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An intelligent framework for sarcasm detection in Arabic tweets using deep learning with Al-Biruni earth radius optimization algorithm

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Alexandria Engineering Journal 2025年 127卷 562-572页

作者： AL-anazi, Reema G. A. Alzaidi, Muhammad Swaileh Eltahir, Majdy M. Almukadi, Wafa Sulaiman Alajmani, Samah Hazzaa Darem, Abdulbasit A. Alshahrani, Mohammed Alhebaishi, Nawaf Department of Arabic Language and Literature College of Humanities and Social Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Software Engineering College of Engineering and Computer Science University of Jeddah Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science College of Science Northern Border University Arar Saudi Arabia Computer Department Applied College Najran University Najran66462 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Social media networks play a major role in expressing people's feelings, reviews, and thoughts. One of the popular linguistic patterns to express or criticize one's ideas with ridicule is sarcasm, where they have unintended and intended meanings in written text. The sarcastic text is used to reverse the polarity of the sentiment. As a result, sarcasm detection in the text positively affects the sentimental analysis (SA) tasks and improves accuracy. The sarcasm recognition of Arabic content is constrained even though Arabic is the most popular language for web content sharing, and yet still naive owing to various challenges, such as the insufficient data sources, the multiple dialects, and the morphological structure of the Arabic language. This study presents an Artificial Intelligence Driven Sarcasm Detection using Optimal Deep Learning (AIDSD-ODL) technique on Arabic tweets. In the AIDSD-ODL technique, a hyperparameter-tuned DL model is utilized to identify sarcastic or non-sarcastic Arabic tweets. At the initial stage, the AIDSD-ODL method undergoes data pre-processing to convert the input tweets into a compatible format. In the next phase, the Glove word embedding process is used in the AIDSD-ODL technique. The self-attention bidirectional long short-term memory (SA-BiLSTM) model is utilized for sarcasm detection. The Al-Biruni Earth Radius (BER) model is implemented for the hyperparameter selection process to enhance the performance of the SA-BiLSTM network. The experimental results of the AIDSD-ODL method are examined under the Arabic tweets dataset. The comparison study of the AIDSD-ODL method portrayed a superior accuracy value of 94.20 % over existing models. © 2025 The Authors

关键词： Deep learning

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Stages Classification on Cervical Cell Images: A Comparative Study 3

Stages Classification on Cervical Cell Images: A Comparative...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Azani Mustafa, Wan Irfan Noor, Mohamad Alquran, Hiam Md Ghani, Miharaini Fahri Hanafi, Hafizul Hidayah Che Lah, Noor Mundher Adnan, Myasar Hameed Abdul Hussein, Abbas Universiti Malaysia Perlis Faculty of Electrical Engineering Technology UniCITI Alam Campus Sungai Chuchuh Perlis Padang Besar Malaysia Yarmouk University Department of Biomedical Systems and Informatics Engineering Irbid Jordan Universiti Putra Malaysia Department of Communication Faculty of Modern Language and Communication Malaysia Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Perak Tanjong Malim Malaysia The Islamic University Najaf Faculty of Engineering Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

The cancer of the cervix is called cervical cancer. An element of a woman's womb is the cervix. Among other diseases that affect women, it came in at number four on the list. According to the World Health Organization's cancer report, there are currently roughly 10 million new cases of cancer recorded year, and by 2020, that number will have doubled to 20 million. With the right screening and awareness campaign, this number can be cut in half. A quarter of cancers are said to be brought on by infections, including hepatitis B, which is connected to liver cancer, and the human papillomavirus, which is connected to cervix cancer. Deep learning techniques have been successfully applied to a wide range of image classification tasks, and have the potential to be highly effective for cervical cell image classification as well. In this project, we propose to use a deep learning-based approach to classify cervical cell images into different categories, such as normal cells, abnormal cells, or cancerous cells. To achieve this goal, we will first pre-process the images to prepare them for analysis, and then extract relevant features. These features will be used to train a deep learning model, which will be fine-tuned and optimized for the specific task of cervical cell classification. In this project, transfer learning method will be by using pre-trained classifier such as ResNet-50, GoogLeNet and EfficientNet-b0. We will evaluate the performance of the model using metrics such as accuracy and compare our results to those obtained using traditional machine learning approaches. From this project, the highest accuracy achieved are 51.49%. The goal to develop a pre-trained classifier transfer learning can be used to accurately and reliably classify cervical cell images in a clinical setting are achieved. © 2023 IEEE.

