[HTML][HTML] Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review
Introduction Wearable sensors have shown promise as a non-intrusive method for collecting
biomarkers that may correlate with levels of elevated stress. Stressors cause a variety of …
biomarkers that may correlate with levels of elevated stress. Stressors cause a variety of …
Different applications of machine learning approaches in materials science and engineering: Comprehensive review
Over the last decades, considerable advancements in artificial intelligence (AI) approaches
have eventuated in their extensive applications in all scientific scopes such as materials …
have eventuated in their extensive applications in all scientific scopes such as materials …
[HTML][HTML] A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches
With the significant increase in WQI applications worldwide and lack of specific application
guidelines, accuracy and reliability of WQI models is a major issue. It has been reported that …
guidelines, accuracy and reliability of WQI models is a major issue. It has been reported that …
[HTML][HTML] Robust machine learning algorithms for predicting coastal water quality index
Coastal water quality assessment is an essential task to keep “good water quality” status for
living organisms in coastal ecosystems. The Water quality index (WQI) is a widely used tool …
living organisms in coastal ecosystems. The Water quality index (WQI) is a widely used tool …
[HTML][HTML] Assessing optimization techniques for improving water quality model
In order to keep the" good" status of coastal water quality, it is essential to monitor and
assess frequently. The Water quality index (WQI) model is one of the most widely used …
assess frequently. The Water quality index (WQI) model is one of the most widely used …
Hyperparameter search for machine learning algorithms for optimizing the computational complexity
For machine learning algorithms, fine-tuning hyperparameters is a computational challenge
due to the large size of the problem space. An efficient strategy for adjusting …
due to the large size of the problem space. An efficient strategy for adjusting …
[HTML][HTML] Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model
The COVID-19 pandemic has significantly impacted various aspects of life, including
environmental conditions. Surface water quality (WQ) is one area affected by lockdowns …
environmental conditions. Surface water quality (WQ) is one area affected by lockdowns …
[HTML][HTML] Marine waters assessment using improved water quality model incorporating machine learning approaches
In marine ecosystems, both living and non-living organisms depend on “good” water quality.
It depends on a number of factors, and one of the most important is the quality of the water …
It depends on a number of factors, and one of the most important is the quality of the water …
Deep learning-based approach for emotion recognition using electroencephalography (EEG) signals using bi-directional long short-term memory (Bi-LSTM)
Emotions are an essential part of daily human communication. The emotional states and
dynamics of the brain can be linked by electroencephalography (EEG) signals that can be …
dynamics of the brain can be linked by electroencephalography (EEG) signals that can be …
[Retracted] An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes
SI Ansarullah, S Mohsin Saif… - Journal of healthcare …, 2022 - Wiley Online Library
Heart disease is a severe disorder, which inflicts an adverse burden on all societies and
leads to prolonged suffering and disability. We developed a risk evaluation model based on …
leads to prolonged suffering and disability. We developed a risk evaluation model based on …