Farah Mohammad^1,2,*, Saad Al-Ahmadi³, Jalal Al-Muhtadi^1,3

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1327-1345, 2025, DOI:10.32604/cmc.2025.060228 - 26 March 2025

Abstract Software defect prediction is a critical component in maintaining software quality, enabling early identification and resolution of issues that could lead to system failures and significant financial losses. With the increasing reliance on user-generated content, social media reviews have emerged as a valuable source of real-time feedback, offering insights into potential software defects that traditional testing methods may overlook. However, existing models face challenges like handling imbalanced data, high computational complexity, and insufficient integration of contextual information from these reviews. To overcome these limitations, this paper introduces the SESDP (Sentiment Analysis-Based Early Software Defect Prediction)… More >

Zheng Zhang, Hengyang Wu^*, Na Wang

Journal on Artificial Intelligence, Vol.7, pp. 17-37, 2025, DOI:10.32604/jai.2025.059607 - 19 March 2025

Abstract This paper proposes a knowledge-enhanced disease diagnosis method based on a prompt learning framework. Addressing challenges such as the complexity of medical terminology, the difficulty of constructing medical knowledge graphs, and the scarcity of medical data, the method retrieves structured knowledge from clinical cases via external knowledge graphs. The method retrieves structured knowledge from external knowledge graphs related to clinical cases, encodes it, and injects it into the prompt templates to enhance the language model’s understanding and reasoning capabilities for the task. We conducted experiments on three public datasets: CHIP-CTC, IMCS-V2-NER, and KUAKE-QTR. The results More >

Konstantinos I. Roumeliotis^1,*, Nikolaos D. Tselikas², Dimitrios K. Nasiopoulos³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2769-2792, 2025, DOI:10.32604/cmc.2025.059567 - 17 February 2025

Abstract In the rapidly evolving landscape of natural language processing (NLP) and sentiment analysis, improving the accuracy and efficiency of sentiment classification models is crucial. This paper investigates the performance of two advanced models, the Large Language Model (LLM) LLaMA model and NLP BERT model, in the context of airline review sentiment analysis. Through fine-tuning, domain adaptation, and the application of few-shot learning, the study addresses the subtleties of sentiment expressions in airline-related text data. Employing predictive modeling and comparative analysis, the research evaluates the effectiveness of Large Language Model Meta AI (LLaMA) and Bidirectional Encoder… More >

Na Deng, Jiuan Zhang^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2235-2254, 2025, DOI:10.32604/cmc.2024.058103 - 17 February 2025

Abstract Rapid and accurate identification of high-quality patents can accelerate the transformation process of scientific and technological achievements, optimize the management of intellectual property rights and enhance the vitality of innovation. Aiming at the shortcomings of the traditional high-value patent assessment method, which is relatively simple and seldom considers the influence of patentees, this paper proposes a high-quality patent method HMFM (High-Value Patent Multi-Feature Fusion Method) that fuses multi-dimensional features. A weighted node importance assessment method in complex network called GLE (Glob-Local-struEntropy) based on improved structural entropy is designed to calculate the influence of the patentee More >

Md Hasibur Rahman, Mohammed Arif Uddin, Zinnat Fowzia Ria, Rashedur M. Rahman^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1637-1666, 2025, DOI:10.32604/cmes.2024.058329 - 27 January 2025

Abstract The rapid growth of digital data necessitates advanced natural language processing (NLP) models like BERT (Bidirectional Encoder Representations from Transformers), known for its superior performance in text classification. However, BERT’s size and computational demands limit its practicality, especially in resource-constrained settings. This research compresses the BERT base model for Bengali emotion classification through knowledge distillation (KD), pruning, and quantization techniques. Despite Bengali being the sixth most spoken language globally, NLP research in this area is limited. Our approach addresses this gap by creating an efficient BERT-based model for Bengali text. We have explored 20 combinations… More > Graphic Abstract

Nahed Alsaleh^1,2, Reem Alnanih^1,*, Nahed Alowidi¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 949-976, 2025, DOI:10.32604/cmc.2024.059351 - 03 January 2025

Abstract App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products. Automating the analysis of these reviews is vital for efficient review management. While traditional machine learning (ML) models rely on basic word-based feature extraction, deep learning (DL) methods, enhanced with advanced word embeddings, have shown superior performance. This research introduces a novel aspect-based sentiment analysis (ABSA) framework to classify app reviews based on key non-functional requirements, focusing on usability factors: effectiveness, efficiency, and satisfaction. We propose a hybrid DL model, combining BERT (Bidirectional Encoder Representations from Transformers) More >

Afam Okonkwo^*, Pius Onobhayedo, Charles Igah

Journal on Artificial Intelligence, Vol.6, pp. 379-401, 2024, DOI:10.32604/jai.2024.058649 - 31 December 2024

Abstract Artificial Intelligence, in general, and particularly Natural language Processing (NLP) has made unprecedented progress recently in many areas of life, automating and enabling a lot of activities such as speech recognition, language translations, search engines, and text-generations, among others. Software engineering and Software Development Life Cycle (SDLC) is also not left out. Indeed, one of the most critical starting points of SDLC is the requirement engineering stage which, traditionally, has been dominated by business analysts. Unfortunately, these analysts have always done the job not just in a monotonous way, but also in an error-prone, tedious,… More >

Arar Al Tawil^1,*, Laiali Almazaydeh², Doaa Qawasmeh³, Baraah Qawasmeh⁴, Mohammad Alshinwan^1,5, Khaled Elleithy⁶

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3395-3412, 2024, DOI:10.32604/cmc.2024.057279 - 18 November 2024

Abstract Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information, a practice known as phishing. This study utilizes three distinct methodologies, Term Frequency-Inverse Document Frequency, Word2Vec, and Bidirectional Encoder Representations from Transformers, to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks. The study uses feature extraction methods to assess the performance of Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron algorithms. The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron (Precision: 0.98, Recall: 0.98, F1-score: 0.98, Accuracy: 0.98). Word2Vec’s More >

Brij B. Gupta^1,2,3,4,*, Akshat Gaurav⁵, Varsha Arya^6,7, Razaz Waheeb Attar⁸, Shavi Bansal⁹, Ahmed Alhomoud¹⁰, Kwok Tai Chui¹¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2165-2183, 2024, DOI:10.32604/cmes.2024.056473 - 31 October 2024

Abstract Phishing attacks present a serious threat to enterprise systems, requiring advanced detection techniques to protect sensitive data. This study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers (BERT) for feature extraction and CNN for classification, specifically designed for enterprise information systems. BERT’s linguistic capabilities are used to extract key features from email content, which are then processed by a convolutional neural network (CNN) model optimized for phishing detection. Achieving an accuracy of 97.5%, our proposed model demonstrates strong proficiency in identifying phishing emails. This approach represents a significant advancement in More >

Mohamed A. Mahdi¹, Suliman Mohamed Fati^2,*, Mohamed A.G. Hazber¹, Shahanawaj Ahamad³, Sawsan A. Saad⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1651-1671, 2024, DOI:10.32604/cmes.2024.052291 - 27 September 2024

Abstract Cyberbullying, a critical concern for digital safety, necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces. To tackle this challenge, our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers (BERT) base model (cased), originally pretrained in English. This model is uniquely adapted to recognize the intricate nuances of Arabic online communication, a key aspect often overlooked in conventional cyberbullying detection methods. Our model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media (SM) tweets showing a notable… More >