Anju Asokan¹, J. Anitha¹, Bogdan Patrut², Dana Danciulescu³, D. Jude Hemanth^1,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 373-388, 2021, DOI:10.32604/cmc.2020.012364 - 30 October 2020

Abstract Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas. Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection. However, many deep learning framework based approaches do not consider both spatial and textural details into account. In order to handle this issue, a Convolutional Neural Network (CNN) based multi-feature extraction and fusion is introduced which considers both spatial and textural features. This method uses CNN to extract the spatio-spectral features from individual channels and fuse them More >

Fereshteh Salimi Nanadegani, Bengt Sunden^*

Frontiers in Heat and Mass Transfer, Vol.14, pp. 1-11, 2020, DOI:10.5098/hmt.14.21

Abstract In this experimental study, mass transfer and hydrodynamic parameters of water/kerosene/acetic acid system in a packed column were investigated, in which the mass transfer direction was set from the continuous phase (saturated water of kerosene and acetic acid) to the dispersed phase (saturated kerosene of water) in all the experiments. To assess the impact of nanoparticles on mass transfer, the experiments were performed in the presence of SiO₂ nanoparticles and absence of the nanoparticles. The results showed that the addition of the nanoparticles to the base fluid (saturated kerosene of water) increased the mass transfer efficiency More >

Feng Yuan, Xiao Shao^*

Journal on Big Data, Vol.2, No.4, pp. 167-176, 2020, DOI:10.32604/jbd.2020.015357 - 24 December 2020

Abstract Traditional image quality assessment methods use the hand-crafted features to predict the image quality score, which cannot perform well in many scenes. Since deep learning promotes the development of many computer vision tasks, many IQA methods start to utilize the deep convolutional neural networks (CNN) for IQA task. In this paper, a CNN-based multi-scale blind image quality predictor is proposed to extract more effectivity multi-scale distortion features through the pyramidal convolution, which consists of two tasks: A distortion recognition task and a quality regression task. For the first task, image distortion type is obtained by More >

Haifeng Song¹, Weiwei Yang^1,*, Haiyan Yuan², Harold Bufford³

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1441-1458, 2020, DOI:10.32604/iasc.2020.011988 - 24 December 2020

Abstract There are two main problems that lead to unsatisfactory classification performance for hyperspectral remote sensing images (HSIs). One issue is that the HSI data used for training in deep learning is insufficient, therefore a deeper network is unfavorable for spatial-spectral feature extraction. The other problem is that as the depth of a deep neural network increases, the network becomes more prone to overfitting. To address these problems, a dual-channel 3D-Multiscale DenseNet (3DMSS) is proposed to boost the discriminative capability for HSI classification. The proposed model has several distinct advantages. First, the model consists of dual… More >

Aziguli Wulamu^1,2,*, Jingyue Sang³, Dezheng Zhang^1,2, Zuxian Shi^1,2

Journal of New Media, Vol.2, No.4, pp. 149-155, 2020, DOI:10.32604/jnm.2020.014115 - 23 December 2020

Abstract Cultivated land extraction is essential for sustainable development and agriculture. In this paper, the network we propose is based on the encoderdecoder structure, which extracts the semantic segmentation neural network of cultivated land from satellite images and uses it for agricultural automation solutions. The encoder consists of two part: the first is the modified Xception, it can used as the feature extraction network, and the second is the atrous convolution, it can used to expand the receptive field and the context information to extract richer feature information. The decoder part uses the conventional upsampling operation More >

JiangtaoLi^1,2,*

Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1047-1061, 2020, DOI:10.32604/iasc.2020.010136

Abstract In view of poor permeability of coal seam and soft coal quality in China's coal mining area, a point hydraulic fracturing method suitable for the occurrence characteristics of coal seam is put forward based on the characteristics of coal seam hydraulic fracturing and the field practical experience of coal seam hydraulic fracturing for many years. A theoretical and mathematical model of hydraulic fracturing is established. Based on the large-scale finite element software ABAQUS, numerical simulation of two dimensional and three dimensional hydraulic fracturing is carried out, and the fracture propagation law and its parameter sensitivity… More >

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 - 11 November 2020

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… More >

Meigui Zhang^*, Kehui Zeng, Jinwei Wang

Journal of Information Hiding and Privacy Protection, Vol.2, No.1, pp. 21-34, 2020, DOI:10.32604/jihpp.2020.010467 - 15 October 2020

Abstract The development of artificial intelligence makes the application of face recognition more and more extensive, which also leads to the security of face recognition technology increasingly prominent. How to design a face anti-spoofing method with high accuracy, strong generalization ability and meeting practical needs is the focus of current research. This paper introduces the research progress of face anti-spoofing algorithm, and divides the existing face anti-spoofing methods into two categories: methods based on manual feature expression and methods based on deep learning. Then, the typical algorithms included in them are classified twice, and the basic More >

Soukaina Mjahed^1,*, Khadija Bouzaachane¹, Ahmad Taher Azar^2,3, Salah El Hadaj¹, Said Raghay¹

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 459-494, 2020, DOI:10.32604/cmes.2020.010791 - 12 October 2020

Abstract This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the “Higgs machine learning challenge 2014” data set. This unsupervised detection goes in this paper analysis through 4 steps: (1) selection of the most informative features from the considered data; (2) definition of the number of clusters based on the elbow criterion. The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters; (3) proposition of a new approach for hybridization of… More >

Malik Javed Akhtar¹, Zahur Ahmad¹, Rashid Amin^{1, *}, Sultan H. Almotiri², Mohammed A. Al Ghamdi², Hamza Aldabbas³

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2639-2663, 2020, DOI:10.32604/cmc.2020.011485 - 16 September 2020

Abstract A large amount of data is present on the web which can be used for useful purposes like a product recommendation, price comparison and demand forecasting for a particular product. Websites are designed for human understanding and not for machines. Therefore, to make data machine-readable, it requires techniques to grab data from web pages. Researchers have addressed the problem using two approaches, i.e., knowledge engineering and machine learning. State of the art knowledge engineering approaches use the structure of documents, visual cues, clustering of attributes of data records and text processing techniques to identify data… More >