Survey on Improving Health Care System by Implementing an Air Ambulance System with the Support of Drones

Sivabalaselvamani, D.; Selvakarthi, D.; Rahunathan, L.; Gayathri, G.; Baskar, M. Mallesh

doi:10.1109/iceca52323.2021.9675859

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…It is also equally important to know about the performance of communication protocols used in the network. In this approach, observational areas are considered separately and implementing new model for improving propagation in VANET and New way of transportation systems were proposed to improve the health systems with the help of drones [30,31]. This work evaluates the routing protocols combined with loss models.…”

Section: Results and Analysismentioning

confidence: 99%

A Hybrid Model for Performance Evaluation of Fixed VANETs using Novel 1C3N and Topology-Based Ad-Hoc Routing Protocols with Packet Loss Control Methods

et al. 2023

Int. Res. J. multidiscip. Technovation

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Vehicular ad-hoc Networks (VANETs) play a significant role in Intelligent Transportation Systems (ITS) Design. Intelligent Transportation Systems are the first mandatory requirements for any smart city. Researchers are vigorously working on ITSs for smart cities and so VANETs have received a lot of attention. In VANETs, Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) message transmissions are carried out using wireless access technologies like IEEE 802.11p and IEEE 1609 WAVE family of standards. The crucial challenge in the implementation of VANETs involves the task of deciding the routing protocol because unlike MANETs, handover in VANETs is extremely high. In this paper, a novel routing technique, One Caption for 3 Nodes (1C3N) algorithm is proposed. This algorithm is implemented along with other topology-based existing routing protocols for the implementation of VANETs in the Coimbatore-Urban Area (Indian Smart City). The performance evaluation is carried out by comparing metrics like goodput, Overhead, Packet delivery ratio (PDR), Packet loss ratio (PLR) and end-to-end delay for four existing VANET routing protocols. The results show that a proper combination of packet loss model with routing protocol enhances the goodput and reduces the overhead for a fixed VANET. It is observed that the proposed 1C3N routing technique provides 60-65% better goodput than the other four algorithms.

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Section: Results and Analysismentioning

confidence: 99%

A Hybrid Model for Performance Evaluation of Fixed VANETs using Novel 1C3N and Topology-Based Ad-Hoc Routing Protocols with Packet Loss Control Methods

et al. 2023

Int. Res. J. multidiscip. Technovation

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“…Sivabalaselvamani et al [18] surveyed the implementation of air ambulance systems with drone support. Their work underscores the importance of advanced sensing and decision-making technologies in enhancing emergency medical services.…”

Section: Related Workmentioning

confidence: 99%

Advanced Object Detection and Decision Making in Autonomous Medical Response Systems

Needhi

2024

Preprint

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This paper focuses on enhancing the object detection and decision-making capabilities of Autonomous Emergency Medical Response Systems (AEMRS). Using advanced sensors, Convolutional Neural Networks (CNN), and reinforcement learning, we propose a model that processes environmental data to identify obstacles and make optimal navigation decisions. The integration of these technologies aims to minimize response time and improve the efficiency of emergency medical services. The perception system utilizes radar, LiDAR, and camera inputs to create a comprehensive understanding of the environment. These data are processed through a series of CNN layers to detect and classify objects such as vehicles, pedestrians, and road signs. On the decision-making front, a Q-learning algorithm is employed to enable the ambulance to learn from its interactions with the environment, continuously improving its route planning and collision avoidance strategies. By combining these advanced AI techniques, the proposed AEMRS can significantly enhance the speed and reliability of emergency responses, ultimately saving more lives. This paper presents the design, implementation, and simulation results of the proposed system, demonstrating its potential to revolutionize emergency medical services.

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“…Sivabalaselvamani et al [18] surveyed the implementation of air ambulance systems with drone support. Their work underscores the importance of advanced sensing and decisionmaking technologies in enhancing emergency medical services.…”

Section: Related Workmentioning

confidence: 99%

Advanced Object Detection and Decision Making in Autonomous Medical Response Systems

Needhi

2024

IJSREM

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Abstract—This paper focuses on enhancing the object detection and decision-making capabilities of Autonomous Emergency Medical Response Systems (AEMRS). Using advanced sensors, Convolutional Neural Networks (CNN), and reinforcement learn- ing, we propose a model that processes environmental data to identify obstacles and make optimal navigation decisions. The integration of these technologies aims to minimize response time and improve the efficiency of emergency medical services. The perception system utilizes radar, LiDAR, and camera inputs to create a comprehensive understanding of the environment. These data are processed through a series of CNN layers to detect and classify objects such as vehicles, pedestrians, and road signs. On the decision-making front, a Q-learning algorithm is employed to enable the ambulance to learn from its interactions with the environment, continuously improving its route planning and collision avoidance strategies. By combining these advanced AI techniques, the proposed AEMRS can significantly enhance the speed and reliability of emergency responses, ultimately saving more lives. This paper presents the design, implementation, and simulation results of the proposed system, demonstrating its potential to revolutionize emergency medical services. Index Terms—Object Detection, Convolutional Neural Net- works (CNN), Reinforcement Learning, Q-Learning, AI-Driven Ambulance, Autonomous Systems, Emergency Medical Services, Real-Time Navigation.

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Survey on Improving Health Care System by Implementing an Air Ambulance System with the Support of Drones

Cited by 7 publications

References 11 publications

A Hybrid Model for Performance Evaluation of Fixed VANETs using Novel 1C3N and Topology-Based Ad-Hoc Routing Protocols with Packet Loss Control Methods

A Hybrid Model for Performance Evaluation of Fixed VANETs using Novel 1C3N and Topology-Based Ad-Hoc Routing Protocols with Packet Loss Control Methods

Advanced Object Detection and Decision Making in Autonomous Medical Response Systems

Advanced Object Detection and Decision Making in Autonomous Medical Response Systems

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