• International journal of advanced smart convergence
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• 제8권2호
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• pp.204-210
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• 2019

Mobile Crowd Computing is one of the most efficient and effective way to collect the Electronic health records and they are very intelligent in processing them. Mobile Crowd Computing can handle, analyze and process the huge volumes of Electronic Health Records (EHR) from the high-performance Cloud Environment. Electronic Health Records are very sensitive, so they need to be secured, authenticated and processed efficiently. However, security, privacy and authentication of Electronic health records(EHR) and Patient health records(PHR) in the Mobile Crowd Computing Environment have become a critical issue that restricts many healthcare services from using Crowd Computing services .Our proposed Efficient Multi-layer Encryption Framework(MLEF) applies a set of multiple security Algorithms to provide access control over integrity, confidentiality, privacy and authentication with cost efficient to the Electronic health records(HER)and Patient health records(PHR). Our system provides the efficient way to create an environment that is capable of capturing, storing, searching, sharing, analyzing and authenticating electronic healthcare records efficiently to provide right intervention to the right patient at the right time in the Mobile Crowd Computing Environment.

• International Journal of Advanced Culture Technology
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• 제6권4호
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• pp.226-232
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• 2018

The rapid pace of growth in internet usage and rich mobile applications and with the advantage of incredible usage of internet enabled mobile devices the Green Mobile Crowd Computing will be the suitable area to research combining with cloud services architecture. Our proposed Framework will deploy the eHealth among various health care sectors and pave a way to create a Green Mobile Application to provide a better and secured way to access the Products/ Information/ Knowledge, eHealth services, experts / doctors globally. This green mobile crowd computing and cloud architecture for healthcare information systems are expected to lower costs, improve efficiency and reduce error by also providing better consumer care and service with great transparency to the patient universally in the field of medical health information technology. Here we introduced novel architecture to use of cloud services with crowd sourcing.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.26-34
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• 2024

Most human emotions are conveyed through facial expressions, which represent the predominant source of emotional data. This research investigates the impact of crowds on human emotions by analysing facial expressions. It examines how crowd behaviour, face recognition technology, and deep learning algorithms contribute to understanding the emotional change according to different level of crowd. The study identifies common emotions expressed during congestion, differences between crowded and less crowded areas, changes in facial expressions over time. The findings can inform urban planning and crowd event management by providing insights for developing coping mechanisms for affected individuals. However, limitations and challenges in using reliable facial expression analysis are also discussed, including age and context-related differences.

• 한국차세대컴퓨팅학회논문지
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• 제15권6호
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• pp.7-14
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• 2019

감시 영상에서 군중 행동의 자동 모니터링 및 감지는 보안, 안전 및 자산 보호와 같은 방대한 응용 프로그램으로 인해 컴퓨터 비전 분야에서 중요한 관심을 받고 있다. 또한 연구 커뮤니티에서 군중 분석 분야가 점차 증가하고 있다. 이를 위해서는 군중들의 행동을 감지하고 분석하는 것이 매우 필요하다. 본 논문에서는 스마트 시티에 설치된 감시 카메라의 비정상적인 활동을 감지하는 딥러닝 기반 방법을 제안하였다. 미세 조정된 VGG-16모델은 트레이닝된 공개적으로 사용 가능한 벤치마크 군중 데이터 셋을 실시간 스트리밍으로 테스트한다. CCTV카메라는 비디오 스트림을 캡쳐하는데, 비정상적인 활동이 감지되면 경보가 발생하여 추가 손실 전에 즉각적인 조치가 이루어지도록 가장 가까운 경찰서로 전송된다. 우리는 제안된 방법이 기존의 첨단 기술 보다 성능이 뛰어남을 실험으로 입증하였다.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.301-309
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• 2009

This study focuses on event-driven social media (EDSM), which supports the production of unique cultural items of small groups by satisfying the conflicting desires of distinctiveness and assimilation that small groups possess. EDSM is a system which promotes the production of idioculture through small group interaction by using an actual event in which people participate in small groups. By setting up an EDSM system in a university festival in which 10,000 to 15,000 people gather in small groups, idioculture production was tested for approximately eight hours and a half. Interaction records gathered from the test, as well as focus group interview data garnered soon after were used to analyze usage patterns of EDSM, types of idiocultures produced, and resulting factors of user experience. Through this, considerations upon designing future EDSM were proposed.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• 한국안전학회지
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• 제33권6호
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• pp.123-127
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• 2018

Recent catastrophic accidents at the underground subway stations in South Korea have proven that the subway evacuation is an important safety concern. Previous studies have used commercial programs for safety assessment or have been focused on development of computing algorithms rather than the basic analysis data which form the foundation of studies. In this study, we designed a new movement recording apparatus which measured and analyzed crowd movements including but not limited to moving velocity, specific flow rate and crowd density. Moreover, We propose new effective analysis method for evacuation studies with this apparatus.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.2948-2963
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• 2015

In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1036-1054
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• 2019

Recently, concomitant with a surge in numbers of Internet of Things (IoT) devices with various sensors, mobile crowdsensing (MCS) has provided a new business model for IoT. For example, a person can share road traffic pictures taken with their smartphone via a cloud computing system and the MCS data can provide benefits to other consumers. In this service model, to encourage people to actively engage in sensing activities and to voluntarily share their sensing data, providing appropriate incentives is very important. However, the sensing data from personal devices can be sensitive to privacy, and thus the privacy issue can suppress data sharing. Therefore, the development of an appropriate privacy protection system is essential for successful MCS. In this study, we address this problem due to the conflicting objectives of privacy preservation and incentive payment. We propose a privacy-preserving mechanism that protects identity and location privacy of sensing users through an on-demand incentive payment and group signatures methods. Subsequently, we apply the proposed mechanism to one example of MCS-an intelligent parking system-and demonstrate the feasibility and efficiency of our mechanism through emulation.

• 한국게임학회 논문지
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• 제13권3호
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• pp.47-54
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• 2013

대부분의 연속된 k-개의 이웃 찾기 알고리즘은 차량, 핸드폰 등 이동하는 객체에 대하여 주기적으로 모니터링을 하는 위치 기반 서비스에서 연구되어 왔다. 이러한 연구들은 쿼리 포인트가 이동 객체에 비하여 매우 적을 뿐만 아니라 쿼리 포인트가 움직이지 않고 고정된 환경을 가정한다. 게임 환경에서 k-개의 이웃을 찾아야 하는 경우는 무리 짓기, 군중 시뮬레이션 및 로봇과 같이 이동 객체가 주변의 이웃 객체를 인식하여 다음 이동을 계산하여야 할 때이다. 따라서 모든 이동 객체가 쿼리 포인트가 되고, 그 결과 이동 객체와 쿼리 포인트의 수가 동일하며, 쿼리 포인트도 움직이게 된다. 본 논문에서는 이러한 게임 환경에서 기존의 위치기반 서비스에서 연구된 k-개의 이웃 찾기 알고리즘들을 적용하여 어떤 알고리즘이 어떤 조건에서 적합한지에 대한 성능을 분석한다.