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

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 the ShuffleNetV2 model network structure… More >

Alia Alshehri, Duaa AlSaeed^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 939-970, 2023, DOI:10.32604/iasc.2023.037096

Abstract One of the most prevalent cancers in women is breast cancer. Early and accurate detection can decrease the mortality rate associated with breast cancer. Governments and health organizations emphasize the significance of early breast cancer screening since it is associated to a greater variety of available treatments and a higher chance of survival. Patients have the best chance of obtaining effective treatment when they are diagnosed early. The detection and diagnosis of breast cancer have involved using various image types and imaging modalities. Breast “infrared thermal” imaging is one of the imaging modalities., a screening instrument used to measure the… More >

Yingkun Song¹, Shunhe Hong¹, Chentao Hu¹, Pingan He², Lingbing Tao¹, Zhixin Tie^1,3,*, Chengfu Ding⁴

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5761-5784, 2023, DOI:10.32604/cmc.2023.038910

Abstract Rapid and precise vehicle recognition and classification are essential for intelligent transportation systems, and road target detection is one of the most difficult tasks in the field of computer vision. The challenge in real-time road target detection is the ability to properly pinpoint relatively small vehicles in complicated environments. However, because road targets are prone to complicated backgrounds and sparse features, it is challenging to detect and identify vehicle kinds fast and reliably. We suggest a new vehicle detection model called MEB-YOLO, which combines Mosaic and MixUp data augmentation, Efficient Channel Attention (ECA) attention mechanism, Bidirectional Feature Pyramid Network (BiFPN)… More >

Song Yu^*, Chen Wang

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5555-5571, 2023, DOI:10.32604/cmc.2023.038162

Abstract PM_2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate, and it is an evaluation indicator of air pollution level. Achieving PM_2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control. The past methods neglected the impact caused by PM_2.5 flow between cities when analyzing the impact of inter-city PM_2.5 concentrations, making it difficult to further improve the prediction accuracy. However, factors including geographical information such as altitude and… More >

Peicheng Shi^1,*, Zhiqiang Liu¹, Heng Qi¹, Aixi Yang²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5615-5637, 2023, DOI:10.32604/cmc.2023.037794

Abstract In complex traffic environment scenarios, it is very important for autonomous vehicles to accurately perceive the dynamic information of other vehicles around the vehicle in advance. The accuracy of 3D object detection will be affected by problems such as illumination changes, object occlusion, and object detection distance. To this purpose, we face these challenges by proposing a multimodal feature fusion network for 3D object detection (MFF-Net). In this research, this paper first uses the spatial transformation projection algorithm to map the image features into the feature space, so that the image features are in the same spatial dimension when fused… More >

Xiuli Si¹, Biao Hong¹, Yuanhui Hu¹, Lidong Chu^2,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6101-6118, 2023, DOI:10.32604/cmc.2023.036829

Abstract Currently, one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development. Therefore, further research in the field of crop disease and pest detection is necessary to address the mentioned problem. Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses, this article proposes a Model-Agnostic Meta-Learning (MAML) attention model based on the meta-learning paradigm. The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention (ECA) module. The module follows the… More >

Kang Xiaofeng¹, Hu Kun^2,*, Ran Li³

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2963-2974, 2023, DOI:10.32604/csse.2023.025908

Abstract Acoustic emission (AE) is a nondestructive real-time monitoring technology, which has been proven to be a valid way of monitoring dynamic damage to materials. The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning. Considering that the huge success of deep learning technologies, where the Recurrent Neural Network (RNN) has been widely applied to sequential classification tasks and Convolutional Neural Network (CNN) has been widely applied to image recognition tasks. A novel three-streams neural network (TSANN) model is proposed in this paper to deal with fault detection tasks. Based on residual connection… More >

Xiaorui Zhang^1,2,3,*, Chunlin Yuan¹, Wei Sun^3,4, Sunil Kumar Jha⁵

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4273-4291, 2023, DOI:10.32604/cmc.2023.036975

Abstract The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image. Studies have shown that certain local regions are more likely to inspire an emotional response than the whole image. However, existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the dataset. Therefore, this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature aggregation. To perform more accurate emotional region prediction, this study designs a multilayer attentional… More >

Zibo Wang^1,3, Chaobin Huo², Yaofang Zhang^1,3, Shengtao Cheng^1,3, Yilu Chen^1,3, Xiaojie Wei⁵, Chao Li⁴, Bailing Wang^1,3,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2957-2979, 2023, DOI:10.32604/cmc.2023.035694

Abstract With the growing discovery of exposed vulnerabilities in the Industrial Control Components (ICCs), identification of the exploitable ones is urgent for Industrial Control System (ICS) administrators to proactively forecast potential threats. However, it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods. To address these challenges, we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph (KG) in which relation paths contain abundant potential evidence to support the reasoning. The reasoning task in this work refers to determining whether a specific relation is valid between an… More >

Xiaorui Zhang^1,2,3,4,*, Yingying Wang¹, Wei Sun^4,5, Qi Cui⁶, Xindong Wei⁷

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3057-3071, 2023, DOI:10.32604/iasc.2023.033578

Abstract Signature verification, which is a method to distinguish the authenticity of signature images, is a biometric verification technique that can effectively reduce the risk of forged signatures in financial, legal, and other business environments. However, compared with ordinary images, signature images have the following characteristics: First, the strokes are slim, i.e., there is less effective information. Second, the signature changes slightly with the time, place, and mood of the signer, i.e., it has high intraclass differences. These challenges lead to the low accuracy of the existing methods based on convolutional neural networks (CNN). This study proposes an end-to-end multi-path attention… More >