Energy management using multi-criteria decision making and machine learning classification algorithms for intelligent system

2022 ◽  
Vol 203 ◽  
pp. 107645
Author(s):  
Hmeda Musbah ◽  
Gama Ali ◽  
Hamed H. Aly ◽  
Timothy A. Little
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Energy Management ◽  
Intelligent System ◽  
Classification Algorithms ◽  
Multi Criteria Decision Making ◽  
Machine Learning Classification

scholarly journals Machine learning classification of new asteroid families members

2020 ◽  
Author(s):  
Valerio Carruba
Keyword(s):  
Machine Learning ◽  
Family Members ◽  
Classification Algorithms ◽  
New Members ◽  
Machine Learning Classification ◽  
New Family ◽  
Robotic Telescope ◽  
Traditional Approaches ◽  
Asteroid Families

<p>Asteroid families are groups of asteroids that are the product of collisions or of the rotational fission of a parent object.  These groups are mainly identified in proper elements or frequencies domains.   Because of robotic telescope surveys, the number of known asteroids has increased from about 10,000 in the early 90's to more than 750,000 nowadays. Traditional approaches for identifying new members of asteroid families, like the hierarchical clustering method (HCM), may   struggle to keep up with the growing rate of new discoveries. Here we used machine learning classification algorithms to identify new family members based on the orbital distribution in proper (a,e,sin(i)) of previously known family constituents. We compared the outcome of nine classification algorithms from stand alone and ensemble approaches.  The Extremely Randomized Trees (ExtraTree) method had the highest precision, enabling to  retrieve up to 97% of family members identified with standard HCM.</p>

scholarly journals Wastewater Plant Reliability Prediction Using the Machine Learning Classification Algorithms

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1518
Author(s):  
Lazar Z. Velimirović ◽  
Radmila Janković ◽  
Jelena D. Velimirović ◽  
Aleksandar Janjić
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Water Levels ◽  
Reliability Prediction ◽  
Classification Algorithms ◽  
Water Pump ◽  
Pump Failure ◽  
Machine Learning Classification ◽  
Capacity Current ◽  
Prediction Systems

One way to optimize wastewater treatment system infrastructure, its operations, monitoring, maintenance and management is through development of smart forecasting, monitoring and failure prediction systems using machine learning modeling. The aim of this paper was to develop a model that was able to predict a water pump failure based on the asymmetrical type of data obtained from sensors such as water levels, capacity, current and flow values. Several machine learning classification algorithms were used for predicting water pump failure. Using the classification algorithms, it was possible to make predictions of future values with a simple input of current values, as well as predicting probabilities of each sample belonging to each class. In order to build a prediction model, an asymmetrical type dataset containing the aforementioned variables was used.

Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making

2021 ◽  
Vol 11 (2) ◽  
pp. 38-52
Author(s):  
Abhinav Juneja ◽  
Sapna Juneja ◽  
Sehajpreet Kaur ◽  
Vivek Kumar
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Healthy Lifestyle ◽  
Age Groups ◽  
Machine Learning Techniques ◽  
Multi Criteria Decision Making ◽  
Learning Techniques ◽  
Supervised Learning Algorithms ◽  
Unsupervised Algorithms ◽  
Early Diagnosis Of Diabetes

Diabetes has become one of the common health issues in people of all age groups. The disease is responsible for many difficulties in lifestyle and is represented by imbalance in hyperglycemia. If kept untreated, diabetes can raise the chance of heart attack, diabetic nephropathy, and other disorders. Early diagnosis of diabetes helps to maintain a healthy lifestyle. Machine learning is a capability of machine to learn from past pattern and occurrences and converge with experience to optimise and give decision. In the current research, the authors have employed machine learning techniques and used multi-criteria decision-making approach in Pima Indian diabetes dataset. To classify the patients, they examined several different supervised and unsupervised predictive models. After detailed analysis, it has been observed that the supervised learning algorithms outweigh the unsupervised algorithms due to the output class being a nominal classified domain.

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