Saima Yasmeen¹, Muhammad Usman Yaseen^1,*, Syed Sohaib Ali², Moustafa M. Nasralla³, Sohaib Bin Altaf Khattak³

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3671-3689, 2023, DOI:10.32604/cmc.2023.041195

Abstract SAR images commonly suffer from speckle noise, posing a significant challenge in their analysis and interpretation. Existing convolutional neural network (CNN) based despeckling methods have shown great performance in removing speckle noise. However, these CNN-based methods have a few limitations. They do not decouple complex background information in a multi-resolution manner. Moreover, they have deep network structures that may result in many parameters, limiting their applicability to mobile devices. Furthermore, extracting key speckle information in the presence of complex background is also a major problem with SAR. The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based… More >

Yang Yang¹, Yuhan Long¹, Yijing Lin², Zhipeng Gao¹, Lanlan Rui¹, Peng Yu^1,3,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3623-3651, 2023, DOI:10.32604/cmc.2023.040250

Abstract With the rapid development of the Internet of Things (IoT), the automation of edge-side equipment has emerged as a significant trend. The existing fault diagnosis methods have the characteristics of heavy computing and storage load, and most of them have computational redundancy, which is not suitable for deployment on edge devices with limited resources and capabilities. This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation. First, we offer a clustering-based self-knowledge distillation approach (Cluster KD), which takes the mean value of the sample diagnosis results, clusters them, and takes the clustering results as the… More >

Lihong Guo^*, Jie Yang, Haitao Wu

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1929-1946, 2023, DOI:10.32604/cmc.2023.039170

Abstract Attribute-based encryption (ABE) is a technique used to encrypt data, it has the flexibility of access control, high security, and resistance to collusion attacks, and especially it is used in cloud security protection. However, a large number of bilinear mappings are used in ABE, and the calculation of bilinear pairing is time-consuming. So there is the problem of low efficiency. On the other hand, the decryption key is not uniquely associated with personal identification information, if the decryption key is maliciously sold, ABE is unable to achieve accountability for the user. In practical applications, shared message requires hierarchical sharing in… More >

So-Eun Jeon, Sun-Jin Lee, Il-Gu Lee^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2407-2419, 2023, DOI:10.32604/iasc.2023.039937

Abstract With the development of the 5th generation of mobile communication (5G) networks and artificial intelligence (AI) technologies, the use of the Internet of Things (IoT) has expanded throughout industry. Although IoT networks have improved industrial productivity and convenience, they are highly dependent on nonstandard protocol stacks and open-source-based, poorly validated software, resulting in several security vulnerabilities. However, conventional AI-based software vulnerability discovery technologies cannot be applied to IoT because they require excessive memory and computing power. This study developed a technique for optimizing training data size to detect software vulnerabilities rapidly while maintaining learning accuracy. Experimental results using a software… More >

Zunliang Chen^1,2, Chengxu Huang^1,2, Lucheng Duan^1,2, Baohua Tan^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1085-1102, 2023, DOI:10.32604/cmc.2023.039451

Abstract In response to the problem of the high cost and low efficiency of traditional water surface litter cleanup through manpower, a lightweight water surface litter detection algorithm based on improved YOLOv5s is proposed to provide core technical support for real-time water surface litter detection by water surface litter cleanup vessels. The method reduces network parameters by introducing the deep separable convolution GhostConv in the lightweight network GhostNet to substitute the ordinary convolution in the original YOLOv5s feature extraction and fusion network; introducing the C3Ghost module to substitute the C3 module in the original backbone and neck networks to further reduce… More >

Sana Parez¹, Naqqash Dilshad², Turki M. Alanazi³, Jong Weon Lee^1,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 515-536, 2023, DOI:10.32604/csse.2023.037992

Abstract A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative,… More >

Zakariae Dlimi^*, Abdellah Ezzati, Said Ben Alla

Computer Systems Science and Engineering, Vol.47, No.1, pp. 79-103, 2023, DOI:10.32604/csse.2023.037099

Abstract The growing advent of the Internet of Things (IoT) users is driving the adoption of cloud computing technologies. The integration of IoT in the cloud enables storage and computational capabilities for IoT users. However, security has been one of the main concerns of cloud-integrated IoT. Existing work attempts to address the security concerns of cloud-integrated IoT through authentication, access control, and blockchain-based methods. However, existing frameworks are somewhat limited by scalability, privacy, and centralized structures. To mitigate the existing problems, we propose a blockchain-based distributed access control method for secure storage in the IoT cloud (BL-DAC). Initially, the BL-DAC performs… More >

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 >

Lei Zhu, Jinyue Gong, Liang Dong^*, Cong Zhang

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5521-5537, 2023, DOI:10.32604/cmc.2023.037336

Abstract SKINNY-64-64 is a lightweight block cipher with a 64-bit block length and key length, and it is mainly used on the Internet of Things (IoT). Currently, faults can be injected into cryptographic devices by attackers in a variety of ways, but it is still difficult to achieve a precisely located fault attacks at a low cost, whereas a Hardware Trojan (HT) can realize this. Temperature, as a physical quantity incidental to the operation of a cryptographic device, is easily overlooked. In this paper, a temperature-triggered HT (THT) is designed, which, when activated, causes a specific bit of the intermediate state… More >

Kun Yang^1,2, Lei Zhao¹, Xianghui Wang¹, Mingyang Zhang¹, Linyan Xue^1,2, Shuang Liu^1,2, Kun Liu^1,2,3,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5159-5176, 2023, DOI:10.32604/cmc.2023.036401

Abstract The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion… More >