Object Detection Using Artificial Intelligence: Predicting Traffic Congestion to Improve Emergency Response to Mass Casualty Incidents

AbstractThe emergency response to mass casualty incidents in Lebanon lacks uniformity. Three recent large-scale incidents have challenged the existing emergency response process and have raised the need to improve and develop incident management for better resilience in times of crisis. We describe some simple emergency management principles that are currently applied in the United States. These principles can be easily adopted by Lebanon and other developing countries to standardize and improve their emergency response systems using existing infrastructure. (Disaster Med Public Health Preparedness. 2013;0:1–6)

Download Full-text

PEMBUATAN PENDETEKSI OBYEK DENGAN METODE YOU ONLY LOOK ONCE (YOLO) UNTUK AUTOMATED TELLER MACHINE (ATM)

Majalah Ilmiah UNIKOM ◽

10.34010/miu.v17i1.2225 ◽

2019 ◽

Vol 17 (1) ◽

pp. 69-76

Author(s):

Mohammad Shiddiq Ghozali

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Object Detection ◽

Open Source ◽

Automated Teller Machine

Perkembangan Teknologi Informasi dan Komunikasi begitu pesat di zaman sekarang ini. Diikuti pula dengan perkembangan di bidang Artificial Intelligence (AI) atau Kecerdasan Buatan. Di Indonesia sendiri masih belum begitu populer dikalangan masyarakat akan tetapi perusahaan-perusahaan IT berlomba-lomba menciptakan inovasi dibidang Kecerdasan Buatan dan penerapan Kecerdasan Buatan disegala aspek kehidupan. Contoh kasus di Automated Teller Machine (ATM), seringkali terjadi kejahatan di ATM seperti pengintaian nomor pin, skimming, lebanese loop dan kejahatan lainnya. Walaupun di ATM sudah terdapat CCTV akan tetapi penjahat menggunakan alat bantu untuk menutupi wajahnya seperti helm, topi, masker dan kacamata hitam. Biasanya didepan pintu masuk ATM terpampang larangan untuk tidak menggunakan helm, topi, masker dan kacamata hitam serta tidak membawa rokok. Akan tetapi larangan itu masih tetap ada yang melanggar, dikarenakan tidak ada tindak lanjut ketika seseorang menggunakan benda-benda yang dilarang dibawa kedalam ATM. Oleh karena itu penulis membuat sistem pendeteksi obyek di bidang Kecerdasan Buatan untuk mendeteksi benda-benda yang dilarang digunakan ketika berada di ATM. Salah satu metode yang digunakan untuk menciptakan Object Detection yaitu You Only Look Once (YOLO). Implementasi ide ini tersedia pada DARKNET (open source neural network). Cara kerja YOLO yaitu dengan melihat seluruh gambar sekali, kemudian melewati jaringan saraf sekali langsung mendeteksi object yang ada. Oleh karena itu disebut You Only Look Once (YOLO). Pada penelitian ini, penulis membuat sistem yang masih dalam bentuk pengembangan, sehingga menjalankannya masih menggunakan command prompt. Keywords : Automated Teller Machine (ATM), Kecerdasan Buatan, Pendeteksi Obyek, You Only Look Once (YOLO)

Download Full-text

Prehospital Triage by Lay Person First Responders: A Scoping Review and Proposal for a new Prehospital Triage Tool

Disaster Medicine and Public Health Preparedness ◽

10.1017/dmp.2021.19 ◽

2021 ◽

pp. 1-11

Author(s):

Wesley D Jetten ◽

Jeroen Seesink ◽

Markus Klimek

Keyword(s):

Scoping Review ◽

Life Support ◽

First Responders ◽

Mass Casualty Incident ◽

Mass Casualty ◽

Mass Casualty Incidents ◽

Lay People ◽

Online Databases ◽

Healthcare Experience ◽

Prehospital Triage

Abstract Objective: The primary aim of this study is to review the available tools for prehospital triage in case of mass casualty incidents and secondly, to develop a tool which enables lay person first responders (LPFRs) to perform triage and start basic life support in mass casualty incidents. Methods: In July 2019, online databases were consulted. Studies addressing prehospital triage methods for lay people were analyzed. Secondly, a new prehospital triage tool for LPFRs was developed. Therefore, a search for prehospital triage models available in literature was conducted and triage actions were extracted. Results: The search resulted in 6188 articles, and after screening, a scoping review of 4 articles was conducted. All articles stated that there is great potential to provide accurate prehospital triage by people with no healthcare experience. Based on these findings, and combined with the pre-existing prehospital triage tools, we developed a, not-yet validated, prehospital triage tool for lay people, which may improve disaster awareness and preparedness and might positively contribute to community resilience. Conclusion: The prehospital triage tool for lay person first responders may be useful and may help professional medical first responders to determine faster, which casualties most urgently need help in a mass casualty incident.

