Fangye Liu^1,#, Jiaping Qu^1,#, Yajun Li¹, Jiawei Fan¹, Yumeng Cui¹, Jingwen Wu¹, Xueqi Gu¹, Xia Li^2,*, He Huang^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1599-1621, 2024, DOI:10.32604/phyton.2024.051386

Abstract Chrysanthemum × morifolium is a horticultural crop which plays a vital role in the flower industry with significant economic value and has a cultivation history of over three thousand years in China. The accumulation of anthocyanins is always affected by light. Here, we revealed that anthocyanin accumulation is highly dependent on light in ‘2021135’ genotype chrysanthemum, while it is light-independent in ‘2001402’ genotype chrysanthemum. However, no literature has been reported regarding the non-photosensitive chrysanthemum in anthocyanins light-independent synthesis pathways. Through the phenotype analysis of 44 F generations, we found that light-independence is a dominant trait which… More >

Li Li^*, Qing Zhang, Youran Kong

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 801-818, 2024, DOI:10.32604/cmc.2024.051916

Abstract Due to the diversity and unpredictability of changes in malicious code, studying the traceability of variant families remains challenging. In this paper, we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants. This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images. The method includes a lightweight classifier and a simulator. The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile, embedded, and other devices. The simulator utilizes… 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

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 >

Khursheed Aurangzeb^*, Syed Irtaza Haider, Musaed Alhussein

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1381-1405, 2024, DOI:10.32604/cmc.2024.048987

Abstract In this work, we aim to introduce some modifications to the Anam-Net deep neural network (DNN) model for segmenting optic cup (OC) and optic disc (OD) in retinal fundus images to estimate the cup-to-disc ratio (CDR). The CDR is a reliable measure for the early diagnosis of Glaucoma. In this study, we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images. Our DNN model is based on modifications to Anam-Net, incorporating an anamorphic depth embedding block. To reduce computational complexity, we employ a fixed filter size for all convolution layers… More >

Zerui Xiao, Wei Liu, Zhiwei Ye^*, Jiatang Yuan, Shishi Liu

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1009-1029, 2024, DOI:10.32604/csse.2023.040475

Abstract Nowadays, construction site safety accidents are frequent, and wearing safety helmets is essential to prevent head injuries caused by object collisions and falls. However, existing helmet detection algorithms have several drawbacks, including a complex structure with many parameters, high calculation volume, and poor detection of small helmets, making deployment on embedded or mobile devices difficult. To address these challenges, this paper proposes a YOLOv5-based multi-head detection safety helmet detection algorithm that is faster and more robust for detecting helmets on construction sites. By replacing the traditional DarkNet backbone network of YOLOv5s with a new backbone… More >

Lirong Yin¹, Lei Wang¹, Siyu Lu^2,*, Ruiyang Wang², Haitao Ren², Ahmed AlSanad³, Salman A. AlQahtani³, Zhengtong Yin⁴, Xiaolu Li⁵, Wenfeng Zheng^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2315-2347, 2024, DOI:10.32604/cmes.2024.050853

Abstract At present, super-resolution algorithms are employed to tackle the challenge of low image resolution, but it is difficult to extract differentiated feature details based on various inputs, resulting in poor generalization ability. Given this situation, this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block (AFB) for feature extraction. This module mainly comprises dynamic convolution, attention mechanism, and pixel-based gating mechanism. Combined with dynamic convolution with scale information, the network can extract more differentiated feature information. The introduction of a channel More >

Md. Atiqur Rahman¹, Mustavi Ibne Masum¹, Khan Md Hasib², M. F. Mridha^3,*, Sultan Alfarhood⁴, Mejdl Safran^4,*, Dunren Che⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2425-2448, 2024, DOI:10.32604/cmes.2024.050760

Abstract Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality. This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging (MRI). It focuses on distinguishing between Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG). LGGs are benign and typically manageable with surgical resection, while HGGs are malignant and more aggressive. The research introduces an innovative custom convolutional neural network (CNN) model, Glioma-CNN. GliomaCNN stands out as a lightweight CNN model compared to its predecessors. The research utilized the BraTS 2020 More >

Rui Zhang^1,2, Tonghai Liu^1,2,*, Wenzheng Liu^1,2, Chaungchuang Yuan^1,2, Xiaoyue Seng^1,2, Tiantian Guo^1,2, Xue Wang^1,2

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1275-1296, 2024, DOI:10.32604/phyton.2024.052397

Abstract Rice diseases can adversely affect both the yield and quality of rice crops, leading to the increased use of pesticides and environmental pollution. Accurate detection of rice diseases in natural environments is crucial for both operational efficiency and quality assurance. Deep learning-based disease identification technologies have shown promise in automatically discerning disease types. However, effectively extracting early disease features in natural environments remains a challenging problem. To address this issue, this study proposes the YOLO-CRD method. This research selected images of common rice diseases, primarily bakanae disease, bacterial brown spot, leaf rice fever, and dry… More >

Abdul Momin¹, Amana Khatoon^1,*, Wajahat Khan¹, Dilsat Bozdoğan Konuşkan², Muhammad Mudasar Aslam³, Muhammad Jamil⁴, Shafiq Ur Rehman⁵, Baber Ali⁶, Alevcan Kaplan⁷, Sana Wahab⁸, Muhammad Nauman Khan^9,*, Sezai Ercisli^10,11, Mohammad Khalid Al-Sadoon¹²

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1117-1128, 2024, DOI:10.32604/phyton.2024.048994

Abstract Sprouts are ready-to-eat and are recognized worldwide as functional components of the human diet. Recent advances in innovative agricultural techniques could enable an increase in the production of healthy food. The use of light-emitting diode (LED) in indoor agricultural production could alter the biological feedback loop, increasing the functional benefits of plant foods such as wheat and lentil sprouts and promoting the bioavailability of nutrients. The effects of white (W), red (R), and blue (B) light were investigated on the growth parameters and nutritional value of wheat and lentil sprouts. In the laboratory, seeds were… More >

Jingting Mei, Yang Yang^*, Zhipeng Gao, Lanlan Rui, Yijing Lin

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4883-4904, 2024, DOI:10.32604/cmc.2024.051860

Abstract Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management. Considering the unique characteristics of edge networks, such as limited resources, complex network faults, and the need for high real-time performance, enhancing and optimizing existing network fault diagnosis methods is necessary. Therefore, this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network (LSNN). Firstly, we use the Izhikevich neurons model to replace the Leaky Integrate and Fire (LIF) neurons model in the LSNN model. Izhikevich… More >