Machine Learning: Fundamentals

Kang, Myeongsu; Jameson, N. Jordan

doi:10.1002/9781119515326.ch4

Cited by 42 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Concerning the categories’ accuracies, the highest was registered at the low level (93%), which might be attributed to the larger dataset for this category. For machine learning models, it is well known that a larger dataset could lead to greater performance accuracy and vice versa [ 44 ]. Of the 750 sets of features, 38% belonged to the low level, and 19%, 22% and 21% to moderate, heavy and severe levels, respectively.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters

Bustos

Cardoso

Rios

et al. 2022

Sensors

View full text Add to dashboard Cite

Physical fatigue is a serious threat to the health and safety of firefighters. Its effects include decreased cognitive abilities and a heightened risk of accidents. Subjective scales and, recently, on-body sensors have been used to monitor physical fatigue among firefighters and safety-sensitive professionals. Considering the capabilities (e.g., noninvasiveness and continuous monitoring) and limitations (e.g., assessed fatiguing tasks and models validation procedures) of current approaches, this study aimed to develop a physical fatigue prediction model combining cardiorespiratory and thermoregulatory measures and machine learning algorithms within a firefighters’ sample. Sensory data from heart rate, breathing rate and core temperature were recorded from 24 participants during an incremental running protocol. Various supervised machine learning algorithms were examined using 21 features extracted from the physiological variables and participants’ characteristics to estimate four physical fatigue conditions: low, moderate, heavy and severe. Results showed that the XGBoosted Trees algorithm achieved the best outcomes with an average accuracy of 82% and accuracies of 93% and 86% for recognising the low and severe levels. Furthermore, this study evaluated different methods to assess the models’ performance, concluding that the group cross-validation method presents the most practical results. Overall, this study highlights the advantages of using multiple physiological measures for enhancing physical fatigue modelling. It proposes a promising health and safety management tool and lays the foundation for future studies in field conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The final version of each algorithm was defined with the hyperparameters combination leading to the best performance. The details of these classifiers and the process of determining their hyperparameters are not reported here, as they can be consulted in various machine learning resources [ 37 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters

Bustos

Cardoso

Rios

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…If the determined design goals are not achieved, the number of data are increased and the BO method is reapplied (Step 5). The number of data are increased because accurate modelling highly depends on the number of sampling points; hence, as the amount of dataset is increased, the accuracy of modelling is also increased [32]. Data generation is automatically stopped when the testing accuracy becomes higher than 90%, because this amount of accuracy demonstrates successful modelling of the design [25].…”

Section: Algorithm 1 Sequentially Automated Optimization Process For Designing Single Microstrip Antennas Based On Buo and Bo Methodsmentioning

confidence: 99%

Automated optimization for broadband flat‐gain antenna designs with artificial neural network

Mir

Kouhalvandi

Matekovits

et al. 2021

IET Microwaves Antenna & Prop

View full text Add to dashboard Cite

An automated optimization process for designing and optimising high-performance single microstrip antennas is presented. It consists of the successive use of two optimization methods, bottom-up optimization (BUO) and Bayesian optimization (BO), which are applied sequentially, resulting in electromagnetic (EM)-based artificial neural network modelling. The BUO method is applied for the initial design of the structure of the antennas whereas the BO approach is successively implemented to predict suitable dimensional parameters, leading to broadband, high flat-gain antennas. The optimization process is performed automatically with the combination of an electronic design automation tool and a numerical analyser. The proposed method is easy to use; it allows one to perform the design with little experience, because both structure modelling and sizing are performed automatically. To verify the power of the proposed EM-based method experimentally, two single microstrip antennas have been designed, optimised, fabricated, and measured. The first antenna has flat-gain performance (6.9-7.2 dB) in a frequency band of 8.8-10 GHz. The second has been designed to perform in the 8.7-to 10-GHz band, where it exhibits flat-gain performance with reduced fluctuation in the range of 6.7-7 dB. The experimental results are in good agreement with the numerical data.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…18 Machine learning algorithms can be divided into four categories according to the learning method: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. 19 Supervised learning uses samples with known labels to train the network; Unsupervised learning uses unlabeled samples to train the network; Semi-supervised learning uses a large number of unlabeled samples and a small number of samples with known labels to train the network; and Reinforcement learning uses unlabeled sample data and a rewards function to train the network. Because the external environment provides little information, reinforcement learning requires experience and experimentation to learn on its own.…”

Section: Introductionmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence technology has been developed rapidly, especially machine learning algorithms, which have shown amazing results in various fields while it also received attention in bridge inspection. This paper summarizes the progress of bridge damage detection research related to artificial intelligence techniques between 2015 and 2021. For structural health monitoring, sensing data is the basis for various data processing methods. The strength and weakness of the sensing data itself directly affect the effectiveness of subsequent processing methods. As a result, this paper classifies bridge damage detection studies into six categories from the types of sensing data: visual image, point cloud, infrared thermal imaging, ground-penetrating radar, vibration response, and other types of data. These six types of damage detection methods were reviewed and summarized respectively. Finally, challenges and future trends were discussed.

show abstract

Machine Learning: Fundamentals

Cited by 42 publications

References 27 publications

Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters

Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters

Automated optimization for broadband flat‐gain antenna designs with artificial neural network

Review of artificial intelligence-based bridge damage detection

Contact Info

Product

Resources

About