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  • Open Access


    A Machine Learning Approach to User Profiling for Data Annotation of Online Behavior

    Moona Kanwal1,2,*, Najeed A. Khan1, Aftab A. Khan3

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2419-2440, 2024, DOI:10.32604/cmc.2024.047223

    Abstract The user’s intent to seek online information has been an active area of research in user profiling. User profiling considers user characteristics, behaviors, activities, and preferences to sketch user intentions, interests, and motivations. Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation. The user’s complete online experience in seeking information is a blend of activities such as searching, verifying, and sharing it on social platforms. However, a combination of multiple behaviors in profiling users has yet to be considered. This research takes a novel approach and explores user intent types… More >

  • Open Access


    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 >

  • Open Access


    Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

    Abdullah Alshehri1,*, Nayeem Khan1, Ali Alowayr1, Mohammed Yahya Alghamdi2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1679-1689, 2023, DOI:10.32604/csse.2023.026526

    Abstract This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics. The framework models the user behavior as sequences of events representing the user activities at such a network. The represented sequences are then fitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users. Thus, the model can recognize frequencies of regular behavior to profile the user manner in the network. The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regular or irregular behavior. The importance of… More >

  • Open Access


    Machine Learning-based Stable P2P IPTV Overlay

    Muhammad Javid Iqbal1,2, Ihsan Ullah2,*, Muhammad Ali2, Atiq Ahmed2, Waheed Noor2, Abdul Basit2

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5381-5397, 2022, DOI:10.32604/cmc.2022.024116

    Abstract Live video streaming is one of the newly emerged services over the Internet that has attracted immense interest of the service providers. Since Internet was not designed for such services during its inception, such a service poses some serious challenges including cost and scalability. Peer-to-Peer (P2P) Internet Protocol Television (IPTV) is an application-level distributed paradigm to offer live video contents. In terms of ease of deployment, it has emerged as a serious alternative to client server, Content Delivery Network (CDN) and IP multicast solutions. Nevertheless, P2P approach has struggled to provide the desired streaming quality due to a number of… More >

  • Open Access


    User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

    Rahmat Budiarto1,*, Ahmad A. Alqarni1, Mohammed Y. Alzahrani1, Muhammad Fermi Pasha2, Mohamed Fazil Mohamed Firdhous3, Deris Stiawan4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2679-2698, 2022, DOI:10.32604/cmc.2022.019847

    Abstract As nearly half of the incidents in enterprise security have been triggered by insiders, it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software (malware) in real-time. Failing to do so may cause a serious loss of reputation as well as business. At the same time, modern network traffic has dynamic patterns, high complexity, and large volumes that make it more difficult to detect malware early. The ability to learn tasks sequentially is crucial to the development of artificial intelligence. Existing neurogenetic computation models with… More >

  • Open Access


    Sales Prediction and Product Recommendation Model Through User Behavior Analytics

    Xian Zhao, Pantea Keikhosrokiani*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3855-3874, 2022, DOI:10.32604/cmc.2022.019750

    Abstract The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down. The general public has responded to call of the government to stay at home. Offline retail stores have been severely affected. Therefore, in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience, this study aims to utilize historical sales data for exploring, building sales prediction and recommendation models. A novel data science life-cycle and process model with Recency, Frequency, and Monetary (RFM) analysis method with the combination of various analytics algorithms are utilized in this study for… More >

  • Open Access


    User Behavior Path Analysis Based on Sales Data

    Wangdong Jiang, Dongling Zhang*, Yapeng Peng, Guang Sun, Ying Cao, Jing Li

    Journal of New Media, Vol.2, No.2, pp. 79-90, 2020, DOI:10.32604/jnm.2020.010088

    Abstract With the rapid development of science and technology and the increasing popularity of the Internet, the number of network users is gradually expanding, and the behavior of network users is becoming more and more complex. Users’ actual demand for resources on the network application platform is closely related to their historical behavior records. Therefore, it is very important to analyze the user behavior path conversion rate. Therefore, this paper analyses and studies user behavior path based on sales data. Through analyzing the user quality of the website as well as the user’s repurchase rate, repurchase rate and retention rate in… More >

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