Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (67)
  • Open Access

    ARTICLE

    An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

    Jun Liu1, Geng Yuan2, Changdi Yang2, Houbing Song3, Liang Luo4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1571-1587, 2023, DOI:10.32604/cmes.2022.023195

    Abstract The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research. The safety criteria for medical imaging are highly stringent, and models are required for an explanation. However, existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs. Thus, the interpretability of CNNs has come into the spotlight. Since medical imaging data are limited, many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public ImageNet datasets by the transfer learning method. Unfortunately, this generates… More >

  • Open Access

    ARTICLE

    Interactive Trajectory Star Coordinates i-tStar and Its Extension i-tStar (3D)

    Jing He1,2, Haonan Chen3,*, Lingxiao Li4, Yebin Zou5

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 211-237, 2023, DOI:10.32604/cmes.2022.020597

    Abstract There are many sources of geographic big data, and most of them come from heterogeneous environments. The data sources obtained in this case contain attribute information of different spatial scales, different time scales and different complexity levels. It is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more difficult. In this case, visualizing high-dimensional spatiotemporal trajectory data is extremely challenging. Therefore, i-tStar and its extension i-tStar (3D) proposed, a trajectory behavior feature for moving objects that are integrated into a view with less effort More > Graphic Abstract

    Interactive Trajectory Star Coordinates i-tStar and Its Extension i-tStar (3D)

  • Open Access

    ARTICLE

    Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges

    Kamal A. ElDahshan, AbdAllah A. AlHabshy, Luay Thamer Mohammed*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 817-839, 2023, DOI:10.32604/cmc.2023.032287

    Abstract This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods. To reduce the volume of big data and minimize model training time (Tt) while maintaining data quality. We contributed to meeting the challenges of big data visualization using the embedded method based “Select from model (SFM)” method by using “Random forest Importance algorithm (RFI)” and comparing it with the filter method by using “Select percentile (SP)” method based chi square “Chi2” tool for selecting the most important features, which are then fed into… More >

  • Open Access

    ARTICLE

    Pedestrian Physical Education Training Over Visualization Tool

    Tamara al Shloul1, Israr Akhter2, Suliman A. Alsuhibany3, Yazeed Yasin Ghadi4, Ahmad Jalal2, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2389-2405, 2022, DOI:10.32604/cmc.2022.027007

    Abstract E-learning approaches are one of the most important learning platforms for the learner through electronic equipment. Such study techniques are useful for other groups of learners such as the crowd, pedestrian, sports, transports, communication, emergency services, management systems and education sectors. E-learning is still a challenging domain for researchers and developers to find new trends and advanced tools and methods. Many of them are currently working on this domain to fulfill the requirements of industry and the environment. In this paper, we proposed a method for pedestrian behavior mining of aerial data, using deep flow… More >

  • Open Access

    ARTICLE

    Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

    Polin Rahman1, Ahmed Rifat1, MD. IftehadAmjad Chy1, Mohammad Monirujjaman Khan1,*, Mehedi Masud2, Sultan Aljahdali2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 757-775, 2023, DOI:10.32604/csse.2023.021469

    Abstract Heart failure is now widely spread throughout the world. Heart disease affects approximately 48% of the population. It is too expensive and also difficult to cure the disease. This research paper represents machine learning models to predict heart failure. The fundamental concept is to compare the correctness of various Machine Learning (ML) algorithms and boost algorithms to improve models’ accuracy for prediction. Some supervised algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), Logistic Regression (LR) are considered to achieve the best results. Some boosting algorithms like Extreme Gradient… More >

  • Open Access

    ARTICLE

    Ransomware Classification Framework Using the Behavioral Performance Visualization of Execution Objects

    Jun-Seob Kim, Ki-Woong Park*

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3401-3424, 2022, DOI:10.32604/cmc.2022.026621

