Zhuoqun Xia¹, Hangyu Hu¹, Wenjing Li^2,3, Qisheng Jiang¹, Lan Pu¹, Yicong Shu¹, Arun Kumar Sangaiah^4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 409-430, 2024, DOI:10.32604/cmes.2024.030052

Abstract Early screening of diabetes retinopathy (DR) plays an important role in preventing irreversible blindness. Existing research has failed to fully explore effective DR lesion information in fundus maps. Besides, traditional attention schemes have not considered the impact of lesion type differences on grading, resulting in unreasonable extraction of important lesion features. Therefore, this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator (MPAG) and a lesion localization module (LLM). Firstly, MPAG is used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained… More >

Junhui Li¹, Xuanzhong Luo^1,2, Changxing Ge³, Cuiping Li^1,*, Changrong Wang⁴

Energy Engineering, Vol.121, No.4, pp. 869-893, 2024, DOI:10.32604/ee.2024.029722

Abstract Peak load and wind energy emission pressure rise more as wind energy penetration keeps growing, which affects the stabilization of the PS (power system). This paper suggests integrated optimal dispatching of thermal power generators and BESS (battery energy storage system) taking wind energy emission grading punishment and deep peak clipping into consideration. Firstly, in order to minimize wind abandonment, a hierarchical wind abandonment penalty strategy based on fuzzy control is designed and introduced, and the optimal grid-connected power of wind energy is determined as a result of minimizing the peak cutting cost of the system. Secondly, considering BESS and thermal… More > Graphic Abstract

Zhimei Li¹, Kuan Tian², Keping Wang², Zhengyi Li², Haoli Qin^1,*, Hu Li^2,*

Journal of Renewable Materials, Vol.11, No.11, pp. 3847-3865, 2023, DOI:10.32604/jrm.2023.030122

Abstract Sustainable acquisition of bioactive compounds from biomass-based platform molecules is a green alternative for existing CO₂-emitting fossil-fuel technologies. Herein, a core–shell magnetic biocarbon catalyst functionalized with sulfonic acid (Fe₃O₄@SiO₂@chitosan-SO₃H, MBC-SO₃H) was prepared to be efficient for the synthesis of various N-substituted pyrroles (up to 99% yield) from bio-based hexanedione and amines under mild conditions. The abundance of Brønsted acid sites in the MBC-SO₃H ensured smooth condensation of 2,5-hexanedione with a variety of amines to produce N-substituted pyrroles. The reaction was illustrated to follow the conventional PallKnorr coupling pathway, which includes three cascade reaction steps: amination, loop closure and dehydration. The… More > Graphic Abstract

Dengyun Li, Baiwen Du, Kai Zhu, Jicheng Yu^*, Siyuan Liang, Changxi Yue

Energy Engineering, Vol.120, No.11, pp. 2611-2628, 2023, DOI:10.32604/ee.2023.028282

Abstract Self-heating and electric field distribution are the primary factors affecting the accuracy of the Ultra High Voltage Direct Current (UHVDC) resistive divider. Reducing the internal temperature rise of the voltage divider caused by self-heating, reducing the maximum electric field strength of the voltage divider, and uniform electric field distribution can effectively improve the UHVDC resistive divider’s accuracy. In this paper, thermal analysis and electric field distribution optimization design of 1200 kV UHVDC resistive divider are carried out: (1) Using the proposed iterative algorithm, the heat dissipation and temperature distribution of the high voltage DC resistive divider are studied, and the… 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

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 the classification accuracy of N-MobileNetXt… More >

Hengyang Liu, Chuncheng Huang^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1745-1761, 2023, DOI:10.32604/cmc.2023.040710

Abstract In the field of medical images, pixel-level labels are time-consuming and expensive to acquire, while image-level labels are relatively easier to obtain. Therefore, it makes sense to learn more information (knowledge) from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training costs. In this paper, using Pixel-Level Labeled Images for Multi-Task Learning (PLDMLT), we focus on grading the severity of fundus images for Diabetic Retinopathy (DR). This is because, for the segmentation task, there is a finely labeled mask, while the severity grading task is without classification… More >

Mohammad Yamin^1,*, Sarah Basahel¹, Saleh Bajaba², Mona Abusurrah³, E. Laxmi Lydia⁴

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1901-1916, 2023, DOI:10.32604/csse.2023.036455

Abstract Recently, there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy (DR). DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed world. Initial detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus images. This article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy (DLLSHDM-DR) on Retinal Fundus Images. The proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making method. In the DLLSHDM-DR technique, image preprocessing is initially… More >

Sesikala Bapatla¹, J. Harikiran^2,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 597-617, 2023, DOI:10.32604/csse.2023.034998

Abstract Diabetes problems can lead to an eye disease called Diabetic Retinopathy (DR), which permanently damages the blood vessels in the retina. If not treated early, DR becomes a significant reason for blindness. To identify the DR and determine the stages, medical tests are very labor-intensive, expensive, and time-consuming. To address the issue, a hybrid deep and machine learning technique-based autonomous diagnostic system is provided in this paper. Our proposal is based on lesion segmentation of the fundus images based on the LuNet network. Then a Refined Attention Pyramid Network (RAPNet) is used for extracting global and local features. To increase… More >

Ting Zhang, Yifei Wang, Xinxin Jin, Zhiwen Gu, Xiaoliang Zhang, Bin He^*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2267-2285, 2023, DOI:10.32604/cmes.2023.023229

Abstract Auto-grading, as an instruction tool, could reduce teachers’ workload, provide students with instant feedback and support highly personalized learning. Therefore, this topic attracts considerable attentions from researchers recently. To realize the automatic grading of handwritten chemistry assignments, the problem of chemical notations recognition should be solved first. The recent handwritten chemical notations recognition solutions belonging to the end-to-end trainable category suffered from the problem of lacking the accurate alignment information between the input and output. They serve the aim of reading notations into electrical devices to better prepare relevant e-documents instead of auto-grading handwritten assignments. To tackle this limitation to… More >

Jianyi Zhu¹, Hui Peng^1,2, Xiaoning Lu^1,2, Jianxiong Lyu^1,2,3, Tianyi Zhan^1,2,*

Journal of Renewable Materials, Vol.11, No.3, pp. 1425-1437, 2023, DOI:10.32604/jrm.2022.023464

Abstract Many tree species are planted in China with variable properties and usage. Toward exploring the structure-properties relationships of wood and classifying the species more reasonably, the physiomechanical properties of the domestic wood species in China were analyzed statistically. According to the correlation analysis, the mechanical properties were closely related to the wood density. Except impact toughness and cleavage strength, the correlation coefficients between mechanical properties and densities were more than 0.8. However, shrinkage properties showed fewer correlations with densities, and the coefficient was no more than 0.7. Primary component analysis was proved to be feasible to explore the information of… More >