Journal ArticleDOI
Recognizing Driving Behavior and Road Anomaly Using Smartphone Sensors
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TLDR
Results showed that the proposed proposed method fordetection of Anomaly Detection showed thatk-nearestﻷnearest-neighborﻴ algorithmﻢdetectedﻵ roadﻅanomaliesﻹdriving-behaviors-detection, moreover, moreover, £2,000,000-2,500,000 moreover than previously thought.Abstract:
Roadtrafficaccidentsarecaused1.25milliondeathsperyearworldwide.Toimproveroadsafety andreducingroadaccidents,arecognitionmethodfordrivingeventsisintroducedinthispaper.The proposedmethoddetectedandclassifiedbothdrivingbehaviorsandroadanomaliespatternsbasedon smartphonesensors(accelerometerandgyroscope).k-NearestNeighborandDynamicTimeWarping algorithmswereutilizedformethodevaluation.Experimentswereconductedtoevaluatek-nearest neighboranddynamictimewarpingalgorithmsaccuracyforroadanomaliesanddrivingbehaviors detection,moreover,drivingbehaviorsclassification.Evaluationresultsshowedthatk-nearestneighbor algorithmdetectedroadanomaliesanddrivingbehaviorswithtotalaccuracy98.67%.Dynamictime warpingalgorithmclassified(normalandabnormal)drivingbehaviorswithtotalaccuracy96.75%. KeywoRDS Anomaly Detection, Behavior Classification, Driving Behavior, Road Anomalies, Smartphone Sensorsread more
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Book ChapterDOI
Industry 4.0: The New Industrial Revolution
TL;DR: This new industrial revolution would possess systems with transformative technologies for managing interconnected systems between its physical assets and computational capabilities, resulting in an escalating need for trained employees proficient in cross-functional capacities and with competencies to cope new processes and IT systems.
Journal ArticleDOI
Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines
Neeraj Gupta,Mahdi Khosravy,Nilesh Patel,Nilanjan Dey,Saurabh Gupta,Saurabh Gupta,Hemant Darbari,Rubén González Crespo +7 more
TL;DR: This research work aims at presenting a bi-level genetic algorithm approach of an optimized data analytic AI technique for monitoring the health of the agriculture vehicles which can be economically utilized on smartphone end-devices using the built-in microphones instead of expensive IoT sensors.
Journal ArticleDOI
Vehicle Driving Behavior Recognition Based on Multi-View Convolutional Neural Network With Joint Data Augmentation
TL;DR: A joint data augmentation (JDA) scheme is proposed, a new multi-view convolutional neural network model (MV-CNN) is designed, and the results show that MV-CNN can obtain the best recall, precision, and F1-score.
Journal ArticleDOI
Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles
Neeraj Gupta,Saurabh Gupta,Saurabh Gupta,Mahdi Khosravy,Nilanjan Dey,Nisheeth Joshi,Rubén González Crespo,Nilesh Patel +7 more
TL;DR: Correlation between the signals captured from costly sensors and Microphone for the generated faults in hydraulic components demonstrates the effectiveness of audio to replace existing HM&D technology.
Journal ArticleDOI
Lightweight Artificial Intelligence Technology for Health Diagnosis of Agriculture Vehicles: Parallel Evolving Artificial Neural Networks by Genetic Algorithm
TL;DR: The developed breakthrough technology can enhance the capability of users on the field for monitoring the agricultural vehicles (AgV)s health by analyzing the acoustic noise using smartphone’s app.
References
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