Jingcheng Zhang¹, Xingjian Zhou¹, Dong Shen¹, Qimeng Yu¹, Lin Yuan^2,*, Yingying Dong³

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 745-762, 2024, DOI:10.32604/phyton.2024.049734

Abstract As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv. oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result of the disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remote sensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutions offer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapid dispersal under suitable conditions, making it difficult to track the disease at a regional scale with… More >

Meng Huang, Honglei Wei^*, Xianyi Zhai

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 531-547, 2024, DOI:10.32604/cmc.2024.048510

Abstract In pursuit of cost-effective manufacturing, enterprises are increasingly adopting the practice of utilizing recycled semiconductor chips. To ensure consistent chip orientation during packaging, a circular marker on the front side is employed for pin alignment following successful functional testing. However, recycled chips often exhibit substantial surface wear, and the identification of the relatively small marker proves challenging. Moreover, the complexity of generic target detection algorithms hampers seamless deployment. Addressing these issues, this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips, termed Van-YOLOv8. Initially, to alleviate the influence of diminutive, low-resolution markings on the precision of… More >

Yiwei Liu¹, Yingnan Zhao^1,*, Yi Chen¹, Zheng Hu¹, Min Xia²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 909-926, 2024, DOI:10.32604/cmc.2024.047901

Abstract Scene text detection is an important task in computer vision. In this paper, we present YOLOv5 Scene Text (YOLOv5ST), an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection. Our primary goal is to enhance inference speed without sacrificing significant detection accuracy, thereby enabling robust performance on resource-constrained devices like drones, closed-circuit television cameras, and other embedded systems. To achieve this, we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation, including replacing standard convolution with depth-wise convolution, adopting the C2 sequence module in place of C3, employing Spatial Pyramid… More >

Xiaohui Yang, Peiyin Zhao^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1521-1538, 2024, DOI:10.32604/cmc.2024.047666

Abstract Electronic medical records (EMR) facilitate the sharing of medical data, but existing sharing schemes suffer from privacy leakage and inefficiency. This article proposes a lightweight, searchable, and controllable EMR sharing scheme, which employs a large attribute domain and a linear secret sharing structure (LSSS), the computational overhead of encryption and decryption reaches a lightweight constant level, and supports keyword search and policy hiding, which improves the high efficiency of medical data sharing. The dynamic accumulator technology is utilized to enable data owners to flexibly authorize or revoke the access rights of data visitors to the data to achieve controllability of… More >

Zhong Qu^1,*, Guoqing Mu¹, Bin Yuan²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 255-273, 2024, DOI:10.32604/cmes.2024.048175

Abstract Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning, with convolutional neural networks (CNN) playing an important role in this field. However, as the performance of crack detection in cement pavement improves, the depth and width of the network structure are significantly increased, which necessitates more computing power and storage space. This limitation hampers the practical implementation of crack detection models on various platforms, particularly portable devices like small mobile devices. To solve these problems, we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules… More > Graphic Abstract

Kai Wang¹, Biao He^2,*, Pijush Samui³, Jian Zhou⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 229-253, 2024, DOI:10.32604/cmes.2024.047569

Abstract Rock bursts represent a formidable challenge in underground engineering, posing substantial risks to both infrastructure and human safety. These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock, leading to severe seismic events and structural damage. Therefore, the development of reliable prediction models for rock bursts is paramount to mitigating these hazards. This study aims to propose a tree-based model—a Light Gradient Boosting Machine (LightGBM)—to predict the intensity of rock bursts in underground engineering. 322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset, which serves… More >

Liliane Nga¹, Benoit Ndiwe^1,2, Achille Bernard Biwolé¹, Antonio Pizzi^3,*, Jean Jalin Eyinga Biwole¹, Joseph Zobo Mfomo¹

Journal of Renewable Materials, Vol.12, No.3, pp. 553-568, 2024, DOI:10.32604/jrm.2023.046568

Abstract Extracts of plant origin, particularly tannins, are attracting growing interest for the sustainable development of materials in the industrial sector. The discovery of new tannins is therefore necessary. The aim of this work was to contribute to the understanding of the properties of Paraberlinia bifoliolata tannin by Matrix Assisted Laser Desorption Ionization Time of Flight Mass Spectroscopy MALDI-TOF/MS and Carbon 13 Nuclear Magnetic Resonance (¹³C NMR). The chemical composition of tannin extracted from Paraberlinia bifoliolata bark was determined, as was the mechanical strength of the resin hardened with Acacia nilotica extracts. Yield by successive water extraction was 35%. MALDI-TOF/MS analysis… More >

Shaha Al-Otaibi¹, Rahim Khan^2,*, Hashim Ali², Aftab Ahmed Khan², Amir Saeed³, Jehad Ali^4,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3805-3823, 2024, DOI:10.32604/cmc.2024.049017

Abstract The Internet of Things (IoT) is a smart networking infrastructure of physical devices, i.e., things, that are embedded with sensors, actuators, software, and other technologies, to connect and share data with the respective server module. Although IoTs are cornerstones in different application domains, the device’s authenticity, i.e., of server(s) and ordinary devices, is the most crucial issue and must be resolved on a priority basis. Therefore, various field-proven methodologies were presented to streamline the verification process of the communicating devices; however, location-aware authentication has not been reported as per our knowledge, which is a crucial metric, especially in scenarios where… More >

Lujuan Deng, Ruochong Fu^*, Zuhe Li, Boyi Liu, Mengze Xue, Yuhao Cui

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4071-4089, 2024, DOI:10.32604/cmc.2024.048200

Abstract Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the… More >

Saba Awan^1,*, Zahid Mehmood^2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.047337

Abstract Highway safety researchers focus on crash injury severity, utilizing deep learning—specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)—as the preferred method for modeling accident severity. Deep learning’s strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic… More >