Rongyu Chen, Lili Pan^*, Yan Zhou, Qianhui Lei

Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 67-76, 2020, DOI:10.32604/jihpp.2020.010472

Abstract With the rapid development of information technology, the speed and efficiency of image retrieval are increasingly required in many fields, and a compelling image retrieval method is critical for the development of information. Feature extraction based on deep learning has become dominant in image retrieval due to their discrimination more complete, information more complementary and higher precision. However, the high-dimension deep features extracted by CNNs (convolutional neural networks) limits the retrieval efficiency and makes it difficult to satisfy the requirements of existing image retrieval. To solving this problem, the high-dimension feature reduction technology is proposed with improved CNN and PCA… More >

Wenyuan Liu^1,2, Zijuan Liu^1,*, Lin Wang¹, Binbin Li¹, Nan Jing¹

Computer Systems Science and Engineering, Vol.33, No.2, pp. 137-147, 2018, DOI:10.32604/csse.2018.33.137

Abstract In recent years, movement detection and gait recognition methods using different techniques emerge in an endless stream. On the one hand, wearable sensors need be worn by the detecting target and the method based on camera requires line of sight. On the other hand, radio frequency signals are easy to be impaired. In this paper, we propose a novel multi-layer filter of channel state information (CSI) to capture moving individuals in dynamic environments and analyze his/her gait periodicity. We design and evaluate an efficient CSI subcarrier feature difference to the multi-layer filtering method leveraging principal component analysis (PCA) and discrete… More >

Wu Zeng¹, Zhanxiong Huo^{1, *}, Yuxuan Xie², Yingxiang Jiang¹, Kun Hu¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1869-1883, 2020, DOI:10.32604/cmc.2020.010139

Abstract Applying computer technology to the field of food safety, and how to identify liquor quickly and accurately, is of vital importance and has become a research focus. In this paper, sparse principal component analysis (SPCA) was applied to seek sparse factors of the mid-infrared (MIR) spectra of five famous vintage year Chinese spirits. The results showed while meeting the maximum explained variance, 23 sparse principal components (PCs) were selected as features in a support vector machine (SVM) model, which obtained a 97% classification accuracy. By comparison principal component analysis (PCA) selected 10 PCs as features but only achieved an 83%… More >

Nana Zhang¹, Kun Zhu¹, Shi Ying^{1, *}, Xu Wang²

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 471-499, 2020, DOI:10.32604/cmc.2020.010117

Abstract Software defect prediction is a research hotspot in the field of software engineering. However, due to the limitations of current machine learning algorithms, we can’t achieve good effect for defect prediction by only using machine learning algorithms. In previous studies, some researchers used extreme learning machine (ELM) to conduct defect prediction. However, the initial weights and biases of the ELM are determined randomly, which reduces the prediction performance of ELM. Motivated by the idea of search based software engineering, we propose a novel software defect prediction model named KAEA based on kernel principal component analysis (KPCA), adaptive genetic algorithm, extreme… More >

Yan Zu^1,2, Qiang Li¹, Chun Yang^2,*

Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 106-106, 2019, DOI:10.32604/mcb.2019.07132

Abstract Extracellular matrix (ECM) elasticity affects the function of a variety of cells. Integrins are transmembrane receptors that considered to be a sensor of cellular mechanical stimulation. The activity of integrins is strongly influenced by glycans through glycosylation events and the establishment of glycan-mediated interactions. Our study found that the level of β1 integrin N-linked glycosylation was significantly down-regulated on softer ECM. Further, sialic acid is a common monosaccharide modified at the end of the sugar chain during N-glycosylation. We subjected the enriched sialylated glycoproteins to gel-based proteomic identification by tandem mass spectrometry and found that the chondrocytes seeded on stiff… More >

A. Rama Mohan Rao¹, S. Krishna Kumar¹, K. Lakshmi¹

Structural Durability & Health Monitoring, Vol.8, No.3, pp. 271-294, 2012, DOI:10.32604/sdhm.2012.008.271

Abstract Current advancements in structural health monitoring, sensor and sensor network technologies have encouraged using large number of sensor networks in monitoring spatially large civil structures like bridges. Large amount of spatial information obtained from these sensor networks will enhance the reliability in truly assessing the state of the health of the structure. However, if sensors go faulty during operation, the feature extraction techniques embedded into SHM scheme may lead to an erroneous conclusion and often end up with false alarms. Hence it is highly desirable to robustly detect the faulty sensors, isolate and correct the data, if the data at… More >

Shuai Yuan¹, Guizhi Wang^1,*, Jibo Chen¹, Wei Guo²

Journal on Artificial Intelligence, Vol.1, No.2, pp. 69-88, 2019, DOI:10.32604/jai.2019.06535

Abstract This paper develops a joint model utilizing the principal component analysis (PCA) and the back propagation (BP) neural network model optimized by the Levenberg Marquardt (LM) algorithm, and as an application of the joint model to investigate the damages caused by typhoons for a coastal province, Fujian Province, China in 2005-2015 (latest). First, the PCA is applied to analyze comprehensively the relationship between hazard factors, hazard bearing factors and disaster factors. Then five integrated indices, overall disaster level, typhoon intensity, damaged condition of houses, medical rescue and self-rescue capability, are extracted through the PCA; Finally, the BP neural network model,… More >

Lianggui Liu^1,*, Wei Li¹, Huiling Jia¹

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 97-106, 2018, DOI:10.32604/cmc.2018.03511

Abstract With the rapid development of mobile communication all over the world, the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities. Mobile phone communication data can be regarded as a type of time series and dynamic time warping (DTW) and derivative dynamic time warping (DDTW) are usually used to analyze the similarity of these data. However, many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series. In this paper, a novel hybrid method based on the combination of dynamic time warping… More >

Chengsheng Yuan^1,2,3, Xinting Li³, Q. M. Jonathan Wu³, Jin Li^4,5, Xingming Sun^1,2

CMC-Computers, Materials & Continua, Vol.53, No.4, pp. 357-372, 2017, DOI:10.3970/cmc.2017.053.357

Abstract Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints, which are made of common fingerprint materials, such as silicon, latex, etc. Thus, to protect our privacy, many fingerprint liveness detection methods are put forward to discriminate fake or true fingerprint. Current work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features, but these methods normally destroy or lose spatial information between pixels. Different from existing methods, convolutional neural network (CNN) can generate high-level semantic representations by learning and concatenating low-level edge and shape features from a large amount of labeled… More >