Download Full-text

Assessing autonomous ship navigation using bridge simulators enhanced by cycle-consistent adversarial networks

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x211021040 ◽

2021 ◽

pp. 1748006X2110210

Author(s):

Andreas Brandsæter ◽

Ottar L Osen

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Decision Support ◽

Object Detection ◽

Autonomous Navigation ◽

Adversarial Networks ◽

Maritime Navigation ◽

Ship Navigation ◽

Autonomous Ship ◽

Ship Operation

The advent of artificial intelligence and deep learning has provided sophisticated functionality for sensor fusion and object detection and classification which have accelerated the development of highly automated and autonomous ships as well as decision support systems for maritime navigation. It is, however, challenging to assess how the implementation of these systems affects the safety of ship operation. We propose to utilize marine training simulators to conduct controlled, repeated experiments allowing us to compare and assess how functionality for autonomous navigation and decision support affects navigation performance and safety. However, although marine training simulators are realistic to human navigators, it cannot be assumed that the simulators are sufficiently realistic for testing the object detection and classification functionality, and hence this functionality cannot be directly implemented in the simulators. We propose to overcome this challenge by utilizing Cycle-Consistent Adversarial Networks (Cycle-GANs) to transform the simulator data before object detection and classification is performed. Once object detection and classification are completed, the result is transferred back to the simulator environment. Based on this result, decision support functionality with realistic accuracy and robustness can be presented and autonomous ships can make decisions and navigate in the simulator environment.

Download Full-text

Postexposure Interventions to Prevent Infection With HBV, HCV, or HIV, and Tetanus in People Wounded During Bombings and Other Mass Casualty Events—United States, 2008: Recommendations of the Centers for Disease Control and Prevention and Disaster Medicine and Public Health Preparedness

Disaster Medicine and Public Health Preparedness ◽

10.1097/dmp.0b013e318187ac66 ◽

2008 ◽

Vol 2 (3) ◽

pp. 150-165 ◽

Cited By ~ 10

Author(s):

Louisa E. Chapman ◽

Ernest E. Sullivent ◽

Lisa A. Grohskopf ◽

Elise M. Beltrami ◽

Joseph F. Perz ◽

...

Keyword(s):

Public Health ◽

Disease Control ◽

Emergency Response ◽

Mass Casualty ◽

Acute Injury ◽

Mass Casualties ◽

Public Health Preparedness ◽

Mass Casualty Event ◽

Public Health Officials ◽

Centers For Disease Control

ABSTRACTPeople wounded during bombings or other events resulting in mass casualties or in conjunction with the resulting emergency response may be exposed to blood, body fluids, or tissue from other injured people and thus be at risk for bloodborne infections such as hepatitis B virus, hepatitis C virus, human immunodeficiency virus, or tetanus. This report adapts existing general recommendations on the use of immunization and postexposure prophylaxis for tetanus and for occupational and nonoccupational exposures to bloodborne pathogens to the specific situation of a mass casualty event. Decisions regarding the implementation of prophylaxis are complex, and drawing parallels from existing guidelines is difficult. For any prophylactic intervention to be implemented effectively, guidance must be simple, straightforward, and logistically undemanding. Critical review during development of this guidance was provided by representatives of the National Association of County and City Health Officials, the Council of State and Territorial Epidemiologists, and representatives of the acute injury care, trauma, and emergency response medical communities participating in the Centers for Disease Control and Prevention’s Terrorism Injuries: Information, Dissemination and Exchange project. The recommendations contained in this report represent the consensus of US federal public health officials and reflect the experience and input of public health officials at all levels of government and the acute injury response community. (Disaster Med Public Health Preparedness. 2008;2:150–165)

Download Full-text

A call for collective learning from mass casualty incidents

Injury ◽

10.1016/j.injury.2021.02.068 ◽

2021 ◽

Author(s):

Amila Ratnayake ◽

Sanjeewa Garusinghe ◽

Miklosh Bala ◽

Tamara J. Worlton

Keyword(s):

Mass Casualty ◽

Collective Learning ◽

Mass Casualty Incidents

Download Full-text

(P2-40) Patient Allocation to Hospitals During Mass-Casualty Incidents

Prehospital and Disaster Medicine ◽

10.1017/s1049023x11004845 ◽

2011 ◽

Vol 26 (S1) ◽

pp. s148-s149 ◽

Cited By ~ 1

Author(s):

K. Ruettger ◽

W. Lenz

Keyword(s):

Emergency Response ◽

Operations Management ◽

Low Frequency ◽

Working Hours ◽

Cost Effective ◽

Mass Casualty ◽

Rescue Service ◽

Hospital Facility ◽

State Register ◽

Helicopter Landing

Due to the limited resources of specialized hospital departments, the allocation of patients to different hospitals according to severity is an extraordinarily complex and time-critical problem. The emergency capacity was determined for all medical centers (n = 135) in the State of Hessen, Germany, for patients of various triage categories (red, yellow, green) during normal working hours, and during weekends and nights and included logistic specifications of a potential helicopter landing. These data were entered into a state register. Using the data from the “acute-care-register”, a Ticket System was developed that allows operations management to assign patients according to the severity of their condition, urgency, and specialization requirements (e.g., neurosurgery, ophthalmology, pediatrics) to a hospital without exceeding the admission and/or treatment capacity of the hospital/facility. During a non-critical period, the order of allocations depending on the distance from the clinic is planned in advance so that no further modifications are necessary during the acute intervention phase of an emergency response. Additional notification of hospital capacities for severe casualties provided during the emergency response can be easily and immediately supplemented. Due to the relatively low frequency of such emergency responses, a cost-effective concept that is easily adaptable to the respective fields of application was decided upon. The system is a sticker set customized for the respective rescue teams. The sets will be carried permanently in the rescue equipment by the organization manager of the rescue service team. The equipment is not dependent on electronic components. The cost per sticker set is approximately US$50. Keeping track of the patient allocations is assured.

Download Full-text