scholarly journals Machine learning approaches to calibrate individual-based infectious disease models

2021 ◽  
Author(s):  
Theresa Reiker ◽  
Monica Golumbeanu ◽  
Andrew Shattock ◽  
Lydia Burgert ◽  
Thomas A. Smith ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Transmission ◽  
Goodness Of Fit ◽  
Epidemiological Data ◽  
Bayesian Optimization ◽  
Model Complexity ◽  
Learning Approaches ◽  
Dimensional Parameter ◽  
Novel Approach ◽  
Transmission Models

AbstractIndividual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose a novel approach to calibrate disease transmission models via a Bayesian optimization framework employing machine learning emulator functions to guide a global search over a multi-objective landscape. We demonstrate our approach by application to an established individual-based model of malaria, optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Outperforming other calibration methodologies, the new approach quickly reaches an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability.One Sentence SummaryWe propose a novel, fast, machine learning-based approach to calibrate disease transmission models that outperforms other methodologies

scholarly journals Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Theresa Reiker ◽  
Monica Golumbeanu ◽  
Andrew Shattock ◽  
Lydia Burgert ◽  
Thomas A. Smith ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Goodness Of Fit ◽  
Epidemiological Data ◽  
Bayesian Optimization ◽  
Model Complexity ◽  
Dimensional Parameter ◽  
Optimization Framework ◽  
Individual Based Models ◽  
Objective Parameter ◽  
History Of

AbstractIndividual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose using a Bayesian optimization framework employing Gaussian process or machine learning emulator functions to calibrate a complex malaria transmission simulator. We demonstrate our approach by optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Our approach quickly outperforms previous calibrations, yielding an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability.

scholarly journals Bituminous Mixtures Experimental Data Modeling Using a Hyperparameters-Optimized Machine Learning Approach

2021 ◽  
Vol 11 (24) ◽  
pp. 11710
Author(s):  
Matteo Miani ◽  
Matteo Dunnhofer ◽  
Fabio Rondinella ◽  
Evangelos Manthos ◽  
Jan Valentin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bayesian Optimization ◽  
Automatic Identification ◽  
Learning Approach ◽  
Ann Model ◽  
Data Set ◽  
Bituminous Mixtures ◽  
Novel Approach ◽  
The Neural Network ◽  
Machine Learning Approach

This study introduces a machine learning approach based on Artificial Neural Networks (ANNs) for the prediction of Marshall test results, stiffness modulus and air voids data of different bituminous mixtures for road pavements. A novel approach for an objective and semi-automatic identification of the optimal ANN’s structure, defined by the so-called hyperparameters, has been introduced and discussed. Mechanical and volumetric data were obtained by conducting laboratory tests on 320 Marshall specimens, and the results were used to train the neural network. The k-fold Cross Validation method has been used for partitioning the available data set, to obtain an unbiased evaluation of the model predictive error. The ANN’s hyperparameters have been optimized using the Bayesian optimization, that overcame efficiently the more costly trial-and-error procedure and automated the hyperparameters tuning. The proposed ANN model is characterized by a Pearson coefficient value of 0.868.

scholarly journals AImmune: a new blood-based machine learning approach to improving immune profiling analysis on COVID-19 patients

2021 ◽  
Author(s):  
Xi Tom Zhang ◽  
Runpeng Harris Han
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Mononuclear Cells ◽  
Data Sets ◽  
Learning Approaches ◽  
Rna Seq ◽  
Real World Data ◽  
Novel Approach ◽  
Massive Number ◽  
Immune Profiling

A massive number of transcriptomic profiles of blood samples from COVID-19 patients has been produced since pandemic COVID-19 begins, however, these big data from primary studies have not been well integrated by machine learning approaches. Taking advantage of modern machine learning arthrograms, we integrated and collected single cell RNA-seq (scRNA-seq) data from three independent studies, identified genes potentially available for interpretation of severity, and developed a high-performance deep learning-based deconvolution model AImmune that can predict the proportion of seven different immune cells from the bulk RNA-seq results of human peripheral mononuclear cells. This novel approach which can be used for clinical blood testing of COVID-19 on the ground that previous research shows that mRNA alternations in blood-derived PBMCs may serve as a severity indicator. Assessed on real-world data sets, the AImmune model outperformed the most recognized immune profiling model CIBERSORTx. The presented study showed the results obtained by the true scRNA-seq route can be consistently reproduced through the new approach AImmune, indicating a potential replacing the costly scRNA-seq technique for the analysis of circulating blood cells for both clinical and research purposes.

