Guanghui Yu¹, Honghui Fan¹, Hongyan Zhou¹, Tao Wu¹, Hongjin Zhu^{1, *}

Journal on Artificial Intelligence, Vol.2, No.3, pp. 125-135, 2020, DOI:10.32604/jai.2020.010501

Abstract When we use traditional computer vision Inspection technology to locate the vehicles, we find that the results were unsatisfactory, because of the existence of diversified scenes and uncertainty. So, we present a new method based on improved SSD model. We adopt ResNet101 to enhance the feature extraction ability of algorithm model instead of the VGG16 used by the classic model. Meanwhile, the new method optimizes the loss function, such as the loss function of predicted offset, and makes the loss function drop more smoothly near zero points. In addition, the new method improves cross entropy loss function of category prediction,… More >

Yanming Wang^{1, 2, 3}, Kebin Jia^{1, 2, 3}, Pengyu Liu^{1, 2, 3, *}

Journal on Artificial Intelligence, Vol.2, No.3, pp. 113-124, 2020, DOI:10.32604/jai.2020.010137

Abstract In recent years, the problem of “Impolite Pedestrian” in front of the zebra crossing has aroused widespread concern from all walks of life. The traffic sector’s governance measures have become more serious. The traditional way of governance is onsite law enforcement, which requires a lot of manpower and material resources and is low efficiency. An enhanced YOLOv3-tiny model is proposed for pedestrians and vehicle detection in traffic monitoring. By modifying the backbone network structure of YOLOv3- tiny model, introducing deep detachable convolution operation, and designing the basic residual block unit of the network, the feature extraction ability of the backbone… More >

Chen Song¹, Xu Cheng^{1, *}, Yongxiang Gu¹, Beijing Chen¹, Zhangjie Fu¹

Journal on Artificial Intelligence, Vol.2, No.2, pp. 59-77, 2020, DOI:10.32604/jai.2020.010193

Abstract Object detection is one of the most fundamental, longstanding and significant problems in the field of computer vision, where detection involves object classification and location. Compared with the traditional object detection algorithms, deep learning makes full use of its powerful feature learning capabilities showing better detection performance. Meanwhile, the emergence of large datasets and tremendous improvement in computer computing power have also contributed to the vigorous development of this field. In the paper, many aspects of generic object detection are introduced and summarized such as traditional object detection algorithms, datasets, evaluation metrics, detection frameworks based on deep learning and state-of-the-art… More >

Xueming Qiao¹, Yingxue Xia¹, Weiyi Zhu², Dongjie Zhu^{3, *}, Liang Kong¹, Chunxu Lin³, Zhenhao Guo³, Yiheng Sun³

Journal on Artificial Intelligence, Vol.2, No.1, pp. 29-37, 2020, DOI:10.32604/jai.2020.09955

Abstract Natural scene recognition has important significance and value in the fields of image retrieval, autonomous navigation, human-computer interaction and industrial automation. Firstly, the natural scene image non-text content takes up relatively high proportion; secondly, the natural scene images have a cluttered background and complex lighting conditions, angle, font and color. Therefore, how to extract text extreme regions efficiently from complex and varied natural scene images plays an important role in natural scene image text recognition. In this paper, a Text extremum region Extraction algorithm based on Joint-Channels (TEJC) is proposed. On the one hand, it can solve the problem that… More >

Xiaojun Pan, Zhuoran Wang, Yanbin Sun^*

Journal of Cyber Security, Vol.2, No.2, pp. 69-83, 2020, DOI:10.32604/jcs.2020.010045

Abstract Programmable Logic Controllers (PLC), core of industrial control systems, is widely used in industrial control systems. The security of PLC is the key to the security of industrial control systems. Nowadays, a large number of industrial control systems are connected to the Internet which exposes the PLC equipment to the Internet, and thus raising security concerns. First of all, we introduce the basic principle of PLC in this paper. Then we analyze the PLC code security, firmware security, network security, virus vulnerability and Modbus communication protocol by reviewing the previous related work. Finally, we make a summary of the current… More >

