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  • Open Access

    ARTICLE

    A 3D Geometry Model of Vocal Tract Based on Smart Internet of Things

    Ming Li1, Kuntharrgyal Khysru2, Haiqiang Shi2,*, Qiang Fang3,*, Jinrong Hu4, Yun Chen5

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 783-798, 2023, DOI:10.32604/csse.2023.034687 - 20 January 2023

    Abstract The Internet of Things (IoT) plays an essential role in the current and future generations of information, network, and communication development and applications. This research focuses on vocal tract visualization and modeling, which are critical issues in realizing inner vocal tract animation. That is applied in many fields, such as speech training, speech therapy, speech analysis and other speech production-related applications. This work constructed a geometric model by observation of Magnetic Resonance Imaging data, providing a new method to annotate and construct 3D vocal tract organs. The proposed method has two advantages compared with previous… More >

  • Open Access

    ARTICLE

    Visualization Techniques via MLBS for Personnel Management in Major Events

    Yu Su1,2,3, Lingjuan Hou2,3,*, Sinan Li1, Zhaochang Jiang1, Haoran Peng4

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 521-536, 2023, DOI:10.32604/csse.2022.028606 - 20 January 2023

    Abstract Mobile location-based services (MLBS) refer to services around geographic location data. Mobile terminals use wireless communication networks (or satellite positioning systems) to obtain users’ geographic location coordinate information based on spatial databases and integrate with other information to provide users with required location-related services. The development of systems based on MLBS has significance and practical value. In this paper a visualization management information system for personnel in major events based on microservices, namely MEPMIS, is designed and implemented by using MLBS. The system consists of a server and a client app, and it has some… More >

  • Open Access

    ARTICLE

    Byte-Level Function-Associated Method for Malware Detection

    Jingwei Hao*, Senlin Luo, Limin Pan

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 719-734, 2023, DOI:10.32604/csse.2023.033923 - 20 January 2023

    Abstract The byte stream is widely used in malware detection due to its independence of reverse engineering. However, existing methods based on the byte stream implement an indiscriminate feature extraction strategy, which ignores the byte function difference in different segments and fails to achieve targeted feature extraction for various byte semantic representation modes, resulting in byte semantic confusion. To address this issue, an enhanced adversarial byte function associated method for malware backdoor attack is proposed in this paper by categorizing various function bytes into three functions involving structure, code, and data. The Minhash algorithm, grayscale mapping, More >

  • Open Access

    ARTICLE

    Identification and Visualization of Spatial and Temporal Trends in Textile Industry

    Umair Yousaf1, Muhammad Asif1, Shahbaz Ahmed1, Noman Tahir1, Azeem Irshad2, Akber Abid Gardezi3, Muhammad Shafiq4,*, Jin-Ghoo Choi4, Habib Hamam5,6,7,8

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4165-4181, 2023, DOI:10.32604/cmc.2023.026607 - 31 October 2022

    Abstract The research volume increases at the study rate, causing massive text corpora. Due to these enormous text corpora, we are drowning in data and starving for information. Therefore, recent research employed different text mining approaches to extract information from this text corpus. These proposed approaches extract meaningful and precise phrases that effectively describe the text's information. These extracted phrases are commonly termed keyphrases. Further, these key phrases are employed to determine the different fields of study trends. Moreover, these key phrases can also be used to determine the spatiotemporal trends in the various research fields.… More >

  • 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 - 27 October 2022

    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 - 29 September 2022

    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 - 22 September 2022

    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

    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 - 01 June 2022

    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

    EFFECTS OF BLOCKAGE LOCATIONS FOR ENHANCED HEAT TRANSFER AND FLOW VISUALIZATION IN A TESTED DUCT WITH DUAL-INCLINED BAFFLES (DIB): A CFD ANALYSIS

    Amnart Boonloia, Withada Jedsadaratanachaib,*

    Frontiers in Heat and Mass Transfer, Vol.18, pp. 1-15, 2022, DOI:10.5098/hmt.18.20

    Abstract Numerical analysis of fluid flow mechanism and heat transfer in a heat exchanger duct (HXD) with dual-inclined baffles (DIB) are reported. Three DIB types are examined: 1. “Type A” is located at the center of the HXD, 2. “Type B” is located on the upper-lower duct walls (as an orifice) and 3. “Type C” is a combination of the type A and B (as double orifices). The impacts of the ratio of DIB heights (b) to the square duct height (H; b/H) on increased heat transfer and friction loss are analyzed. Laminar flow (Re =… More >

  • Open Access

    ARTICLE

    VISUALIZATION OF INDUCED COUNTER-ROTATING VORTICES FOR ELECTRIC VEHICLES BATTERY MODULE THERMAL MANAGEMENT

    A.C. Budimana,*, S. M. Hasheminejadb, Sudirjaa, A. Mitayanic, S. H. Winotod

    Frontiers in Heat and Mass Transfer, Vol.19, pp. 1-6, 2022, DOI:10.5098/hmt.19.9

    Abstract Streamwise development of counter-rotating vortices induced by three different types of chevron Vortex Generators (VGs) placed upstream an Electric Vehicles (EV) dummy battery module is experimentally visualized using a smoke-wire method. From the single chevron reference case, the mushroom-like vortices do not collapse until passing the module. When more chevrons are used in line, the vortices become more prominent. It can also be observed that the vortex sizes and shapes are significantly influenced by the spanwise base length of the chevron. The induced vortices from all three VGs suggest a potential heat transfer augmentation for More >

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