scholarly journals Machine learning landscapes and predictions for patient outcomes

2017 ◽  
Vol 4 (7) ◽  
pp. 170175 ◽  
Author(s):  
Ritankar Das ◽  
David J. Wales
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Patient Outcomes ◽  
Global Minimum ◽  
Regularization Parameter ◽  
Vital Signs ◽  
Training Data ◽  
Time Intervals ◽  
Computational Tools ◽  
Minimum Solution

The theory and computational tools developed to interpret and explore energy landscapes in molecular science are applied to the landscapes defined by local minima for neural networks. These machine learning landscapes correspond to fits of training data, where the inputs are vital signs and laboratory measurements for a database of patients, and the objective is to predict a clinical outcome. In this contribution, we test the predictions obtained by fitting to single measurements, and then to combinations of between 2 and 10 different patient medical data items. The effect of including measurements over different time intervals from the 48 h period in question is analysed, and the most recent values are found to be the most important. We also compare results obtained for neural networks as a function of the number of hidden nodes, and for different values of a regularization parameter. The predictions are compared with an alternative convex fitting function, and a strong correlation is observed. The dependence of these results on the patients randomly selected for training and testing decreases systematically with the size of the database available. The machine learning landscapes defined by neural network fits in this investigation have single-funnel character, which probably explains why it is relatively straightforward to obtain the global minimum solution, or a fit that behaves similarly to this optimal parameterization.

Convolutional Neural Network

2022 ◽  
pp. 1559-1575
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

scholarly journals NOVEL APPROACHES TO DEVELOPMENT OF ARTIFICIAL INTELLIGENCE ALGORITHMS IN THE LUNG CANCER DIAGNOSTICS

2019 ◽  
pp. 8-18 ◽  
Author(s):  
A. A. Meldo ◽  
L. V. Utkin ◽  
T. N. Trofimova ◽  
M. A. Ryabinin ◽  
V. M. Moiseenko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Neural Networks ◽  
Diagnostic System ◽  
Cancer Diagnostics ◽  
Point Of View ◽  
Training Data ◽  
Medical Community ◽  
Social Significance ◽  
Feature Dimension

The relevance of developing an intelligent automated diagnostic system (IADS) for lung cancer (LC) detection stems from the social significance of this disease and its leading position among all cancer diseases. Theoretically, the use of IADS is possible at a stage of screening as well as at a stage of adjusted diagnosis of LC. The recent approaches to training the IADS do not take into account the clinical and radiological classification as well as peculiarities of the LC clinical forms, which are used by the medical community. This defines difficulties and obstacles of using the available IADS. The authors are of the opinion that the closeness of a developed IADS to the «doctor’s logic» contributes to a better reproducibility and interpretability of the IADS usage results. Most IADS described in the literature have been developed on the basis of neural networks, which have several disadvantages that affect reproducibility when using the system. This paper proposes a composite algorithm using machine learning methods such as Deep Forest and Siamese neural network, which can be regarded as a more efficient approach for dealing with a small amount of training data and optimal from the reproducibility point of view. The open datasets used for training IADS include annotated objects which in some cases are not confirmed morphologically. The paper provides a description of the LIRA dataset developed by using the diagnostic results of St. Petersburg Clinical Research Center of Specialized Types of Medical Care (Oncology), which includes only computed tomograms of patients with the verified diagnosis. The paper considers stages of the machine learning process on the basis of the shape features, of the internal structure features as well as a new developed system of differential diagnosis of LC based on the Siamese neural networks. A new approach to the feature dimension reduction is also presented in the paper, which aims more efficient and faster learning of the system.

Creep Rupture Forecasting

2014 ◽  
Vol 2 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Stylianos Chatzidakis ◽  
Miltiadis Alamaniotis ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Process Model ◽  
Production Systems ◽  
Learning Algorithms ◽  
Difficult Problem ◽  
Network Size ◽  
Creep Rupture ◽  
Machine Learning Algorithms ◽  
Training Data

Creep rupture is becoming increasingly one of the most important problems affecting behavior and performance of power production systems operating in high temperature environments and potentially under irradiation as is the case of nuclear reactors. Creep rupture forecasting and estimation of the useful life is required to avoid unanticipated component failure and cost ineffective operation. Despite the rigorous investigations of creep mechanisms and their effect on component lifetime, experimental data are sparse rendering the time to rupture prediction a rather difficult problem. An approach for performing creep rupture forecasting that exploits the unique characteristics of machine learning algorithms is proposed herein. The approach seeks to introduce a mechanism that will synergistically exploit recent findings in creep rupture with the state-of-the-art computational paradigm of machine learning. In this study, three machine learning algorithms, namely General Regression Neural Networks, Artificial Neural Networks and Gaussian Processes, were employed to capture the underlying trends and provide creep rupture forecasting. The current implementation is demonstrated and evaluated on actual experimental creep rupture data. Results show that the Gaussian process model based on the Matérn kernel achieved the best overall prediction performance (56.38%). Significant dependencies exist on the number of training data, neural network size, kernel selection and whether interpolation or extrapolation is performed.

