Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (30)
  • Open Access

    ARTICLE

    Fast CU Partition for VVC Using Texture Complexity Classification Convolutional Neural Network

    Yue Zhang1,3,4, Pengyu Liu1,2,3,4,*, Xiaowei Jia5, Shanji Chen2, Tianyu Liu1,3,4, Chang Liu1,3,4

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3545-3556, 2022, DOI:10.32604/cmc.2022.028226

    Abstract Versatile video coding (H.266/VVC), which was newly released by the Joint Video Exploration Team (JVET), introduces quad-tree plus multi-type tree (QTMT) partition structure on the basis of quad-tree (QT) partition structure in High Efficiency Video Coding (H.265/HEVC). More complicated coding unit (CU) partitioning processes in H.266/VVC significantly improve video compression efficiency, but greatly increase the computational complexity compared. The ultra-high encoding complexity has obstructed its real-time applications. In order to solve this problem, a CU partition algorithm using convolutional neural network (CNN) is proposed in this paper to speed up the H.266/VVC CU partition process. Firstly, 64 × 64 CU… More >

  • Open Access

    ARTICLE

    Mango Leaf Stress Identification Using Deep Neural Network

    Vinay Gautam1,*, Jyoti Rani2

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 849-864, 2022, DOI:10.32604/iasc.2022.025113

    Abstract Mango is a widely growing and consumable fruit crop. The quantity and quality of production are most important to satisfy the needs of the huge population. Numerous research has been conducted to increase the yield of the crop. But a good number of crop harvests were destroyed due to various factors and leaf stress is one of them. The various types of stresses include biotic and abiotic that impact the mangoes productivity. But here the focus is on biotic stress factors such as fungus and bacteria. The effect of the stress can be reduced in the preliminary stage by taking… More >

  • Open Access

    ARTICLE

    LF-CNN: Deep Learning-Guided Small Sample Target Detection for Remote Sensing Classification

    Chengfan Li1,2, Lan Liu3,*, Junjuan Zhao1, Xuefeng Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 429-444, 2022, DOI:10.32604/cmes.2022.019202

    Abstract Target detection of small samples with a complex background is always difficult in the classification of remote sensing images. We propose a new small sample target detection method combining local features and a convolutional neural network (LF-CNN) with the aim of detecting small numbers of unevenly distributed ground object targets in remote sensing images. The k-nearest neighbor method is used to construct the local neighborhood of each point and the local neighborhoods of the features are extracted one by one from the convolution layer. All the local features are aggregated by maximum pooling to obtain global feature representation. The classification… More >

  • Open Access

    ARTICLE

    An Improved Genetic Algorithm for Automated Convolutional Neural Network Design

    Rahul Dubey*, Jitendra Agrawal

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 747-763, 2022, DOI:10.32604/iasc.2022.020975

    Abstract Extracting the features from an image is a cumbersome task. Initially, this task was performed by domain experts through a process known as handcrafted feature design. A deep embedding technique known as convolutional neural networks (CNNs) later solved this problem by introducing the feature learning concept, through which the CNN is directly provided with images. This CNN then learns the features of the image, which are subsequently given as input to the further layers for an intended task like classification. CNNs have demonstrated astonishing performance in several practicable applications in the last few years. Nevertheless, the pursuance of CNNs primarily… More >

  • Open Access

    ARTICLE

    Alzheimer Disease Detection Empowered with Transfer Learning

    Taher M. Ghazal1,2, Sagheer Abbas3, Sundus Munir3,4, M. A. Khan5, Munir Ahmad3, Ghassan F. Issa2, Syeda Binish Zahra4, Muhammad Adnan Khan6,*, Mohammad Kamrul Hasan1

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5005-5019, 2022, DOI:10.32604/cmc.2022.020866

    Abstract Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems like this. The proposed system… More >

  • Open Access

    ARTICLE

    A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

    Ahmed Reda*, Sherif Barakat, Amira Rezk

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1381-1399, 2022, DOI:10.32604/cmc.2022.019809

    Abstract Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a COVID-19 patient, or a normal… More >

  • Open Access

    ARTICLE

    A Pregnancy Prediction System based on Uterine Peristalsis from Ultrasonic Images

    Kentaro Mori1,*, Kotaro Kitaya2, Tomomoto Ishikawa2, Yutaka Hata3

    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 335-352, 2021, DOI:10.32604/iasc.2021.01010

    Abstract In infertility treatment, it is required to improve a success rate of the treatment. A purpose of this study is to develop a prediction system for pregnancy outcomes using ultrasonic images. In infertility treatment, it is typical to evaluate the endometrial shape by using ultrasonic images. The convolutional neural network (CNN) system developed in the current study predicted pregnancy outcome by velocity information. The velocity information has a movement feature of uterine. It is known that a uterine movement is deep related to infertility. Experiments compared the velocity-based and shape-based systems. The shape-based systems predict the optimal uterine features for… More >

  • Open Access

    ARTICLE

    Automatic BIM Indoor Modelling from Unstructured Point Clouds Using a Convolutional Neural Network

    Uuganbayar Gankhuyag, Ji-Hyeong Han*

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 133-152, 2021, DOI:10.32604/iasc.2021.015227

    Abstract The automated reconstruction of building information modeling (BIM) objects from unstructured point cloud data for indoor as-built modeling is still a challenging task and the subject of much ongoing research. The most important part of the process is to detect the wall geometry clearly. A popular method is first to segment and classify point clouds, after which the identified segments should be clustered according to their corresponding objects, such as walls and clutter. To perform this process, a major problem is low-quality point clouds that are noisy, cluttered and that contain missing parts in the data. Moreover, the size of… More >

  • Open Access

    ABSTRACT

    Characterization of Coronary Atherosclerotic Plaque Composition Based on Convolutional Neural Network (CNN)

    Yifan Yin1, Chunliu He1, Biao Xu2, Zhiyong Li1,*

    Molecular & Cellular Biomechanics, Vol.16, Suppl.1, pp. 57-57, 2019, DOI:10.32604/mcb.2019.05732

    Abstract The tissue composition and morphological structure of atherosclerotic plaques determine its stability or vulnerability. Intravascular optical coherence tomography (IVOCT) has rapidly become the method of choice for assessing the pathology of the coronary arterial wall in vivo due to its superior resolution. However, in clinical practice, the analysis of plaque composition of OCT images mainly relies on the interpretation of images by well-trained experts, which is a time-consuming, labor-intensive procedure and it is also subjective. The purpose of this study is to use the Convolutional neural network (CNN) method to automatically extract the best feature information from the OCT images… More >

  • Open Access

    ARTICLE

    Investigation on the Chinese Text Sentiment Analysis Based on Convolutional Neural Networks in Deep Learning

    Feng Xu1, Xuefen Zhang2,*, Zhanhong Xin1, Alan Yang3

    CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 697-709, 2019, DOI:10.32604/cmc.2019.05375

    Abstract Nowadays, the amount of wed data is increasing at a rapid speed, which presents a serious challenge to the web monitoring. Text sentiment analysis, an important research topic in the area of natural language processing, is a crucial task in the web monitoring area. The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data. Deep learning is a hot research topic of the artificial intelligence in the recent years. By now, several research groups have studied the sentiment analysis of English texts using deep learning methods. In contrary, relatively few works have so far… More >

Displaying 21-30 on page 3 of 30. Per Page