Palak Bari, Gurnur Bedi, Khushi Joshi, Anupama Jawale^*

Journal on Artificial Intelligence, Vol.7, pp. 499-508, 2025, DOI:10.32604/jai.2025.072531 - 17 November 2025

Abstract This study investigates sarcasm detection in text using a dataset of 8095 sentences compiled from MUStARD and HuggingFace repositories, balanced across sarcastic and non-sarcastic classes. A sequential baseline model (LSTM) is compared with transformer-based models (RoBERTa and XLNet), integrated with attention mechanisms. Transformers were chosen for their proven ability to capture long-range contextual dependencies, whereas LSTM serves as a traditional benchmark for sequential modeling. Experimental results show that RoBERTa achieves 0.87 accuracy, XLNet 0.83, and LSTM 0.52. These findings confirm that transformer architectures significantly outperform recurrent models in sarcasm detection. Future work will incorporate multimodal More >

Yiming Guo, Hongyu Ma^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3485-3505, 2025, DOI:10.32604/cmc.2025.067590 - 23 September 2025

Abstract In dynamic 5G network environments, user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching. Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device (D2D) cooperative caching, limiting the reduction of transmission latency. To address this issue, this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning. First, a Transformer-based geolocation prediction model is designed, leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.… More >

Md Shakhawat Hossain^1,2,#,*, Md Sahilur Rahman^2,#, Munim Ahmed², Anowar Hussen³, Zahid Ullah⁴, Mona Jamjoom⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3193-3215, 2025, DOI:10.32604/cmc.2025.065230 - 03 July 2025

Abstract One in every eight men in the US is diagnosed with prostate cancer, making it the most common cancer in men. Gleason grading is one of the most essential diagnostic and prognostic factors for planning the treatment of prostate cancer patients. Traditionally, urological pathologists perform the grading by scoring the morphological pattern, known as the Gleason pattern, in histopathology images. However, this manual grading is highly subjective, suffers intra- and inter-pathologist variability and lacks reproducibility. An automated grading system could be more efficient, with no subjectivity and higher accuracy and reproducibility. Automated methods presented previously… More >

Suliman Mohamed Fati^1,*, Mohammed A. Mahdi², Mohamed A.G. Hazber², Shahanawaj Ahamad³, Sawsan A. Saad⁴, Mohammed Gamal Ragab⁵, Mohammed Al-Shalabi²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2109-2131, 2025, DOI:10.32604/cmes.2025.063092 - 30 May 2025

Abstract Cyberbullying on social media poses significant psychological risks, yet most detection systems oversimplify the task by focusing on binary classification, ignoring nuanced categories like passive-aggressive remarks or indirect slurs. To address this gap, we propose a hybrid framework combining Term Frequency-Inverse Document Frequency (TF-IDF), word-to-vector (Word2Vec), and Bidirectional Encoder Representations from Transformers (BERT) based models for multi-class cyberbullying detection. Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships, fused with BERT’s contextual embeddings to capture syntactic and semantic complexities. We evaluate the framework on a publicly available dataset of 47,000 annotated social… More >

Faheem Shaukat¹, Naveed Ejaz^1,2, Rashid Kamal^3,4, Tamim Alkhalifah^5,*, Sheraz Aslam^6,7,*, Mu Mu⁴

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5595-5622, 2025, DOI:10.32604/cmc.2025.061702 - 19 May 2025

Abstract Automated and accurate movie genre classification is crucial for content organization, recommendation systems, and audience targeting in the film industry. Although most existing approaches focus on audiovisual features such as trailers and posters, the text-based classification remains underexplored despite its accessibility and semantic richness. This paper introduces the Genre Attention Model (GAM), a deep learning architecture that integrates transformer models with a hierarchical attention mechanism to extract and leverage contextual information from movie plots for multi-label genre classification. In order to assess its effectiveness, we assess multiple transformer-based models, including Bidirectional Encoder Representations from Transformers… More >

Song Liu^1,2, Hongying Zhang^1,*, Xue Li¹, Xi Yang^1,3

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1969-1984, 2025, DOI:10.32604/cmc.2024.057662 - 17 February 2025

Abstract Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retinexformer introduces channel self-attention mechanisms in the IG-MSA. However, it fails to effectively capture long-range spatial dependencies, leaving room for improvement. Based on the Retinexformer deep learning framework, we designed the Retinexformer+ network. The “+” signifies our advancements in extracting long-range spatial dependencies. We introduced multi-scale dilated convolutions in illumination estimation to expand the receptive field. These convolutions effectively capture the weakening semantic dependency between pixels… More >

Shorouq Alshawabkeh, Li Wu^*, Daojun Dong, Yao Cheng, Liping Li

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 561-577, 2025, DOI:10.32604/cmc.2024.057213 - 03 January 2025

Abstract Detecting pavement cracks is critical for road safety and infrastructure management. Traditional methods, relying on manual inspection and basic image processing, are time-consuming and prone to errors. Recent deep-learning (DL) methods automate crack detection, but many still struggle with variable crack patterns and environmental conditions. This study aims to address these limitations by introducing the MaskerTransformer, a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network (Mask R-CNN) with the global contextual awareness of Vision Transformer (ViT). The research focuses on leveraging the strengths of both architectures… 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 >

Huiling Lu^1,*, Tao Zhou^2,3

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4371-4393, 2024, DOI:10.32604/cmc.2024.047827 - 20 June 2024

Abstract The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis. However, in PET/CT (Positron Emission Tomography/Computed Tomography) lung images, the lesion shapes are complex, the edges are blurred, and the sample numbers are unbalanced. To solve these problems, this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model (MCIF-Transformer Mask RCNN) for PET/CT lung tumor instance segmentation, The main innovative works of this paper are as follows: Firstly, the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images. The pixel dependence relationship… More >

R. Sujatha, K. Nimala^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963 - 27 February 2024

Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for… More >