IJISA Vol. 16, No. 6, 8 Dec. 2024
Cover page and Table of Contents: PDF (size: 734KB)
PDF (734KB), PP.1-19
Views: 0 Downloads: 0
Artificial Intelligence, Emergency Services, Machine Learning, Deep Learning, Data Analysis, Decision-Making, Healthcare, Public Safety, Data Insights, Innovative Technology
The dynamic force of artificial intelligence (AI) is reshaping our world, not in the distant future, but today. Its transformative potential, adaptability, and capacity to liberate human potential are becoming evident in a multitude of domains. AI's ability to process vast datasets, offer data-driven recommendations, and enhance decision-making processes underscores its pivotal role in addressing complex challenges. This article explores AI's current impact and its potential for further growth. It reviews 77 articles across diverse domains, highlighting AI's role in emergency services. Through an in-depth analysis of these studies, the paper provides a broad overview of the current state of AI in emergency services, identifying key trends, challenges, and future opportunities. By examining the methodologies, datasets, AI and deep learning techniques, feature selection processes, evaluation metrics, and prediction models used in each study, the paper aims to offer a thorough understanding of AI's role in this critical sector. This extensive body of knowledge is intended to be a valuable resource for researchers, practitioners, and policymakers. It supports the ongoing advancement of AI-driven emergency services, with the goal of saving lives, optimizing resource allocation, and enhancing response times in critical situations. Ultimately, this collaborative effort seeks to foster the development of more resilient and responsive emergency systems that can effectively mitigate risks and deliver timely aid to those in need. By advancing the capabilities of emergency response systems, AI enhances the precision and efficiency of critical interventions, ultimately leading to better outcomes and improved resilience in crisis situations.
Roxane Elias Mallouhy, Naoufal Sirri, Irum Nahvi, Christophe Guyeux, "AI’s Current Impact and Future Potential in Emergency Services: A Comprehensive Review and Analysis", International Journal of Intelligent Systems and Applications(IJISA), Vol.16, No.6, pp.1-19, 2024. DOI:10.5815/ijisa.2024.06.01
[1]Ajiga David Iyanuoluwa, Ndubuisi Ndubuisi Leonard, Asuzu Onyeka Franca, et al, "AI-driven predictive analytics in retail: a review of emerging trends and customer engagement strategies", International Journal of Management & Entrepreneurship Research, 2024, vol. 6, no 2, p. 307-321. doi.org/10.51594/ijmer.v6i2.772.
[2]Aafar Abbas, Bibi Nabila, Naqvi Rizwan Ali, et al, "Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations", Frontiers in Plant Science, 2024, vol. 15, p. 1356260. doi.org/10.3389/fpls.2024.1356260.
[3]Chatterjee Swastika, Das Soumyajit, Ganguly Karabi, et al, "Advancements in robotic surgery: innovations, challenges and future prospects", Journal of Robotic Surgery, 2024, vol. 18, no 1, p. 28. doi.org/10.1007/s11701-023-01801-w.
[4]Jiménez-Luna José, Grisoni Francesca, Weskamp Nils, et al, "Artificial intelligence in drug discovery: recent advances and future perspectives", Expert opinion on drug discovery, 2021, vol. 16, no 9, p. 949-959. doi.org/10.1080/17460441.2021.1909567.
[5]Michałowska Maria, Ogłoziński Mariusz, "Autonomous vehicles and road safety", 17th International Conference on Transport Systems Telematics, TST 2017, Katowice–Ustroń, Poland, April 5-8, 2017, Selected Papers 17. Springer International Publishing, 2017. p. 191-202. doi.org/10.1007/978-3-319-66251-0_16.
[6]Chong Terrence, Yu Ting, Keeling Debbie Isobel, et al, "AI-chatbots on the services frontline addressing the challenges and opportunities of agency", Journal of Retailing and Consumer Services, 2021, vol. 63, p. 102735. doi.org/10.1016/j.jretconser.2021.102735.
[7]Dikshit Srishti, Atiq Areeba, Shahid Mohammad, et al, "The use of artificial intelligence to optimize the routing of vehicles and reduce traffic congestion in urban areas", EAI Endorsed Transactions on Energy Web, 2023, vol. 10. doi.org/10.4108/ew.4613.
[8]Baldominos Alejandro, Blanco Iván, Moreno Antonio José, et al, "Identifying real estate opportunities using machine learning", Applied sciences, 2018, vol. 8, no 11, p. 2321. doi.org/10.3390/app8112321.
