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.

A hierarchical approach to mood classification in blogs

2011 ◽  
Vol 18 (1) ◽  
pp. 61-81 ◽  
Author(s):  
FAZEL KESHTKAR ◽  
DIANA INKPEN
Keyword(s):  
Machine Learning ◽  
Error Analysis ◽  
Learning Approach ◽  
Hierarchical Approach ◽  
Data Set ◽  
Novel Approach ◽  
Machine Learning Approach ◽  
Sentiment Orientation ◽  
Mood Classification

AbstractIn this article, we explore the task of mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard machine learning approach. We also show that using sentiment orientation features improves the performance of classification. We used the Livejournal blog corpus as a data set to train and evaluate our method. We present extensive error analysis and discuss the difficulty of the task.

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

scholarly journals Symptoms Based Multiple Disease Prediction Model using Machine Learning Approach

2021 ◽  
Vol 10 (9) ◽  
pp. 67-72
Author(s):  
Talasila Bhanuteja ◽  
◽  
Kilaru Venkata Narendra Kumar ◽  
Kolli Sai Poornachand ◽  
Chennupati Ashish ◽  
...  
Keyword(s):  
Machine Learning ◽  
Well Being ◽  
Local Area ◽  
Learning Approach ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Tree Classifier ◽  
Machine Learning Approach ◽  
Disease Forecast

The turn of events and misuse of a few noticeable Data mining strategies in various genuine application regions (for example Trade, Medical management and Natural science) has induced the usage of such methods in Machine Learning (ML) constrains, to distinct helpful snippets of information of the predefined information in medical services networks, biomedical fields and so forth The exact examination of clinical data set advantages in early illness expectation, patient consideration and local area administrations. The methodology of Machine Learning (ML) has been effectively utilized in grouped technologies including Disease forecast. The objective of generating classifier framework utilizing Machine Learning (ML) models is to massively assist with addressing the well-being related issues by helping the doctors to foresee and analyze illnesses at a beginning phase. Sample information of 4920 patient’s records determined to have 41 illnesses was chosen for examination. A reliant variable was made out of 41 sicknesses. 95 of 132 autonomous variables (symptoms) firmly identified with infections were chosen and advanced. This examination work completed shows the illness expectation framework created utilizing Machine learning calculations like Random Forest, Decision Tree Classifier and LightGBM. The paper confers the relative investigation of the consequences of the above-mentioned algorithms are utilized efficiently.

scholarly journals Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing

2021 ◽  
Vol 28 (1) ◽  
pp. 1-41
Author(s):  
Prerna Chikersal ◽  
Afsaneh Doryab ◽  
Michael Tumminia ◽  
Daniella K. Villalba ◽  
Janine M. Dutcher ◽  
...  
Keyword(s):  
Machine Learning ◽  
Depressive Symptoms ◽  
Symptom Severity ◽  
Ground Truth ◽  
Learning Approach ◽  
Novel Approach ◽  
Passive Sensing ◽  
Machine Learning Approach ◽  
Fitness Trackers ◽  
Feature Extraction Technique

We present a machine learning approach that uses data from smartphones and fitness trackers of 138 college students to identify students that experienced depressive symptoms at the end of the semester and students whose depressive symptoms worsened over the semester. Our novel approach is a feature extraction technique that allows us to select meaningful features indicative of depressive symptoms from longitudinal data. It allows us to detect the presence of post-semester depressive symptoms with an accuracy of 85.7% and change in symptom severity with an accuracy of 85.4%. It also predicts these outcomes with an accuracy of >80%, 11–15 weeks before the end of the semester, allowing ample time for pre-emptive interventions. Our work has significant implications for the detection of health outcomes using longitudinal behavioral data and limited ground truth. By detecting change and predicting symptoms several weeks before their onset, our work also has implications for preventing depression.

scholarly journals Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

2021 ◽  
Vol 13 (5) ◽  
pp. 969
Author(s):  
Ka Lok Chan ◽  
Ehsan Khorsandi ◽  
Song Liu ◽  
Frank Baier ◽  
Pieter Valks
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Satellite Observations ◽  
Learning Approach ◽  
Annual Average ◽  
The Neural Network ◽  
Machine Learning Approach

In this paper, we present the estimation of surface NO2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO2 concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO2 concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO2 concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO2 concentrations over Germany from 2018 to 2020. Estimated surface NO2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO2 in Germany. The estimated surface NO2 concentrations provide comprehensive information of NO2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µµg/m3, respectively. While the annual average NO2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µµg/m3. In addition, we used the surface NO2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO2 levels in Germany. In general, 10–30% lower surface NO2 concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus.

scholarly journals Single-trial FRPs: A Machine Learning Approach Towards the Study of the Neural Underpinning of Reading Disorders

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Christoforos Christoforou ◽  
Timothy C. Papadopoulos ◽  
Maria Theodorou
Keyword(s):  
Machine Learning ◽  
Naming Task ◽  
Rapid Automatized Naming ◽  
Learning Approach ◽  
Reading Disorders ◽  
Single Trial ◽  
Developmental Neuroscience ◽  
Novel Approach ◽  
Machine Learning Approach ◽  
Related Potentials

Understanding the neural underpinning of reading disorders, such as dyslexia, is a fundamental question in developmental neuroscience. However, identifying and isolating informative neural components elicited during free-naming paradigms (i.e. unprompted and unconstrained naming tasks) has proven a challenging methodological task. These methodological barriers have hindered the study of the neural underpinnings of reading disorders. In this paper, we proposed a machine learning approach for detecting neural components during free-naming, overcoming much of the current methodological challenges. We propose a new neural-based metric to differentiate groups of children with dyslexia (DYS) and their chronological age controls (CAC) in a free-naming task. Our approach combines electroencephalography (EEG) and eye-tracking measures to generate single-trial fixation-related potentials (sFRPs) and formulate an optimization problem to extract naming-related neural components, informative of group differences. Our approach is validated on a real dataset involving children with dyslexia and CAC performing a Rapid-Automatized Naming (RAN) task. Our results demonstrate the validity of the proposed metric as an indicator of the neural-based markers of reading disorders. Importantly, our proposed framework provides a novel approach that can facilitate the study of neural correlates of reading disorders under paradigms current methods are unable to.

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