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Search Results (14)
  • Open Access

    ARTICLE

    Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

    Yu Li, Mingxiao Li, Dongyang Ou*, Junjie Guo, Fangyuan Pan

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 893-909, 2024, DOI:10.32604/cmes.2023.031350 - 30 December 2023

    Abstract With the rapid development of mobile Internet, spatial crowdsourcing has become more and more popular. Spatial crowdsourcing consists of many different types of applications, such as spatial crowd-sensing services. In terms of spatial crowd-sensing, it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models. Besides collecting sensing data, spatial crowdsourcing also includes spatial delivery services like DiDi and Uber. Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications. Previous research conducted task assignments via traditional matching approaches or using simple… More > Graphic Abstract

    Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

  • Open Access

    ARTICLE

    Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

    Tamilvizhi Thanarajan1, Youseef Alotaibi2, Surendran Rajendran3,*, Krishnaraj Nagappan4

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1995-2013, 2023, DOI:10.32604/cmc.2023.039644 - 30 August 2023

    Abstract Autism spectrum disorder (ASD) can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics, like changes in behavior, social disabilities, and difficulty communicating with others. Eye tracking (ET) has become a useful method to detect ASD. One vital aspect of moral erudition is the aptitude to have common visual attention. The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection. Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD, but it is important to… More >

  • Open Access

    ARTICLE

    Visual Motion Segmentation in Crowd Videos Based on Spatial-Angular Stacked Sparse Autoencoders

    Adel Hafeezallah1, Ahlam Al-Dhamari2,3,*, Syed Abd Rahman Abu-Bakar2

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 593-611, 2023, DOI:10.32604/csse.2023.039479 - 26 May 2023

    Abstract Visual motion segmentation (VMS) is an important and key part of many intelligent crowd systems. It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes, which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades. Trajectory clustering has become one of the most popular methods in VMS. However, complex data, such as a large number of samples and parameters, makes it difficult for trajectory clustering to work well with accurate motion segmentation… More >

  • Open Access

    ARTICLE

    Abnormal Crowd Behavior Detection Using Optimized Pyramidal Lucas-Kanade Technique

    G. Rajasekaran1,*, J. Raja Sekar2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2399-2412, 2023, DOI:10.32604/iasc.2023.029119 - 19 July 2022

    Abstract Abnormal behavior detection is challenging and one of the growing research areas in computer vision. The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain events. In this work, Pyramidal Lucas Kanade algorithm is optimized using EMEHOs to achieve the objective. First stage, OPLKT-EMEHOs algorithm is used to generate the optical flow from MIIs. Second stage, the MIIs optical flow is applied as input to 3 layer CNN for detect the abnormal crowd behavior. University of Minnesota (UMN) dataset is used to evaluate the proposed More >

  • Open Access

    ARTICLE

    Game Outlier Behavior Detection System Based on Dynamic Time Warp Algorithm

    Shinjin Kang1, Soo Kyun Kim2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 219-237, 2022, DOI:10.32604/cmes.2022.018413 - 24 January 2022

    Abstract This paper proposes a methodology for using multi-modal data in gameplay to detect outlier behavior. The proposed methodology collects, synchronizes, and quantifies time-series data from webcams, mouses, and keyboards. Facial expressions are varied on a one-dimensional pleasure axis, and changes in expression in the mouth and eye areas are detected separately. Furthermore, the keyboard and mouse input frequencies are tracked to determine the interaction intensity of users. Then, we apply a dynamic time warp algorithm to detect outlier behavior. The detected outlier behavior graph patterns were the play patterns that the game designer did not More >

  • Open Access

    ARTICLE

    Brain Storm Optimization Based Clustering for Learning Behavior Analysis

    Yu Xue1,2,*, Jiafeng Qin1, Shoubao Su2, Adam Slowik3

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 211-219, 2021, DOI:10.32604/csse.2021.016693 - 20 July 2021

    Abstract Recently, online learning platforms have proven to help people gain knowledge more conveniently. Since the outbreak of COVID-19 in 2020, online learning has become a mainstream mode, as many schools have adopted its format. The platforms are able to capture substantial data relating to the students’ learning activities, which could be analyzed to determine relationships between learning behaviors and study habits. As such, an intelligent analysis method is needed to process efficiently this high volume of information. Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning More >

  • Open Access

    ARTICLE

    Outlier Behavior Detection for Indoor Environment Based on t-SNE Clustering

    Shinjin Kang1, Soo Kyun Kim2,*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3725-3736, 2021, DOI:10.32604/cmc.2021.016828 - 06 May 2021

    Abstract In this study, we propose a low-cost system that can detect the space outlier utilization of residents in an indoor environment. We focus on the users’ app usage to analyze unusual behavior, especially in indoor spaces. This is reflected in the behavioral analysis in that the frequency of using smartphones in personal spaces has recently increased. Our system facilitates autonomous data collection from mobile app logs and Google app servers and generates a high-dimensional dataset that can detect outlier behaviors. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was applied for effective singular… More >

  • Open Access

    ARTICLE

    Detecting Outlier Behavior of Game Player Players Using Multimodal Physiology Data

    Shinjin Kang1, Taiwoo Park2,*

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 205-214, 2020, DOI:10.31209/2019.100000141

    Abstract This paper describes an outlier detection system based on a multimodal physiology data clustering algorithm in a PC gaming environment. The goal of this system is to provide information on a game player’s abnormal behavior with a bio-signal analysis. Using this information, the game platform can easily identify players with abnormal behavior in specific events. To do this, we propose a mouse device that measures the wearer's skin conductivity, temperature, and motion. We also suggest a Dynamic Time Warping (DTW) based clustering algorithm. The developed system examines the biometric information of 50 players in a More >

  • Open Access

    ARTICLE

    Closed Solution for Initial Post-Buckling Behavior Analysis of a Composite Beam with Shear Deformation Effect

    Yongping Yu1, Lihui Chen1, Shaopeng Zheng1, Baihui Zeng1, Weipeng Sun2, ∗

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 185-200, 2020, DOI:10.32604/cmes.2020.07997 - 01 April 2020

    Abstract This paper is focused on the post-buckling behavior of the fixed laminated composite beams with effects of axial compression force and the shear deformation. The analytical solutions are established for the original control equations (that is not simplified) by applying the Maclaurin series expansion, Chebyshev polynomials, the harmonic balance method and the Newton’s method. The validity of the present method is verified via comparing the analytical approximate solutions with the numerical ones which are obtained by the shooting method. The present third analytical approximate solutions can give excellent agreement with the numerical solutions for a More >

  • Open Access

    ARTICLE

    Analysis of Bus Ride Comfort Using Smartphone Sensor Data

    Hoong-Chor Chin1, Xingting Pang1, Zhaoxia Wang2,3,*

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 455-463, 2019, DOI:10.32604/cmc.2019.05664

    Abstract Passenger comfort is an important indicator that is often used to measure the quality of public transport services. It may also be a crucial factor in the passenger’s choice of transport mode. The typical method of assessing passenger comfort is through a passenger interview survey which can be tedious. This study aims to investigate the relationship between bus ride comfort based on ride smoothness and the vehicle’s motion detected by the smartphone sensors. An experiment was carried out on a bus fixed route within the University campus where comfort levels were rated on a 3-point… More >

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