Jing Yang, Bin Ji, Shasha Li^*, Jun Ma, Jie Yu

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4303-4315, 2024, DOI:10.32604/cmc.2023.035594

Abstract Named entity recognition (NER) is a fundamental task of information extraction (IE), and it has attracted considerable research attention in recent years. The abundant annotated English NER datasets have significantly promoted the NER research in the English field. By contrast, much fewer efforts are made to the Chinese NER research, especially in the scientific domain, due to the scarcity of Chinese NER datasets. To alleviate this problem, we present a Chinese scientific NER dataset–SciCN, which contains entity annotations of titles and abstracts derived from 3,500 scientific papers. We manually annotate a total of 62,059 entities, and these entities are classified… More >

Yongcan Zhu^1,2, Jiawen Wang¹, Ye Zhang^1,2, Long Zhao¹, Botao Jiang¹, Xinbo Huang^1,*

Energy Engineering, Vol.121, No.4, pp. 895-911, 2024, DOI:10.32604/ee.2023.041002

Abstract The accumulation of snow and ice on PV modules can have a detrimental impact on power generation, leading to reduced efficiency for prolonged periods. Thus, it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules. To address this issue, the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images. Furthermore, the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules, allowing for the establishment of a residual ice and… More >

Shuai Chen¹, Yinwei Ma², Zhongshu Wang², Zongmei Xu³, Song Zhang¹, Jianle Li¹, Hao Xu¹, Zhanjun Wu^1,*

Structural Durability & Health Monitoring, Vol.18, No.2, pp. 125-141, 2024, DOI:10.32604/sdhm.2024.042594

Abstract The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life. To this end, distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages, such as lightweight and ease of embedding. However, identifying the precise location of damage from the optical fiber signals remains a critical challenge. In this paper, a novel approach which namely Modified Sliding Window Principal Component Analysis (MSWPCA) was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors. The proposed method is able to extract signal… More > Graphic Abstract

Bijon Mallik¹, Md Abdur Rahim¹, Abu Saleh Musa Miah², Keun Soo Yun^3,*, Jungpil Shin²

Computer Systems Science and Engineering, Vol.48, No.2, pp. 555-570, 2024, DOI:10.32604/csse.2023.045981

Abstract In the digital age, non-touch communication technologies are reshaping human-device interactions and raising security concerns. A major challenge in current technology is the misinterpretation of gestures by sensors and cameras, often caused by environmental factors. This issue has spurred the need for advanced data processing methods to achieve more accurate gesture recognition and predictions. Our study presents a novel virtual keyboard allowing character input via distinct hand gestures, focusing on two key aspects: hand gesture recognition and character input mechanisms. We developed a novel model with LSTM and fully connected layers for enhanced sequential data processing and hand gesture recognition.… More >

Hae Sung Park¹, Yong Suk Choi^2,*

Computer Systems Science and Engineering, Vol.48, No.2, pp. 451-477, 2024, DOI:10.32604/csse.2023.043245

Abstract Much like humans focus solely on object movement to understand actions, directing a deep learning model’s attention to the core contexts within videos is crucial for improving video comprehension. In the recent study, Video Masked Auto-Encoder (VideoMAE) employs a pre-training approach with a high ratio of tube masking and reconstruction, effectively mitigating spatial bias due to temporal redundancy in full video frames. This steers the model’s focus toward detailed temporal contexts. However, as the VideoMAE still relies on full video frames during the action recognition stage, it may exhibit a progressive shift in attention towards spatial contexts, deteriorating its ability… More >

Irfan Haider¹, Muhammad Attique Khan^1,*, Muhammad Nazir¹, Taerang Kim², Jae-Hyuk Cha²

Computer Systems Science and Engineering, Vol.48, No.2, pp. 529-554, 2024, DOI:10.32604/csse.2023.042080

Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for… More >

Jungpil Shin^1,*, Md. Al Mehedi Hasan², Abu Saleh Musa Miah¹, Kota Suzuki¹, Koki Hirooka¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2605-2625, 2024, DOI:10.32604/cmes.2023.046334

Abstract Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities. In Japan, approximately 360,000 individuals with hearing and speech disabilities rely on Japanese Sign Language (JSL) for communication. However, existing JSL recognition systems have faced significant performance limitations due to inherent complexities. In response to these challenges, we present a novel JSL recognition system that employs a strategic fusion approach, combining joint skeleton-based handcrafted features and pixel-based deep learning features. Our system incorporates two distinct streams: the first stream extracts crucial handcrafted features, emphasizing the capture of hand and body movements within JSL gestures. Simultaneously,… More >

Yizhao Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3029-3045, 2024, DOI:10.32604/cmes.2024.046270

Abstract Near-fault impulsive ground-shaking is highly destructive to engineering structures, so its accurate identification ground-shaking is a top priority in the engineering field. However, due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods, the generalization and accuracy of the identification process are low. To address these problems, an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed. Firstly, ground-shaking characteristics were analyzed and ground-shaking the data was annotated using Baker’s method. Secondly, the Principal Component Analysis (PCA) method was used to extract the most relevant features related to impulsive ground-shaking. Thirdly, a… More >

Yanqiong Zhang, Xianwei Jiang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2399-2450, 2024, DOI:10.32604/cmes.2023.045731

Abstract Sign language, a visual-gestural language used by the deaf and hard-of-hearing community, plays a crucial role in facilitating communication and promoting inclusivity. Sign language recognition (SLR), the process of automatically recognizing and interpreting sign language gestures, has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world. The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR. This paper presents a comprehensive and up-to-date analysis of the advancements, challenges, and opportunities in deep learning-based sign language recognition, focusing on the… More >

Shahriar Md Arman¹, Tao Yang^1,*, Shahadat Shahed², Alanoud Al Mazroa³, Afraa Attiah⁴, Linda Mohaisen⁴

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2087-2110, 2024, DOI:10.32604/cmc.2024.047870

Abstract The rapid growth of smart technologies and services has intensified the challenges surrounding identity authentication techniques. Biometric credentials are increasingly being used for verification due to their advantages over traditional methods, making it crucial to safeguard the privacy of people’s biometric data in various scenarios. This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems. It proposes a noble and thorough taxonomy survey for privacy-preserving techniques, as well as a systematic framework for categorizing the field’s existing literature. We review the state-of-the-art methods and address their advantages and limitations in the context of various biometric… More >