Linguo Li^{1, 2}, Lijuan Sun¹, Jian Guo¹, Shujing Li^{2, *}, Ping Jiang³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 761-775, 2020, DOI:10.32604/cmc.2020.010158

Abstract As an indispensable task in crop protection, the detection of crop diseases directly impacts the income of farmers. To address the problems of low crop-disease identification precision and detection abilities, a new method of detection is proposed based on improved genetic algorithm and extreme learning machine. Taking five different typical diseases with common crops as the objects, this method first preprocesses the images of crops and selects the optimal features for fusion. Then, it builds a model of crop disease identification for extreme learning machine, introduces the hill-climbing algorithm to improve the traditional genetic algorithm, optimizes the initial weights and… More >

Peipeng Yu¹, Zhihua Xia^{1, *}, Jianwei Fei¹, Sunil Kumar Jha^{1, 2}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 743-760, 2020, DOI:10.32604/cmc.2020.011391

Abstract Coronaviruses are a well-known family of viruses that can infect humans or animals. Recently, the new coronavirus (COVID-19) has spread worldwide. All countries in the world are working hard to control the coronavirus disease. However, many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system, which leads to the mass spread of diseases. As a powerful tool, artificial intelligence (AI) has been successfully applied to solve various complex problems ranging from big data analysis to computer vision. In the process of epidemic control, many algorithms… More >

Jun Li^{1, 2}, Jieren Cheng^{2, *}, Naixue Xiong³, Lougao Zhan⁴, Yuan Zhang¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 723-741, 2020, DOI:10.32604/cmc.2020.011272

Abstract Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health. However, big data faces many ongoing serious challenges in the process of collection, storage, and use. Among them, data security and privacy problems have attracted extensive interest. In an effort to overcome this challenge, this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions (SGX). First of all, we define SGX as a trusted edge computing node, design data access module, data protection module, and data integrity check module, to achieve… More >

Jinrong Hu¹, Zhiqin Lei¹, Xiaoying Li^{2, *}, Yongqun He³, Jiliu Zhou¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 705-722, 2020, DOI:10.32604/cmc.2020.09878

Abstract The quality of ultrasound scanning images is usually damaged by speckle noise. This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing algorithm. Unlike single statistical moment-based speckle reduction algorithms, this method adaptively smooths the speckle regions while preserving the margin and tissue structure to achieve high detectability. The criterion of a speckle region is defined by the similarity value obtained by matching the histogram of the current processing window and the reference window derived from the speckle region in advance. Then, according to the similarity value and tissue… More >

Jinxin Zuo^{1, 3}, Yueming Lu^{1, 3, *}, Hui Gao^{2, 3}, Ruohan Cao^{2, 3}, Ziyv Guo^{2, 3}, Jim Feng⁴

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 683-704, 2020, DOI:10.32604/cmc.2020.010793

Abstract The development of the Internet of Things (IoT) calls for a comprehensive information security evaluation framework to quantitatively measure the safety score and risk (S&R) value of the network urgently. In this paper, we summarize the architecture and vulnerability in IoT and propose a comprehensive information security evaluation model based on multi-level decomposition feedback. The evaluation model provides an idea for information security evaluation of IoT and guides the security decision maker for dynamic protection. Firstly, we establish an overall evaluation indicator system that includes four primary indicators of threat information, asset, vulnerability, and management, respectively. It also includes eleven… More >

Fei Li^{1, *}, Jiayan Zhang¹, Edward Szczerbicki², Jiaqi Song¹, Ruxiang Li ¹, Renhong Diao¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 653-681, 2020, DOI:10.32604/cmc.2020.011264

Abstract The increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated the intrusion detection system based on the in-vehicle system. We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior. In order to verify the accuracy and efficiency… More >

Jinfeng Li¹, Li Feng^{1, *}, Longqing Zhang², Hongning Dai¹, Lei Yang¹, Liwei Tian¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 639-651, 2020, DOI:10.32604/cmc.2020.010309

Abstract Fueled by the booming online games, there is an increasing demand for monitoring online games in various settings. One of the application scenarios is the monitor of computer games in school computer labs, for which an intelligent game recognition method is required. In this paper, a method to identify game processes in accordance with private working sets (i.e., the amount of memory occupied by a process but cannot be shared among other processes) is introduced. Results of the W test showed that the memory sizes occupied by the legitimate processes (e.g., the processes of common native windows applications) and game… More >

Xiujuan Wang¹, Qianqian Zheng^{1, *}, Kangfeng Zheng², Yi Sui¹, Jiayue Zhang¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 617-638, 2020, DOI:10.32604/cmc.2020.011165

Abstract Malicious social robots are the disseminators of malicious information on social networks, which seriously affect information security and network environments. Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks. Supervised classification based on manual feature extraction has been widely used in social robot detection. However, these methods not only involve the privacy of users but also ignore hidden feature information, especially the graph feature, and the label utilization rate of semi-supervised algorithms is low. Aiming at the problems of shallow feature extraction and low label utilization rate in existing social network robot detection… More >

Lejun Zhang^{1, 2}, Zhijie Zhang¹, Weizheng Wang³, Rasheed Waqas¹, Chunhui Zhao^{1, 4}, Seokhoon Kim⁵, Huiling Chen^{6, *}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 597-616, 2020, DOI:10.32604/cmc.2020.011554

Abstract As an extension of the traditional encryption technology, information hiding has been increasingly used in the fields of communication and network media, and the covert communication technology has gradually developed. The blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance, which can effectively alleviate the disadvantages and problems of traditional covert communication. However, its combination with covert communication thus far has been mostly at the theoretical level. The BLOCCE method, as an early result of the combination of blockchain and covert communication technology, has the problems of low information embedding efficiency, the use… More >

Xu Han^{1, #}, Huijun Yang^{1, 4, *}, Qiufeng Shen^{1, #}, Jiangtao Yang², Huihui Liang¹, Cancan Bao¹, Shuang Cang³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 579-596, 2020, DOI:10.32604/cmc.2020.011262

Abstract Although predecessors have made great contributions to the semantic segmentation of 3D indoor scenes, there still exist some challenges in the debris recognition of terrain data. Compared with hundreds of thousands of indoor point clouds, the amount of terrain point cloud is up to millions. Apart from that, terrain point cloud data obtained from remote sensing is measured in meters, but the indoor scene is measured in centimeters. In this case, the terrain debris obtained from remote sensing mapping only have dozens of points, which means that sufficient training information cannot be obtained only through the convolution of points. In… More >