Integration of Ensemble and Evolutionary Machine Learning Algorithms for Monitoring Diver Behavior Using Physiological Signals

Koohestani, Afsaneh; Abdar, Moloud; Khosravi, Abbas; Nahavandi, Saeid; Koohestani, Mahereh

doi:10.1109/access.2019.2926444

Cited by 38 publications

(5 citation statements)

References 72 publications

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“…For the construction of a useful ensemble method, it needs to consider multiple measures, e.g., subsampling the training examples, manipulating the features, manipulating the output and random integration of algorithms. Bagging, boosting, and voting are the common techniques for combining the algorithms [ 57 ].…”

Section: Resultsmentioning

confidence: 99%

A Performance Comparison of Classification Algorithms for Rose Plants

Malik

Aslam

Nasr

et al. 2022

Computational Intelligence and Neuroscience

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One of the key roles of Botanists is to be able to recognize flowers. This role has become highly challenging given that the number of discovered flower types are nearing half a million. To support Botanists, Information Technology offers promising solutions. Specifically, machine learning techniques are intrinsically appealing due to being precise enough as required. To this aim, two observations on flower leaves are relevant and leverage flower identification: one, flower plants exhibit unique features in their leaves thus allow distinction of their co-located flowers; two, leaves have a much longer life than flowers thus preserve identity properties longer. This paper proposes the use of machine learning-based identification of rose types by leveraging the features from their leaves. For this purpose, the performance of Naive Bayes, Generalized Linear Model, Multilayer Perceptron, Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine has been analyzed. This study optimizes the RF model by investigating and tuning its various parameters such as the number of trees, the depth of trees, and splitting criteria. The best results are achieved with gain ratio because it takes more distinct values to avoid the problems associated with Information Gain. Optimizing the number of trees and the depth of trees of RF yield better accuracy than other models. Extensive experiments are performed to analyze the results of ensemble algorithms by using the voting method for each instance. Results suggest that the performance of ensemble classifiers is superior to that of individual models.

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Section: Resultsmentioning

confidence: 99%

A Performance Comparison of Classification Algorithms for Rose Plants

Malik

Aslam

Nasr

et al. 2022

Computational Intelligence and Neuroscience

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“…In our situation, the outputs of the fundamental classifiers (CNN, SVM, and MLP) are mixed by a majority vote, indicating that a sample window of the sensor signal is allocated to a specific class if at least fifty percent of the classifiers produced that class. Other combination strategies, such as the variant of weighted voting [26], have been proposed in the literature, but they were deemed inadequate for the present application. Another advantage of using ensemble voting models is the certainty level.…”

Section: Resultsmentioning

confidence: 99%

Safe Deep Driving Behavior Detection (S3D)

et al. 2022

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The human factor is one of the most critical parameters in car accidents and even traffic occurrences. Driving style affected by human factors comprises driving events (maneuvers) and driver behaviors. Driving event detection is the fundamental step of identifying driving style and facilitates predicting potentially unsafe behaviors, preventing accidents, and imposing restrictions on high-risk drivers. This paper proposes a deep hybrid model to detect safe driver behaviors and driving events using real-time smartphone sensor signals. The ensemble of Multi-layer Perceptron, Support-Vector Machine, and Convolutional Neural Network classifiers process each driving event sample. In order to evaluate our model, we develop an Android Application to capture smartphone sensor signal data. We capture about 24000 driving data from 50 drivers. Results indicate that the fusion model performs better than each individual classifier in terms of Accuracy, False Positive Rate (FPR), and Specificity (96.75, 0.004, and 0.996). This research gives insights to Auto-mobile developers to focus on the speed and cost efficiency of smartphone driver monitoring platforms. Although some insurance and freight management companies utilize smartphones as their monitoring platforms, the market share of these use cases is meager and could improve rapidly with the promotion of new smartphones with better processing and storage.

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“…Koohestani et al [17] focus on analyzing the driving experience using a variety of machine learning approaches. The suggested system's optimization section contains two primary phases.…”

Section: Related Workmentioning

confidence: 99%

Machine learning based driver monitoring system: A case study for the Kayoola EVS

Ziryawulawo¹,

Kirabo²,

Mwikirize

et al. 2023

SAIEE Afr. Res. J.

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With the ever-growing traffic density, the number of road accidents has continued to increase. Finding solutions to reduce road accidents and improve traffic safety has become a top priority for Kiira Motors Corporation, a Ugandan state-owned automotive company. The company seeks to develop intelligent driver assistance systems for its market entry product, the Kayoola EVS bus. A machine learning-based driver monitoring system that would monitor driver drowsiness and send out an alarm in case drowsiness is detected has been developed in an attempt to reduce drowsiness-related accidents. The system consists of a camera positioned in such a way as to keep track of the driver's face. The camera is interfaced with a Raspberry Pi minicomputer which carries out the computations and analysis and when drowsiness is detected, an alarm is triggered. Dangerous driver behavior including distraction and fatigue has long been recognized as the main contributing factor in traffic accidents. This paper therefore presents the development of a driver monitoring system for the Kayoola Electric City Bus to address the increasing occurrences of road accidents. The machine learningbased driver monitoring system is designed to be non-intrusive with continuous real-time operation.

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Integration of Ensemble and Evolutionary Machine Learning Algorithms for Monitoring Diver Behavior Using Physiological Signals

Cited by 38 publications

References 72 publications

A Performance Comparison of Classification Algorithms for Rose Plants

A Performance Comparison of Classification Algorithms for Rose Plants

Safe Deep Driving Behavior Detection (S3D)

Machine learning based driver monitoring system: A case study for the Kayoola EVS

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