scholarly journals Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 686
Author(s):  
Jui-Sheng Chou ◽  
Dinh-Nhat Truong ◽  
Chih-Fong Tsai
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Historical Data ◽  
Computation Time ◽  
Machine Learning Techniques ◽  
Efficient Tool ◽  
Intelligent Machine ◽  
Industrial Experiments ◽  
Learning Techniques ◽  
Regression Problems

Machine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine various single models. To construct or find the best model is very complex and time-consuming, so this study develops a new platform, called intelligent Machine Learner (iML), to automatically build popular models and identify the best one. The iML platform is benchmarked with WEKA by analyzing publicly available datasets. After that, four industrial experiments are conducted to evaluate the performance of iML. In all cases, the best models determined by iML are superior to prior studies in terms of accuracy and computation time. Thus, the iML is a powerful and efficient tool for solving regression problems in engineering informatics.

scholarly journals Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques

2021 ◽  
Vol 309 ◽  
pp. 01163
Author(s):  
K. Anuradha ◽  
Deekshitha Erlapally ◽  
G. Karuna ◽  
V. Srilakshmi ◽  
K. Adilakshmi
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Random Forest ◽  
Regression Models ◽  
Solar Power ◽  
Machine Learning Techniques ◽  
Pv Systems ◽  
Solar Power Generation ◽  
Learning Techniques ◽  
Solar Power Forecasting

Solar power is generated using photovoltaic (PV) systems all over the world. Because the output power of PV systems is alternating and highly dependent on environmental circumstances, solar power sources are unpredictable in nature. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. Because of the unpredictability in photovoltaic generating, it’s crucial to plan ahead for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The impacts of various environmental conditions on the output of a PV system are discussed. Machine Learning (ML) algorithms have shown great results in time series forecasting and so can be used to anticipate power with weather conditions as model inputs. The use of multiple machine learning, Deep learning and artificial neural network techniques to perform solar power forecasting. Here in this regression models from machine learning techniques like support vector machine regressor, random forest regressor and linear regression model from which random forest regressor beaten the other two regression models with vast accuracy.

Predicting Medical Resources Required to be Dispatched After Earthquake and Flood, Using Historical Data and Machine Learning Techniques

2018 ◽  
Vol 8 (2) ◽  
pp. 13-35
Author(s):  
Homer Papadopoulos ◽  
Antonis Korakis
Keyword(s):  
Machine Learning ◽  
Web Services ◽  
Large Scale ◽  
Historical Data ◽  
Machine Learning Techniques ◽  
Medical Resources ◽  
Simulation Tools ◽  
Medical Response ◽  
Emergency Medical ◽  
Learning Techniques

This article presents a method to predict the medical resources required to be dispatched after large-scale disasters to satisfy the demand. The historical data of past incidents (earthquakes, floods) regarding the number of victims requested emergency medical services and hospitalisation, simulation tools, web services and machine learning techniques have been combined. The authors adopted a twofold approach: a) use of web services and simulation tools to predict the potential number of victims and b) use of historical data and self-trained algorithms to “learn” from these data and provide relative predictions. Comparing actual and predicted victims needed hospitalisation showed that the proposed models can predict the medical resources required to be dispatched with acceptable errors. The results are promoting the use of electronic platforms able to coordinate an emergency medical response since these platforms can collect big heterogeneous datasets necessary to optimise the performance of the suggested algorithms.

Predicting Medical Resources Required to be Dispatched After Earthquake and Flood, Using Historical Data and Machine Learning Techniques

2020 ◽  
pp. 38-66
Author(s):  
Homer Papadopoulos ◽  
Antonis Korakis
Keyword(s):  
Machine Learning ◽  
Web Services ◽  
Large Scale ◽  
Historical Data ◽  
Machine Learning Techniques ◽  
Medical Resources ◽  
Simulation Tools ◽  
Medical Response ◽  
Emergency Medical ◽  
Learning Techniques

This article presents a method to predict the medical resources required to be dispatched after large-scale disasters to satisfy the demand. The historical data of past incidents (earthquakes, floods) regarding the number of victims requested emergency medical services and hospitalisation, simulation tools, web services and machine learning techniques have been combined. The authors adopted a twofold approach: a) use of web services and simulation tools to predict the potential number of victims and b) use of historical data and self-trained algorithms to “learn” from these data and provide relative predictions. Comparing actual and predicted victims needed hospitalisation showed that the proposed models can predict the medical resources required to be dispatched with acceptable errors. The results are promoting the use of electronic platforms able to coordinate an emergency medical response since these platforms can collect big heterogeneous datasets necessary to optimise the performance of the suggested algorithms.

Application of Machine Learning Techniques for the Analysis of National Bridge Inventory and Bridge Element Data

2019 ◽  
Vol 2673 (7) ◽  
pp. 99-110 ◽  
Author(s):  
Graziano Fiorillo ◽  
Hani Nassif
Keyword(s):  
Machine Learning ◽  
Asset Management ◽  
Historical Data ◽  
Machine Learning Techniques ◽  
The Past ◽  
Precise Evaluation ◽  
Learning Techniques ◽  
Good State ◽  
Wide Range ◽  
National Bridge Inventory

The MAP-21 Act requires information on bridge assets to be at the element level for management operations in the U.S.A. This approach has the objective of improving future predictions of the performance of bridge assets for a more precise evaluation of condition and correct allocation of management funds to keep bridges in a good state of repair. Although bridge element conditions were introduced in the 1990s, the application of such data had never been mandatory for bridge asset management until 2014, therefore, the amount of historical data on bridge element (BE) condition is still limited. On the other hand, National Bridge Inventory (NBI) ratings have been collected since the 1970s and a wide range of data are available. Therefore, it is natural to ask whether BE condition can be predicted using NBI data. In the past, researchers statistically related BE and NBI data, but little has been done to revert NBI to BE. This paper addresses both challenges of mapping BE–NBI condition data using several machine learning techniques. The results of the analysis of these techniques applied to a sample of about 9,000 bridges from northeastern states of the U.S.A. shows that between 79.8% and 100% of the NBI ratings for deck, superstructure, and substructure can be predicted within a rating error of ± 1. The back-mapping operation of NBI time-dependent ratings to BE deterioration profiles for deck, superstructure, and substructure can also be predicted accurately with a probability greater than 50% at the 95% confidence level.

scholarly journals Machine learning techniques to derive bioclimatic classifications for Colombia

2021 ◽  
Author(s):  
Richard Rios ◽  
Elkin A. Noguera-Urbano ◽  
Jairo Espinosa ◽  
Jose Manuael Ochoa
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Logistic Regression ◽  
Regression Models ◽  
Principal Component ◽  
Component Analysis ◽  
Machine Learning Techniques ◽  
Study Region ◽  
Logistic Regression Models ◽  
Learning Techniques

Bioclimatic classifications seek to divide a study region into geographic areas with similar bioclimatic characteristics. In this study we proposed two bioclimatic classifications for Colombia using machine learning techniques. We firstly characterized the precipitation space of Colombia using principal component analysis. Based on Lang classification, we then projected all background sites in the precipitation space with their corresponding categories. We sequentially fit logistic regression models to re-classify all background sites in the precipitation space with six redefined Lang categories. New categories were the used to define a new modified Lang and Caldas-Lang classifications.

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