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 >

Qiang Ma^1,2,3,4, Zepeng Li^1,2, Kai Yang^1,2,*, Shaofeng Zhang^1,2, Zhuopei Wei^1,2

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1607-1634, 2025, DOI:10.32604/sdhm.2025.069876 - 17 November 2025

Abstract Effective fault identification is crucial for bearings, which are critical components of mechanical systems and play a pivotal role in ensuring overall safety and operational efficiency. Bearings operate under variable service conditions, and their diagnostic environments are complex and dynamic. In the process of bearing diagnosis, fault datasets are relatively scarce compared with datasets representing normal operating conditions. These challenges frequently cause the practicality of fault detection to decline, the extraction of fault features to be incomplete, and the diagnostic accuracy of many existing models to decrease. In this work, a transfer-learning framework, designated DSCNN-HA-TL,… More >

Yating Xu¹, Mansheng Xiao^1,*, Mengxing Gao¹, Zhenzhen Liu¹, Zeyu Xiao²

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1635-1656, 2025, DOI:10.32604/sdhm.2025.068822 - 17 November 2025

Abstract During the operation, maintenance and upkeep of concrete buildings, surface cracks are often regarded as important warning signs of potential damage. Their precise segmentation plays a key role in assessing the health of a building. Traditional manual inspection is subjective, inefficient and has safety hazards. In contrast, current mainstream computer vision–based crack segmentation methods still suffer from missed detections, false detections, and segmentation discontinuities. These problems are particularly evident when dealing with small cracks, complex backgrounds, and blurred boundaries. For this reason, this paper proposes a lightweight building surface crack segmentation method, HL-YOLO, based on… More >

Xinjian Li¹, Yu Zhang^1,2,*, Zewen Wang¹, Zhenyun Song¹

Energy Engineering, Vol.122, No.11, pp. 4603-4619, 2025, DOI:10.32604/ee.2025.065799 - 27 October 2025

Abstract Power prediction has been critical in large-scale wind power grid connections. However, traditional wind power prediction methods have long suffered from problems, for instance low prediction accuracy and poor reliability. For this purpose, a hybrid prediction model (VMD-LSTM-Attention) has been proposed, which integrates the variational modal decomposition (VMD), the long short-term memory (LSTM), and the attention mechanism (Attention), and has been optimized by improved dung beetle optimization algorithm (IDBO). Firstly, the algorithm’s performance has been significantly enhanced through the implementation of three key strategies, namely the elite group strategy of the Logistic-Tent map, the nonlinear… More >

Boyan Cui^1,2, Huijuan Wang^1,*, Yongjun Qi^1,*, Hongce Chen¹, Quanbo Yuan^1,3, Dongran Liu¹, Xuehua Zhou¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5531-5553, 2025, DOI:10.32604/cmc.2025.070101 - 23 October 2025

Abstract The proliferation of malware and the emergence of adversarial samples pose severe threats to global cybersecurity, demanding robust detection mechanisms. Traditional malware detection methods suffer from limited feature extraction capabilities, while existing Vision Transformer (ViT)-based approaches face high computational complexity due to global self-attention, hindering their efficiency in handling large-scale image data. To address these issues, this paper proposes a novel hybrid enhanced Vision Transformer architecture, HERL-ViT, tailored for malware detection. The detection framework involves five phases: malware image visualization, image segmentation with patch embedding, regional-local attention-based feature extraction, enhanced feature transformation, and classification. Methodologically,… More >

Jiao Chen¹, Xiao Wang^1,*, Zhiqin He¹, Yi Chen², Chao Ma¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5423-5450, 2025, DOI:10.32604/cmc.2025.067952 - 23 October 2025

Abstract This research proposes an innovative solution to the inherent challenges faced by landslide displacement prediction models based on data-driven methods, such as the need for extensive historical datasets for training, the reliance on manual feature selection, and the difficulty in effectively utilizing landslide historical data. We have developed a dual-channel deep learning prediction model that integrates multimodal decomposition and an attention mechanism to overcome these challenges and improve prediction performance. The proposed methodology follows a three-stage framework: (1) Empirical Mode Decomposition (EMD) effectively segregates cumulative displacement and feature factors; (2) We have developed a Double… More >

Gang Li^1,#, Ru Wang^1,#, Yang Zhang^2,*, Chuanyun Xu², Xinyu Fan¹, Zheng Zhou¹, Pengfei Lv¹, Zihan Ruan¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3267-3288, 2025, DOI:10.32604/cmc.2025.067763 - 23 September 2025

Abstract Currently, challenges such as small object size and occlusion lead to a lack of accuracy and robustness in small object detection. Since small objects occupy only a few pixels in an image, the extracted features are limited, and mainstream downsampling convolution operations further exacerbate feature loss. Additionally, due to the occlusion-prone nature of small objects and their higher sensitivity to localization deviations, conventional Intersection over Union (IoU) loss functions struggle to achieve stable convergence. To address these limitations, LR-Net is proposed for small object detection. Specifically, the proposed Lossless Feature Fusion (LFF) method transfers spatial… More >

Zihan Jia, Bowen Ma, Dongyue Chen^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2873-2891, 2025, DOI:10.32604/cmc.2025.067460 - 23 September 2025

Abstract In real-world scenarios, dual-view X-ray machines have outnumbered single-view X-ray machines due to their ability to provide comprehensive internal information about the baggage, which is important for identifying prohibited items that are not visible in one view due to rotation or overlap. However, existing work still focuses mainly on single-view, and the limited dual-view based work only performs simple information fusion at the feature or decision level and lacks effective utilization of the complementary information hidden in dual view. To this end, this paper proposes an end-to-end dual-view prohibited item detection method, the core of… More >

Hazem Farah¹, Akram Bennour^1,*, Hama Soltani¹, Mouaaz Nahas², Rashiq Rafiq Marie³, Mohammed Al-Sarem^3,4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3335-3348, 2025, DOI:10.32604/cmc.2025.067226 - 23 September 2025

Abstract This study presents an advanced method for post-mortem person identification using the segmentation of skeletal structures from chest X-ray images. The proposed approach employs the Attention U-Net architecture, enhanced with gated attention mechanisms, to refine segmentation by emphasizing spatially relevant anatomical features while suppressing irrelevant details. By isolating skeletal structures which remain stable over time compared to soft tissues, this method leverages bones as reliable biometric markers for identity verification. The model integrates custom-designed encoder and decoder blocks with attention gates, achieving high segmentation precision. To evaluate the impact of architectural choices, we conducted an… More >

Nouman Ahmad^*, Changsheng Zhang

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3321-3334, 2025, DOI:10.32604/cmc.2025.067044 - 23 September 2025

Abstract Source code vulnerabilities present significant security threats, necessitating effective detection techniques. Rigid rule-sets and pattern matching are the foundation of traditional static analysis tools, which drown developers in false positives and miss context-sensitive vulnerabilities. Large Language Models (LLMs) like BERT, in particular, are examples of artificial intelligence (AI) that exhibit promise but frequently lack transparency. In order to overcome the issues with model interpretability, this work suggests a BERT-based LLM strategy for vulnerability detection that incorporates Explainable AI (XAI) methods like SHAP and attention heatmaps. Furthermore, to ensure auditable and comprehensible choices, we present a… More >