[9]Wang Sai, "Factors related to user perceptions of artificial intelligence (AI)-based content moderation on social media", Computers in Human Behavior, 2023, vol. 149, p. 107971. doi.org/10.1016/j.chb.2023.107971.
[10]Rajan Arathy, Binu, V. P, "Enhancement and security in surveillance video system", The 2016 International Conference on Next Generation Intelligent Systems (ICNGIS). IEEE, 2016. p. 1-5. doi.org/10.1109/ICNGIS.2016.7854056.
[11]Chintalapati Srikrishna, Pandey Shivendra Kumar, "Artificial intelligence in marketing: A systematic literature review", International Journal of Market Research, 2022, vol. 64, no 1, p. 38-68. doi.org/10.1177/14707853211018428.
[12]Raji Mustafa Ayobami, Olodo Hameedat Bukola, Oke Timothy Tolulope, et al, "E-commerce and consumer behavior: A review of AI-powered personalization and market trends", GSC Advanced Research and Reviews, 2024, vol. 18, no 3, p. 066-077. doi.org/10.30574/gscarr.2024.18.3.0090.
[13]Cerna Selene, Guyeux Christophe, Arcolezi Héber, et al, "A comparison of LSTM and XGBoost for predicting firemen interventions", World Conference on Information Systems and Technologies. Cham : Springer International Publishing, 2020. p. 424-434. doi.org/10.1007/978-3-030-45691-7_39.
[14]Kang Da-Young, Cho Kyung-Jae, Kwon Oyeon, et al, "Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services", Scandinavian journal of trauma, resuscitation and emergency medicine, 2020, vol. 28, p. 1-8. doi.org/10.1186/s13049-020-0713-4.
[15]Madaio Michael, Chen Shang-Tse, Haimson Oliver, et al, "Firebird: Predicting fire risk and prioritizing fire inspections in Atlanta", Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. p. 185-194. doi.org/10.1145/2939672.2939682.
[16]Gerber Matthew, "Predicting crime using Twitter and kernel density estimation", Decision Support Systems, 2014, vol. 61, p. 115-125. doi.org/10.1016/j.dss.2014.02.003.
[17]Fernandes Paulo Martins, "Fire spread prediction in shrub fuels in Portugal", Forest ecology and management, 2001, vol. 144, no 1-3, p. 67-74. doi.org/10.1016/S0378-1127(00)00363-7.
[18]Kühl Niklas, Schemmer Max, Goutier Marc, "Artificial intelligence and machine learning", Electronic Markets, 2022, vol. 32, no 4, p. 2235-2244. https://doi.org/10.1007/s12525-022-00598-0.
[19]Gamal Donia, Alfonse Marco, El-Horbaty El-Sayed, et al, "Analysis of machine learning algorithms for opinion mining in different domains", Machine Learning and Knowledge Extraction, 2018, vol. 1, no 1, p. 224-234. doi.org/10.3390/make1010014.
[20]Grekousis George, "Artificial neural networks and deep learning in urban geography: A systematic review and meta-analysis", Computers, Environment and Urban Systems, 2019, vol. 74, p. 244-256. doi.org/10.1016/j.compenvurbsys.2018.10.008.
[21]Zheng Ming, Li Tong, Zhu Rui, et al. Traffic accident’s severity prediction: A deep-learning approach-based CNN network. IEEE Access, 2019, vol. 7, p. 39897-39910. doi.org/10.1109/ACCESS.2019.2903319.
[22]Liu Song, Liu Xiaojie, Lyu Qing, et al, "Comprehensive system based on a DNN and LSTM for predicting sinter composition", Applied Soft Computing, 2020, vol. 95, p. 106574. doi.org/10.1016/j.asoc.2020.106574.
[23]Myagmar-Ochir Yanjinlkham, Kim Wooseong, "A survey of video surveillance systems in smart city", Electronics, 2023, vol. 12, no 17, p. 3567. doi.org/10.3390/electronics12173567.
[24]Rahman Anichur, Hasan Kamrul, Kundu Dipanjali, et al, "On the ICN-IoT with federated learning integration of communication: Concepts, security-privacy issues, applications, and future perspectives", Future Generation Computer Systems, 2023, vol. 138, p. 61-88. doi.org/10.1016/j.future.2022.08.004.
[25]Yan Wei Qi, "Introduction to intelligent surveillance: surveillance data capture, transmission, and analytics", Springer, 2019.