关键词： Cancer Cell Cervical Classify Image

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Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model

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Computer systems Science & Engineering 2024年第5期48卷 1387-1403页

作者： Abdelwahed Motwakel Badriyya B.Al-onazi Jaber S.Alzahrani Ayman Yafoz Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Amgad Atta Abdelmageed Department of Computer and Self Development Prince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Handwritten character recognition becomes one of the challenging research *** studies were presented for recognizing letters of various *** availability of Arabic handwritten characters databases was *** a quarter of a billion people worldwide write and speak *** historical books and files indicate a vital data set for many Arab nationswritten in ***,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)*** intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic *** initially pre-processes the handwritten images to improve their quality of ***,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature ***,the deep echo state network(DESN)model is utilized to classify handwritten ***,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition *** simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR *** comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

关键词： Arabic language handwritten character recognition deep learning classification parameter tuning

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Dialog chatbot as an interactive online tool in enhancing ESP vocabulary learning

Saudi Journal of Language Studies

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Saudi Journal of language Studies 2023年第2期3卷 76-86页

作者： Fawaz Qasem Mukhtar Ghaleb Hassan Saleh Mahdi Ahmed Al Khateeb Hind Al Fadda Department of English College of Sciences and Arts and the Applied College at Al-Namas University of Bisha Bisha Saudi Arabia Department of Information Systems College of Computing and Information Technology University of Bisha Al Namas Saudi Arabia Faculty of Computer Science and Information Technology Sana'a University Sana'a Yemen Department of English Hodeidah University Hodeidah Yemen English Language Centre Taif University Taif Saudi Arabia Department of English Language College of Arts King Faisal University Al-Ahsa Saudi Arabia Department of Curriculum and Instructions King Saud University Riyadh Saudi Arabia

Purpose Based on an experimental study on English for Specific Purposes (ESP) students, at the Business department at the University of Bisha, the purpose of the study is to examine the effect of chatbot use on learning ESP in online classrooms during COVID-19 and find out how Dialogflow chabot can be a useful and interactive online platform to help ESP learners in learning vocabulary well. Design/methodology/approach The research paper is based on an experimental study of two groups, an experiential group and a controlled group. Two tests were carried out. Pre-tests and post-test of vocabulary knowledge were conducted for both groups to explore the usefulness of using the Dialogflow chatbot in learning ESP vocabulary. A designed chatbot content was prepared and included all the vocabulary details related to words' synonyms and a brief explanation of words’ meanings. An informal interview is another tool used in the study. The purpose of using the interview with the participants was to elicit more data from the participants about using the chatbot and about how and in what aspects chatbot using the conversational program was useful and productive. Findings The findings of the study explored that the use of chatbots plays a major role in enhancing and learning ESP vocabulary. That was clear as the results showed that the students who used the chatbot Dialogflow in the experimental group outperformed their counterparts in the control group. Research limitations/implications The study displays an important pedagogical implication as the use of chatbots could be applied in several settings to improve language learning in general or learning ESP courses in particular. Chatbot creates an interesting environment to foster build good interactions where negotiation of meaning takes place clearly seems to be of great benefit to help learners advance in their L2 lexical development. Originality/value Examining and exploring whether the use of chatbots play

关键词： Chatbot English for specific purposes (ESP) ESP vocabulary English as second language (ESL) Online platform

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A conceptual framework for determining metaverse adoption in higher institutions of gulf area: An empirical study using hybrid SEM-ANN approach

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Computers and Education: Artificial Intelligence 2022年 3卷

作者： Akour, Iman A. Al-Maroof, Rana Saeed Alfaisal, Raghad Salloum, Said A. Information Systems Department College of Computing & Informatics University of Sharjah United Arab Emirates English Language & Linguistics Department Al Buraimi University College Al Buraimi Oman Faculty of Art Computing and Creative Industries Universiti Pendidikan Sultan Idris Malaysia School of Science Engineering and Environment University of Salford United Kingdom

