Zheyi Chen^1,, Liuchang Xu^1,, Hongting Zheng¹, Luyao Chen¹, Amr Tolba^2,3, Liang Zhao⁴, Keping Yu^5,*, Hailin Feng^1,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1753-1808, 2024, DOI:10.32604/cmc.2024.052618

Abstract Since the 1950s, when the Turing Test was introduced, there has been notable progress in machine language intelligence. Language modeling, crucial for AI development, has evolved from statistical to neural models over the last two decades. Recently, transformer-based Pre-trained Language Models (PLM) have excelled in Natural Language Processing (NLP) tasks by leveraging large-scale training corpora. Increasing the scale of these models enhances performance significantly, introducing abilities like context learning that smaller models lack. The advancement in Large Language Models, exemplified by the development of ChatGPT, has made significant impacts both academically and industrially, capturing widespread… More >

Sameer Qazi^1,*, Muhammad Bilal Kadri², Muhammad Naveed^1,*, Bilal A. Khawaja³, Sohaib Zia Khan⁴, Muhammad Mansoor Alam^5,6,7, Mazliham Mohd Su’ud⁶

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3289-3314, 2024, DOI:10.32604/cmc.2024.048893

Abstract COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected were schools, colleges, and universities. The education system of entire nations had shifted to online education during this time. Many shortcomings of Learning Management Systems (LMSs) were detected to support education in an online mode that spawned the research in Artificial Intelligence (AI) based tools that are being developed by the research community to improve the effectiveness of LMSs. This paper presents a detailed survey of the different enhancements to LMSs, which… More >

Sohail Muhammad¹, Muzammil Khan², Sarwar Shah Khan^2,3,*

Journal on Artificial Intelligence, Vol.6, pp. 193-209, 2024, DOI:10.32604/jai.2024.052099

Abstract Retrieving information from evolving digital data collection using a user’s query is always essential and needs efficient retrieval mechanisms that help reduce the required time from such massive collections. Large-scale time consumption is certain to scan and analyze to retrieve the most relevant textual data item from all the documents required a sophisticated technique for a query against the document collection. It is always challenging to retrieve a more accurate and fast retrieval from a large collection. Text summarization is a dominant research field in information retrieval and text processing to locate the most appropriate… More >

Glody Muka^1,*, Patrick Mukala^1,2,*

Journal on Artificial Intelligence, Vol.6, pp. 211-223, 2024, DOI:10.32604/jai.2024.049685

Abstract Reviews have a significant impact on online businesses. Nowadays, online consumers rely heavily on other people's reviews before purchasing a product, instead of looking at the product description. With the emergence of technology, malicious online actors are using techniques such as Natural Language Processing (NLP) and others to generate a large number of fake reviews to destroy their competitors’ markets. To remedy this situation, several researches have been conducted in the last few years. Most of them have applied NLP techniques to preprocess the text before building Machine Learning (ML) or Deep Learning (DL) models… More >

Jinqiao Dai, Pengsen Cheng, Jiayong Liu^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 547-565, 2024, DOI:10.32604/cmc.2024.051745

Abstract Generating diverse and factual text is challenging and is receiving increasing attention. By sampling from the latent space, variational autoencoder-based models have recently enhanced the diversity of generated text. However, existing research predominantly depends on summarization models to offer paragraph-level semantic information for enhancing factual correctness. The challenge lies in effectively generating factual text using sentence-level variational autoencoder-based models. In this paper, a novel model called fact-aware conditional variational autoencoder is proposed to balance the factual correctness and diversity of generated text. Specifically, our model encodes the input sentences and uses them as facts to… More >

G. Sunil Santhosh Kumar^1,2,*, M. Rudra Kumar³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 695-713, 2024, DOI:10.32604/cmc.2024.050143

Abstract This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost, a machine learning algorithm renowned for its efficiency and performance. The framework proposed herein utilizes the fusion of diversified feature formats, specifically, metadata, textual, and pattern features. The goal is to enhance the system’s ability to discern and generalize transformation rules from source to destination formats in varied contexts. Firstly, the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model. Subsequent sections expound… More >

Badriyya B. Al-onazi¹, Jaber S. Alzahrani², Najm Alotaibi³, Hussain Alshahrani⁴, Mohamed Ahmed Elfaki⁴, Radwa Marzouk⁵, Heba Mohsen⁶, Abdelwahed Motwakel^7,*

Intelligent Automation & Soft Computing, Vol.39, No.3, pp. 567-583, 2024, DOI:10.32604/iasc.2023.033835

Abstract In recent years, the usage of social networking sites has considerably increased in the Arab world. It has empowered individuals to express their opinions, especially in politics. Furthermore, various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different scales. This is attributed to business owners’ understanding of social media’s importance for business development. However, 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 nouns. Hate speech… More >

M. Navya Pratyusha, Ranjan Kumar^*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2957-2976, 2024, DOI:10.32604/cmes.2024.051581

Abstract In the real world, one of the most common problems in project management is the unpredictability of resources and timelines. An efficient way to resolve uncertainty problems and overcome such obstacles is through an extended fuzzy approach, often known as neutrosophic logic. Our rigorous proposed model has led to the creation of an advanced technique for computing the triangular single-valued neutrosophic number. This innovative approach evaluates the inherent uncertainty in project durations of the planning phase, which enhances the potential significance of the decision-making process in the project. Our proposed method, for the first time… More >

Adéla Hamplová^1,*, Alexey Lyavdansky^2,*, Tomáš Novák¹, Ondřej Svojše¹, David Franc¹, Arnošt Veselý¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2869-2889, 2024, DOI:10.32604/cmes.2024.050791

Abstract This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions, employing two state-of-the-art deep learning algorithms, namely YOLOv8 and Roboflow 3.0. The goal is to contribute to the preservation and understanding of historical texts, showcasing the potential of modern deep learning methods in archaeological research. Our research culminates in several key findings and scientific contributions. We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context. We also created… More >

Ronghao Pan, José Antonio García-Díaz^*, Rafael Valencia-García

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2849-2868, 2024, DOI:10.32604/cmes.2024.049631

Abstract Large Language Models (LLMs) are increasingly demonstrating their ability to understand natural language and solve complex tasks, especially through text generation. One of the relevant capabilities is contextual learning, which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates. In recent years, the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior. In this study, we investigate the ability of different LLMs, ranging from zero-shot… More >