Comparison study of Regression Models for the prediction of post-Graduation admissions using Machine Learning Techniques

2021 ◽  
Author(s):  
Naveen S. Sapare ◽  
Sahana M. Beelagi
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Machine Learning Techniques ◽  
Comparison Study ◽  
Learning Techniques

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.

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.

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.

Preliminary Bulbous Bow Design Tool Applying K Nearest Neighbours Classification and Regression Model

2021 ◽  
Vol 163 (A3) ◽  
Author(s):  
A Grech La Rosa ◽  
E Anderlini ◽  
G Thomas
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Longitudinal Section ◽  
Design Tool ◽  
Machine Learning Techniques ◽  
Preliminary Design ◽  
Learning Techniques ◽  
Design Attributes ◽  
Nearest Neighbours ◽  
Bulbous Bow

Designing bulbous bows for ships remains a challenging task.  Their impact on different design attributes as well as their change in performance when operating off their intended design condition renders this as a multidimensional problem.  This paper explores the application of machine learning techniques to a sample of in-service vessel data to develop a preliminary design tool.  The ships' data was analysed together with their bulbous bow data to generate machine learning models using a supervised approach.  The K Nearest Neighbours Classifier and Regression models were used as the basis of the tool.  Together, these models can be used to predict whether to install a bulbous bow and the recommended dimensionless coefficients for new vessels. Generating this preliminary bulbous bow design tool required the introduction of new dimensionless coefficients that discretise the bulbous bow's longitudinal section.  The preliminary design tool gives the designer the ability to determine whether a bulbous bow should be fitted and, if so, to obtain an initial estimate of the bulbous bow required for the vessel being designed, based on key input parameters that relate to the ship and its operation.  The new design tool is demonstrated to provide preliminary design details for bulbous bows through the case studies. 

scholarly journals A Comprehensive Survey on Machine Learning

2021 ◽  
Vol 1 (1) ◽  
pp. 1-17
Author(s):  
Astha Singh ◽  
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Correct Model ◽  
Learning Techniques ◽  
Unseen Data ◽  
Comprehensive Survey ◽  
Statistical Agencies ◽  
High Level

The objective of this briefing is to present an overview of the topic, machine learning techniques currently in use or in consideration at statistical agencies worldwide. It is important to know the main reason why real-world scenarios should start exploring the use of machine learning techniques, terminology, approach and about few popular libraries in python, what regression is, by completely throwing light on simple as well as multiple linear and non-linear regression models and their applications, classification techniques, various clustering techniques. The material presented in this paper is the result of a study based on different models and the study of various datasets (analysis and choice of the correct model are important). While Machine Learning involves concepts of automation, it requires human guidance. Machine Learning involves a high level of generalization to get a system that performs well on yet-unseen data instances. Topics like regression, classification, and clustering, the report covers the insight of various techniques and their applications.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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