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  • Open Access


    Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

    Mesfer Al Duhayyim1, Haya Mesfer Alshahrani2, Fahd N. Al-Wesabi3, Mohammed Alamgeer4, Anwer Mustafa Hilal5,*, Mohammed Rizwanullah5

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6257-6270, 2022, DOI:10.32604/cmc.2022.021212

    Abstract Cybersecurity encompasses various elements such as strategies, policies, processes, and techniques to accomplish availability, confidentiality, and integrity of resource processing, network, software, and data from attacks. In this scenario, the rising popularity of Online Social Networks (OSN) is under threat from spammers for which effective spam bot detection approaches should be developed. Earlier studies have developed different approaches for the detection of spam bots in OSN. But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning (DL) models needs to be explored. With this motivation, the current research article… More >

  • Open Access


    Graph Transformer for Communities Detection in Social Networks

    G. Naga Chandrika1, Khalid Alnowibet2, K. Sandeep Kautish3, E. Sreenivasa Reddy4, Adel F. Alrasheedi2, Ali Wagdy Mohamed5,6,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5707-5720, 2022, DOI:10.32604/cmc.2022.021186

    Abstract Graphs are used in various disciplines such as telecommunication, biological networks, as well as social networks. In large-scale networks, it is challenging to detect the communities by learning the distinct properties of the graph. As deep learning has made contributions in a variety of domains, we try to use deep learning techniques to mine the knowledge from large-scale graph networks. In this paper, we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs. The advantages of neural attention are widely seen in the field of NLP and computer vision, which has… More >

  • Open Access


    CGraM: Enhanced Algorithm for Community Detection in Social Networks

    Kalaichelvi Nallusamy*, K. S. Easwarakumar

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 749-765, 2022, DOI:10.32604/iasc.2022.020189

    Abstract Community Detection is used to discover a non-trivial organization of the network and to extract the special relations among the nodes which can help in understanding the structure and the function of the networks. However, community detection in social networks is a vast and challenging task, in terms of detected communities accuracy and computational overheads. In this paper, we propose a new algorithm Enhanced Algorithm for Community Detection in Social Networks – CGraM, for community detection using the graph measures eccentricity, harmonic centrality and modularity. First, the centre nodes are identified by using the eccentricity and harmonic centrality, next a… More >

  • Open Access


    Applying Machine Learning Techniques for Religious Extremism Detection on Online User Contents

    Shynar Mussiraliyeva1, Batyrkhan Omarov1,*, Paul Yoo1,2, Milana Bolatbek1

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 915-934, 2022, DOI:10.32604/cmc.2022.019189

    Abstract In this research paper, we propose a corpus for the task of detecting religious extremism in social networks and open sources and compare various machine learning algorithms for the binary classification problem using a previously created corpus, thereby checking whether it is possible to detect extremist messages in the Kazakh language. To do this, the authors trained models using six classic machine-learning algorithms such as Support Vector Machine, Decision Tree, Random Forest, K Nearest Neighbors, Naive Bayes, and Logistic Regression. To increase the accuracy of detecting extremist texts, we used various characteristics such as Statistical Features, TF-IDF, POS, LIWC, and… More >

  • Open Access


    Advanced Community Identification Model for Social Networks

    Farhan Amin1, Jin-Ghoo Choi2, Gyu Sang Choi2,*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1687-1707, 2021, DOI:10.32604/cmc.2021.017870

    Abstract Community detection in social networks is a hard problem because of the size, and the need of a deep understanding of network structure and functions. While several methods with significant effort in this direction have been devised, an outstanding open problem is the unknown number of communities, it is generally believed that the role of influential nodes that are surrounded by neighbors is very important. In addition, the similarity among nodes inside the same cluster is greater than among nodes from other clusters. Lately, the global and local methods of community detection have been getting more attention. Therefore, in this… More >

  • Open Access


    Social Network Rumor Recognition Based on Enhanced Naive Bayes

    Lei Guo*

    Journal of New Media, Vol.3, No.3, pp. 99-107, 2021, DOI:10.32604/jnm.2021.019649

    Abstract In recent years, with the increasing popularity of social networks, rumors have become more common. At present, the solution to rumors in social networks is mainly through media censorship and manual reporting, but this method requires a lot of manpower and material resources, and the cost is relatively high. Therefore, research on the characteristics of rumors and automatic identification and classification of network message text is of great significance. This paper uses the Naive Bayes algorithm combined with Laplacian smoothing to identify rumors in social network texts. The first is to segment the text and remove the stop words after… More >

  • Open Access


    Research on Feature Extraction Method of Social Network Text

    Zheng Zhang*, Shu Zhou

    Journal of New Media, Vol.3, No.2, pp. 73-80, 2021, DOI:10.32604/jnm.2021.018923

    Abstract The development of various applications based on social network text is in full swing. Studying text features and classifications is of great value to extract important information. This paper mainly introduces the common feature selection algorithms and feature representation methods, and introduces the basic principles, advantages and disadvantages of SVM and KNN, and the evaluation indexes of classification algorithms. In the aspect of mutual information feature selection function, it describes its processing flow, shortcomings and optimization improvements. In view of its weakness in not balancing the positive and negative correlation characteristics, a balance weight attribute factor and feature difference factor… More >

  • Open Access


    Machine Learning Approach for COVID-19 Detection on Twitter

    Samina Amin1,*, M. Irfan Uddin1, Heyam H. Al-Baity2, M. Ali Zeb1, M. Abrar Khan1

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2231-2247, 2021, DOI:10.32604/cmc.2021.016896

    Abstract Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows that social media analysis can be used as a crisis detector (e.g., understanding the sentiment of social media users regarding various pandemic outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020. Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions. This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages (tweets). For this… More >

  • Open Access


    Detecting Information on the Spread of Dengue on Twitter Using Artificial Neural Networks

    Samina Amin1,*, M. Irfan Uddin1, M. Ali Zeb1, Ala Abdulsalam Alarood2, Marwan Mahmoud3, Monagi H. Alkinani4

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1317-1332, 2021, DOI:10.32604/cmc.2021.014733

    Abstract Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning techniques. Many analytical and statistical models are available to infer a variety of user sentiments in posts on social media. The amount of data generated by social media platforms, such as Twitter, that can be used to track diseases is increasing rapidly. This paper proposes a method for the classification of tweets related to the outbreak of dengue using machine learning algorithms. An artificial neural network (ANN)-based method is developed using Global Vector… More >

  • Open Access


    An AIoT Monitoring System for Multi-Object Tracking and Alerting

    Wonseok Jung1, Se-Han Kim2, Seng-Phil Hong3, Jeongwook Seo4,*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 337-348, 2021, DOI:10.32604/cmc.2021.014561

    Abstract Pig farmers want to have an effective solution for automatically detecting and tracking multiple pigs and alerting their conditions in order to recognize disease risk factors quickly. In this paper, therefore, we propose a novel monitoring system using an Artificial Intelligence of Things (AIoT) technique combining artificial intelligence and Internet of Things (IoT). The proposed system consists of AIoT edge devices and a central monitoring server. First, an AIoT edge device extracts video frame images from a CCTV camera installed in a pig pen by a frame extraction method, detects multiple pigs in the images by a faster region-based convolutional… More >

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