it is particularly important to establish a customer churn early warning model. In this paper, we will use the random forest method to establish a customer churn early warning model, focusing on the churn of bank credit card customers and predicting the possibility of future churn… More >

  • Open Access

    ARTICLE

    Evaluating Neural Dialogue Systems Using Deep Learning and Conversation History

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 155-165, 2022, DOI:10.32604/jai.2022.032390
    Abstract Neural talk models play a leading role in the growing popular building of conversational managers. A commonplace criticism of those systems is that they seldom understand or use the conversation data efficiently. The development of profound concentration on innovations has increased the use of neural models for a discussion display. In recent years, deep learning (DL) models have achieved significant success in various tasks, and many dialogue systems are also employing DL techniques. The primary issues involved in the generation of the dialogue system are acquiring perspectives into instinctual linguistics, comprehension provision, and conversation assessment. In this paper, we mainly… More >

  • Open Access

    ARTICLE

    X-ray Based COVID-19 Classification Using Lightweight EfficientNet

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 167-187, 2022, DOI:10.32604/jai.2022.032974
    Abstract The world has been suffering from the Coronavirus (COVID-19) pandemic since its appearance in late 2019. COVID-19 spread has led to a drastic increase of the number of infected people and deaths worldwide. Imminent and accurate diagnosis of positive cases emerged as a natural alternative to reduce the number of serious infections and limit the spread of the disease. In this paper, we proposed an X-ray based COVID-19 classification system that aims at diagnosing positive COVID-19 cases. Specifically, we adapted lightweight versions of EfficientNet as backbone of the proposed recognition system. Particularly, lightweight EfficientNet networks were used to build classification… More >

  • Open Access

    ARTICLE

    Ensemble Classifier-Based Features Ranking on Employee Attrition

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 189-199, 2022, DOI:10.32604/jai.2022.034064
    Abstract The departure of good employee incurs direct and indirect cost and impacts for an organization. The direct cost arises from hiring to training of the relevant employee. The replacement time and lost productivity affect the running of business processes. This work presents the use of ensemble classifier to identify important attributes that affects attrition significantly. The data consists of attributes related to job function, education level, satisfaction towards work and working relationship, compensation, and frequency of business travel. Both bagging and boosting classifiers were used for testing. The results show that the selected features (nine selected features) achieve the same… More >

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