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  • Open Access

    ARTICLE

    Neural Machine Translation Models with Attention-Based Dropout Layer

    Huma Israr1,*, Safdar Abbas Khan1, Muhammad Ali Tahir1, Muhammad Khuram Shahzad1, Muneer Ahmad1, Jasni Mohamad Zain2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2981-3009, 2023, DOI:10.32604/cmc.2023.035814 - 31 March 2023

    Abstract In bilingual translation, attention-based Neural Machine Translation (NMT) models are used to achieve synchrony between input and output sequences and the notion of alignment. NMT model has obtained state-of-the-art performance for several language pairs. However, there has been little work exploring useful architectures for Urdu-to-English machine translation. We conducted extensive Urdu-to-English translation experiments using Long short-term memory (LSTM)/Bidirectional recurrent neural networks (Bi-RNN)/Statistical recurrent unit (SRU)/Gated recurrent unit (GRU)/Convolutional neural network (CNN) and Transformer. Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively, with a scalable data set, make precise predictions on unseen… More >

  • Open Access

    ARTICLE

    Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

    Manar Ahmed Hamza1,*, Hala J. Alshahrani2, Abdulkhaleq Q. A. Hassan3, Abdulbaset Gaddah4, Nasser Allheeib5, Suleiman Ali Alsaif6, Badriyya B. Al-onazi7, Heba Mohsen8

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4137-4154, 2023, DOI:10.32604/cmc.2023.035237 - 31 March 2023

    Abstract Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions. The number of social media users has been increasing over the last few years, which have allured researchers’ interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a better way. Irony and sarcasm detection is a complex task in Natural Language Processing (NLP). Irony detection has inferences in advertising, sentiment analysis (SA), and opinion mining. For the last few years, irony-aware… More >

  • Open Access

    ARTICLE

    A Data Mining Approach to Detecting Bias and Favoritism in Public Procurement

    Yeferson Torres-Berru1,2,*, 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 >

  • Open Access

    ARTICLE

    Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

    Manar Ahmed Hamza1,*, Hala J. Alshahrani2, Jaber S. Alzahrani3, Heba Mohsen4, Mohamed I. Eldesouki5, Mohammed Rizwanullah1

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2619-2635, 2023, DOI:10.32604/csse.2023.034519 - 09 February 2023

    Abstract Aspect-Based Sentiment Analysis (ABSA) on Arabic corpus has become an active research topic in recent days. ABSA refers to a fine-grained Sentiment Analysis (SA) task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided text. Most of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as lexicons. In literature, concerning the Arabic language text analysis, the authors made use of regular Machine Learning (ML) techniques that rely on a group of rare sources and tools.… More >

  • Open Access

    ARTICLE

    Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

    Hae Sun Jung1, Seon Hong Lee1, Haein Lee1, Jang Hyun Kim2,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2231-2246, 2023, DOI:10.32604/csse.2023.034466 - 09 February 2023

    Abstract Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market. As the history of the Bitcoin market is short and price volatility is high, studies have been conducted on the factors affecting changes in Bitcoin prices. Experiments have been conducted to predict Bitcoin prices using Twitter content. However, the amount of data was limited, and prices were predicted for only a short period (less than two years). In this study, data from Reddit and LexisNexis, covering a period of more than four years, were collected. These data were utilized… More >

  • Open Access

    ARTICLE

    Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets

    Aisha M. Mashraqi, Hanan T. Halawani*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2555-2570, 2023, DOI:10.32604/csse.2023.031246 - 09 February 2023

    Abstract Sentiment Analysis (SA) is one of the Machine Learning (ML) techniques that has been investigated by several researchers in recent years, especially due to the evolution of novel data collection methods focused on social media. In literature, it has been reported that SA data is created for English language in excess of any other language. It is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic language. An earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the text.… More >

  • Open Access

    ARTICLE

    A GDPR Compliant Approach to Assign Risk Levels to Privacy Policies

    Abdullah R. Alshamsan1, Shafique A. Chaudhry1,2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4631-4647, 2023, DOI:10.32604/cmc.2023.034039 - 28 December 2022

    Abstract Data privacy laws require service providers to inform their customers on how user data is gathered, used, protected, and shared. The General Data Protection Regulation (GDPR) is a legal framework that provides guidelines for collecting and processing personal information from individuals. Service providers use privacy policies to outline the ways an organization captures, retains, analyzes, and shares customers’ data with other parties. These policies are complex and written using legal jargon; therefore, users rarely read them before accepting them. There exist a number of approaches to automating the task of summarizing privacy policies and assigning… More >

  • Open Access

    ARTICLE

    Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

    Badriyya B. Al-onazi1, Saud S. Alotaib2, Saeed Masoud Alshahrani3,*, Najm Alotaibi4, Mrim M. Alnfiai5, Ahmed S. Salama6, Manar Ahmed Hamza7

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5447-5465, 2023, DOI:10.32604/cmc.2023.033564 - 28 December 2022

    Abstract The text classification process has been extensively investigated in various languages, especially English. Text classification models are vital in several Natural Language Processing (NLP) applications. The Arabic language has a lot of significance. For instance, it is the fourth mostly-used language on the internet and the sixth official language of the United Nations. However, there are few studies on the text classification process in Arabic. A few text classification studies have been published earlier in the Arabic language. In general, researchers face two challenges in the Arabic text classification process: low accuracy and high dimensionality… More >

  • Open Access

    ARTICLE

    Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

    R. Brindha1, S. Nandagopal2, H. Azath3, V. Sathana4, Gyanendra Prasad Joshi5, Sung Won Kim6,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5901-5914, 2023, DOI:10.32604/cmc.2023.030784 - 28 December 2022

    Abstract Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’ sensitive data. E-mails, instant messages and phone calls are some of the common modes used in cyberattacks. Though the security models are continuously upgraded to prevent cyberattacks, hackers find innovative ways to target the victims. In this background, there is a drastic increase observed in the number of phishing emails sent to potential targets. This scenario necessitates the importance of designing an effective classification model. Though numerous conventional models are available in the literature for… 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 - 03 November 2022

    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… More >

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