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  • Open Access

    ARTICLE

    Pathological Examination: Features of Ocular Tumors
    Examen Anatomopathologique: Particularités des Tumeurs Oculaires

    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 >

  • Open Access

    ARTICLE

    Gly-LysPred: Identification of Lysine Glycation Sites in Protein Using Position Relative Features and Statistical Moments via Chou’s 5 Step Rule

    Shaheena Khanum1, Muhammad Adeel Ashraf2, Asim Karim1, Bilal Shoaib3, Muhammad Adnan Khan4, Rizwan Ali Naqvi5, Kamran Siddique6, Mohammed Alswaitti6,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2165-2181, 2021, DOI:10.32604/cmc.2020.013646

    Abstract Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide. It is a clinically important attribute to numerous age-related, metabolic, and chronic diseases such as diabetes, Alzheimer’s, renal failure, etc. Identification of a non-enzymatic reaction are quite challenging in research. Manual identification in labs is a very costly and time-consuming process. In this research, we developed an accurate, valid, and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites. Comprehensive techniques using position relative features are used for feature extraction. An algorithm named as a random forest with some preprocessing… More >

  • Open Access

    ARTICLE

    3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

    Khalil Khan1, Jehad Ali2, Kashif Ahmad3, Asma Gul4, Ghulam Sarwar5, Sahib Khan6, Qui Thanh Hoai Ta7, Tae-Sun Chung8, Muhammad Attique9,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1757-1770, 2021, DOI:10.32604/cmc.2020.013590

    Abstract Face image analysis is one among several important cues in computer vision. Over the last five decades, methods for face analysis have received immense attention due to large scale applications in various face analysis tasks. Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation. In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model. We have developed an end to end face parts segmentation framework through deep convolutional neural networks (DCNNs). For training a deep face parts parsing model, we label face images… More >

  • Open Access

    ARTICLE

    Image Retrieval Based on Deep Feature Extraction and Reduction with Improved CNN and PCA

    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 >

  • Open Access

    REVIEW

    PGCA-Net: Progressively Aggregating Hierarchical Features with the Pyramid Guided Channel Attention for Saliency Detection

    Jiajie Mai1, Xuemiao Xu2,*, Guorong Xiao3, Zijun Deng2, Jiaxing Chen2

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 847-855, 2020, DOI:10.32604/iasc.2020.010119

    Abstract The Salient object detection aims to segment out the most visually distinctive objects in an image, which is a challenging task in computer vision. In this paper, we present the PGCA-Net equipped with the pyramid guided channel attention fusion block (PGCAFB) for the saliency detection task. Given an input image, the hierarchical features are extracted using a deep convolutional neural network (DCNN), then starting from the highest-level semantic features, we stage-by-stage restore the spatial saliency details by aggregating the lowerlevel detailed features. Since for the weak discriminative ability of the shallow detailed features, directly introducing them to the semantic features… More >

  • Open Access

    ARTICLE

    The Application of Sparse Reconstruction Algorithm for Improving Background Dictionary in Visual Saliency Detection

    Lei Feng1,2, Haibin Li1,*, Yakun Gao1, Yakun Zhang1

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 831-839, 2020, DOI:10.32604/iasc.2020.010117

    Abstract In the paper, we apply the sparse reconstruction algorithm of improved background dictionary to saliency detection. Firstly, after super-pixel segmentation, two bottom features are extracted: the color information of LAB and the texture features of the image by Gabor filter. Secondly, the convex hull theory is used to remove object region in boundary region, and K-means clustering algorithm is used to continue to simplify the background dictionary. Finally, the saliency map is obtained by calculating the reconstruction error. Compared with the mainstream algorithms, the accuracy and efficiency of this algorithm are better than those of other algorithms. More >

  • Open Access

    REVIEW

    Importance of Features Selection, Attributes Selection, Challenges and Future Directions for Medical Imaging Data: A Review

    Nazish Naheed1, Muhammad Shaheen1, Sajid Ali Khan1, Mohammed Alawairdhi2,*, Muhammad Attique Khan3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 315-344, 2020, DOI:10.32604/cmes.2020.011380

    Abstract In the area of pattern recognition and machine learning, features play a key role in prediction. The famous applications of features are medical imaging, image classification, and name a few more. With the exponential growth of information investments in medical data repositories and health service provision, medical institutions are collecting large volumes of data. These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality. On the other hand, this growth also made it difficult to comprehend and utilize data for various purposes. The results of imaging data can become biased because of… More >

  • Open Access

    ARTICLE

    A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features

    Wei Sun1, 2, *, Xiaorui Zhang2, 3, Xiaozheng He4, Yan Jin1, Xu Zhang3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2489-2510, 2020, DOI:10.32604/cmc.2020.012343

    Abstract Vehicle type recognition (VTR) is an important research topic due to its significance in intelligent transportation systems. However, recognizing vehicle type on the real-world images is challenging due to the illumination change, partial occlusion under real traffic environment. These difficulties limit the performance of current stateof-art methods, which are typically based on single-stage classification without considering feature availability. To address such difficulties, this paper proposes a twostage vehicle type recognition method combining the most effective Gabor features. The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier (SKNNC). Further… More >

  • Open Access

    ARTICLE

    Security of Chip Bank Card in Remote Payment Based on Risk Feature

    Zheng Fang1,∗, Junyun Cai1, Lifang Tian2

    Computer Systems Science and Engineering, Vol.35, No.4, pp. 299-305, 2020, DOI:10.32604/csse.2020.35.299

    Abstract Payment is a necessary thing in people’s daily life, and the development of the Internet makes it possible that people can shop at home. As for chip bank card, it is an important payment method that has been developed in recent years and plays a key role in remote payment. In this study, firstly, the risk features of chip bank cards were analyzed from the general remote payment scheme. Then, based on the security technology theory, a chip bank card remote payment model using elliptic curve hybrid encryption algorithm and identity authentication technology was constructed. In terms of security testing,… More >

  • Open Access

    ARTICLE

    Automated Inspection of Char Morphologies in Colombian Coals Using Image Analysis

    Deisy Chaves1,5,*, Maria Trujillo1, Edward Garcia2, Juan Barraza2, Edward Lester3, Maribel Barajas4, Billy Rodriguez4, Manuel Romero4, Laura Fernández-Robles5

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 397-405, 2020, DOI:10.32604/iasc.2020.013916

    Abstract Precise automated determination of char morphologies formed by coal during combustion can lead to more efficient industrial control systems for coal combustion. Commonly, char particles are manually classified following the ICCP decision tree which considers four morphological features. One of these features is unfused material, and this class of material not characteristic of Colombian coals. In this paper, we propose new machine learning algorithms to classify the char particles in an image based system. Our hypothesis is that supervised classification methods can outperform the 4 ‘class’ ICCP criteria. In this paper we evaluate several morphological features and specifically assess the… More >

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