M. A. Rahim Khan^*, Manoj Kumar Jain

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1359-1373, 2020, DOI:10.32604/iasc.2020.013849

Abstract Repacked mobile applications and obfuscation attacks constitute a significant threat to the Android technological ecosystem. A novel method using the Constant Key Point Selection and Limited Binary Pattern Feature (CKPS: LBP) extraction-based Hashing has been proposed to identify repacked Android applications in previous works. Although the approach was efficient in detecting the repacked Android apps, it was not suitable for detecting obfuscation attacks. Additionally, the time complexity needed improvement. This paper presents an optimization technique using Scalable Bivariant Feature Transformation extract optimum feature-points extraction, and the Harris method applied for optimized image hashing. The experiments produced better results than the… More >

Fahd N. Al-Wesabi^1,*, Huda G. Iskandar², Mohammad Alamgeer³, Mokhtar M. Ghilan²

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1267-1283, 2020, DOI:10.32604/iasc.2020.013782

Abstract In this paper, a robust approach INLPETWA (an Intelligent Natural Language Processing and English Text Watermarking Approach) is proposed to tampering detection of English text by integrating zero text watermarking and hidden Markov model as a soft computing and natural language processing techniques. In the INLPETWA approach, embedding and detecting the watermark key logically conducted without altering the plain text. Second-gram and word mechanism of hidden Markov model is used as a natural text analysis technique to extracts English text features and use them as a watermark key and embed them logically and validates them during detection process to detect… More >

Lingyun Xiang^1,2,3, Guoqing Guo², Qian Li⁴, Chengzhang Zhu^5,*, Jiuren Chen⁶, Haoliang Ma²

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1375-1390, 2020, DOI:10.32604/iasc.2020.013382

Abstract Current works on spam detection in product reviews tend to ignore the temporal relevance among reviews in the user or product entity, resulting in poor detection performance. To address this issue, the present paper proposes a spam detection method that jointly learns comprehensive temporal features from both behavioral and text features in user and product entities. We first extract the behavioral features of a single review, then employ a convolutional neural network (CNN) to learn the text features of this review. We next combine the behavioral features with the text features of each review and train a Long-Short-Term Memory (LSTM)… More >

Ujala Yasmeen¹, Jamal Hussain Shah¹, Muhammad Attique Khan², Ghulam Jillani Ansari¹, Saeed ur Rehman¹, Muhammad Sharif¹, Seifedine Kadry³, Yunyoung Nam^4,*

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1251-1266, 2020, DOI:10.32604/iasc.2020.012775

Abstract Detection of textual data from scene text images is a very thought-provoking issue in the field of computer graphics and visualization. This challenge is even more complicated when edge intelligent devices are involved in the process. The low-quality image having challenges such as blur, low resolution, and contrast make it more difficult for text detection and classification. Therefore, such exigent aspect is considered in the study. The technology proposed is comprised of three main contributions. (a) After synthetic blurring, the blurred image is preprocessed, and then the deblurring process is applied to recover the image. (b) Subsequently, the standard maximal… 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

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 channels that can extract both… More >

Boxia Hu^1,2,*, Huihuang Zhao¹, Yufei Yang^1,3, Bo Zhou⁴, Alex Noel Joseph Raj⁵

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1549-1560, 2020, DOI:10.32604/iasc.2020.011721

Abstract Face tracking is one of the most challenging research topics in computer vision. This paper proposes a framework to track multiple faces in video sequences automatically and presents an improved method based on feature fusion and neural network for multiple faces tracking in a video. The proposed method mainly includes three steps. At first, it is face detection, where an existing method is used to detect the faces in the first frame. Second, faces tracking with feature fusion. Given a video that has multiple faces, at first, all faces in the first frame are detected correctly by using an existing… More >

Tao Zhang^1,*, Jinxing Niu¹, Shuo Liu¹, Taotao Pan¹, Brij B. Gupta^2,3

Computer Systems Science and Engineering, Vol.36, No.1, pp. 271-280, 2021, DOI:10.32604/csse.2021.014181

Abstract Three-dimensional (3D) reconstruction using structured light projection has the characteristics of non-contact, high precision, easy operation, and strong real-time performance. However, for actual measurement, projection modulated images are disturbed by electronic noise or other interference, which reduces the precision of the measurement system. To solve this problem, a 3D measurement algorithm of structured light based on deep learning is proposed. The end-to-end multi-convolution neural network model is designed to separately extract the coarse- and fine-layer features of a 3D image. The point-cloud model is obtained by nonlinear regression. The weighting coefficient loss function is introduced to the multi-convolution neural network,… More >

Wasfi Shatanawi^1,2,3, Ali Raza^4,5,*, Muhammad Shoaib Arif⁴, Muhammad Rafiq⁶, Mairaj Bibi⁷, Muhammad Mohsin⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.1, pp. 201-215, 2021, DOI:10.32604/cmes.2021.012111

Abstract Nonlinear stochastic modelling plays an important character in the different fields of sciences such as environmental, material, engineering, chemistry, physics, biomedical engineering, and many more. In the current study, we studied the computational dynamics of the stochastic dengue model with the real material of the model. Positivity, boundedness, and dynamical consistency are essential features of stochastic modelling. Our focus is to design the computational method which preserves essential features of the model. The stochastic non-standard finite difference technique is most efficient as compared to other techniques used in literature. Analysis and comparison were explored in favour of convergence. Also, we… More >

Sophie Gardrat^*, Vincent Cockenpot

Oncologie, Vol.22, No.4, pp. 195-202, 2020, DOI:10.32604/oncologie.2020.013698

Abstract The pathological examination of ocular tumors has specificities in terms of macroscopic management, microscopic analysis, and molecular examinations requiring special attention. We discuss here the difficulties encountered in the reception in the pathological anatomy laboratory of conjunctival samples, enucleation and orbital exenteration pieces, then detail the diagnostic and theranostic, microscopic and molecular characteristics of ocular tumor pathologies. Conjunctival tumors (epithelial, melanocytic and lymphoid), choroidal tumors (including uveal melanoma) and retinoblastoma are treated. Because of their low frequency and their features, these tumors should be the subject of anatomo-clinical discussions.

Résumé:
L’examen anatomopathologique des tumeurs oculaires comporte des spécificités en termes… More >

Yukun Lv, Jiaxi Yang, Jiawen Wang^*, Runcheng Zhang, Shuang Yang, Wentao Li

FDMP-Fluid Dynamics & Materials Processing, Vol.16, No.6, pp. 1203-1217, 2020, DOI:10.32604/fdmp.2020.010489

Abstract Aiming at the problem that the total pressure loss of the flue of the electric precipitator of the 350 MW unit of a power plant to the inlet of the draft fan is too large, the numerical simulation software Fluent and the standard k-ε model was used to simulate the flue, the results show that the main part of the flue mean total pressure loss is derived from the confluence header and elbow. In order to reduce the loss and consider the cost of transformation, the concept of twodimensional feature surface is established, gradually proposed three sets of flue transformation… More >