Tianyu Wang^1,2, Kongyang Chen^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2297-2314, 2024, DOI:10.32604/cmes.2024.049964

Abstract Crowdsourcing holds broad applications in information acquisition and dissemination, yet encounters challenges pertaining to data quality assessment and user reputation management. Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores, thereby elevating the quality and dependability of crowdsourced data. However, these mechanisms face several challenges in traditional crowdsourcing systems: 1) platform security lacks robust guarantees and may be susceptible to attacks; 2) there exists a potential for large-scale privacy breaches; and 3) incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations, occasionally lacking… More >

Yun-Jung Choi^1,#,*, Jae-Won Kwak^2,#, Hae-In Namgung³

International Journal of Mental Health Promotion, Vol.26, No.6, pp. 439-447, 2024, DOI:10.32604/ijmhp.2024.050458

Abstract This study evaluated the state of anxiety, depression, post-traumatic stress disorder, general mental health, and mental well-being among citizens after a crowd-crush disaster in Korea. Individuals who experienced the crowd crush had significantly higher anxiety, depression, and post-traumatic stress disorder (PTSD) scores than those who did not (p < 0.001). Additionally, people who avoided the disaster area had significantly higher depression and PTSD scores than those who did not avoid the area (p < 0.001). Those who directly witnessed the Seoul Halloween crowd crush had a significant difference in PTSD levels in either group than those More >

Waleed Khalid Al-Ghanem¹, Emad Ul Haq Qazi^2,*, Muhammad Hamza Faheem², Syed Shah Amanullah Quadri³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4001-4020, 2024, DOI:10.32604/cmc.2024.048208

Abstract Estimation of crowd count is becoming crucial nowadays, as it can help in security surveillance, crowd monitoring, and management for different events. It is challenging to determine the approximate crowd size from an image of the crowd’s density. Therefore in this research study, we proposed a multi-headed convolutional neural network architecture-based model for crowd counting, where we divided our proposed model into two main components: (i) the convolutional neural network, which extracts the feature across the whole image that is given to it as an input, and (ii) the multi-headed layers, which make it easier More >

Mingze Li, Diwen Zheng, Shuhua Lu^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2105-2122, 2024, DOI:10.32604/cmc.2024.048928

Abstract Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis, achieving tremendous success recently with the development of deep learning. However, there have been still many challenges including crowd multi-scale variations and high network complexity, etc. To tackle these issues, a lightweight Res-connection multi-branch network (LRMBNet) for highly accurate crowd counting and localization is proposed. Specifically, using improved ShuffleNet V2 as the backbone, a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters. A light multi-branch structure with different expansion rate… More >

Maria Gul¹, Imran Ali Khan¹, Gohar Zaman², Atta Rahman^3,*, Jamaluddin Mir², Sardar Asad Ali Biabani^4,5, May Issa Aldossary⁶, Mustafa Youldash⁷, Ashraf Saadeldeen⁸, Maqsood Mahmud⁹, Asiya Abdus Salam⁶, Dania Alkhulaifi³, Abdullah AlTurkey³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1631-1657, 2024, DOI:10.32604/cmc.2024.048289

Abstract Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, many researchers proposed game theoretic models to avoid and remove obstacles for crowd evacuation. Game theoretical models aim to study and analyze the strategic behaviors of individuals within a crowd and their interactions during the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. These models consider a group of individuals as homogeneous objects with the same goals, involve complex mathematical formulation, and cannot model real-world scenarios such as panic, environmental information, crowds that move dynamically,… More >

Kai Wei¹, Song Yu², Qingxian Pan^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 607-622, 2024, DOI:10.32604/cmc.2024.048240

Abstract Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation. While traditional methods for task allocation can help reduce costs and improve efficiency, they may encounter challenges when dealing with abnormal data flow nodes, leading to decreased allocation accuracy and efficiency. To address these issues, this study proposes a novel two-part invalid detection task allocation framework. In the first step, an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data. Compared to the baseline method, the model achieves an approximately 4% increase in the F1 value More >

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

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

Jiaxing Wang, Lanlan Rui, Yang Yang^*, Zhipeng Gao, Xuesong Qiu

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2355-2384, 2023, DOI:10.32604/cmc.2023.034993

Abstract Crowdsensing, as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information, has received extensive attention in data collection. Since crowdsensing relies on user equipment to consume resources to obtain information, and the quality and distribution of user equipment are uneven, crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data, which affects the widespread use of crowdsensing. This paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of… More >

Cuiyu Wang, Xinyu Li, Yiping Gao^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1849-1870, 2023, DOI:10.32604/cmes.2023.028098

Abstract Job shop scheduling (JS) is an important technology for modern manufacturing. Flexible job shop scheduling (FJS) is critical in JS, and it has been widely employed in many industries, including aerospace and energy. FJS enables any machine from a certain set to handle an operation, and this is an NP-hard problem. Furthermore, due to the requirements in real-world cases, multi-objective FJS is increasingly widespread, thus increasing the challenge of solving the FJS problems. As a result, it is necessary to develop a novel method to address this challenge. To achieve this goal, a novel collaborative More >

Adel Hafeezallah¹, Ahlam Al-Dhamari^2,3,*, Syed Abd Rahman Abu-Bakar²

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

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 >