Deep Learning, Predictive Modelling and Nano/Bio-Sensing Technologies for Mitigation of the COVID-19 Pandemic

2021 ◽  
pp. 3-16
Author(s):  
Asim Kar ◽  
Anuradha Kar
Keyword(s):  
Deep Learning ◽  
Predictive Modelling

scholarly journals Predictive modelling of turbofan engine components condition using machine and deep learning methods

2021 ◽  
Vol 23 (2) ◽  
pp. 359-370
Author(s):  
Michał Matuszczak ◽  
Mateusz Żbikowski ◽  
Andrzej Teodorczyk
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Predictive Modelling ◽  
Continuous Variable ◽  
Environmental Data ◽  
Bayesian Optimization ◽  
Aviation Industry ◽  
Learning Models ◽  
Turbofan Engine ◽  
Machine Learning Models

The article proposes an approach based on deep and machine learning models to predict a component failure as an enhancement of condition based maintenance scheme of a turbofan engine and reviews currently used prognostics approaches in the aviation industry. Component degradation scale representing its life consumption is proposed and such collected condition data are combined with engines sensors and environmental data. With use of data manipulation techniques, a framework for models training is created and models' hyperparameters obtained through Bayesian optimization. Models predict the continuous variable representing condition based on the input. Best performed model is identified by detemining its score on the holdout set. Deep learning models achieved 0.71 MSE score (ensemble meta-model of neural networks) and outperformed significantly machine learning models with their best score at 1.75. The deep learning models shown their feasibility to predict the component condition within less than 1 unit of the error in the rank scale.

Predictive Modelling and Monitoring of Industrial Equipment Through Advanced Deep Learning Algorithms

2020 ◽  
Author(s):  
Martin Cimmino ◽  
Matteo Calabrese ◽  
Dimos Kapetis ◽  
Sture Lygren ◽  
Daniele Vanzan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Predictive Modelling ◽  
Industrial Equipment

scholarly journals Riboflow: using deep learning to classify riboswitches with ~99% accuracy

2019 ◽  
Author(s):  
Keshav Aditya R. Premkumar ◽  
Ramit Bharanikumar ◽  
Ashok Palaniappan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Critical Role ◽  
Predictive Modelling ◽  
Superior Performance ◽  
Genetic Circuits ◽  
Roc Curve Analysis ◽  
Link Type

AbstractRiboswitches are cis-regulatory genetic elements that use an aptamer to control gene expression. Specificity to cognate ligand and diversity of such ligands have expanded the functional repetoire of riboswitches to mediate mounting apt responses to sudden metabolic demands and signal changes in environmental conditions. Given their critical role in microbial life, and novel uses in synthetic biology, riboswitch characterisation remains a challenging computational problem. Here we have addressed the issue with advanced deep learning frameworks, namely convolutional neural networks (CNN), and bidirectional recurrent neural networks (RNN) with Long Short-Term Memory (LSTM). Using a comprehensive dataset of 32 ligand classes and a stratified train-validate-test approach, we demonstrated the superior performance of both the deep models (CNN and RNN) relative to other conventional machine learning classifiers on all key performance metrics, including the ROC curve analysis. In particular, the bidirectional LSTM RNN emerged as the best-performing learning method for identifying the ligand-specificity of riboswitches with an accuracy > 0.99 and macro-averaged F-score of 0.96. A dynamic update functionality is inbuilt to account for the discovery of new riboswitches and extend the predictive modelling to any number of new additional classes. Our work would be valuable in the design and assembly of genetic circuits and the development of the next generation of antibiotics. The software is freely available as a Python package and standalone resource for wide use in genome annotation and biotechnology workflows.AvailabilityPyPi package: riboflow @ https://pypi.org/project/riboflowRepository with Standalone suite of tools: https://github.com/RiboswitchClassifierLanguage: Python 3.6 with numpy, keras, and tensorflow libraries.Licence: MIT

scholarly journals Magnetic Resonance Imaging-based Deep Learning Predictive Models of Brain Disorders: A Systematic Review of Modelling Practices, Transparency, and Interpretability

2021 ◽  
Author(s):  
Shane O'Connell ◽  
Dara M Cannon ◽  
Pilib O Broin
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Performance Metrics ◽  
Anatomical Variation ◽  
Predictive Modelling ◽  
Reproducible Research ◽  
Brain Disorders ◽  
Resonance Imaging ◽  
Depressive Episodes