Hui Yao¹, Liqun Wang³, Jia Guo¹, Weimin Liu⁴, Jingjing Li¹, Yingxiao Wang², Linhong Deng^1,*, Mingxing Ouyang^1,2,3,*

Molecular & Cellular Biomechanics, Vol.17, No.3, pp. 101-111, 2020, DOI:10.32604/mcb.2020.09595

Abstract Prostate cancer is the most common cancer among men beyond 50 years old, and ranked the second in mortality. The level of Prostate-specific antigen (PSA) in serum has been a routine biomarker for clinical assessment of the cancer development, which is detected mostly by antibody-based immunoassays. The proteolytic activity of PSA also has important functions. Here a genetically encoded biosensor based on fluorescence resonance energy transfer (FRET) technology was developed to measure PSA activity. In vitro assay showed that the biosensor containing a substrate peptide ‘RLSSYYSGAG’ had 400% FRET change in response to 1 µg/ml PSA within 90 min, and… More >

Yun Tan¹, Jiaohua Qin^{1, *}, Hao Tang², Xuyu Xiang¹, Ling Tan², Neal N. Xiong³

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1797-1817, 2020, DOI:10.32604/cmc.2020.010802

Abstract With the development of the internet of medical things (IoMT), the privacy protection problem has become more and more critical. In this paper, we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography. For a given group of medical images of one patient, DenseNet is used to regroup the images based on feature similarity comparison. Then the mapping indexes can be constructed based on LBP feature and hash generation. After mapping the privacy information with the hash sequences, the corresponding mapped indexes of secret information will be packed together with the medical images group and… More >

Chunlai Du¹, Shenghui Liu¹, Lei Si², Yanhui Guo^{2, *}, Tong Jin¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1785-1796, 2020, DOI:10.32604/cmc.2020.010091

Abstract In recent years, the number of exposed vulnerabilities has grown rapidly and more and more attacks occurred to intrude on the target computers using these vulnerabilities such as different malware. Malware detection has attracted more attention and still faces severe challenges. As malware detection based traditional machine learning relies on exports’ experience to design efficient features to distinguish different malware, it causes bottleneck on feature engineer and is also time-consuming to find efficient features. Due to its promising ability in automatically proposing and selecting significant features, deep learning has gradually become a research hotspot. In this paper, aiming to detect… More >

Juan R. Bermejo Higuera^{1, *}, Javier Bermejo Higuera¹, Juan A. Sicilia Montalvo¹, Javier Cubo Villalba¹, Juan José Nombela Pérez¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1555-1577, 2020, DOI:10.32604/cmc.2020.010885

Abstract To detect security vulnerabilities in a web application, the security analyst must choose the best performance Security Analysis Static Tool (SAST) in terms of discovering the greatest number of security vulnerabilities as possible. To compare static analysis tools for web applications, an adapted benchmark to the vulnerability categories included in the known standard Open Web Application Security Project (OWASP) Top Ten project is required. The information of the security effectiveness of a commercial static analysis tool is not usually a publicly accessible research and the state of the art on static security tool analyzers shows that the different design and… More >

Mohammad Shorfuzzaman^{1, *}, Mehedi Masud¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1359-1381, 2020, DOI:10.32604/cmc.2020.011326

Abstract Coronavirus disease (COVID-19) is an extremely infectious disease and possibly causes acute respiratory distress or in severe cases may lead to death. There has already been some research in dealing with coronavirus using machine learning algorithms, but few have presented a truly comprehensive view. In this research, we show how convolutional neural network (CNN) can be useful to detect COVID-19 using chest X-ray images. We leverage the CNN-based pre-trained models as feature extractors to substantiate transfer learning and add our own classifier in detecting COVID-19. In this regard, we evaluate performance of five different pre-trained models with fine-tuning the weights… More >