The metaverse is a kind of imagined world with immersive digital spaces that increase, allowing a more interactive environment in educational settings. The metaverse is an expansion of the synchronous communication that embraces an effective number of users to share different experiences. The study aims to investigate the students' perceptions towards metaverse system for educational purposes in the Gulf area. The conceptual model comprises the adoption properties, namely trialability, observability, compatibility, and complexity, users' satisfaction, personal innovativeness, and Technology Acceptance Model (TAM) constructs. The novelty of the paper lies in its conceptual model that correlates both personal-based characteristics and technology-based features. In addition, the novel approach of hybrid analysis will be used in the current study to perform deep-learning-based analysis of structural equation modelling (SEM) and artificial neural network (ANN). Moreover, the importance-performance map analysis (IPMA) is used in the current study to evaluate the involved factors for their importance and performance. The study identified Perceived Usefulness (PU) to be an essential predictor of the factor of Users’ Intention to Use the Metaverse System (MS). The fact was discovered during ANN and IPMA analysis. Furthermore, this study is practically significant, as it helped the concerned authorities in educational sector in understanding the significance of each factor and allowed them to make efforts and plans according to the order of significance of factors. Another important implication of the study is methodological in nature. It validates that deep ANN architecture can offer deep insight into non-linear relationships shared by various factors of a theoretical model. © 2022 The Authors

关键词： Neural networks

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Overview of the ehealth knowledge discovery challenge at iberlef 2020

Overview of the ehealth knowledge discovery challenge at ibe...

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2020 Iberian languages Evaluation Forum, IberLEF 2020

作者： Piad-Morffis, Alejandro Gutiérrez, Yoan Cañizares-Diaz, Hian Estévez-Velarde, Suilan Montoyo, Andrés Almeida-Cruz, Yudivian School of Math and Computer Science University of Havana La Habana10400 Cuba Department of Language and Computing Systems University of Alicante Alicante03690 Spain University Institute for Computing Research University of Alicante Alicante03690 Spain

This paper summarises the results of the third edition of the eHealth Knowledge Discovery (KD) challenge, hosted at the Iberian language Evaluation Forum 2020. The eHealth-KD challenge proposes two computational tasks involving the identification of semantic entities and relations in natural language text, focusing on Spanish language health documents. In this edition, besides text extracted from medical sources, Wikipedia content was introduced into the corpus, and a novel transfer-learning evaluation scenario was designed that challenges participants to create systems that provide crossdomain generalisation. A total of eight teams participated with a variety of approaches including deep learning end-to-end systems as well as rule-based and knowledge-driven techniques. This paper analyses the most successful approaches and highlights the most interesting challenges for future research in this field. © 2020 Copyright for this paper by its authors. Use permitted under.

关键词： eHealth

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Chaotic Elephant Herd Optimization with Machine Learning for Arabic Hate Speech Detection

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Intelligent Automation & Soft computing 2024年第3期39卷 567-583页

作者： Badriyya B.Al-onazi Jaber S.Alzahrani Najm Alotaibi Hussain Alshahrani Mohamed Ahmed Elfaki Radwa Marzouk Heba Mohsen Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies Riyadh12735Saudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqra11911Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New CairoNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipAl-Kharj16278Saudi Arabia

In recent years,the usage of social networking sites has considerably increased in the Arab *** has empowered individuals to express their opinions,especially in ***,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different *** is attributed to business owners’understanding of social media’s importance for business ***,the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and *** speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil *** this background,the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection(CEHOML-HSD)model in the context of the Arabic *** presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and *** attain this,the CEHOML-HSD model follows different sub-processes as discussed *** the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF ***,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic ***,the CEHO approach is employed for fine-tuning the parameters involved in *** CEHO approach is developed by combining the chaotic functions with the classical EHO *** design of the CEHO algorithm for parameter tuning shows the novelty of the work.A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD *** comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.

关键词： Arabic language machine learning elephant herd optimization TF-IDF vectorizer hate speech detection

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UH-matcom at ehealth-kd challenge 2020: Deep-learning and ensemble models for knowledge discovery in Spanish documents

UH-matcom at ehealth-kd challenge 2020: Deep-learning and en...

