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  • Open Access

    ARTICLE

    The Cloud Manufacturing Resource Scheduling Optimization Method Based on Game Theory

    Xiaoxuan Yang*, Zhou Fang

    Journal on Artificial Intelligence, Vol.4, No.4, pp. 229-243, 2022, DOI:10.32604/jai.2022.035368

    Abstract In order to optimize resource integration and optimal scheduling problems in the cloud manufacturing environment, this paper proposes to use load balancing, service cost and service quality as optimization goals for resource scheduling, however, resource providers have resource utilization requirements for cloud manufacturing platforms. In the process of resource optimization scheduling, the interests of all parties have conflicts of interest, which makes it impossible to obtain better optimization results for resource scheduling. Therefore, a multithreaded auto-negotiation method based on the Stackelberg game is proposed to resolve conflicts of interest in the process of resource scheduling. The cloud manufacturing platform first… More >

  • Open Access

    ARTICLE

    ResCD-FCN: Semantic Scene Change Detection Using Deep Neural Networks

    S. Eliza Femi Sherley1,*, J. M. Karthikeyan1, N. Bharath Raj1, R. Prabakaran2, A. Abinaya1, S. V. V. Lakshmi3

    Journal on Artificial Intelligence, Vol.4, No.4, pp. 215-227, 2022, DOI:10.32604/jai.2022.034931

    Abstract Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic (labels/categories) details before and after the timelines are analyzed. Periodical land change analysis is used for many real time applications for valuation purposes. Majority of the research works are focused on Convolutional Neural Networks (CNN) which tries to analyze changes alone. Semantic information of changes appears to be missing, there by absence of communication between the different semantic timelines and changes detected over the region happens. To overcome this limitation, a… More >

  • Open Access

    ARTICLE

    Iris Recognition Based on Multilevel Thresholding Technique and Modified Fuzzy c-Means Algorithm

    Slim Ben Chaabane1,2,*, Rafika Harrabi1,2, Anas Bushnag1, Hassene Seddik2

    Journal on Artificial Intelligence, Vol.4, No.4, pp. 201-214, 2022, DOI:10.32604/jai.2022.032850

    Abstract Biometrics represents the technology for measuring the characteristics of the human body. Biometric authentication currently allows for secure, easy, and fast access by recognizing a person based on facial, voice, and fingerprint traits. Iris authentication is one of the essential biometric methods for identifying a person. This authentication type has become popular in research and practical applications. Unlike the face and hands, the iris is an internal organ, protected and therefore less likely to be damaged. However, the number of helpful information collected from the iris is much greater than the other biometric human organs. This work proposes a new… More >

  • Open Access

    ARTICLE

    Ensemble Classifier-Based Features Ranking on Employee Attrition

    Yok-Yen Nguwi*

    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 >

  • Open Access

    ARTICLE

    X-ray Based COVID-19 Classification Using Lightweight EfficientNet

    Tahani Maazi Almutairi*, Mohamed Maher Ben Ismail, Ouiem Bchir

    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

    Evaluating Neural Dialogue Systems Using Deep Learning and Conversation History

    Inshirah Ali AlMutairi*, Ali Mustafa Qamar

    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

    Research on Early Warning of Customer Churn Based on Random Forest

    Zizhen Qin, Yuxin Liu, Tianze Zhang*

    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

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

    Jie Luo*, Qingbing Ji, Lvlin Ni

    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

    REVIEW

    Deep Learning-Based 3D Instance and Semantic Segmentation: A Review

    Siddiqui Muhammad Yasir1, Hyunsik Ahn2,*

    Journal on Artificial Intelligence, Vol.4, No.2, pp. 99-114, 2022, DOI:10.32604/jai.2022.031235

    Abstract The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation. Segmentation is challenging with point cloud data due to substantial redundancy, fluctuating sample density and lack of apparent organization. The research area has a wide range of robotics applications, including intelligent vehicles, autonomous mapping and navigation. A number of researchers have introduced various methodologies and algorithms. Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I. methods. However, due to the specific problems of processing point clouds… More >

  • Open Access

    ARTICLE

    Research on the Dissemination and Influencing Factors of Big Data and Artificial Intelligence Related Courses in Colleges and Universities-Taking MOOC as an Example

    Zhu Junyan1, Min Yuguo2, Li Yudi3, Chen Xiaoyu4,*, Zhou Yu5

    Journal on Artificial Intelligence, Vol.4, No.2, pp. 115-132, 2022, DOI:10.32604/jai.2022.030353

    Abstract The rapid development of information technologies such as artificial intelligence, Internet and big data has promoted the deep integration of technology and education, especially the rise of large-scale online courses, which provides a great opportunity for curriculum teaching reform in colleges and universities. At the same time, artificial intelligence, as a cutting-edge technology, has good development prospects and has become a popular professional course in colleges and universities, artificial intelligence technology has become the focus of subject education in many universities. The combination of online education and AI courses will also greatly enhance the enthusiasm of users and expand the… More >

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