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  • Open Access

    ARTICLE

    Text Classification for Azerbaijani Language Using Machine Learning

    Umid Suleymanov1, Behnam Kiani Kalejahi1,2,*, Elkhan Amrahov1, Rashid Badirkhanli1

    Computer Systems Science and Engineering, Vol.35, No.6, pp. 467-475, 2020, DOI:10.32604/csse.2020.35.467

    Abstract Text classification systems will help to solve the text clustering problem in the Azerbaijani language. There are some text-classification applications for foreign languages, but we tried to build a newly developed system to solve this problem for the Azerbaijani language. Firstly, we tried to find out potential practice areas. The system will be useful in a lot of areas. It will be mostly used in news feed categorization. News websites can automatically categorize news into classes such as sports, business, education, science, etc. The system is also used in sentiment analysis for product reviews. For More >

  • Open Access

    ARTICLE

    Improvement of the Firework Algorithm for Classification Problems

    Yu Xue, Sow Alpha Amadou*, Yan Zhao

    Journal of Cyber Security, Vol.2, No.4, pp. 191-196, 2020, DOI:10.32604/jcs.2020.014045 - 07 December 2020

    Abstract Attracted numerous analysts’ consideration, classification is one of the primary issues in Machine learning. Numerous evolutionary algorithms (EAs) were utilized to improve their global search ability. In the previous years, many scientists have attempted to tackle this issue, yet regardless of the endeavors, there are still a few inadequacies. Based on solving the classification problem, this paper introduces a new optimization classification model, which can be applied to the majority of evolutionary computing (EC) techniques. Firework algorithm (FWA) is one of the EC methods, Although the Firework algorithm (FWA) is a proficient algorithm for solving More >

  • Open Access

    ARTICLE

    Two Stage Classification with CNN for Colorectal Cancer Detection

    Pallabi Sharma1,*, Kangkana Bora2, Kunio Kasugai3, Bunil Kumar Balabantaray1

    Oncologie, Vol.22, No.3, pp. 129-145, 2020, DOI:10.32604/oncologie.2020.013870

    Abstract In this paper, we address a current problem in medical image processing, the detection of colorectal cancer from colonoscopy videos. According to worldwide cancer statistics, colorectal cancer is one of the most common cancers. The process of screening and the removal of pre-cancerous cells from the large intestine is a crucial task to date. The traditional manual process is dependent on the expertise of the medical practitioner. In this paper, a two-stage classification is proposed to detect colorectal cancer. In the first stage, frames of colonoscopy video are extracted and are rated as significant if More >

  • Open Access

    ARTICLE

    Event Trigger Recognition Based on Positive and Negative Weight Computing and its Application

    Tao Liao1,‡, Weicheng Fu1,†, Shunxiang Zhang1,*, Zongtian Liu2,§

    Computer Systems Science and Engineering, Vol.35, No.5, pp. 311-319, 2020, DOI:10.32604/csse.2020.35.311

    Abstract Event trigger recognition is a sub-task of event extraction, which is important for text classification, topic tracking and so on. In order to improve the effectiveness of using word features as a benchmark, a new event trigger recognition method based on positive and negative weight computing is proposed. Firstly, the associated word feature, the part-of-speech feature and the dependency feature are combined. Then, the combination of these three features with positive and negative weight computing is used to identify triggers. Finally, the text classification is carried out based on the event triggers. Findings from our More >

  • Open Access

    ARTICLE

    A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models

    Xing Deng1,2, Haijian Shao1,2,*, Liang Shi3, Xia Wang4,5, Tongling Xie6

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 579-596, 2020, DOI:10.32604/cmes.2020.011920 - 12 October 2020

    Abstract The Coronavirus Disease 2019 (COVID-19) is wreaking havoc around the world, bring out that the enormous pressure on national health and medical staff systems. One of the most effective and critical steps in the fight against COVID-19, is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation imaging. In this paper, five keras-related deep learning models: ResNet50, InceptionResNetV2, Xception, transfer learning and pre-trained VGGNet16 is applied to formulate an classification–detection approaches of COVID-19. Two benchmark methods SVM (Support Vector Machine), CNN (Convolutional Neural Networks) are provided to compare with More >

  • Open Access

    ARTICLE

    A Perspective of the Machine Learning Approach for the Packet Classification in the Software Defined Network

    B. Indira1,*, K. Valarmathi2

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 795-805, 2020, DOI:10.32604/iasc.2020.010114

    Abstract Packet classification is a major bottleneck in Software Defined Network (SDN). Each packet has to be classified based on the action specified in each rule in the given flow table. To perform classification, the system requires much of the CPU clock time. Therefore, developing an efficient packet classification algorithm is critical for high speed inter networking. Existing works make use of exact matching, range matching and longest prefix matching for classification and these techniques sometime enlarges rule databases, thus resulting in huge memory consumption and inefficient searching performance. In order to select an efficient packet… More >

  • Open Access

    ARTICLE

    The Data Classification Query Optimization Method for English Online Examination System Based on Grid Image Analysis

    Kun Liu*

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 749-754, 2020, DOI:10.32604/iasc.2020.010109

    Abstract In the English network examination system, the big data distribution is highly coupled, the cost of data query is large, and the precision is not good. In order to improve the ability of the data classification and query in the English network examination system, a method of data classification and query in the English network examination system is proposed based on the grid region clustering and frequent itemset feature extraction of the association rules. Using the grid image analysis to improve the statistical analysis of the English performance analysis, the collection and storage structure analysis… More >

  • Open Access

    ARTICLE

    Object Detection and Fuzzy-Based Classification Using UAV Data

    Abdul Qayyum1,*, Iftikhar Ahmad2, Mohsin Iftikhar3, Moona Mazher4

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 693-702, 2020, DOI:10.32604/iasc.2020.010103

    Abstract UAV (Unmanned Aerial Vehicle) equipped with remote sensing devices can acquire spatial data with a relevant area of interest. In this paper, we have acquired UAV data for high voltage power poles, urban areas and vegetation/trees near power lines. For object classification, the proposed approach based on the fuzzy classifier is compared with the traditional minimum distance classifier and maximum likelihood classifier on our three defined segments of UAV images. The performance evaluation of all the classifiers was based on the statistics parameters which included the mean, standard deviation and PDF (probability density function) of More >

  • Open Access

    ARTICLE

    Reversible Data Hiding in Encrypted Images Based on Prediction and Adaptive Classification Scrambling

    Lingfeng Qu1, Hongjie He1, Shanjun Zhang2, Fan Chen1, *

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2623-2638, 2020, DOI:10.32604/cmc.2020.09723 - 16 September 2020

    Abstract Reversible data hiding in encrypted images (RDH-EI) technology is widely used in cloud storage for image privacy protection. In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images, we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling. First, the prediction error image is obtained by a novel prediction method before encryption. Then, the image pixel values are divided into two categories by the threshold range, which is selected adaptively according to the image content. Multiple high-significant bits of pixels 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 - 16 September 2020

    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… More >

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