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  • Open Access

    ARTICLE

    Social Engineering Attack Classifications on Social Media Using Deep Learning

    Yichiet Aun1,*, Ming-Lee Gan1, Nur Haliza Binti Abdul Wahab2, Goh Hock Guan1

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4917-4931, 2023, DOI:10.32604/cmc.2023.032373

    Abstract In defense-in-depth, humans have always been the weakest link in cybersecurity. However, unlike common threats, social engineering poses vulnerabilities not directly quantifiable in penetration testing. Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware. Social Engineering (SE) in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic. In this paper, a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory (RNN-LSTM) to identify well-disguised SE threats in social media posts. We use a custom dataset crawled from hundreds of… More >

  • Open Access

    ARTICLE

    Complete Cototal Roman Domination Number of a Graph for User Preference Identification in Social Media

    J. Maria Regila Baby*, K. Uma Samundesvari

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2405-2415, 2023, DOI:10.32604/csse.2023.032240

    Abstract Many graph domination applications can be expanded to achieve complete cototal domination. If every node in a dominating set is regarded as a record server for a PC organization, then each PC affiliated with the organization has direct access to a document server. It is occasionally reasonable to believe that this gateway will remain available even if one of the scrape servers fails. Because every PC has direct access to at least two documents’ servers, a complete cototal dominating set provides the required adaptability to non-critical failure in such scenarios. In this paper, we presented a method for calculating a… More >

  • Open Access

    ARTICLE

    Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

    Kuldeep Chouhan1, Mukesh Yadav2, Ranjeet Kumar Rout3, Kshira Sagar Sahoo4, NZ Jhanjhi5,*, Mehedi Masud6, Sultan Aljahdali6

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1113-1128, 2023, DOI:10.32604/csse.2023.030842

    Abstract Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres… More >

  • Open Access

    ARTICLE

    A Machine Learning-Based Technique with Intelligent WordNet Lemmatize for Twitter Sentiment Analysis

    S. Saranya*, G. Usha

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 339-352, 2023, DOI:10.32604/iasc.2023.031987

    Abstract Laterally with the birth of the Internet, the fast growth of mobile strategies has democratised content production owing to the widespread usage of social media, resulting in a detonation of short informal writings. Twitter is microblogging short text and social networking services, with posted millions of quick messages. Twitter analysis addresses the topic of interpreting users’ tweets in terms of ideas, interests, and views in a range of settings and fields. This type of study can be useful for a variation of academics and applications that need knowing people’s perspectives on a given topic or event. Although sentiment examination of… More >

  • Open Access

    ARTICLE

    Anomaly Detection in Social Media Texts Using Optimal Convolutional Neural Network

    Swarna Sudha Muppudathi1, Valarmathi Krishnasamy2,*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1027-1042, 2023, DOI:10.32604/iasc.2023.031165

    Abstract Social Networking Sites (SNSs) are nowadays utilized by the whole world to share ideas, images, and valuable contents by means of a post to reach a group of users. The use of SNS often inflicts the physical and the mental health of the people. Nowadays, researchers often focus on identifying the illegal behaviors in the SNS to reduce its negative influence. The state-of-art Natural Language processing techniques for anomaly detection have utilized a wide annotated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum accuracy. To overcome these issues, the proposed… More >

  • Open Access

    ARTICLE

    Popularity Prediction of Social Media Post Using Tensor Factorization

    Navdeep Bohra1,2, Vishal Bhatnagar3, Amit Choudhary4, Savita Ahlawat2, Dinesh Sheoran2, Ashish Kumari2,*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 205-221, 2023, DOI:10.32604/iasc.2023.030708

    Abstract The traditional method of doing business has been disrupted by social media. In order to develop the enterprise, it is essential to forecast the level of interaction that a new post would receive from social media users. It is possible for the user’s interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detection strategies that are user-based or population-based are unable to keep up with these shifts, which leads to inaccurate forecasts. This work makes a prediction about how popular the post will… More >

  • Open Access

    ARTICLE

    Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

    Sandeep Kumar1, Muhammad Badruddin Khan2, Mozaherul Hoque Abul Hasanat2, Abdul Khader Jilani Saudagar2,*, Abdullah AlTameem2, Mohammed AlKhathami2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 897-914, 2023, DOI:10.32604/cmc.2023.031867

    Abstract Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed… More >

  • Open Access

    ARTICLE

    A Top-down Method of Extraction Entity Relationship Triples and Obtaining Annotated Data

    Zhiqiang Hu1, Zheng Ma1, Jun Shi1, Zhipeng Li1, Xun Shao1,2, Yangzhao Yang1,*, Yong Liao1, Zhenyuan Gao1, Jie Zhang1

    Journal of Quantum Computing, Vol.4, No.1, pp. 13-22, 2022, DOI:10.32604/jqc.2022.026785

    Abstract The extraction of entity relationship triples is very important to build a knowledge graph (KG), meanwhile, various entity relationship extraction algorithms are mostly based on data-driven, especially for the current popular deep learning algorithms. Therefore, obtaining a large number of accurate triples is the key to build a good KG as well as train a good entity relationship extraction algorithm. Because of business requirements, this KG’s application field is determined and the experts’ opinions also must be satisfied. Considering these factors we adopt the top-down method which refers to determining the data schema firstly, then filling the specific data according… More >

  • Open Access

    ARTICLE

    Seeker Optimization with Deep Learning Enabled Sentiment Analysis on Social Media

    Hanan M. Alghamdi1, Saadia H.A. Hamza2, Aisha M. Mashraqi3, Sayed Abdel-Khalek4,5,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5985-5999, 2022, DOI:10.32604/cmc.2022.031732

    Abstract World Wide Web enables its users to connect among themselves through social networks, forums, review sites, and blogs and these interactions produce huge volumes of data in various forms such as emotions, sentiments, views, etc. Sentiment Analysis (SA) is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive, negative, and neutral. However, Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing (NLP). Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array… More >

  • Open Access

    ARTICLE

    Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

    Mesfer Al Duhayyim1,*, Heba G. Mohamed2, Saud S. Alotaibi3, Hany Mahgoub4,5, Abdullah Mohamed6, Abdelwahed Motwakel7, Abu Sarwar Zamani7, Mohamed I. Eldesouki8

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5011-5024, 2022, DOI:10.32604/cmc.2022.031096

    Abstract Cyberbullying (CB) is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB. The recently developed deep learning (DL) models pave the way to design CB classifier models with maximum performance. At the same time, optimal hyperparameter tuning process plays a vital role to enhance overall results. This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification (TLGODL-CBC) model in Social Media. The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context. Initially, the input data is cleaned and pre-processed to make… More >

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