TING LAN, SHENG QIN, XIAOJIN GONG, PING ZHENG, JIAXIN YAN, YANG LIU^*

BIOCELL, Vol.45, No.5, pp. 1313-1319, 2021, DOI:10.32604/biocell.2021.015923

Abstract Invasive stratified mucin-producing carcinoma (ISMC) is a recently described histologic variant of high-risk human papillomavirus (HPV)-associated endocervical adenocarcinoma, as the putative invasive counterpart of the stratified mucin-producing intraepithelial lesion (SMILE). ISMC can display variable architectural patterns and usually coexists with other more conventional types of HPV-associated carcinomas, which makes diagnosis and differential diagnosis of ISMC is difficult for pathologists. Moreover, the prognosis of ISMC is still controversial. We analyzed 6 ISMCs with detailed pathological and clinical information. Intraepithelial lesion, including 1 high-grade squamous intraepithelial lesion and 1 SMILE, was found. Various architectures were observed (including nest, glandular, solid, trabecular, and… More >

Hanan A. Hosni Mahmoud^*, Amal H. Alharbi, Doaa S. Khafga

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 803-814, 2021, DOI:10.32604/iasc.2021.018607

Abstract In this paper, we aim to apply deep learning convolution neural network (Deep-CNN) technology to classify breast masses in mammograms. We develop a Deep-CNN combined with multi-feature extraction and transfer learning to detect breast cancer. The Deep-CNN is utilized to extract features from mammograms. A support vector machine (SVM) is then trained on the Deep-CNN features to classify normal, benign, and cancer cases. The scoring features from the Deep-CNN are coupled with texture features and used as inputs to the final classifier. Two texture features are included: texture features of spatial dependency and gradient-based histograms. Both are employed to locate… More >

Jingyuan Yang¹, Jie Zhou¹, Jiaojiao Jin¹, Yi Li², Xia Zhang³, Teng Li³, Mengdi Zhang³, Xiaodong Cai³, Chaodong Yang³, Cunyu Zhou^3,*

Phyton-International Journal of Experimental Botany, Vol.90, No.6, pp. 1685-1696, 2021, DOI:10.32604/phyton.2021.015533

Abstract Huanglian (Coptis chinensis Franch.) is a slow-growing perennial medicinal herb with considerable economic value. This study aimed to determine the structural characteristics and the levels of berberine deposits in the organs and tissues of Huanglian using light and epifluorescence microscopy. The adventitious roots are composed of primary and secondary structures with endodermis, exodermis, and phellem. The rhizome structures are composed of primary and secondary structures with cuticle and phellem. The leaves are composed of sclerenchymatous rings, isolateral mesophyll, and thin cuticles. We detected berberine in the xylem walls of the roots and rhizomes as well as in the sclerenchymatous rings… More >

Huizhong Sun¹, Guosheng Xu^1,*, Hewei Yu², Minyan Ma³, Yanhui Guo¹, Ruijie Quan⁴

Journal of Quantum Computing, Vol.3, No.2, pp. 55-63, 2021, DOI:10.32604/jqc.2021.017365

Abstract Language detection models based on system calls suffer from certain false negatives and detection blind spots. Hence, the normal behavior sequences of some malware applications for a short period can become malicious behavior within a certain time window. To detect such behaviors, we extract a multidimensional time distribution feature matrix on the basis of statistical analysis. This matrix mainly includes multidimensional time distribution features, multidimensional word pair correlation features, and multidimensional word frequency distribution features. A multidimensional time distribution model based on neural networks is built to detect the overall abnormal behavior within a given time window. Experimental evaluation is… More >

Y. Sreeraman^*, S. Lakshmana Pandian

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 539-557, 2021, DOI:10.32604/iasc.2021.017256

Abstract Causal relationships in a data play vital role in decision making. Identification of causal association in data is one of the important areas of research in data analytics. Simple correlations between data variables reveal the degree of linear relationship. Partial correlation explains the association between two variables within the control of other related variables. Partial association test explains the causality in data. In this paper a couple of causal relationship discovery strategies are proposed using the design of partial association tree that makes use of partial association test among variables. These decision trees are different from normal decision trees in… More >

Jehyeok Rew, Hyungjoon Kim, Eenjun Hwang^*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 801-817, 2021, DOI:10.32604/cmc.2021.017892

Abstract Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation… More >

Jehad Ali¹, Byungkyu Lee², Jimyung Oh², Jungtae Lee³, Byeong-hee Roh^1,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 267-282, 2021, DOI:10.32604/cmc.2021.017813

Abstract The controller in software-defined networking (SDN) acts as strategic point of control for the underlying network. Multiple controllers are available, and every single controller retains a number of features such as the OpenFlow version, clustering, modularity, platform, and partnership support, etc. They are regarded as vital when making a selection among a set of controllers. As such, the selection of the controller becomes a multi-criteria decision making (MCDM) problem with several features. Hence, an increase in this number will increase the computational complexity of the controller selection process. Previously, the selection of controllers based on features has been studied by… More >

Javeria Naz¹, Muhammad Attique Khan¹, Majed Alhaisoni², Oh-Young Song^3,*, Usman Tariq⁴, Seifedine Kadry⁵

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 607-625, 2021, DOI:10.32604/cmc.2021.017101

Abstract An automated system is proposed for the detection and classification of GI abnormalities. The proposed method operates under two pipeline procedures: (a) segmentation of the bleeding infection region and (b) classification of GI abnormalities by deep learning. The first bleeding region is segmented using a hybrid approach. The threshold is applied to each channel extracted from the original RGB image. Later, all channels are merged through mutual information and pixel-based techniques. As a result, the image is segmented. Texture and deep learning features are extracted in the proposed classification task. The transfer learning (TL) approach is used for the extraction… More >

Tina Babu¹, Deepa Gupta¹, Tripty Singh^1,*, Shahin Hameed², Mohammed Zakariah³, Yousef Ajami Alotaibi⁴

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 99-128, 2021, DOI:10.32604/cmc.2021.016341

Abstract Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification. This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes: normal, well, moderate, and poor. The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature, Gabor wavelet, wavelet moments, HSV histogram, color auto-correlogram, color moments, and morphological features that can be used to characterize different grades. Besides, the classifier is modeled as a multiclass structure with six binary class Bayesian optimized… More >

Xiajiong Shen^1,3, Shuaimin Jiang^2,3, Lei Zhang^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1069-1085, 2021, DOI:10.32604/cmes.2021.015736

Abstract The emergence of smart contracts has increased the attention of industry and academia to blockchain technology, which is tamper-proofing, decentralized, autonomous, and enables decentralized applications to operate in untrustworthy environments. However, these features of this technology are also easily exploited by unscrupulous individuals, a typical example of which is the Ponzi scheme in Ethereum. The negative effect of unscrupulous individuals writing Ponzi scheme-type smart contracts in Ethereum and then using these contracts to scam large amounts of money has been significant. To solve this problem, we propose a detection model for detecting Ponzi schemes in smart contracts using bytecode. In… More >