Emotion Recognition With Facial Expression Using Machine Learning for Social Network and Healthcare

2020 ◽  
pp. 273-282
Author(s):  
Anju Yadav ◽  
Venkatesh Gauri Shankar ◽  
Vivek Kumar Verma
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Facial Expression ◽  
Practical Importance ◽  
Single Layer ◽  
Expression Recognition ◽  
Emotional States ◽  
Learning Approaches ◽  
Novel Approach ◽  
Challenges And Opportunities

In this chapter, machine learning application on facial expression recognition (FER) is studied for seven emotional states (disgust, joy, surprise, anger, sadness, contempt, and fear) based on FER describing coefficient. FER has many practical importance in various area like social network, robotics, healthcare, etc. Further, a literature review of existing machine learning approaches for FER is discussed, and a novel approach for FER is given for static and dynamic images. Then the results are compared with the other existing approaches. The chapter also covers additional related issues of applications, various challenges, and opportunities in future FER. For security-based face detection systems that can identify an individual, in any form of expression he introduces himself. Doctors will use this system to find the intensity of illness or pain of a deaf and dumb patient. The proposed model is based on machine learning application with three types of prototypes, which are pre-trained model, single layer augmented model, and multi-layered augmented model, having a combined accuracy of approx. 99%.

scholarly journals A Novel Approach to Enhance the Generalization Capability of the Hourly Solar Diffuse Horizontal Irradiance Models on Diverse Climates

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4868
Author(s):  
Raghuram Kalyanam ◽  
Sabine Hoffmann
Keyword(s):  
Machine Learning ◽  
Diffuse Radiation ◽  
Absolute Error ◽  
Training Data ◽  
Learning Approaches ◽  
Simulation Tools ◽  
Energy Applications ◽  
Novel Approach ◽  
Machine Learning Model ◽  
Different Climates

Solar radiation data is essential for the development of many solar energy applications ranging from thermal collectors to building simulation tools, but its availability is limited, especially the diffuse radiation component. There are several studies aimed at predicting this value, but very few studies cover the generalizability of such models on varying climates. Our study investigates how well these models generalize and also show how to enhance their generalizability on different climates. Since machine learning approaches are known to generalize well, we apply them to truly understand how well they perform on different climates than they are originally trained. Therefore, we trained them on datasets from the U.S. and tested on several European climates. The machine learning model that is developed for U.S. climates not only showed low mean absolute error (MAE) of 23 W/m2, but also generalized very well on European climates with MAE in the range of 20 to 27 W/m2. Further investigation into the factors influencing the generalizability revealed that careful selection of the training data can improve the results significantly.

Bayesian optimization based machine learning approaches for prediction of plug-in electric vehicle state-of-charge

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Subhasish Deb ◽  
Arup Kumar Goswami ◽  
Rahul Lamichane Chetri ◽  
Rajesh Roy
Keyword(s):  
Machine Learning ◽  
Electric Vehicle ◽  
State Of Charge ◽  
Bayesian Optimization ◽  
Machine Learning Techniques ◽  
Consumption Pattern ◽  
Learning Approaches ◽  
Prediction Analysis ◽  
Learning Techniques ◽  
Boosting Method

Abstract The growing popularity of plug-in electric vehicle (PEV) around the world makes complexity in power sector. The distribution system is subjected to overload due to the random penetration of PEVs in charging depending on their level of state-of-charge (SOC). The accurate calculation and prediction of SOC considering their travel distance makes significant impact on the level of SOC. Therefore, the accurate SOC prediction of PEVs is need of the hour in transportation sector. However, the prediction of SOC allows the PEVs owners to decide the charging/discharging mode or priority based charging. Recently, machine learning techniques are gaining popularity in prediction analysis of different parameters. This article proposes machine learning approaches in combination with Bayesian optimization (BO) for prediction analysis of PEVs SOC. The gradient boosting method (GBM) and random forest method (RFM) are used as machine learning approaches in this work. The energy consumption pattern, different battery capacities and total trip distance of PEVs are included in calculation for the estimation of accurate SOC. A satisfactory result of SOC prediction has been observed using both GBM-BO and RFM-BO. The comparative study of results reveals the performance and efficacy of GBM-BO against RFM-BO in the PEVs SOC prediction analysis. Moreover, the hybrid machine learning techniques with BO performs better than individual machine learning techniques in the prediction analysis of PEVs SOC.

