An approach on automated rescue system with intelligent traffic lights for emergency services

Sanjana, K R; Lavanya, S.; Jinila, Y. Bevish

doi:10.1109/iciiecs.2015.7193205

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…In 1992, 3M upgraded its infrared transmitter by including an encoder. Therefore, it is crucial in the travel time concept for emergency vehicles that emphasizes the importance of the Internet of Things (IoT) implementation [11][12], the development of radio for traffic light control [13 -17], RFID-based traffic light prioritization [18], and emergency vehicle traffic signal coordination [19][20][21][22]. Understanding various basic concepts of travel time for emergency vehicles is important to know the reaction to how and why these various concepts affect the emergency vehicles' travel time.…”

Section: Underlying Concept Of Travel Time For Emergency Vehiclementioning

confidence: 99%

A New Design and Simulation of Smart Sensing for Emergency Vehicle

Mahmod Abd Hakim Mohamad,

Nurfarahin Onn,

Mohd Shahir Yahya

et al. 2023

ARASET

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All emergency vehicles are designed and certified to better respond to emergencies. Also, emergency vehicles have the legal right to violate the highway code to get to a certain area as quickly as possible. The victim's existence is helpless in the event of a stroke of fate or an emergency. Because of the visitors, emergency vehicles also have to give way to get to the scene. This change can also further increase the victim's chance of death. Therefore, to solve the problem, it is crucial to plan and limit travel time so that emergency vehicles use their maximum capacity. Preventive service can be a means for emergency vehicles to reach their intended vacation spot in a much shorter time. So, there is a call to address this emergency vehicle action. This look at strategies is an intelligent detection utility to deal with alarming traffic jams. This mission aims to simulate the intelligent detection of emergency vehicles through simulation software. The purpose of e-Buzz: Simulation of Smart Sensing for Emergency Vehicles is to assist the government that may be responsible for responding to emergencies, including ambulance, police, fire, and many others. The main element to ensure that this simulation works is based on the type of sensor used and its usefulness and responsibility. The sensor can detect the sound of emergency vehicles and respond immediately to them by signalling smaller parallel visitors to change the screen from pink to green or vice versa. Therefore, this flow reduces the travel time required for emergencies.

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Section: Underlying Concept Of Travel Time For Emergency Vehiclementioning

confidence: 99%

A New Design and Simulation of Smart Sensing for Emergency Vehicle

Mahmod Abd Hakim Mohamad,

Nurfarahin Onn,

Mohd Shahir Yahya

et al. 2023

ARASET

View full text Add to dashboard Cite

show abstract

“…However, random search and Bayesian optimization framework can help for further hyper parameter simplification. In [25] the author proposes a model for eliminating the curse of dimensionality. They employ a learning distributed representation of words which allows to train sentences about an exponential number of semantically neighboring sentences.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Truth Discovery Algorithm for Research the Genuineness of Given Text Corpus

2019

ijrte

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Lot of research has gone into Natural language processing and the state of the art algorithms in deep learning that unambiguously helps in converting an English text into a data structure without loss of meaning. Also with the advent of neural networks for learning word representations as vectors has helped a lot in revolutionizing the automatic feature extraction from text data corpus. A combination of word embedding and the use of a deep learning algorithm like a convolution neural network helped in better accuracy for text classification. In this era of Internet of things and the voluminous amounts of data that is overwhelming the users determining the veracity of the data is a very challenging task. There are many truth discovery algorithms in literature that help in resolving the conflicts that arise due to multiple sources of data. These algorithms help in estimating the trustworthiness of the data and reliability of the sources. In this paper, a convolution based truth discovery with multitasking is proposed to estimate the genuineness of the data for a given text corpus. The proposed algorithm has been tested on analysing the genuineness of Quora questions dataset and experimental results showed an improved accuracy and speed over other existing approaches.

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“…Consequently, this article proposes Hybrid Neural primarily based on machine learning concept for rain prediction supported weather information consisting of options specifically vapor content, ratio, air pressure, and temperature. the information is gathered by dumdum bullet earth science center located at state [13,14,15,16,17].…”

Section: Jrefonaa M Lakshmi Allu Chaya Satya Kiran Anantha Ravimentioning

confidence: 99%

Rainfall Prediction using Apriori Algorithm

2019

ijrte

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The growth of machine learning techniques has paved way for predicting various areas and weather prediction is one of the most commonly used application. One of the most interesting part of research is rainfall prediction Rainfall prediction is done by various researchers as it is one of the prime aspects in detecting the rainfall and measuring its intensity beforehand to reduce the number of loss of lives and properties. Nowadays, severe rainfall is one of the major reason is most of disasters such as floods and landslides. Predicting its occurrence well before could be very much helpful in further disaster management operations. In this paper, we have predicted rainfall by making use of various machine learning techniques and is been deployed by using it in predicting Chennai rainfall dataset. The research work makes use of Apriori Algorithm to demonstrate the feature set and to look out for the possibility of rainfall. Preprocessing of the dataset is done in order to extract the features of the dataset and then the Apriori Algorithm is used. The designed model is proposed to produce about 95% of accuracy, potency and lesser time when compared to other model

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An approach on automated rescue system with intelligent traffic lights for emergency services

Cited by 10 publications

References 4 publications

A New Design and Simulation of Smart Sensing for Emergency Vehicle

A New Design and Simulation of Smart Sensing for Emergency Vehicle

Deep Learning Based Truth Discovery Algorithm for Research the Genuineness of Given Text Corpus

Rainfall Prediction using Apriori Algorithm

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