Khudran M. Alzhrani^*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 483-500, 2022, DOI:10.32604/iasc.2022.023914 - 05 January 2022

Abstract Individuals inadvertently allow emotions to drive their rational thoughts to predetermined conclusions regarding political partiality issues. Being well-informed about the subject in question mitigates emotions’ influence on humans’ cognitive reasoning, but it does not eliminate bias. By nature, humans tend to pick a side based on their beliefs, personal interests, and principles. Hence, journalists’ political leaning is defining factor in the rise of the polarity of political news coverage. Political bias studies usually align subjects or controversial topics of the news coverage to a particular ideology. However, politicians as private citizens or public officials are… 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 >

Khaled M. Fouad^1,3, Sahar F. Sabbeh^1,2,*, Walaa Medhat^1,3

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3647-3665, 2022, DOI:10.32604/cmc.2022.021449 - 07 December 2021

Abstract Nowadays, an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication. However, because user-generated content is unregulated, it may contain offensive content such as fake news, insults, and harassment phrases. The identification of fake news and rumors and their dissemination on social media has become a critical requirement. They have adverse effects on users, businesses, enterprises, and even political regimes and governments. State of the art has tackled the English language for news and used feature-based algorithms. This paper proposes a… More >

Nur-Hana Samsudin^1,*, Mark Lee²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1025-1039, 2022, DOI:10.32604/iasc.2022.022180 - 17 November 2021

Abstract In a polyglot speech synthesis, it is possible to use one language resource for another language. However, if the adaptation is not implemented carefully, the foreignness of the sound will be too noticeable for the listeners. This paper presents the analysis of respondents’ acceptance of a series of listening tests. The research goal was to find out in the absence of phonemes of a particular language, would it be possible for the phonemes to be replaced with another language’s phonemes. This will be especially beneficial for under-resourced language either in the case for 1) the… More >

Nahla H. Barakat^*, Ahmed H. ElSabbagh

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1207-1224, 2022, DOI:10.32604/iasc.2022.022104 - 17 November 2021

Abstract Social media recently became convenient platforms for different groups with common concerns to share their experiences, including Adverse Drug Reactions (ADRs). In this paper, we propose a two stage intelligent algorithm which we call “Simi_to_Prob”, that utilizes social media forums; for ranking ADRs, and evaluating the ADRs prevalence considering different age and gender groups as its first stage. In the second stage, ADRs are predicted utilizing a different data set from the Food and Drug Administration (FDA). In particular, Natural Language Processing (NLP) is used on social media to extract ranked lists of ADRs, which… More >

Alaa A. El-Demerdash, Sherif E. Hussein, John FW Zaki^*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 941-959, 2022, DOI:10.32604/cmc.2022.021839 - 03 November 2021

Abstract Sentiment analysis attracts the attention of Egyptian Decision-makers in the education sector. It offers a viable method to assess education quality services based on the students’ feedback as well as that provides an understanding of their needs. As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels, this research uses a dataset for tweets' sentiments to assess a few machine learning techniques. After dataset preprocessing to remove symbols, necessary stemming and lemmatization is performed for features extraction. This is followed by several machine learning techniques and… More >

Muhammad Shahid Bhatti^1,*, Azmat Ullah¹, Rohaya Latip², Abid Sohail¹, Anum Riaz¹, Rohail Hassan³

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 125-141, 2022, DOI:10.32604/cmc.2022.020083 - 03 November 2021

Abstract Text classification of low resource language is always a trivial and challenging problem. This paper discusses the process of Urdu news classification and Urdu documents similarity. Urdu is one of the most famous spoken languages in Asia. The implementation of computational methodologies for text classification has increased over time. However, Urdu language has not much experimented with research, it does not have readily available datasets, which turn out to be the primary reason behind limited research and applying the latest methodologies to the Urdu. To overcome these obstacles, a medium-sized dataset having six categories is… More >

Muhammad Aasim Qureshi^1,*, Muhammad Asif¹, Mohd Fadzil Hassan², Ghulam Mustafa¹, Muhammad Khurram Ehsan¹, Aasim Ali¹, Unaza Sajid¹

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4987-5004, 2022, DOI:10.32604/cmc.2022.020544 - 11 October 2021

Abstract In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of… More >

Chinu Singla¹, Fahd N. Al-Wesabi^2,3, Yash Singh Pathania¹, Badria Sulaiman Alfurhood⁴, Anwer Mustafa Hilal^5,*, Mohammed Rizwanullah⁵, Manar Ahmed Hamza⁵, Mohammad Mahzari⁶

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6365-6380, 2022, DOI:10.32604/cmc.2022.020480 - 11 October 2021

Abstract The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man. Analyzing this data can be critical for any organization. Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society. Sentiment analysis in Twitter mitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter. Resources used for analyzing tweet emotions are also briefly… More >

Walaa Gad^1,*, Anas Alokla¹, Waleed Nazih², Mustafa Aref¹, Abdel-badeeh Salem¹

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3117-3132, 2022, DOI:10.32604/cmc.2022.019884 - 27 September 2021

Abstract Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language. Pseudo-code explains and describes the content of the code without using syntax or programming language technologies. However, writing Pseudo-code to each code instruction is laborious. Recently, neural machine translation is used to generate textual descriptions for the source code. In this paper, a novel deep learning-based transformer (DLBT) model is proposed for automatic Pseudo-code generation from the source code. The proposed model uses deep learning which is based on Neural Machine Translation (NMT)… More >