A Model for Assessing the Nature of Car Crashes using Convolutional Neural Networks

Mothe, Saiteja

doi:10.30534/ijeter/2020/41832020

Cited by 7 publications

(6 citation statements)

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“…This is evident in very recently published research where authors outlined the use of different optimisers in their research analysis including Adam, Stochastic Gradient Descent (SGD), AdaDelta, AdaGrad, RMSProp [14,15], and many other applications outlined in the literature [16]. [17] and also to test the model performance of different applications including sign language recognition [18], optimising car crash detection [19], prediction of water leakage using Adam, RMSProp and AdaDelta [20], SGD, Adam, AdaGrad, AdaDelta, Nadam, RMSProp for bushfire prediction [21] among others. This article provides a comprehensive compilation of optimisers used in DL research.…”

Section: Research Methodology and Motivationmentioning

confidence: 99%

Advances in Optimisation Algorithms and Techniques for Deep Learning

Nwankpa¹

2020

Adv. sci. technol. eng. syst. j.

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In the last decade, deep learning(DL) has witnessed excellent performances on a variety of problems, including speech recognition, object recognition, detection, and natural language processing (NLP) among many others. Of these applications, one common challenge is to obtain ideal parameters during the training of the deep neural networks (DNN). These typical parameters are obtained by some optimisation techniques which have been studied extensively. These research have produced state-of-art(SOTA) results on speed and memory improvements for deep neural networks(NN) architectures. However, the SOTA optimisers have continued to be an active research area with no compilations of the existing optimisers reported in the literature. This paper provides an overview of the recent advances in optimisation algorithms and techniques used in DNN, highlighting the current SOTA optimisers, improvements made on these optimisation algorithms and techniques, alongside the trends in the development of optimisers used in training DL based models. The results of the search of the Scopus database for the optimisers in DL provides the articles reported as the summary of the DL optimisers. From what we can tell, there is no comprehensive compilation of the optimisation algorithms and techniques so far developed and used in DL research and applications, and this paper summarises these facts.

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Section: Research Methodology and Motivationmentioning

confidence: 99%

Advances in Optimisation Algorithms and Techniques for Deep Learning

Nwankpa¹

2020

Adv. sci. technol. eng. syst. j.

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“…A safety system that avoids unintended acceleration of the vehicle resulting from pedal mix-up has been demonstrated in [16]. A neural network-based method for analyzing the nature of car crashes has been proposed in [17]. A system for modernizing the management of bulk goods transportation and logistics was proposed in [18].…”

Section: Existing Methodsmentioning

confidence: 99%

NodeMCU and IoT-based Safety and Security Ecosystem for Heavy Vehicles

Patwari¹

2020

IJETER

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This paper explains the design and implementation of a safety system for heavy vehicles. A small-scale embedded system based on Node MicroController Unit (NodeMCU) is proposed and prototyped here. Peripherals such as Global Positioning System (GPS) receiver (for location updates), tilt sensor (for accident detection), gas sensor (for alcohol activity detection), RFID tags and reader (to monitor entry and exit of vehicles in restricted areas), a touch sensor (to indicate an emergency) etc., have been used in this system. A major improvement over existing accident alert systems is that the proposed design makes use of ultrasonic sensors to indicate over-height of the vehicles approaching underpasses (tunnels or rail over road bridges) on highways. A sound alarm is triggered if the vehicle's height is more than the entry point of the underpass so that the driver can slow down the vehicle. A working prototype of the overall system has been implemented and tested successfully. The data gathered by all sensors is sent to the cloud using the Message Queuing Telemetry Transport (MQTT) protocol and the user can monitor all the updates using his/her android smartphone.

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“…This parameter characterizes the autonomy degree so that every processor is in reasoning what manner it can utilize its autonomous assets. Inside favorable circumstances, many processors are responsible for holding their very self-bit of planning goals [9]. The processors need to cooperate with a higher-level objective.…”

Section: Cooperativementioning

confidence: 99%

“…It appears in the frameworks underneath. In data mining and estimations, different leveled batching assessment is a methodology for bunch examination which hopes to fabricate a chain of the significance of bundles, for instance, tree-type structure subject to the movement [9].…”

Section: K-meansmentioning

confidence: 99%

Load Balancing Analyzer: A Recommendation System using Machine Learning

Tata¹

2020

IJETER

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The Parallel and distributed systems focus on the concept of load balancing. A tangle is split into a hard and fast number of processors that are to be executed in parallel. However, there may be a state that some processors will complete their tasks before other processes and reach idle state as the work is unevenly divided or perhaps some processors complete before the others. Ideally, we like all the processors to have the minimum wait time. Achieving the above goal by spreading the tasks is eventually named as load balancing. In the paper, we stress on clustering techniques. The basis of the join-idle-queue algorithm is seen by using clustering. The technique of load balancing uses Support Vector Machine (SVM) and clustering techniques (K-means, Hierarchical). A comparative study of the above techniques is done utilizing load balancing.

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A Model for Assessing the Nature of Car Crashes using Convolutional Neural Networks

Cited by 7 publications

References 0 publications

Advances in Optimisation Algorithms and Techniques for Deep Learning

Advances in Optimisation Algorithms and Techniques for Deep Learning

NodeMCU and IoT-based Safety and Security Ecosystem for Heavy Vehicles

Load Balancing Analyzer: A Recommendation System using Machine Learning

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