Kalaichelvi Nallusamy^*, K. S. Easwarakumar

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 749-765, 2022, DOI:10.32604/iasc.2022.020189 - 22 September 2021

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… More >

Shynar Mussiraliyeva¹, Batyrkhan Omarov^1,*, Paul Yoo^1,2, Milana Bolatbek¹

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 915-934, 2022, DOI:10.32604/cmc.2022.019189 - 07 September 2021

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 More >

Farhan Amin¹, Jin-Ghoo Choi², Gyu Sang Choi^2,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1687-1707, 2021, DOI:10.32604/cmc.2021.017870 - 21 July 2021

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 >

Lei Guo^*

Journal of New Media, Vol.3, No.3, pp. 99-107, 2021, DOI:10.32604/jnm.2021.019649 - 13 July 2021

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 More >

Zheng Zhang^*, Shu Zhou

Journal of New Media, Vol.3, No.2, pp. 73-80, 2021, DOI:10.32604/jnm.2021.018923 - 23 April 2021

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 More >

Samina Amin^1,*, 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 - 13 April 2021

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… More >

Samina Amin^1,*, 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 - 12 January 2021

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… More >

Wonseok Jung¹, Se-Han Kim², Seng-Phil Hong³, Jeongwook Seo^4,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 337-348, 2021, DOI:10.32604/cmc.2021.014561 - 12 January 2021

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 More >

Hongri Liu^1,2,3, Xu Zhang^1,3, Jingjing Li^1,3, Bailing Wang^1,3,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2539-2553, 2021, DOI:10.32604/cmc.2021.012213 - 28 December 2020

Abstract

A collective user web behavior simulation is an import means for generating a large-scale user network behavior in a network testbed or cyber range. Existing studies almost focus on individual web behavior analysis and prediction, which cannot simulate human dynamics that widely exist in large-scale users’ behaviors. To address these issues, we propose a novel collective user web behavior simulation method, in which an algorithm for constructing a connected virtual social network is proposed, and then a collective user web behavior simulation algorithm is designed on the virtual social network. In the simulation method, a

… More >

Xinxin Lu^1,*, Hong Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 281-297, 2020, DOI:10.32604/cmes.2020.010948 - 18 September 2020

Abstract As an interdisciplinary comprehensive subject involving multidisciplinary knowledge, emotional analysis has become a hot topic in psychology, health medicine and computer science. It has a high comprehensive and practical application value. Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research. The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period, so as to understand their normal state, abnormal state and the reason of state change from… More >