Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

He, Qiang; Wang, Jin

doi:10.3390/pr8080951

Cited by 11 publications

(7 citation statements)

References 193 publications

(257 reference statements)

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“…This can be considered as "big data" in the context of these experiments. Big data in ML is characterized by data volume (size or scale), variety (multitype), velocity (batch or streaming), and veracity (uncertainty, quality, and accuracy) [12][13][14]. ML is about modeling data [15] and Processes 2021, 9, 413 3 of 29 combines statistics, optimization and computer science [16].…”

Section: Introductionmentioning

confidence: 99%

Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

et al. 2021

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The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a stability analysis using the Gibbs Free Energy of mixing and the NRTL model, provide a good understanding with calculation issues, when using HYSYS V9 and Aspen Plus V9 software, this shows that significant phase equilibrium uncertainties still exist. To clarify these matters, n-octane and water blends, are good surrogates of naphtha/water mixtures. Runs were developed in a CREC vapor–liquid (VL_ Cell operated with octane–water mixtures under dynamic conditions and used to establish the two-phase (liquid–vapor) and three phase (liquid–liquid–vapor) domains. Results obtained demonstrate that the two phase region (full solubility in the liquid phase) of n-octane in water at 100 °C is in the 10-4 mol fraction range, and it is larger than the 10-5 mol fraction predicted by Aspen Plus and the 10-7 mol fraction reported in the technical literature. Furthermore, and to provide an effective and accurate method for predicting the number of phases, a machine learning (ML) technique was implemented and successfully demonstrated, in the present study.

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Section: Introductionmentioning

confidence: 99%

Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

et al. 2021

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“…We performed the training and tuning model without cross-validation strategy on 88 subjects (i.e., 3718 slices) of training data. As reported in [ 27 ], cross-validation is known as internal validation. Using both techniques together can lead to high variance and non-optimized hyper-parameters.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Assisted Localization of Polycystic Kidney on Contrast-Enhanced CT Images

et al. 2020

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Total Kidney Volume (TKV) is essential for analyzing the progressive loss of renal function in Autosomal Dominant Polycystic Kidney Disease (ADPKD). Conventionally, to measure TKV from medical images, a radiologist needs to localize and segment the kidneys by defining and delineating the kidney’s boundary slice by slice. However, kidney localization is a time-consuming and challenging task considering the unstructured medical images from big data such as Contrast-enhanced Computed Tomography (CCT). This study aimed to design an automatic localization model of ADPKD using Artificial Intelligence. A robust detection model using CCT images, image preprocessing, and Single Shot Detector (SSD) Inception V2 Deep Learning (DL) model is designed here. The model is trained and evaluated with 110 CCT images that comprise 10,078 slices. The experimental results showed that our derived detection model outperformed other DL detectors in terms of Average Precision (AP) and mean Average Precision (mAP). We achieved mAP = 94% for image-wise testing and mAP = 82% for subject-wise testing, when threshold on Intersection over Union (IoU) = 0.5. This study proves that our derived automatic detection model can assist radiologist in locating and classifying the ADPKD kidneys precisely and rapidly in order to improve the segmentation task and TKV calculation.

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“…Given that the imbalance in sympathetic and parasympathetic nervous activity has been proposed as a physiological correlate of cognitive fatigue, the knowledge derived from traditional biomarker research is particularly informative and should be incorporated into machine learning models to aid development and validation. Specifically, such domain-specific knowledge can help with the selection of parameters, features, or models, which could result in models that are more theoretically coherent, physiologically sound, generalisable, and interpretable [122]. In addition, using multiple biomarkers to build a multivariate model could potentially improve overall predictive power [123].…”

Section: Towards a Biomarker-informed Machine Learning Model Of Cognitive Fatiguementioning

confidence: 99%

Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Lee

Gan

Christopoulos

2021

Sensors

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Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and provides informed recommendations for future research. Traditionally, cognitive fatigue is introspectively assessed through self-report or objectively inferred from a decline in behavioural performance. However, more recently, researchers have attempted to explore the biological underpinnings of cognitive fatigue to understand and measure this phenomenon. In particular, there is evidence indicating that the imbalance between sympathetic and parasympathetic nervous activity appears to be a physiological correlate of cognitive fatigue. This imbalance has been indexed through various heart rate variability indices that have also been proposed as putative biomarkers of cognitive fatigue. Moreover, in contrast to traditional inferential methods, there is also a growing research interest in using data-driven approaches to assessing cognitive fatigue. The ubiquity of wearables with the capability to collect large amounts of physiological data appears to be a major facilitator in the growth of data-driven research in this area. Preliminary findings indicate that such large datasets can be used to accurately predict cognitive fatigue through various machine learning approaches. Overall, the potential of combining domain-specific knowledge gained from biomarker research with machine learning approaches should be further explored to build more robust predictive models of cognitive fatigue.

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Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

Cited by 11 publications

References 193 publications

Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

Deep Learning Assisted Localization of Polycystic Kidney on Contrast-Enhanced CT Images

Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

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