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Instant Ambulance Connection Rescue Radar

Published: 13 May 2024 Publication History

Abstract

In the field of emergency medical services, the element of time significantly influences patient outcomes. The groundbreaking Instant Ambulance Connection- Rescue Radar system is introduced to expedite responses to medical emergencies. This research paper explores the system's structure, approach, and potential impact, highlighting its role in providing swift and efficient emergency medical services. The primary objective of this system is to bridge the gap between patients and ambulance services, simplifying ambulance requests and hospital visit scheduling. A user-friendly Android app is central to its functionality, enabling one-tap ambulance requests and harnessing modern technologies like the Internet of Things and the Google Maps API to transmit user information and precise locations to local ambulance drivers rapidly. This system also empowers patients with real-time access to ambulance availability and the ability to track the ambulance's location in real-time. By enhancing communication between patients, ambulance drivers, and healthcare institutions, the system significantly improves the efficiency of the emergency medical response network.

References

[1]
Abdallah, Z.S., Du, L., Webb, G.I. 2017. Data Preparation. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_62.
[2]
Ndung'u Rachael Njeri.2022. Data Preparation for Machine Learning Modelling. International Journal of Computer Applications Technology and Research. 11, 231-235.
[3]
Chandak, A., Ganorkar, P., Sharma, S., Bagmar, A., & Tiwari, S. 2019. Car Price Prediction Using Machine Learning. International Journal of Computer Sciences and Engineering, 7(5), 444–450.
[4]
Chen, X., Jeong, J. C. 2007. Enhanced recursive feature elimination. Sixth International Conference on Machine Learning and Applications (ICMLA 2007), 429–435.
[5]
Chu, X., Ilyas, I. F., Krishnan, S., Wang, J. 2016. Data cleaning: Overview and emerging challenges. Proceedings of the 2016 International Conference on Management of Data, 2201–2206.
[6]
Cui, B., Ye, Z., Zhao, H., Renqing, Z., Meng, L., & Yang, Y.2022. Used Car Price Prediction Based on the Iterative Framework of XGBoost+ LightGBM. Electronics, 11(18), 2932.
[7]
Gajera, P., Gondaliya, A., & Kavathiya, J. 202). Old Car Price Prediction With Machine Learning. Int. Res. J. Mod. Eng. Technol. Sci, 3, 284–290.
[8]
Gegic, E., Isakovic, B., Keco, D., Masetic, Z., & Kevric, J. 2019. Car price prediction using machine learning techniques. TEM Journal, 8(1), 113.
[9]
Jordan, M. I., & Mitchell, T. M. 2015. Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
[10]
Karakoç, M. M., Çelik, G., & Varol, A. 2020. Car Price Prediction Using An Artificial Neural Network. Eastern Anatolian Journal of Science, 6(2), 44–48.
[11]
Liu, E., Li, J., Zheng, A., Liu, H., & Jiang, T. 2022. Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network. Sustainability, 14(15), 8993.
[12]
Montgomery, D. C., Peck, E. A., & Vining, G. G. 2021. Introduction to linear regression analysis. John Wiley & Sons.
[13]
Rahm, E., Do, H. H. 2000. Data cleaning: Problems and current approaches. IEEE Data Eng. Bull., 23(4), 3–13.
[14]
Samruddhi, K., & Kumar, R. A. 2020. Used Car Price Prediction using K-Nearest Neighbor Based Model. Int. J. Innov. Res. Appl. Sci. Eng.(IJIRASE), 4, 629–632.
[15]
Siva, R., & Adimoolam, M. 2022. Linear Regression Algorithm Based Price Prediction of Car and Accuracy Comparison with Support Vector Machine Algorithm. ECS Transactions, 107(1), 12953.
[16]
Su, X., Yan, X., & Tsai, C.-L. 2012. Linear regression. Wiley Interdisciplinary Reviews: Computational Statistics, 4(3), 275–294.
[17]
Tsagris, M., & Fafalios, S. 2022. Advanced Car Price Modelling and Prediction. In M. K. Terzioğlu (Ed.), Advances in Econometrics, Operational Research, Data Science and Actuarial Studies: Techniques and Theories . 479–494. https://doi.org/10.1007/978-3-030-85254-2_29.

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ICIMMI '23: Proceedings of the 5th International Conference on Information Management & Machine Intelligence
November 2023
1215 pages
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Published: 13 May 2024

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