Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches

Malik, Meenakshi; Nandal, Rainu; Dalal, Surjeet; Jalglan, Vivek; Le, Dac‐Nhuong

doi:10.32604/iasc.2022.020249

Cited by 14 publications

(3 citation statements)

References 48 publications

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“…Heart sounds may be used to indicate CHF, according to the authors of reference [18]. Using the suggested method, it is possible to distinguish between healthy people and those with chronic heart failure and to identify diverse stages of the disease.…”

Section: Related Workmentioning

confidence: 99%

Application of Machine Learning for Cardiovascular Disease Risk Prediction

Dalal

Goel

Onyema

et al. 2023

Computational Intelligence and Neuroscience

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Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neural networks, random forests, Bayesian networks, C5.0, and QUEST were compared for this dataset. On training and testing data sets, the results acquired a high accuracy (99.1 percent), which is significantly superior to previous methods. Ahead-of-time detection and diagnosis of cardiac disease, as well as better treatment outcomes, are strong possibilities for the suggested prediction model. Additionally, it may help patients better manage their illness or life forms in order to increase their chances of recovery/survival. The result showed greater accuracy and promising signs that machine-learning algorithms can indeed assist in early identification of the disease and improvement of the treatment outcome.

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Section: Related Workmentioning

confidence: 99%

Application of Machine Learning for Cardiovascular Disease Risk Prediction

Dalal

Goel

Onyema

et al. 2023

Computational Intelligence and Neuroscience

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“…Authors in [54] presented an approach for classifying driving behavior, based on stacked LSTM Recurrent Neural Networks. Using a smartphone's inbuilt sensors to record data from nine distinct sensors.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Driver Behavior Detection in Time Series Decade review

Saber,

Ghoniemy,

Al-Qutt

2023

IJICIS

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Driver's behavior is expressed by the intentional and unintentional actions the driver performs while driving a motor vehicle. This behavior could be influenced by several factors such as fatigue, drowsiness, vehicle surroundings, and distraction state. Driver's behavior could be normal, risky or aggressive. Risky and aggressive behaviors, such as harsh braking and rapid acceleration, can lead to traffic accidents. Monitoring, analyzing and improving driver's behavior can reduce traffic collisions and enhance road safety. Different approaches have been followed for the detection and identification of driver's behavior. Rule-based Machine learning (ML) and deep learning (DL) approaches have succeeded to mine dynamical characteristics of time series. However, they have some challenges that make them unsuitable for many classification tasks including the selection of efficient architectures and corresponding hyper-parameters, as well as slow training and limited labeled data. Fusion and attention mechanisms through hybrid approaches were found to be more suitable for time series sensor data analysis. Transfer learning addresses useful approaches for making use of learning applied to other applications. Maneuver detection represents a serious characteristic of driver's behavior identification. A recent approach for extracting maneuvers from high-frequency telematics data is through time series motifs detection algorithms. Motifs extraction is preferred over ML and DL approaches as it does not requie labels, which is extremely time-consuming to collect. This work focuses on the latest techniques for classifying driver's behavior in time series data, and summarizes the pros and cons of the different categories.

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“…The first layer of a convolutional network is the convolutional layer. The fullyconnected layer comes last in a neural network architecture, following any number of layers that may or may not be convolutional or pooling layers [40]. Adding more layers to the CNN allows it to recognize more subtle differences in a picture.…”

Section: Architecturementioning

confidence: 99%

Optimized Deep Learning with Learning without Forgetting (LwF) for Weather Classification for Sustainable Transportation and Traffic Safety

Dalal

Seth²,

Rădulescu

et al. 2023

Sustainability

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Unfortunately, accidents caused by bad weather have regularly made headlines throughout history. Some of the more catastrophic events to recently make news include a plane crash, ship collision, railway derailment, and several vehicle accidents. The public’s attention has been directed to the severe issue of safety and security under extreme weather conditions, and many studies have been conducted to highlight the susceptibility of transportation services to environmental factors. An automated method of determining the weather’s state has gained importance with the development of new technologies and the rise of a new industry: intelligent transportation. Humans are well-suited for determining the temperature from a single photograph. Nevertheless, this is a more challenging problem for a fully autonomous system. The objective of this research is developing a good weather classifier that uses only a single image as input. To resolve quality-of-life challenges, we propose a modified deep-learning method to classify the weather condition. The proposed model is based on the Yolov5 model, which has been hyperparameter tuned with the Learning-without-Forgetting (LwF) approach. We took 1499 images from the Roboflow data repository and divided them into training, validation, and testing sets (70%, 20%, and 10%, respectively). The proposed model has gained 99.19% accuracy. The results demonstrated that the proposed model gained a much higher accuracy level in comparison with existing approaches. In the future, this proposed model may be implemented in real-time.

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Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches

Cited by 14 publications

References 48 publications

Application of Machine Learning for Cardiovascular Disease Risk Prediction

Application of Machine Learning for Cardiovascular Disease Risk Prediction

Driver Behavior Detection in Time Series Decade review

Optimized Deep Learning with Learning without Forgetting (LwF) for Weather Classification for Sustainable Transportation and Traffic Safety

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