[26]Costa Daniel, Peixoto João Paulo, Jesus Thiago, et al, "A survey of emergencies management systems in smart cities", IEEE Access, 2022, vol. 10, p. 61843-61872. doi.org/10.1109/ACCESS.2022.3180033.
[27]Došilović Filip Karlo, Brčić Mario, Hlupić Nikica, "Explainable artificial intelligence: A survey", The 41st International convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE, 2018. p. 0210-0215. doi.org/10.23919/MIPRO.2018.8400040.
[28]Liu Chen, Chen Jiming, Liu Haoyu, et al, "Towards Efficient Traffic Incident Detection via Explicit Edge-Level Incident Modeling", IEEE Internet of Things Journal, 2024. doi.org/10.1109/JIOT.2024.3371482.
[29]Ahmed Abdulaziz, Ashour Omar, Ali Haneen, et al, "An integrated optimization and machine learning approach to predict the admission status of emergency patients", Expert Systems with Applications, 2022, vol. 202, p. 117314. doi.org/10.1016/j.eswa.2022.117314.
[30]Willmott Cort, MATSUURA, Kenji, "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance", Climate research, 2005, vol. 30, no 1, p. 79-82. doi:10.3354/cr030079.
[31]Hyndman Rob, Koehler Anne, "Another look at measures of forecast accuracy", International journal of forecasting, 2006, vol. 22, no 4, p. 679-688. doi.org/10.1016/j.ijforecast.2006.03.001.
[32]Wheelwright Steven, Makridakis Spyros, Hyndman Rob, "Forecasting: methods and applications", John Wiley & Sons, 1998.
[33]Pearson Karl, "VII. Note on regression and inheritance in the case of two parents", proceedings of the royal society of London, 1895, vol. 58, no 347-352, p. 240-242. doi.org/10.1098/rspl.1895.0041.
[34]Sokolova Marina, Lapalme Guy, "A systematic analysis of performance measures for classification tasks", Information processing & management, 2009, vol. 45, no 4, p. 427-437. doi.org/10.1016/j.ipm.2009.03.002.
[35]Powers David, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation", arXiv preprint arXiv:2010.16061, 2020. doi.org/10.48550/arXiv.2010.16061.
[36]Hanley James, Mcneil Barbara, "The meaning and use of the area under a receiver operating characteristic (ROC) curve", Radiology, 1982, vol. 143, no 1, p. 29-36. doi.org/10.1148/radiology.143.1.7063747.
[37]Ren Honglei, Song You, Wang Jingwen, et al, "A deep learning approach to the citywide traffic accident risk prediction", The 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. p. 3346-3351. doi.org/10.1109/ITSC.2018.8569437.
[38]Arcolezi Héber, Couchot Jean-François, Cerna Selene, et al, "Forecasting the number of firefighter interventions per region with local-differential-privacy-based data", Computers & Security, 2020, vol. 96, p. 101888. doi.org/10.1016/j.cose.2020.101888.
[39]Nahuis Selene, Arcolezi Héber, et al, "Long short-term memory for predicting firemen interventions", The 6th International Conference on Control, Decision and Information Technologies (CoDIT). Ieee, 2019. p. 1132-1137. doi.org/10.1109/CoDIT.2019.8820671.
[40] Cerna Selene, Royer Guillaume, et al, "Predicting fire brigades operational breakdowns: A real case study", Mathematics, 2020, vol. 8, no 8, p. 1383. doi.org/10.3390/math8081383.
[41]Arcolezi Héber, Cerna Selene, et al, "Preserving geo-indistinguishability of the emergency scene to predict ambulance response time", Mathematical and Computational Applications, 2021, vol. 26, no 3, p. 56. doi.org/10.3390/mca26030056.
[42]Elias Mallouhy Roxane, Abou Jaoude Chady, et al, "Time series forecasting for the number of firefighters interventions", The International Conference on Advanced Information Networking and Applications. Cham: Springer International Publishing, 2021. p. 39-50. doi.org/10.1007/978-3-030-75100-5_4.
[43]Abou Jaoude Chady, et al, "Machine learning for predicting firefighters’ interventions per type of mission", The 8th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2022. p. 1196-1200. doi.org/10.1109/CoDIT55151.2022.9804035.
[44]Arcolezi, Héber, Cerna Selene, Couchot Jean-François, et al, "Privacy-preserving prediction of victim’s mortality and their need for transportation to health facilities", IEEE Transactions on Industrial Informatics, 2021, vol. 18, no 8, p. 5592-5599. doi.org/10.1109/TII.2021.3123588.