scholarly journals Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen

2019 ◽  
Vol 16 (11) ◽  
pp. 1992 ◽  
Author(s):  
Gebreab K. Zewdie ◽  
David J. Lary ◽  
Estelle Levetin ◽  
Gemechu F. Garuma
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Land Surface ◽  
Deep Neural Networks ◽  
Airborne Pollen ◽  
Training Data ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Ambrosia Pollen ◽  
Extreme Gradient Boosting

Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a method for the robust estimation of the concentration of airborne Ambrosia pollen using a suite of machine learning approaches including deep learning and ensemble learners. Each of these machine learning approaches utilize data from the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric weather and land surface reanalysis. The machine learning approaches used for developing a suite of empirical models are deep neural networks, extreme gradient boosting, random forests and Bayesian ridge regression methods for developing our predictive model. The training data included twenty-four years of daily pollen concentration measurements together with ECMWF weather and land surface reanalysis data from 1987 to 2011 is used to develop the machine learning predictive models. The last six years of the dataset from 2012 to 2017 is used to independently test the performance of the machine learning models. The correlation coefficients between the estimated and actual pollen abundance for the independent validation datasets for the deep neural networks, random forest, extreme gradient boosting and Bayesian ridge were 0.82, 0.81, 0.81 and 0.75 respectively, showing that machine learning can be used to effectively forecast the concentrations of airborne pollen.

Bridging Finite Element and Machine Learning Modeling: Stress Prediction of Arterial Walls in Atherosclerosis

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Ali Madani ◽  
Ahmed Bakhaty ◽  
Jiwon Kim ◽  
Yara Mubarak ◽  
Mohammad R. K. Mofrad
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element ◽  
Deep Neural Networks ◽  
Prediction Models ◽  
Plaque Rupture ◽  
Training Data ◽  
Von Mises Stress ◽  
And Performance ◽  
Von Mises

Finite element and machine learning modeling are two predictive paradigms that have rarely been bridged. In this study, we develop a parametric model to generate arterial geometries and accumulate a database of 12,172 2D finite element simulations modeling the hyperelastic behavior and resulting stress distribution. The arterial wall composition mimics vessels in atherosclerosis–a complex cardiovascular disease and one of the leading causes of death globally. We formulate the training data to predict the maximum von Mises stress, which could indicate risk of plaque rupture. Trained deep learning models are able to accurately predict the max von Mises stress within 9.86% error on a held-out test set. The deep neural networks outperform alternative prediction models and performance scales with amount of training data. Lastly, we examine the importance of contributing features on stress value and location prediction to gain intuitions on the underlying process. Moreover, deep neural networks can capture the functional mapping described by the finite element method, which has far-reaching implications for real-time and multiscale prediction tasks in biomechanics.

Neural Networks for Prediction and Classification

2011 ◽  
pp. 865-869 ◽  
Author(s):  
Kate A. Smith
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Training Data ◽  
Computational Tools ◽  
Unseen Data ◽  
Future Events ◽  
Natural Groups

Neural networks are simple computational tools for examining data and developing models that help to identify interesting patterns or structures. The data used to develop these models is known as training data. Once a neural network has been exposed to the training data, and has learnt the patterns that exist in that data, it can be applied to new data thereby achieving a variety of outcomes. Neural networks can be used to: • learn to predict future events based on the patterns that have been observed in the historical training data; • learn to classify unseen data into pre-defined groups based on characteristics observed in the training data; • learn to cluster the training data into natural groups based on the similarity of characteristics in the training data.

Emerging Technologies in a Modern Competitive Scenario

2021 ◽  
pp. 1-16
Author(s):  
George Leal Jamil ◽  
Alexis Rocha da Silva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Statistical Analysis ◽  
Differential Privacy ◽  
Social Issues ◽  
Training Data ◽  
Sensitive Data ◽  
Machine Learning Model ◽  
Highly Sensitive ◽  
Inference Attacks

Users' personal, highly sensitive data such as photos and voice recordings are kept indefinitely by the companies that collect it. Users can neither delete nor restrict the purposes for which it is used. Learning how to machine learning that protects privacy, we can make a huge difference in solving many social issues like curing disease, etc. Deep neural networks are susceptible to various inference attacks as they remember information about their training data. In this chapter, the authors introduce differential privacy, which ensures that different kinds of statistical analysis don't compromise privacy and federated learning, training a machine learning model on a data to which we do not have access to.

