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 >

Xuan Chen^1,*, Zhiping Wan¹, Jiatong Wang²

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1531-1548, 2020, DOI:10.32604/iasc.2020.011723 - 24 December 2020

Abstract Addressing the shortcomings of unmanned path planning, such as significant error and low precision, a path-planning algorithm based on the whale optimization algorithm (WOA)-optimized double-blinking restricted Boltzmann machine-back propagation (RBM-BP) deep neural network model is proposed. The model consists mainly of two twin RBMs and one BP neural network. One twin RBM is used for feature extraction of the unmanned path location, and the other RBM is used for the path similarity calculation. The model uses the WOA algorithm to optimize parameters, which reduces the number of training sessions, shortens the training time, and reduces… More >

Yang Yang^1,*, Jianxin Wang¹, Zhipeng Gao¹, Yonghua Huo², Xuesong Qiu¹

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1459-1475, 2020, DOI:10.32604/iasc.2020.011688 - 24 December 2020

Abstract With the vigorous development of the Internet industry and the iterative updating of web service technologies, there are increasing web services with the same or similar functions in the ocean of platforms on the Internet. The issue of selecting the most reliable web service for users has received considerable critical attention. Aiming to solve this task, we propose a service reliability inference method based on deep neural network (SRI-XDFM) in this article. First, according to the pattern of the raw data in our scenario, we improve the performance of embedding by extracting self-correlated information with More >

Tianyu Wang¹, Wael A. Altabey^1,2, Mohammad Noori^3,*, Ramin Ghiasi¹

Structural Durability & Health Monitoring, Vol.14, No.4, pp. 315-338, 2020, DOI:10.32604/sdhm.2020.011083 - 04 December 2020

Abstract Beam-like structures are a class of common but important structures in engineering. Over the past few centuries, extensive research has been carried out to obtain the static and dynamic response of beam-like structures. Although building the finite element model to predict the response of these structures has proven to be effective, it is not always suitable in all the application cases because of high computational time or lack of accuracy. This paper proposes a novel approach to predict the deflection response of beam-like structures based on a deep neural network and the governing differential equation More >

Kun Zhu¹, Nana Zhang¹, Qing Zhang², Shi Ying^{1, *}, Xu Wang³

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1467-1486, 2020, DOI:10.32604/cmc.2020.011415 - 20 August 2020

Abstract Software defect prediction plays a very important role in software quality assurance, which aims to inspect as many potentially defect-prone software modules as possible. However, the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features. In addition, software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques. To address these two issues, we propose the following two solutions in this paper: (1) We leverage a novel non-linear manifold learning method - SOINN Landmark… More >

Zhaoquan Gu¹, Yu Su¹, Chenwei Liu¹, Yinyu Lyu¹, Yunxiang Jian¹, Hao Li², Zhen Cao³, Le Wang^{1, *}

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1437-1452, 2020, DOI:10.32604/cmc.2020.011834 - 20 August 2020

Abstract The license plate recognition system (LPRS) has been widely adopted in daily life due to its efficiency and high accuracy. Deep neural networks are commonly used in the LPRS to improve the recognition accuracy. However, researchers have found that deep neural networks have their own security problems that may lead to unexpected results. Specifically, they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images, resulting in incorrect license plate recognition. There are some classic methods to generate adversarial examples, but they cannot be adopted on More >

Abdu Gumaei^{1, 2, *}, Mabrook Al-Rakhami^{1, 2}, Hussain AlSalman², Sk. Md. Mizanur Rahman³, Atif Alamri^{1, 2}

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1033-1057, 2020, DOI:10.32604/cmc.2020.011740 - 20 August 2020

Abstract Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them. Deep learning has gained momentum for identifying activities through sensors, smartphones or even surveillance cameras. However, it is often difficult to train deep learning models on constrained IoT devices. The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing, which we call DL-HAR. The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model More >

Yatian Shen¹, Yubo Mai², Xiajiong Shen², Wenke Ding^{2, *}, Mengjiao Guo³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 529-541, 2020, DOI:10.32604/cmc.2020.011139 - 23 July 2020

Abstract Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic roles. At the same time, the predicate-argument structure in a sentence is important information for semantic role labeling task. In this work, we introduce the auxiliary deep neural network model, which models semantic dependency between part-of-speech and semantic roles and incorporates the information of predicate-argument into semantic role labeling. Based on the framework of joint learning, part-of-speech tagging is used as an auxiliary task to improve the result of the semantic role labeling. In addition, we introduce the argument recognition layer More >

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

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 279-308, 2020, DOI:10.32604/cmc.2020.011001 - 23 July 2020

Abstract Software defect prediction plays an important role in software quality assurance. However, the performance of the prediction model is susceptible to the irrelevant and redundant features. In addition, previous studies mostly regard software defect prediction as a single objective optimization problem, and multi-objective software defect prediction has not been thoroughly investigated. For the above two reasons, we propose the following solutions in this paper: (1) we leverage an advanced deep neural network—Stacked Contractive AutoEncoder (SCAE) to extract the robust deep semantic features from the original defect features, which has stronger discrimination capacity for different classes… More >

Xintao Duan^{1, *}, Daidou Guo¹, Chuan Qin²

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 721-745, 2020, DOI:10.32604/cmes.2020.09463 - 20 July 2020

Abstract Image steganography is a technique that hides secret information into the cover image to protect information security. The current image steganography is mainly to embed a smaller secret image in an area such as a texture of a larger-sized cover image, which will cause the size of the secret image to be much smaller than the cover image. Therefore, the problem of small steganographic capacity needs to be solved urgently. This paper proposes a steganography framework that combines image compression. In this framework, the Vector Quantized Variational AutoEncoder (VQ-VAE) is used to achieve the compression More >