A Lightweight In-Vehicle Alcohol Detection using Smart Sensing and Supervised Learning

Al‐Haija, Qasem Abu; Krichen, Moez

doi:10.20944/preprints202207.0261.v1

Cited by 7 publications

References 33 publications

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Real‐time Internet of LoRa Things (IoLT)‐based accident detection and prevention system in vehicular networks towards smart city

Vinodhini,

Rajkumar,

Subramaniam

2023

Int J Communication

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SummaryVehicle utilization has increased on a broader scale in today's reality. Because of insufficient emergency services, increased motor traffic has resulted in the rise of traffic collisions, and widespread accidents lead to the demise of lives and property. Generally, the consequence death rate happens each year, originating from delays in rescue activities. However, vehicles are embedded with trend technology. Still, the accident count will rise day by day due to delays in transmitting the information to the concerned person or delays in rescue activities. The proposed work offers an effective solution to the aforementioned issue by assisting low‐power and long‐range (LoRa) architecture with edge node ultrasonic sensors for identifying the abnormalities and to provide an efficient transport ambulance from the unexpected mishap spot to the closest hospital where emergency well‐being can be provided with the help of cloud technology. Also, emergency information can be sent to the cloud immediately, and its response is the way of alerting the surroundings and notifying the appropriate hospital with the aid of a GPS‐enabled LoRa‐shield. Further, comparing the proposed technology with other LPWAN (low‐power wide area networks) in terms of coverage, cost, capacity, and energy consumption proves efficient communication in the development of intelligent vehicular networks towards a smart city.

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Real‐time Internet of LoRa Things (IoLT)‐based accident detection and prevention system in vehicular networks towards smart city

Vinodhini,

Rajkumar,

Subramaniam

2023

Int J Communication

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A Robust Vehicular Control System with Collision Detection and Avoidance Mechanism using SVM Classification

Roy,

Saha,

Dutta

2023

2023 IEEE World Conference on Applied Intelligence and Computing (AIC)

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PDF Malware Detection Based on Optimizable Decision Trees

2022

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Portable document format (PDF) files are one of the most universally used file types. This has incentivized hackers to develop methods to use these normally innocent PDF files to create security threats via infection vector PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PDF files from malware PDF files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight and accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at a high detection performance and low detection overhead.

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A Lightweight In-Vehicle Alcohol Detection using Smart Sensing and Supervised Learning

Cited by 7 publications

References 33 publications

Real‐time Internet of LoRa Things (IoLT)‐based accident detection and prevention system in vehicular networks towards smart city

Real‐time Internet of LoRa Things (IoLT)‐based accident detection and prevention system in vehicular networks towards smart city

A Robust Vehicular Control System with Collision Detection and Avoidance Mechanism using SVM Classification

PDF Malware Detection Based on Optimizable Decision Trees

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