Using Machine Learning for processing Big Data of Copernicus Satellite Sensors at the Example of the TROPOMI / Sentinel-5 Precursor and Sentinel-4 Cloud Product

2020 ◽  
Author(s):  
Fabian Romahn ◽  
Athina Argyrouli ◽  
Ronny Lutz ◽  
Diego Loyola ◽  
Victor Molina Garcia
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Neural Networks ◽  
General Procedure ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Critical Parameters ◽  
Observation Data ◽  
Computational Performance ◽  
Retrieval Algorithms

<p>The satellites of the Copernicus program show the increasing relevance of properly handling the huge amount of Earth observation data, nowadays common in remote sensing. This is further challenging if the processed data has to be provided in near real time (NRT), like the cloud product from TROPOMI / Sentinel-5<!-- The slash has spaces before “TROPOMI” and after “Sentinel-5”, which is inconsistent with the lack of spaces in the title. --> Precursor (S5P) or the upcoming Sentinel-4 (S4) mission.</p><p>In order to solve the inverse problems that arise in the retrieval of cloud products, as well as in similar remote sensing problems, usually complex radiative transfer models (RTMs) are used. These are very accurate, however also computationally very expensive and therefore often not feasible in combination with NRT requirements. With the recent significant breakthroughs in machine learning, easier application through better software and more powerful hardware, the methods of this field have become very interesting as a way to improve the classical remote sensing algorithms.</p><p>In this presentation we show how artificial neural networks (ANNs) can be used to replace the original RTM in the ROCINN (Retrieval Of Cloud Information using Neural Networks) algorithm with sufficient accuracy while increasing the computational performance at the same time by several orders of magnitude.</p><p>We developed a general procedure which consists of smart sampling, generation and scaling of the training data, as well as training, validation and finally deployment of the ANN into the operational processor. In order to minimize manual work, the procedure is highly automated and uses latest technologies such as TensorFlow. It is applicable for any kind of RTMs and thus can be used for many retrieval algorithms like it is already done for ROCINN in S5P and will be soon for ROCINN in the context of S4. Regarding the final performance of the generated ANN, there are several critical parameters which have a high impact (e.g. the structure of the ANN). These will be evaluated in detail. Furthermore, we also show general limitations of ANNs in comparison with RTMs, how this can lead to unexpected side effects and ways to cope with these issues.</p><p>With the example of ROCINN, as part of the operational S5P and upcoming S4 cloud product, we show the great potential of machine learning techniques in improving the performance of classical retrieval algorithms and thus increasing their capability to deal with much larger data quantities. However, we also highlight the importance of a proper configuration and possible limitations.</p>

scholarly journals Deep learning surrogate models for spatial and visual connectivity

2019 ◽  
Vol 18 (1) ◽  
pp. 53-66 ◽  
Author(s):  
Sherif Tarabishy ◽  
Stamatios Psarras ◽  
Marcin Kosicki ◽  
Martha Tsigkari
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Distributed Computation ◽  
Training Data ◽  
Connectivity Analysis ◽  
Floor Plan ◽  
Learning Models ◽  
Computationally Intensive ◽  
Analysis Engine ◽  
Machine Learning Models

Spatial and visual connectivity are important metrics when developing workplace layouts. Calculating those metrics in real time can be difficult, depending on the size of the floor plan being analysed and the resolution of the analyses. This article investigates the possibility of considerably speeding up the outcomes of such computationally intensive simulations by using machine learning to create models capable of identifying the spatial and visual connectivity potential of a space. To that end, we present the entire process of investigating different machine learning models and a pipeline for training them on such task, from the incorporation of a bespoke spatial and visual connectivity analysis engine through a distributed computation pipeline, to the process of synthesizing training data and evaluating the performance of different neural networks.

scholarly journals Performance of Machine Learning Algorithms and Diversity in Data

2018 ◽  
Vol 210 ◽  
pp. 04019 ◽  
Author(s):  
Hyontai SUG
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Real World ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Real World Data ◽  
Random Data ◽  
Data Set ◽  
World Data

Recent world events in go games between human and artificial intelligence called AlphaGo showed the big advancement in machine learning technologies. While AlphaGo was trained using real world data, AlphaGo Zero was trained using massive random data, and the fact that AlphaGo Zero won AlphaGo completely revealed that diversity and size in training data is important for better performance for the machine learning algorithms, especially in deep learning algorithms of neural networks. On the other hand, artificial neural networks and decision trees are widely accepted machine learning algorithms because of their robustness in errors and comprehensibility respectively. In this paper in order to prove that diversity and size in data are important factors for better performance of machine learning algorithms empirically, the two representative algorithms are used for experiment. A real world data set called breast tissue was chosen, because the data set consists of real numbers that is very good property for artificial random data generation. The result of the experiment proved the fact that the diversity and size of data are very important factors for better performance.

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