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2020 Iberian languages Evaluation Forum, IberLEF 2020

作者： Consuegra-Ayalaa, Juan Pablo Palomar, Manuel Faculty of Math and Computer Science University of Habana La Habana10200 Cuba University of Alicante Alicante03690 Spain Department of Language and Computing Systems University of Alicante Alicante03690 Spain

The eHealth-KD challenge hosted at IberLEF 2020 proposes a set of resources and evaluation scenarios to encourage the development of systems for the automatic extraction of knowledge from unstructured text. This paper describes the system presented by team UH-MatCom in the challenge. Several deeplearning models are trained and ensembled to automatically extract relevant entities and relations from plain text documents. State of the art techniques such as BERT, Bi-LSTM, and CRF are applied. The use of external knowledge sources such as ConceptNet is explored. The system achieved average results in the challenge, ranking fifth across all different evaluation scenarios. The ensemble method produced a slight improvement in performance. Additional work needs to be done for the relation extraction task to successfully benefit from external knowledge sources. © 2020 Copyright for this paper by its authors. Use permitted under.

关键词： eHealth

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It is not as good as you think! Evaluating simultaneous machine translation on interpretation data

arXiv

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arXiv 2021年

作者： Zhao, Jinming Arthur, Philip Haffari, Gholamreza Cohn, Trevor Shareghi, Ehsan Department of Data Science & AI Monash University Oracle Digital Assistant Oracle Corp Language Technology Lab University of Cambridge School of Computing and Information Systems The University of Melbourne

Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora. We argue that SiMT systems should be trained and tested on real interpretation data. To illustrate this argument, we propose an interpretation test set and conduct a realistic evaluation of SiMT trained on offline translations. Our results, on our test set along with 3 existing smaller scale language pairs, highlight the difference of up-to 13.83 BLEU score when SiMT models are evaluated on translation vs interpretation data. In the absence of interpretation training data, we propose a translation-to-interpretation (T2I) style transfer method which allows converting existing offline translations into interpretation-style data, leading to up-to 2.8 BLEU improvement. However, the evaluation gap remains notable, calling for constructing large-scale interpretation corpora better suited for evaluating and developing SiMT systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Computer aided language translation

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Integrating Artificial Intelligence in Improving Educational System: The Mediating role of Smart Learning

Integrating Artificial Intelligence in Improving Educational...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Iman A. Akour Hevron Muhammad Alshurideh Haitham M. Alzoubi Muhammad Turki Alshurideh Yasin Ahmad Antouz Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Foreign Languages Faculty of English Language and Literature The University of Jordan Amman Jordan School of Business Skyline University College Sharjah UAE Applied Science Research Center Applied Science Private University Amman Jordan Department of Management College of Business University of Sharjah Sharjah UAE Department of Marketing School of Business The University of Jordan Amman Jordan Department of Management College of Business University of Sharjah Sharjah United Arab Emirates

One of the contemporary developments in educational technology is artificial intelligence (AI) in the education. The use of AI will lead to a significant change in governance across the board for UAE institutes of learning. The potential use of AI in education includes an examination of the educational consequences, including how teachers would be enriched, how students would learn, and how fast and accurate judgements could be made in education institutions. Given this situation, AI assistance is absolutely necessary. Despite the fact that it has been around for close to 30 years, educators are still confused of how to use it pedagogically on a broader scale and how it may actually have a big impact on teaching and learning in higher education. Through an empirical review, this research aims to present an empirical analysis of the application of artificial intelligence in educational system with mediating effect of smart learning. This research carried out empirical evidence from 28 educational institutions based in UAE. The managerial and professors were approached in order to answer the online questionnaire. 195 valid responses were considered for data analysis using SmartPLS. Model assessment done on the basis of various tests, for instance, reliability, discriminant validity, PLS-SEM. The findings suggest a significant relationship between artificial intelligence and educational system, and mediating role of smart learning is predicted to be significant. Hence, the intention to use artificial intelligence with smart learning can enhance the academic learning outcomes.

关键词： Wireless communication Wireless sensor networks Technological innovation Educational technology Learning (artificial intelligence) Sensor systems Internet

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