Chaozhi Cai^*, Tiexin Xu, Jianhua Ren, Yingfang Xue

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 125-144, 2025, DOI:10.32604/sdhm.2024.052813 - 15 November 2024

Abstract A bearing fault diagnosis method based on the Markov transition field (MTF) and SEnet (SE)-IShufflenetV2 model is proposed in this paper due to the problems of complex working conditions, low fault diagnosis accuracy, and poor generalization of rolling bearing. Firstly, MTF is used to encode one-dimensional time series vibration signals and convert them into time-dependent and unique two-dimensional feature images. Then, the generated two-dimensional dataset is fed into the SE-IShufflenetV2 model for training to achieve fault feature extraction and classification. This paper selects the bearing fault datasets from Case Western Reserve University and Paderborn University… More >

Meena N. Alnuaimi¹, Nourah S. Alqahtani¹, Mohammed Gollapalli², Atta Rahman^1,*, Alaa Alahmadi¹, Aghiad Bakry¹, Mustafa Youldash³, Dania Alkhulaifi¹, Rashad Ahmed⁴, Hesham Al-Musallam¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2609-2623, 2024, DOI:10.32604/cmes.2024.055303 - 31 October 2024

Abstract Human beings are often affected by a wide range of skin diseases, which can be attributed to genetic factors and environmental influences, such as exposure to sunshine with ultraviolet (UV) rays. If left untreated, these diseases can have severe consequences and spread, especially among children. Early detection is crucial to prevent their spread and improve a patient’s chances of recovery. Dermatology, the branch of medicine dealing with skin diseases, faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance, type of skin, and… More >

Zhilin Li^1,2, Yuxin Li¹, Chunyu Yan¹, Peng Yan¹, Xiutong Li¹, Mei Yu¹, Tingchi Wen^4,5, Benliang Xie^1,2,3,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 679-694, 2024, DOI:10.32604/cmc.2024.051526 - 18 July 2024

Abstract Diseases in tea trees can result in significant losses in both the quality and quantity of tea production. Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations. However, existing methods face challenges such as a high number of parameters and low recognition accuracy, which hinders their application in tea plantation monitoring equipment. This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves, to address these challenges. The proposed method first embeds a Coordinate Attention (CA) module into the original MobileNetV2 network, enabling the model to locate disease 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 - 11 March 2024

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… More >

Javed Rashid^1,2, Imran Khan¹, Irshad Ahmed Abbasi³, Muhammad Rizwan Saeed⁴, Mubbashar Saddique^5,*, Mohamed Abbas^6,7

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3897-3920, 2023, DOI:10.32604/cmc.2023.040356 - 08 October 2023

Abstract Many plant species have a startling degree of morphological similarity, making it difficult to split and categorize them reliably. Unknown plant species can be challenging to classify and segment using deep learning. While using deep learning architectures has helped improve classification accuracy, the resulting models often need to be more flexible and require a large dataset to train. For the sake of taxonomy, this research proposes a hybrid method for categorizing guava, potato, and java plum leaves. Two new approaches are used to form the hybrid model suggested here. The guava, potato, and java plum More >

Yanlei Xu, Xue Cong, Yuting Zhai, Zhiyuan Gao, Helong Yu^*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3019-3037, 2023, DOI:10.32604/iasc.2023.040330 - 11 September 2023

Abstract To solve inefficient water stress classification of spinach seedlings under complex background, this study proposed an automatic classification method for the water stress level of spinach seedlings based on the N-MobileNetXt (NCAM+MobileNetXt) network. Firstly, this study reconstructed the Sandglass Block to effectively increase the model accuracy; secondly, this study introduced the group convolution module and a two-dimensional adaptive average pool, which can significantly compress the model parameters and enhance the model robustness separately; finally, this study innovatively proposed the Normalization-based Channel Attention Module (NCAM) to enhance the image features obviously. The experimental results showed that More >

Yue Yu^*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2681-2694, 2023, DOI:10.32604/fdmp.2023.028250 - 25 June 2023

Abstract A triethylenetetramine epoxy mixture was synthesized through the reaction of a low-molecular-weight liquid epoxy resin with triethylenetetramine (TETA). Then triethyltetramine (TETA) was injected dropwise into a propylene glycol methyl ether (PM) solution for chain extension reaction. A hydrophilic and flexible polyether segment was introduced into the hardener molecule. The effects of TETA/DGEPG, reaction temperature and reaction time on the epoxy conversion of polyethylene glycol diglycidyl ether (DGEPG) were studied. In addition, several alternate strategies to add epoxy resin to the high-speed dispersion machine and synthesize MEA DGEBA adduct (without catalyst and with bisphenol A diglycidyl More >

Shah Faisal¹, Kashif Javed¹, Sara Ali¹, Areej Alasiry², Mehrez Marzougui², Muhammad Attique Khan^3,*, Jae-Hyuk Cha^4,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 895-914, 2023, DOI:10.32604/cmc.2023.039781 - 08 June 2023

Abstract Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset More >

Jianbo Lu^1,2,*, Ruxin Shi², Jin Tong³, Wenqi Cheng⁴, Xiaoya Ma^1,3, Xiaobin Liu²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 525-544, 2023, DOI:10.32604/iasc.2023.038287 - 29 April 2023

Abstract In the deep learning approach for identifying plant diseases, the high complexity of the network model, the large number of parameters, and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources. In this study, a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed. In the proposed model, the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions; the efficient channel attention module is added into… More >

Qiong Luo^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3141-3158, 2023, DOI:10.32604/csse.2023.038017 - 03 April 2023

Abstract The products of an archival culture in colleges and universities are the final result of the development of archival cultural resources, and the development of archival cultural effects in colleges and universities should be an important part of improving the artistic level of libraries. The existing RippleNet model doesn’t consider the influence of key nodes on recommendation results, and the recommendation accuracy is not high. Therefore, based on the RippleNet model, this paper introduces the influence of complex network nodes into the model and puts forward the Cn RippleNet model. The performance of the model More >