Arar Al Tawil^1,*, Laiali Almazaydeh², Doaa Qawasmeh³, Baraah Qawasmeh⁴, Mohammad Alshinwan^1,5, Khaled Elleithy⁶

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3395-3412, 2024, DOI:10.32604/cmc.2024.057279 - 18 November 2024

Abstract Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information, a practice known as phishing. This study utilizes three distinct methodologies, Term Frequency-Inverse Document Frequency, Word2Vec, and Bidirectional Encoder Representations from Transformers, to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks. The study uses feature extraction methods to assess the performance of Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron algorithms. The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron (Precision: 0.98, Recall: 0.98, F1-score: 0.98, Accuracy: 0.98). Word2Vec’s More >

Jiawen Li^1,2, Yuesheng Huang¹, Yayi Lu¹, Leijun Wang^1,*, Yongqi Ren¹, Rongjun Chen¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1581-1599, 2024, DOI:10.32604/cmc.2024.052666 - 18 July 2024

Abstract In the context of the accelerated pace of daily life and the development of e-commerce, online shopping is a mainstream way for consumers to access products and services. To understand their emotional expressions in facing different shopping experience scenarios, this paper presents a sentiment analysis method that combines the e-commerce review keyword-generated image with a hybrid machine learning-based model, in which the Word2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence (AI). Subsequently, a hybrid Convolutional Neural Network and Support Vector Machine (CNN-SVM) model… More >

Yeferson Torres-Berru^1,2,*, Vivian F. Lopez-Batista¹, Lorena Conde Zhingre³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3501-3516, 2023, DOI:10.32604/iasc.2023.035367 - 15 March 2023

Abstract In a public procurement process, corruption can occur at each stage, favoring a participant with a previous agreement, which can result in over-pricing and purchases of substandard products, as well as gender discrimination. This paper’s aim is to detect biased purchases using a Spanish Language corpus, analyzing text from the questions and answers registry platform by applicants in a public procurement process in Ecuador. Additionally, gender bias is detected, promoting both men and women to participate under the same conditions. In order to detect gender bias and favoritism towards certain providers by contracting entities, the… More >

Hanxu Wang, Xin Li^*

Journal on Big Data, Vol.4, No.1, pp. 41-60, 2022, DOI:10.32604/jbd.2022.027717 - 04 May 2022

Abstract With the explosive growth of Internet text information, the task of text classification is more important. As a part of text classification, Chinese news text classification also plays an important role. In public security work, public opinion news classification is an important topic. Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time. This paper introduces a combined-convolutional neural network text classification model based on word2vec and improved TF-IDF: firstly, the word vector is… More >

Shweta Paliwal¹, Amit Kumar Mishra^2,*, Ram Krishn Mishra³, Nishad Nawaz⁴, M. Senthilkumar⁵

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5345-5362, 2022, DOI:10.32604/cmc.2022.025858 - 21 April 2022

Abstract Machine Learning is revolutionizing the era day by day and the scope is no more limited to computer science as the advancements are evident in the field of healthcare. Disease diagnosis, personalized medicine, and Recommendation system (RS) are among the promising applications that are using Machine Learning (ML) at a higher level. A recommendation system helps inefficient decision-making and suggests personalized recommendations accordingly. Today people share their experiences through reviews and hence designing of recommendation system based on users’ sentiments is a challenge. The recommendation system has gained significant attention in different fields but considering More >

Mohd Anul Haq¹, Mohd Abdul Rahim Khan^1,*, Mohammed Alshehri²

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 619-635, 2022, DOI:10.32604/iasc.2022.021430 - 05 January 2022

Abstract The growth of edge computing, the Internet of Things (IoT), and cloud computing have been accompanied by new security issues evolving in the information security infrastructure. Recent studies suggest that the cost of insider attacks is higher than the external threats, making it an essential aspect of information security for organizations. Efficient insider threat detection requires state-of-the-art Artificial Intelligence models and utility. Although significant have been made to detect insider threats for more than a decade, there are many limitations, including a lack of real data, low accuracy, and a relatively low false alarm, which… More >

Lelisa Adeba Jilcha¹, Jin Kwak^2,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2883-2899, 2022, DOI:10.32604/cmc.2022.023167 - 07 December 2021

Abstract In the contemporary world, digital content that is subject to copyright is facing significant challenges against the act of copyright infringement. Billions of dollars are lost annually because of this illegal act. The current most effective trend to tackle this problem is believed to be blocking those websites, particularly through affiliated government bodies. To do so, an effective detection mechanism is a necessary first step. Some researchers have used various approaches to analyze the possible common features of suspected piracy websites. For instance, most of these websites serve online advertisement, which is considered as their… More >

Jin Wang¹, Changqing Zhao¹, Shiming He^1,*, Yu Gu², Osama Alfarraj³, Ahed Abugabah⁴

Computer Systems Science and Engineering, Vol.41, No.3, pp. 1207-1222, 2022, DOI:10.32604/csse.2022.022365 - 10 November 2021

Abstract System logs record detailed information about system operation and are important for analyzing the system's operational status and performance. Rapid and accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more and more complex, and the number of system logs gradually increases, which brings challenges to analyze system logs. Some recent studies show that logs can be unstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a long time to train models.… More >

Ramsha Saeed¹, Saddaf Rubab¹, Sara Asif¹, Malik M. Khan¹, Saeed Murtaza¹, Seifedine Kadry², Yunyoung Nam^3,*, Muhammad Attique Khan^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 533-547, 2021, DOI:10.32604/cmes.2021.014355 - 19 April 2021

Abstract The substantial competition among the news industries puts editors under the pressure of posting news articles which are likely to gain more user attention. Anticipating the popularity of news articles can help the editorial teams in making decisions about posting a news article. Article similarity extracted from the articles posted within a small period of time is found to be a useful feature in existing popularity prediction approaches. This work proposes a new approach to estimate the popularity of news articles by adding semantics in the article similarity based approach of popularity estimation. A semantically More >

Muhammad Yahya Saeed¹, Muhammad Awais¹, Muhammad Younas¹, Muhammad Arif Shah^2,*, Atif Khan³, M. Irfan Uddin⁴, Marwan Mahmoud⁵

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2409-2423, 2021, DOI:10.32604/cmc.2021.014330 - 28 December 2020

Abstract Text Summarization is an essential area in text mining, which has procedures for text extraction. In natural language processing, text summarization maps the documents to a representative set of descriptive words. Therefore, the objective of text extraction is to attain reduced expressive contents from the text documents. Text summarization has two main areas such as abstractive, and extractive summarization. Extractive text summarization has further two approaches, in which the first approach applies the sentence score algorithm, and the second approach follows the word embedding principles. All such text extractions have limitations in providing the basic… More >