scholarly journals Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields

2019 ◽  
Author(s):  
James McDonagh ◽  
Ardita Shkurti ◽  
David J. Bray ◽  
Richard L. Anderson ◽  
Edward O. Pyzer-Knapp
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
Dissipative Particle Dynamics ◽  
Simulation Method ◽  
Particle Dynamics ◽  
Bayesian Optimization ◽  
Coarse Grained ◽  
Proof Of Concept ◽  
Novel Approach ◽  
Molecular Force

This work demonstrates the use of open literature data to force field paramterization via a novel approach applying Bayesian optimization. We have selected Dissipative Particle Dynamics (DPD) as the simulation method in this proof-of-concept work.

scholarly journals Between comments and repeat visit: capturing repeat visitors with a hybrid approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jina Kim ◽  
Yeonju Jang ◽  
Kunwoo Bae ◽  
Soyoung Oh ◽  
Nam Jeong Jeong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Service Industry ◽  
Hybrid Approach ◽  
Learning Approaches ◽  
The Novel ◽  
Content Type ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Novel Approach ◽  
Repeat Visit

PurposeUnderstanding customers' revisiting behavior is highlighted in the field of service industry and the emergence of online communities has enabled customers to express their prior experience. Thus, purpose of this study is to investigate customers' reviews on an online hotel reservation platform, and explores their postbehaviors from their reviews.Design/methodology/approachThe authors employ two different approaches and compare the accuracy of predicting customers' post behavior: (1) using several machine learning classifiers based on sentimental dimensions of customers' reviews and (2) conducting the experiment consisted of two subsections. In the experiment, the first subsection is designed for participants to predict whether customers who wrote reviews would visit the hotel again (referred to as Prediction), while the second subsection examines whether participants want to visit one of the particular hotels when they read other customers' reviews (dubbed as Decision).FindingsThe accuracy of the machine learning approaches (73.23%) is higher than that of the experimental approach (Prediction: 58.96% and Decision: 64.79%). The key reasons of users' predictions and decisions are identified through qualitative analyses.Originality/valueThe findings reveal that using machine learning approaches show the higher accuracy of predicting customers' repeat visits only based on employed sentimental features. With the novel approach of integrating customers' decision processes and machine learning classifiers, the authors provide valuable insights for researchers and providers of hospitality services.

Predicting online shopping cart abandonment with machine learning approaches

2020 ◽  
pp. 147078532097252
Author(s):  
Theresa Maria Rausch ◽  
Nicholas Daniel Derra ◽  
Lukas Wolf
Keyword(s):  
Machine Learning ◽  
Online Shopping ◽  
Model Complexity ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Online Behavior ◽  
Clickstream Data ◽  
Shopping Cart ◽  
Auc Value ◽  
Shopping Cart Abandonment

Excessive online shopping cart abandonment rates constitute a major challenge for e-commerce companies and can inhibit their success within their competitive environment. Simultaneously, the emergence of the Internet’s commercial usage results in steadily growing volumes of data about consumers’ online behavior. Thus, data-driven methods are needed to extract valuable knowledge from such big data to automatically identify online shopping cart abandoners. Hence, this contribution analyzes clickstream data of a leading German online retailer comprising 821,048 observations to predict such abandoners by proposing different machine learning approaches. Thereby, we provide methodological insights to gather a comprehensive understanding of the practicability of classification methods in the context of online shopping cart abandonment prediction: our findings indicate that gradient boosting with regularization outperforms the remaining models yielding an F1-Score of 0.8569 and an AUC value of 0.8182. Nevertheless, as gradient boosting tends to be computationally infeasible, a decision tree or boosted logistic regression may be suitable alternatives, balancing the trade-off between model complexity and prediction accuracy.

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