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Detection of mass panic using internet of things and machine learning / Gehan Yahya Alsalat, Mohammad Elramly ; Supervised Aly Aly Fahmy , Mohammad Elramly

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Gehan Yahya Alsalat, Mohammad Elramly, 2019Description: 97Leaves : ill; 30cmOther title:
  • اكتشاف الذعر الجماعي باستخدام الانترنت الأشيتاء وتعلم الألة [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Computer and Information - Department of Computer Science Summary: The increase of emergency situations that cause mass panic in mass gatherings, such as terrorist attacks, random shooting, stampede, and fires, sheds light on the fact that advancements in technology should contribute in timely detecting and reporting serious crowd abnormal behaviour. The new paradigm of the {u2018}Internet of Things{u2019} (IoT) can contribute to that. In this study, a method for real-time detection of abnormal crowd behaviour in mass gatherings is proposed. This system is based on advanced wireless connections, wearable sensors and machine learning technologies. It is a new crowdsourcing approach that considers humans themselves as the surveillance devices that exist everywhere. A sufficient number of the event{u2019}s attendees are supposed to wear an electronic wristband which contains a heart rate sensor, motion sensors and an assisted-GPS, and has a wireless connection. It detects the abnormal behaviour by detecting heart rate increase and abnormal motion. Due to the unavailability of public bio-dataset on mass panic, dataset of this study was collected from 89 subjects wearing the above-mentioned wristband and generating 1054 data samples. Two types of data collected were: firstly, the data of normal daily activities and secondly, the data of abnormal activities resembling the behaviour of escape panic
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة CaI01.20.03.M.Sc.2019.Ge.D (Browse shelf(Opens below)) Not for loan 01010110078682000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة CaI01.20.03.M.Sc.2019.Ge.D (Browse shelf(Opens below)) 78682.CD Not for loan 01020110078682000

Thesis (M.Sc.) - Cairo University - Faculty of Computer and Information - Department of Computer Science

The increase of emergency situations that cause mass panic in mass gatherings, such as terrorist attacks, random shooting, stampede, and fires, sheds light on the fact that advancements in technology should contribute in timely detecting and reporting serious crowd abnormal behaviour. The new paradigm of the {u2018}Internet of Things{u2019} (IoT) can contribute to that. In this study, a method for real-time detection of abnormal crowd behaviour in mass gatherings is proposed. This system is based on advanced wireless connections, wearable sensors and machine learning technologies. It is a new crowdsourcing approach that considers humans themselves as the surveillance devices that exist everywhere. A sufficient number of the event{u2019}s attendees are supposed to wear an electronic wristband which contains a heart rate sensor, motion sensors and an assisted-GPS, and has a wireless connection. It detects the abnormal behaviour by detecting heart rate increase and abnormal motion. Due to the unavailability of public bio-dataset on mass panic, dataset of this study was collected from 89 subjects wearing the above-mentioned wristband and generating 1054 data samples. Two types of data collected were: firstly, the data of normal daily activities and secondly, the data of abnormal activities resembling the behaviour of escape panic

Issued also as CD

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