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Search Results (9)
  • Open Access


    Fine-Grained Pornographic Image Recognition with Multi-Instance Learning

    Zhiqiang Wu*, Bing Xie

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 299-316, 2023, DOI:10.32604/csse.2023.038586

    Abstract Image has become an essential medium for expressing meaning and disseminating information. Many images are uploaded to the Internet, among which some are pornographic, causing adverse effects on public psychological health. To create a clean and positive Internet environment, network enforcement agencies need an automatic and efficient pornographic image recognition tool. Previous studies on pornographic images mainly rely on convolutional neural networks (CNN). Because of CNN’s many parameters, they must rely on a large labeled training dataset, which takes work to build. To reduce the effect of the database on the recognition performance of pornographic images, many researchers view pornographic… More >

  • Open Access


    Multi-Classification of Polyps in Colonoscopy Images Based on an Improved Deep Convolutional Neural Network

    Shuang Liu1,2,3, Xiao Liu1, Shilong Chang1, Yufeng Sun4, Kaiyuan Li1, Ya Hou1, Shiwei Wang1, Jie Meng5, Qingliang Zhao6, Sibei Wu1, Kun Yang1,2,3,*, Linyan Xue1,2,3,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5837-5852, 2023, DOI:10.32604/cmc.2023.034720

    Abstract Achieving accurate classification of colorectal polyps during colonoscopy can avoid unnecessary endoscopic biopsy or resection. This study aimed to develop a deep learning model that can automatically classify colorectal polyps histologically on white-light and narrow-band imaging (NBI) colonoscopy images based on World Health Organization (WHO) and Workgroup serrAted polypS and Polyposis (WASP) classification criteria for colorectal polyps. White-light and NBI colonoscopy images of colorectal polyps exhibiting pathological results were firstly collected and classified into four categories: conventional adenoma, hyperplastic polyp, sessile serrated adenoma/polyp (SSAP) and normal, among which conventional adenoma could be further divided into three sub-categories of tubular adenoma,… More >

  • Open Access


    Multi-Classification and Distributed Reinforcement Learning-Based Inspection Swarm Offloading Strategy

    Yuping Deng1, Tao Wu1, Xi Chen2,*, Amir Homayoon Ashrafzadeh3

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1157-1174, 2022, DOI:10.32604/iasc.2022.022606

    Abstract In meteorological and electric power Internet of Things scenarios, in order to extend the service life of relevant facilities and reduce the cost of emergency repair, the intelligent inspection swarm is introduced to cooperate with monitoring tasks, which collect and process the current scene data through a variety of sensors and cameras, and complete tasks such as emergency handling and fault inspection. Due to the limitation of computing resources and battery life of patrol inspection equipment, it will cause problems such as slow response in emergency and long time for fault location. Mobile Edge Computing is a promising technology, which… More >

  • Open Access


    A Novel Framework for Multi-Classification of Guava Disease

    Omar Almutiry1, Muhammad Ayaz2, Tariq Sadad3, Ikram Ullah Lali4, Awais Mahmood1,*, Najam Ul Hassan5, Habib Dhahri1

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1915-1926, 2021, DOI:10.32604/cmc.2021.017702

    Abstract Guava is one of the most important fruits in Pakistan, and is gradually boosting the economy of Pakistan. Guava production can be interrupted due to different diseases, such as anthracnose, algal spot, fruit fly, styler end rot and canker. These diseases are usually detected and identified by visual observation, thus automatic detection is required to assist formers. In this research, a new technique was created to detect guava plant diseases using image processing techniques and computer vision. An automated system is developed to support farmers to identify major diseases in guava. We collected healthy and unhealthy images of different guava… More >

  • Open Access


    Multi-Classification Network for Identifying COVID-19 Cases Using Deep Convolutional Neural Networks

    Sajib Sarker, Ling Tan*, Wenjie Ma, Shanshan Rong, Osibo Benjamin Kwapong, Oscar Famous Darteh

    Journal on Internet of Things, Vol.3, No.2, pp. 39-51, 2021, DOI:10.32604/jiot.2021.014877

    Abstract The novel coronavirus 2019 (COVID-19) rapidly spreading around the world and turns into a pandemic situation, consequently, detecting the coronavirus (COVID-19) affected patients are now the most critical task for medical specialists. The deficiency of medical testing kits leading to huge complexity in detecting COVID-19 patients worldwide, resulting in the number of infected cases is expanding. Therefore, a significant study is necessary about detecting COVID-19 patients using an automated diagnosis method, which hinders the spreading of coronavirus. In this paper, the study suggests a Deep Convolutional Neural Network-based multi-classification framework (COVMCNet) using eight different pre-trained architectures such as VGG16, VGG19,… More >

  • Open Access


    M-IDM: A Multi-Classification Based Intrusion Detection Model in Healthcare IoT

    Jae Dong Lee1,2, Hyo Soung Cha1, Shailendra Rathore2, Jong Hyuk Park2,*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1537-1553, 2021, DOI:10.32604/cmc.2021.014774

    Abstract In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed a novel intrusion classification architecture… More >

  • Open Access


    Multi-Purpose Forensics of Image Manipulations Using Residual- Based Feature

    Anjie Peng1, Kang Deng1, Shenghai Luo1, Hui Zeng1, 2, *

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2217-2231, 2020, DOI:10.32604/cmc.2020.011006

    Abstract The multi-purpose forensics is an important tool for forge image detection. In this paper, we propose a universal feature set for the multi-purpose forensics which is capable of simultaneously identifying several typical image manipulations, including spatial low-pass Gaussian blurring, median filtering, re-sampling, and JPEG compression. To eliminate the influences caused by diverse image contents on the effectiveness and robustness of the feature, a residual group which contains several highpass filtered residuals is introduced. The partial correlation coefficient is exploited from the residual group to purely measure neighborhood correlations in a linear way. Besides that, we also combine autoregressive coefficient and… More >

  • Open Access


    Scalable Skin Lesion Multi-Classification Recognition System

    Fan Liu1, Jianwei Yan2, Wantao Wang2, Jian Liu2, *, Junying Li3, Alan Yang4

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 801-816, 2020, DOI:10.32604/cmc.2020.07039

    Abstract Skin lesion recognition is an important challenge in the medical field. In this paper, we have implemented an intelligent classification system based on convolutional neural network. First of all, this system can classify whether the input image is a dermascopic image with an accuracy of 99%. And then diagnose the dermoscopic image and the non-skin mirror image separately. Due to the limitation of the data, we can only realize the recognition of vitiligo by non-skin mirror. We propose a vitiligo recognition based on the probability average of three structurally identical CNN models. The method is more efficient and robust than… More >

  • Open Access


    Improved Logistic Regression Algorithm Based on Kernel Density Estimation for Multi-Classification with Non-Equilibrium Samples

    Yang Yu1, Zeyu Xiong1,*, Yueshan Xiong1, Weizi Li2

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 103-118, 2019, DOI:10.32604/cmc.2019.05154

    Abstract Logistic regression is often used to solve linear binary classification problems such as machine vision, speech recognition, and handwriting recognition. However, it usually fails to solve certain nonlinear multi-classification problem, such as problem with non-equilibrium samples. Many scholars have proposed some methods, such as neural network, least square support vector machine, AdaBoost meta-algorithm, etc. These methods essentially belong to machine learning categories. In this work, based on the probability theory and statistical principle, we propose an improved logistic regression algorithm based on kernel density estimation for solving nonlinear multi-classification. We have compared our approach with other methods using non-equilibrium samples,… More >

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