Meijing Li^*, Runqing Huang, Xianxian Qi

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2283-2299, 2024, DOI:10.32604/cmc.2024.053630

Abstract Chinese Clinical Named Entity Recognition (CNER) is a crucial step in extracting medical information and is of great significance in promoting medical informatization. However, CNER poses challenges due to the specificity of clinical terminology, the complexity of Chinese text semantics, and the uncertainty of Chinese entity boundaries. To address these issues, we propose an improved CNER model, which is based on multi-feature fusion and multi-scale local context enhancement. The model simultaneously fuses multi-feature representations of pinyin, radical, Part of Speech (POS), word boundary with BERT deep contextual representations to enhance the semantic representation of text… More >

Gonghui Deng, Dunzhi Wu, Weizhen Chen^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1985-2003, 2024, DOI:10.32604/cmc.2024.052174

Abstract The task of food image recognition, a nuanced subset of fine-grained image recognition, grapples with substantial intra-class variation and minimal inter-class differences. These challenges are compounded by the irregular and multi-scale nature of food images. Addressing these complexities, our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion, grounded in the ConvNeXt architecture. Our model employs hybrid attention (HA) mechanisms to pinpoint critical discriminative regions within images, substantially mitigating the influence of background noise. Furthermore, it introduces a multi-stage local fusion (MSLF) module, fostering long-distance dependencies between feature maps at… More >

Baoxia Li¹, Wenzhuo Chen¹, Xiaojiang Tang¹, Shaohuang Bian¹, Yang Liu², Junwei Guo², Dan Zhang², Feng Huang^2,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2221-2236, 2024, DOI:10.32604/cmc.2024.049862

Abstract Complex plasma widely exists in thin film deposition, material surface modification, and waste gas treatment in industrial plasma processes. During complex plasma discharge, the configuration, distribution, and size of particles, as well as the discharge glow, strongly depend on discharge parameters. However, traditional manual diagnosis methods for recognizing discharge parameters from discharge images are complicated to operate with low accuracy, time-consuming and high requirement of instruments. To solve these problems, by combining the two mechanisms of attention mechanism (strengthening the extraction of the channel feature) and shortcut connection (enabling the input information to be directly… More >

Md. Mahbubur Rahman Tusher¹, Fahmid Al Farid^2,*, Md. Al-Hasan¹, Abu Saleh Musa Miah¹, Susmita Roy Rinky¹, Mehedi Hasan Jim¹, Sarina Mansor², Md. Abdur Rahim³, Hezerul Abdul Karim^2,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2633-2656, 2024, DOI:10.32604/cmc.2024.049296

Abstract The context of recognizing handwritten city names, this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script. In today’s technology-driven era, where precise tools for reading handwritten text are essential, this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting. The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems, particularly in critical areas such as postal automation and document processing. Notably, no prior research has specifically targeted the unique needs of Bangla handwritten city name… More >

Xianxian Yin¹, Shimin Yin¹, Yiming Bu², Xiukun Wei^3,*

Structural Durability & Health Monitoring, Vol.18, No.5, pp. 623-640, 2024, DOI:10.32604/sdhm.2024.050195

Abstract As an important component of load transfer, various fatigue damages occur in the track as the rail service life and train traffic increase gradually, such as rail corrugation, rail joint damage, uneven thermite welds, rail squats fastener defects, etc. Real-time recognition of track defects plays a vital role in ensuring the safe and stable operation of rail transit. In this paper, an intelligent and innovative method is proposed to detect the track defects by using axle-box vibration acceleration and deep learning network, and the coexistence of the above-mentioned typical track defects in the track system… More >

Bo Cao^1,*, Qinghua Xing², Longyue Li², Huaixi Xing¹, Zhanfu Song¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1251-1275, 2024, DOI:10.32604/cmc.2024.052842

Abstract As a core part of battlefield situational awareness, air target intention recognition plays an important role in modern air operations. Aiming at the problems of insufficient feature extraction and misclassification in intention recognition, this paper designs an air target intention recognition method (KGTLIR) based on Knowledge Graph and Deep Learning. Firstly, the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism. Meanwhile, the accuracy, recall, and F1-score after iteration are introduced More >

Seolhee Kim¹, Sang-Duck Lee^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 195-215, 2024, DOI:10.32604/cmc.2024.052070

Abstract Damage to parcels reduces customer satisfaction with delivery services and increases return-logistics costs. This can be prevented by detecting and addressing the damage before the parcels reach the customer. Consequently, various studies have been conducted on deep learning techniques related to the detection of parcel damage. This study proposes a deep learning-based damage detection method for various types of parcels. The method is intended to be part of a parcel information-recognition system that identifies the volume and shipping information of parcels, and determines whether they are damaged; this method is intended for use in the… 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 >

Mohamed Karray^1,*, Nesrine Triki^2,*, Mohamed Ksantini²

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 119-138, 2024, DOI:10.32604/cmc.2024.051562

Abstract Advanced Driver Assistance Systems (ADAS) technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road. Traffic Sign Recognition System (TSRS) is one of the most important components of ADAS. Among the challenges with TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time. Accordingly, this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules. Firstly, the Speed Limit Detection (SLD) module uses… More >

Jian Luo^1,*, Bo Xu¹, Tardi Tjahjadi², Jian Yi¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 235-261, 2024, DOI:10.32604/cmc.2024.050018

Abstract Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes. This paper proposes a novel targeted 3-dimensional (3D) gait model (3DGait) represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model. The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing. The 3DGait recognition method involves 2-dimensional (2D) to 3DGait data learning based on 3D virtual samples, a semantic gait parameter estimation Long Short Time Memory (LSTM) network (3D-SGPE-LSTM), a feature fusion… More >