Brain disorders are characterised by impaired cognition, mood alteration, psychosis, depressive episodes, and neurodegeneration, and comprise several psychiatric and neurological disorders. Clinical diagnoses primarily rely on a combination of life history information and questionnaires, with a distinct lack of discriminative biomarkers in use for psychiatric disorders. Given that symptoms across brain conditions are associated with functional alterations of cognitive and emotional processes, which can correlate with anatomical variation, structural magnetic resonance imaging (MRI) data of the brain are an important focus of research studies, particularly for predictive modelling. With the advent of large MRI data consortiums (such as the Alzheimer's Disease Neuroimaging Initiative) facilitating a greater number of MRI based classification studies, convolutional neural networks (CNNs), which are multi layer representation based models particularly well suited to image processing, have become increasingly popular for research into brain conditions. Despite this, modelling practices, the degree of transparency, and considerations of interpretability vary widely across studies, making them difficult to both compare and/or reproduce. Modelling practices here refers to issues surrounding the data splitting procedure, the presence or absence of repeat experiments, the critical appraisal of performance metrics, and the overall reliability of the modelling approach. Transparency refers to how detailed the authors' methodological descriptions are, and the availability of code. Finally, interpretability refers to the attempt made by the authors to identify structural brain alterations driving model predictions; this is particularly important as the application of deep learning systems becomes more widespread in clinical settings. Here, we conduct a systematic literature review of 55 studies carrying out CNN based predictive modelling of brain disorders using MRI data and critique their modelling practices, transparency, and considerations of interpretability; we furthermore propose several practical recommendations aimed at promoting comprehensive, clear, and reproducible research into brain disorders using MRI based deep learning models.

A Surrogate Data-Based Approach for Validating Deep Learning Model Used in Healthcare

2020 ◽  
pp. 132-146
Author(s):  
Meenakshi Srivastava
Keyword(s):  
Deep Learning ◽  
Predictive Modelling ◽  
Surrogate Data ◽  
Learning Model ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Huge Data ◽  
Small Data Sets ◽  
Deep Learning Model

IoT-based communication between medical devices has encouraged the healthcare industry to use automated systems which provide effective insight from the massive amount of gathered data. AI and machine learning have played a major role in the design of such systems. Accuracy and validation are considered, since copious training data is required in a neural network (NN)-based deep learning model. This is hardly feasible in medical research, because the size of data sets is constrained by complexity and high cost experiments. The availability of limited sample data validation of NN remains a concern. The prediction of outcomes on a NN trained on a smaller data set cannot guarantee performance and exhibits unstable behaviors. Surrogate data-based validation of NN can be viewed as a solution. In the current chapter, the classification of breast tissue data by a NN model has been detailed. In the absence of a huge data set, a surrogate data-based validation approach has been applied. The discussed study can be applied for predictive modelling for applications described by small data sets.

scholarly journals An approach for predicting the effects of endocrine disrupting chemicals on human health using deep learning

2020 ◽  
Author(s):  
Pantelis Karatzas ◽  
Yiannis Kiouvrekis ◽  
Haralambos Sarimveis ◽  
Petros Stefaneas
Keyword(s):  
Deep Learning ◽  
Endocrine Disrupting Chemicals ◽  
Predictive Modelling ◽  
Quantitative Structure Activity Relationship ◽  
Specific Information ◽  
Complementary Method ◽  
Chemical Structures ◽  
Qsar Modelling ◽  
Endocrine Disrupting ◽  
Deep Learning Neural Network

In recent years, deep neural networks, especially those exhibiting synergistic properties, have been at the cutting edge of image processing, producing very good results. So far, they have been able to successfully address issues of classification, recognition and recognition of objects depicted on images. In this paper, a novel idea is presented, where images of chemical structures are used as input information in deep learning neural network architectures aiming at the generation of Quantitative Structure Activity Relationship (QSAR) models, i.e. models that predict properties, activities or adverse effects of chemicals. The proposed method was applied to a case study of particular interest, which is the prediction of endocrine disrupting potential of chemicals. Two different deep learning architectures were applied. The produced ImageNet model proved successful, in terms of accuracy, performance and robustness on training and validation sets. The new approach is proposed to the community as an alternative or complementary method to current practices in QSAR modelling, which can automate and improve the creation of predictive models. Key words: deep learning; QSAR modelling; endocrine disruption; machine learning; predictive modelling o human tissue and cells. Our aim was to determine whether this specific information could be used for modelling purposes.

scholarly journals The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review

2018 ◽  
Vol 7 (3) ◽  
pp. 803-816 ◽  
Author(s):  
Alanna Vial ◽  
David Stirling ◽  
Matthew Field ◽  
Montserrat Ros ◽  
Christian Ritz ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Predictive Modelling
Sign in / Sign up

Export Citation Format

Share Document