S. Praveenkumar^*, T. Karthick

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1073-1073, 2024, DOI:10.32604/csse.2024.054414

Abstract This article has no abstract. More >

Rudraneel Dutta^*, Shreya Mohanty

Journal on Artificial Intelligence, Vol.6, pp. 177-192, 2024, DOI:10.32604/jai.2024.050706

Abstract Traditionally, exam preparation involves manually analyzing past question papers to identify and prioritize key topics. This research proposes a data-driven solution to automate this process using techniques like Document Layout Segmentation, Optical Character Recognition (OCR), and Latent Dirichlet Allocation (LDA) for topic modelling. This study aims to develop a system that utilizes machine learning and topic modelling to identify and rank key topics from historical exam papers, aiding students in efficient exam preparation. The research addresses the difficulty in exam preparation due to the manual and labour-intensive process of analyzing past exam papers to identify… More >

Jie-Di Weng¹, Yong-Zheng Jiang^1,*, Long-Chao Chen¹, Xu Zhang¹, Guan-Yong Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2533-2557, 2024, DOI:10.32604/cmes.2024.051232

Abstract Applying numerical simulation technology to investigate fluid-solid interaction involving complex curved boundaries is vital in aircraft design, ocean, and construction engineering. However, current methods such as Lattice Boltzmann (LBM) and the immersion boundary method based on solid ratio (IMB) have limitations in identifying custom curved boundaries. Meanwhile, IBM based on velocity correction (IBM-VC) suffers from inaccuracies and numerical instability. Therefore, this study introduces a high-accuracy curve boundary recognition method (IMB-CB), which identifies boundary nodes by moving the search box, and corrects the weighting function in LBM by calculating the solid ratio of the boundary nodes,… More > Graphic Abstract

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 >

Salah Alghyaline^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4905-4924, 2024, DOI:10.32604/cmc.2024.052016

Abstract Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles. Arabic is morphologically rich, and its characters have a high similarity. The Arabic language includes 28 characters. Each character has up to four shapes according to its location in the word (at the beginning, middle, end, and isolated). This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters. The proposed architectures were derived from the popular CNN architectures, such as VGG, ResNet, and Inception, to make them applicable to recognizing character-size images. The experimental results on three More >

Shiwen Song, Rui Zhang, Min Hu^*, Feiyao Huang

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5243-5271, 2024, DOI:10.32604/cmc.2024.050879

Abstract Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security. Currently, with the emergence of massive high-resolution multi-modality images, the use of multi-modality images for fine-grained recognition has become a promising technology. Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples. The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features. The attention mechanism helps the model to pinpoint the key information in the image, resulting in a… More >

Yunfeng Cai¹, Ran Qin¹, Jin Tang¹, Long Zhang¹, Xiaotian Bi¹, Qing Yang^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4979-4994, 2024, DOI:10.32604/cmc.2024.050435

Abstract Electric power training is essential for ensuring the safety and reliability of the system. In this study, we introduce a novel Abnormal Action Recognition (AAR) system that utilizes a Lightweight Pose Estimation Network (LPEN) to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios. The LPEN network, comprising three stages—MobileNet, Initial Stage, and Refinement Stage—is employed to swiftly extract image features, detect human key points, and refine them for accurate analysis. Subsequently, a Pose-aware Action Analysis Module (PAAM) captures the positional coordinates of human skeletal points in each frame. Finally, More >

Yi-Chun Lai¹, Shu-Yin Chiang², Yao-Chiang Kan³, Hsueh-Chun Lin^4,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3783-3803, 2024, DOI:10.32604/cmc.2024.050376

Abstract Artificial intelligence (AI) technology has become integral in the realm of medicine and healthcare, particularly in human activity recognition (HAR) applications such as fitness and rehabilitation tracking. This study introduces a robust coupling analysis framework that integrates four AI-enabled models, combining both machine learning (ML) and deep learning (DL) approaches to evaluate their effectiveness in HAR. The analytical dataset comprises 561 features sourced from the UCI-HAR database, forming the foundation for training the models. Additionally, the MHEALTH database is employed to replicate the modeling process for comparative purposes, while inclusion of the WISDM database, renowned… More > Graphic Abstract

Yan Xiang^1,2, Xuedong Zhao^1,2, Junjun Guo^1,2,*, Zhiliang Shi³, Enbang Chen³, Xiaobo Zhang³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4149-4167, 2024, DOI:10.32604/cmc.2024.050229

Abstract Chinese named entity recognition (CNER) has received widespread attention as an important task of Chinese information extraction. Most previous research has focused on individually studying flat CNER, overlapped CNER, or discontinuous CNER. However, a unified CNER is often needed in real-world scenarios. Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER. Nevertheless, how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge. In this study, we enhance the character-pair grid representation… More >

Karshiev Sanjar¹, Soyoun Bang¹, Sookhee Ryue², Heechul Jung^1,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3569-3583, 2024, DOI:10.32604/cmc.2024.048312

Abstract The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe, navigable routes. Traditional approaches primarily focus on broad applications such as wayfinding, obstacle detection, and fall prevention. However, there is a notable discrepancy in applying these technologies to more specific scenarios, like identifying distinct food crop types or recognizing faces. This study proposes a real-time application designed for visually impaired individuals, aiming to bridge this research-application gap. It introduces a system capable of detecting 20 different food crop types… More >