Samuel Kofi Akpatsa^1,*, Hang Lei¹, Xiaoyu Li¹, Victor-Hillary Kofi Setornyo Obeng¹, Ezekiel Mensah Martey¹, Prince Clement Addo², Duncan Dodzi Fiawoo³

Journal of Quantum Computing, Vol.4, No.1, pp. 1-11, 2022, DOI:10.32604/jqc.2022.026658

Abstract The ability of pre-trained BERT model to achieve outstanding performances on many Natural Language Processing (NLP) tasks has attracted the attention of researchers in recent times. However, the huge computational and memory requirements have hampered its widespread deployment on devices with limited resources. The concept of knowledge distillation has shown to produce smaller and faster distilled models with less trainable parameters and intended for resource-constrained environments. The distilled models can be fine-tuned with great performance on a wider range of tasks, such as sentiment classification. This paper evaluates the performance of DistilBERT model and other pre-canned text classifiers on a… More >

Abdul Rahaman Wahab Sait^1,*, Mohamad Khairi Ishak²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2553-2567, 2023, DOI:10.32604/csse.2023.029603

Abstract Sentiment analysis (SA) is the procedure of recognizing the emotions related to the data that exist in social networking. The existence of sarcasm in textual data is a major challenge in the efficiency of the SA. Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection, punctuations, and sentiment shift that are vital indicators of sarcasm. With the advent of deep-learning, recent works, leveraging neural networks in learning lexical and contextual features, removing the need for handcrafted feature. In this aspect, this study designs a deep learning with natural language processing enabled SA (DLNLP-SA)… More >

Hanan M. Alghamdi¹, Saadia H.A. Hamza², Aisha M. Mashraqi³, Sayed Abdel-Khalek^4,5,*

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 >

Sara A. Althubiti¹, Fayadh Alenezi², Romany F. Mansour^3,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3529-3544, 2022, DOI:10.32604/cmc.2022.028981

Abstract The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster rate. At most of the times, the intention of fake news is to misinform the people and make manipulated societal insights. The spread of low-quality news in social networking sites has a negative influence upon people as well as the society. In order to overcome the ever-increasing dissemination of fake news, automated detection models are developed using Artificial Intelligence (AI) and Machine Learning (ML) methods. The latest advancements in Deep Learning (DL) models and complex Natural Language… More >

Dhivya Chandrasekaran^*, Vijay Mago

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2257-2274, 2022, DOI:10.32604/cmc.2022.027236

Abstract Student mobility or academic mobility involves students moving between institutions during their post-secondary education, and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student. In general, this process involves domain experts comparing the learning outcomes of the courses, to decide on offering transfer credits to the incoming students. This manual implementation is not only labor-intensive but also influenced by undue bias and administrative complexity. The proposed research article focuses on identifying a model that exploits the advancements in the field of Natural Language Processing (NLP) to effectively automate… More >

Md. Anwar Hussen Wadud¹, M. F. Mridha¹, Jungpil Shin^2,*, Kamruddin Nur³, Aloke Kumar Saha⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.027841

Abstract Offensive messages on social media, have recently been frequently used to harass and criticize people. In recent studies, many promising algorithms have been developed to identify offensive texts. Most algorithms analyze text in a unidirectional manner, where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences. In addition, there are many separate models for identifying offensive texts based on monolingual and multilingual, but there are a few models that can detect both monolingual and multilingual-based offensive texts. In this study, a detection system has been developed for both monolingual and multilingual offensive texts by… More >

Hafsa Naveed¹, Abid Sohail², Jasni Mohamad Zain^3,*, Noman Saleem⁴, Rao Faizan Ali⁵, Shahid Anwar⁶

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 15-30, 2023, DOI:10.32604/iasc.2023.023277

Abstract Question and answer websites such as Quora, Stack Overflow, Yahoo Answers and Answer Bag are used by professionals. Multiple users post questions on these websites to get the answers from domain specific professionals. These websites are multilingual meaning they are available in many different languages. Current problem for these types of websites is to handle meaningless and irrelevant content. In this paper we have worked on the Quora insincere questions (questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers) dataset in order to identify user insincere questions,… More >

V. R. Kavitha¹, K. Nimala², A. Beno³, K. C. Ramya⁴, Seifedine Kadry⁵, Byeong-Gwon Kang⁶, Yunyoung Nam^7,*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 579-593, 2023, DOI:10.32604/csse.2023.024553

Abstract Image Captioning is an emergent topic of research in the domain of artificial intelligence (AI). It utilizes an integration of Computer Vision (CV) and Natural Language Processing (NLP) for generating the image descriptions. It finds use in several application areas namely recommendation in editing applications, utilization in virtual assistance, etc. The development of NLP and deep learning (DL) models find useful to derive a bridge among the visual details and textual semantics. In this view, this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning (OHHO-DLIC) technique. The OHHO-DLIC technique involves the design of distinct levels… More >

R. Bhaskaran^1,*, S. Saravanan¹, M. Kavitha², C. Jeyalakshmi³, Seifedine Kadry⁴, Hafiz Tayyab Rauf⁵, Reem Alkhammash⁶

Computer Systems Science and Engineering, Vol.44, No.1, pp. 235-247, 2023, DOI:10.32604/csse.2023.024399

Abstract Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover,… More >

Anil Sharma^1,*, Suresh Kumar²

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1989-2005, 2022, DOI:10.32604/iasc.2022.026095

Abstract Information Retrieval (IR) systems are developed to fetch the most relevant content matching the user’s information needs from a pool of information. A user expects to get IR results based on the conceptual contents of the query rather than keywords. But traditional IR approaches index documents based on the terms that they contain and ignore semantic descriptions of document contents. This results in a vocabulary gap when queries and documents use different terms to describe the same concept. As a solution to this problem and to improve the performance of IR systems, we have designed a Shallow Neural Network and… More >