    Abstract A ransomware attack that interrupted the operation of Colonial Pipeline (a large U.S. oil pipeline company), showed that security threats by malware have become serious enough to affect industries and social infrastructure rather than individuals alone. The agents and characteristics of attacks should be identified, and appropriate strategies should be established accordingly in order to respond to such attacks. For this purpose, the first task that must be performed is malware classification. Malware creators are well aware of this and apply various concealment and avoidance techniques, making it difficult to classify malware. This study focuses… More >

  • Open Access

    ARTICLE

    Visualization Detection of Solid–Liquid Two-Phase Flow in Filling Pipeline by Electrical Capacitance Tomography Technology

    Ningbo Jing1, Mingqiao Li1, Lang Liu2,*, Yutong Shen1, Peijiao Yang1, Xuebin Qin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 465-476, 2022, DOI:10.32604/cmes.2022.018965

    Abstract During mine filling, the caking in the pipeline and the waste rock in the filling slurry may cause serious safety accidents such as pipe blocking or explosion. Therefore, the visualization of the inner mine filling of the solid–liquid two-phase flow in the pipeline is important. This paper proposes a method based on capacitance tomography for the visualization of the solid–liquid distribution on the section of a filling pipe. A feedback network is used for electrical capacitance tomography reconstruction. This reconstruction method uses radial basis function neural network fitting to determine the relationship between the capacitance… More >

  • Open Access

    ARTICLE

    Complex Network Formation and Analysis of Online Social Media Systems

    Hafiz Abid Mahmood Malik*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1737-1750, 2022, DOI:10.32604/cmes.2022.018015

    Abstract To discover and identify the influential nodes in any complex network has been an important issue. It is a significant factor in order to control over the network. Through control on a network, any information can be spread and stopped in a short span of time. Both targets can be achieved, since network of information can be extended and as well destroyed. So, information spread and community formation have become one of the most crucial issues in the world of SNA (Social Network Analysis). In this work, the complex network of twitter social network has… More >

  • Open Access

    ARTICLE

    Deep Stacked Ensemble Learning Model for COVID-19 Classification

    G. Madhu1, B. Lalith Bharadwaj1, Rohit Boddeda2, Sai Vardhan1, K. Sandeep Kautish3, Khalid Alnowibet4, Adel F. Alrasheedi4, Ali Wagdy Mohamed5,6,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5467-5469, 2022, DOI:10.32604/cmc.2022.020455

    Abstract COVID-19 is a growing problem worldwide with a high mortality rate. As a result, the World Health Organization (WHO) declared it a pandemic. In order to limit the spread of the disease, a fast and accurate diagnosis is required. A reverse transcript polymerase chain reaction (RT-PCR) test is often used to detect the disease. However, since this test is time-consuming, a chest computed tomography (CT) or plain chest X-ray (CXR) is sometimes indicated. The value of automated diagnosis is that it saves time and money by minimizing human effort. Three significant contributions are made by… More >

  • Open Access

    ARTICLE

    Classification and Visualization of Surrounding Rock Mass Stability Based on Multi-Dimensional Cloud Model

    Liming Xue1,*, Wenlong Shen1, Zhixue Zheng2,*, Jiming Chen1, Hongtao Liu3

    Energy Engineering, Vol.118, No.6, pp. 1799-1810, 2021, DOI:10.32604/EE.2021.015124

    Abstract The classification of the stability of surrounding rock is an uncertain system with multiple indices. The Multi-dimensional Cloud Model provides an advanced solution through the use of an improved model of One-dimensional Cloud Model. Setting each index as a one-dimensional attribute, the Multi-dimensional Cloud Model can set the digital characteristics of each index according to the cloud theory. The Multi-dimensional cloud generator can calculate the certainty of each grade, and then determine the stability levels of the surrounding rock according to the principle of maximum certainty. Using this model to 5 coal mine roadway surrounding… More >

Displaying 21-30 on page 3 of 67. Per Page