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 >

Jun Wang, Changfu Si, Zhen Wang, Qiang Fu^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4297-4318, 2024, DOI:10.32604/cmc.2024.050223

Abstract Nowadays, with the rapid development of industrial Internet technology, on the one hand, advanced industrial control systems (ICS) have improved industrial production efficiency. However, there are more and more cyber-attacks targeting industrial control systems. To ensure the security of industrial networks, intrusion detection systems have been widely used in industrial control systems, and deep neural networks have always been an effective method for identifying cyber attacks. Current intrusion detection methods still suffer from low accuracy and a high false alarm rate. Therefore, it is important to build a more efficient intrusion detection model. This paper… More >

Chengcheng Fan^1,2,*, Zhiruo Fang³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4925-4943, 2024, DOI:10.32604/cmc.2024.049710

Abstract Anchor-free object-detection methods achieve a significant advancement in field of computer vision, particularly in the realm of real-time inferences. However, in remote sensing object detection, anchor-free methods often lack of capability in separating the foreground and background. This paper proposes an anchor-free method named probability-enhanced anchor-free detector (ProEnDet) for remote sensing object detection. First, a weighted bidirectional feature pyramid is used for feature extraction. Second, we introduce probability enhancement to strengthen the classification of the object’s foreground and background. The detector uses the logarithm likelihood as the final score to improve the classification of the More >

Chunming Wu¹, Shupeng Zheng^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4395-4411, 2024, DOI:10.32604/cmc.2024.049665

Abstract Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently. To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios, a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network (MSCNN) and Long Short-Term Memory (LSTM) fused with attention mechanism is proposed. To adaptively extract the essential spatial feature information of various sizes, the model creates a multi-scale feature extraction module using the convolutional neural network (CNN) learning process.… More >

Waleed Khalid Al-Ghanem¹, Emad Ul Haq Qazi^2,*, Muhammad Hamza Faheem², Syed Shah Amanullah Quadri³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4001-4020, 2024, DOI:10.32604/cmc.2024.048208

Abstract Estimation of crowd count is becoming crucial nowadays, as it can help in security surveillance, crowd monitoring, and management for different events. It is challenging to determine the approximate crowd size from an image of the crowd’s density. Therefore in this research study, we proposed a multi-headed convolutional neural network architecture-based model for crowd counting, where we divided our proposed model into two main components: (i) the convolutional neural network, which extracts the feature across the whole image that is given to it as an input, and (ii) the multi-headed layers, which make it easier More >

Feng Zhao, Guangdi Liu^*, Xiaoqiang Chen, Ying Wang

Energy Engineering, Vol.121, No.7, pp. 1865-1882, 2024, DOI:10.32604/ee.2024.048209

Abstract In light of the prevailing issue that the existing convolutional neural network (CNN) power quality disturbance identification method can only extract single-scale features, which leads to a lack of feature information and weak anti-noise performance, a new approach for identifying power quality disturbances based on an adaptive Kalman filter (KF) and multi-scale channel attention (MS-CAM) fused convolutional neural network is suggested. Single and composite-disruption signals are generated through simulation. The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal, and subsequent integration of multi-scale features into the conventional CNN… More >

Md. Abdur Rahim¹, Fahmid Al Farid², Abu Saleh Musa Miah³, Arpa Kar Puza¹, Md. Nur Alam⁴, Md. Najmul Hossain⁵, Sarina Mansor², Hezerul Abdul Karim^2,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1689-1710, 2024, DOI:10.32604/cmes.2024.048714

Abstract Handwriting is a unique and significant human feature that distinguishes them from one another. There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification. However, such systems are susceptible to forgery, posing security risks. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures… More >

Jiaxiang Luo^1,2, Weien Zhou^2,3, Bingxiao Du^1,*, Daokui Li¹, Wen Yao^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1919-1947, 2024, DOI:10.32604/cmes.2024.048118

Abstract In recent years, there has been significant research on the application of deep learning (DL) in topology optimization (TO) to accelerate structural design. However, these methods have primarily focused on solving binary TO problems, and effective solutions for multi-material topology optimization (MMTO) which requires a lot of computing resources are still lacking. Therefore, this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO design. The framework employs convolutional neural network (CNN) to construct a surrogate model for solving MMTO, and the obtained surrogate model can rapidly generate multi-material structure topologies… More >

Muhamad Irsan^1,2,*, Rosilah Hassan², Anwar Hassan Ibrahim³, Mohamad Khatim Hasan², Meng Chun Lam², Wan Mohd Hirwani Wan Hussain⁴

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 157-174, 2024, DOI:10.32604/iasc.2024.042193

Abstract One of the major causes of road accidents is sleepy drivers. Such accidents typically result in fatalities and financial losses and disadvantage other road users. Numerous studies have been conducted to identify the driver’s sleepiness and integrate it into a warning system. Most studies have examined how the mouth and eyelids move. However, this limits the system’s ability to identify drowsiness traits. Therefore, this study designed an Accident Detection Framework (RPK) that could be used to reduce road accidents due to sleepiness and detect the location of accidents. The drowsiness detection model used three facial… More >

Chaozhi Cai^*, Xiaoyu Guo, Yingfang Xue, Jianhua Ren

Structural Durability & Health Monitoring, Vol.18, No.3, pp. 321-339, 2024, DOI:10.32604/sdhm.2024.045831

Abstract Bleachers play a crucial role in practical engineering applications, and any damage incurred during their operation poses a significant threat to the safety of both life and property. Consequently, it becomes imperative to conduct damage diagnosis and health monitoring of bleachers. The intricate structure of bleachers, the varied types of potential damage, and the presence of similar vibration data in adjacent locations make it challenging to achieve satisfactory diagnosis accuracy through traditional time-frequency analysis methods. Furthermore, field environmental noise can adversely impact the accuracy of bleacher damage diagnosis. To enhance the accuracy and anti-noise capabilities… More > Graphic Abstract