Statistical models for shear strength of RC beam-column joints using machine-learning techniques

2014 ◽  
Vol 43 (14) ◽  
pp. 2075-2095 ◽  
Author(s):  
Jong-Su Jeon ◽  
Abdollah Shafieezadeh ◽  
Reginald DesRoches
Keyword(s):  
Machine Learning ◽  
Shear Strength ◽  
Statistical Models ◽  
Machine Learning Techniques ◽  
Rc Beam ◽  
Learning Techniques

Metaheuristic based MLP-SARIMAX HybridizationOne Hour Ahead Solar Radiation Forecasting

2021 ◽  
Author(s):  
Hugo Abreu Mendes ◽  
João Fausto Lorenzato Oliveira ◽  
Paulo Salgado Gomes Mattos Neto ◽  
Alex Coutinho Pereira ◽  
Eduardo Boudoux Jatoba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Time Series ◽  
Solar Radiation ◽  
Statistical Models ◽  
Clean Energy ◽  
Machine Learning Techniques ◽  
Exogenous Variables ◽  
Learning Techniques ◽  
Photovoltaic Plants

Within the context of clean energy generation, solar radiation forecast is applied for photovoltaic plants to increase maintainability and reliability. Statistical models of time series like ARIMA and machine learning techniques help to improve the results. Hybrid Statistical + ML are found in all sorts of time series forecasting applications. This work presents a new way to automate the SARIMAX modeling, nesting PSO and ACO optimization algorithms, differently from R's AutoARIMA, its searches optimal seasonality parameter and combination of the exogenous variables available. This work presents 2 distinct hybrid models that have MLPs as their main elements, optimizing the architecture with Genetic Algorithm. A methodology was used to obtain the results, which were compared to LSTM, CLSTM, MMFF and NARNN-ARMAX topologies found in recent works. The obtained results for the presented models is promising for use in automatic radiation forecasting systems since it outperformed the compared models on at least two metrics.

scholarly journals Modeling the Temporal Population Distribution of Ae. aegypti Mosquito using Big Earth Observation Data.

2020 ◽  
Author(s):  
Oladimeji Mudele ◽  
Fabio M. Bayer ◽  
Lucas Zanandrez ◽  
Alvaro Eiras ◽  
Paolo Gamba
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Earth Observation ◽  
Machine Learning Techniques ◽  
Distribution Model ◽  
Support Vector ◽  
World Population ◽  
Observation Data ◽  
Vector Population ◽  
Learning Techniques

<div>Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most</div>

scholarly journals Modeling the Temporal Population Distribution of Ae. aegypti Mosquito using Big Earth Observation Data.

2020 ◽  
Author(s):  
Oladimeji Mudele ◽  
Fabio M. Bayer ◽  
Lucas Zanandrez ◽  
Alvaro Eiras ◽  
Paolo Gamba
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Earth Observation ◽  
Machine Learning Techniques ◽  
Distribution Model ◽  
Support Vector ◽  
World Population ◽  
Observation Data ◽  
Vector Population ◽  
Learning Techniques

<div>Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most</div>

scholarly journals mosquitoes.pdf

2019 ◽  
Author(s):  
Oladimeji Mudele ◽  
Fabio M. Bayer ◽  
Lucas Zanandrez ◽  
Alvaro Eiras ◽  
Paolo Gamba
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Land Surface ◽  
Vegetation Index ◽  
Machine Learning Techniques ◽  
Distribution Model ◽  
Support Vector ◽  
World Population ◽  
Vector Population ◽  
Learning Techniques

<div>Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most</div>

Information Retrieval in Conjunction With Deep Learning

2020 ◽  
pp. 300-311 ◽  
Author(s):  
Anu Bajaj ◽  
Tamanna Sharma ◽  
Om Prakash Sangwan
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Deep Learning ◽  
Statistical Models ◽  
Web Search ◽  
Machine Learning Techniques ◽  
Trend Detection ◽  
Social Media Analytics ◽  
Learning Techniques ◽  
Long Time

Information is second level of abstraction after data and before knowledge. Information retrieval helps fill the gap between information and knowledge by storing, organizing, representing, maintaining, and disseminating information. Manual information retrieval leads to underutilization of resources, and it takes a long time to process, while machine learning techniques are implications of statistical models, which are flexible, adaptable, and fast to learn. Deep learning is the extension of machine learning with hierarchical levels of learning that make it suitable for complex tasks. Deep learning can be the best choice for information retrieval as it has numerous resources of information and large datasets for computation. In this chapter, the authors discuss applications of information retrieval with deep learning (e.g., web search by reducing the noise and collecting precise results, trend detection in social media analytics, anomaly detection in music datasets, and image retrieval).

scholarly journals mosquitoes.pdf

2019 ◽  
Author(s):  
Oladimeji Mudele ◽  
Fabio M. Bayer ◽  
Lucas Zanandrez ◽  
Alvaro Eiras ◽  
Paolo Gamba
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Land Surface ◽  
Vegetation Index ◽  
Machine Learning Techniques ◽  
Distribution Model ◽  
Support Vector ◽  
World Population ◽  
Vector Population ◽  
Learning Techniques

<div>Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most</div>

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