A Data-Mining Approach for Energy Behavioural Analysis to Ease Predictive Modelling for the Smart City

2018 ◽  
Author(s):  
Lavinia Chiara Tagliabue ◽  
Stefano Rinaldi ◽  
Mario Favalli Ragusini ◽  
Giovanni Tardioli ◽  
Angelo Luigi Camillo Ciribini
Keyword(s):  
Data Mining ◽  
Smart City ◽  
Predictive Modelling ◽  
Data Mining Approach ◽  
Behavioural Analysis

scholarly journals Data-Driven Approach for Incident Management in a Smart City

2020 ◽  
Vol 10 (22) ◽  
pp. 8281
Author(s):  
Luís B. Elvas ◽  
Carolina F. Marreiros ◽  
João M. Dinis ◽  
Maria C. Pereira ◽  
Ana L. Martins ◽  
...  
Keyword(s):  
Data Mining ◽  
Smart City ◽  
City Planning ◽  
Smart Cities ◽  
Data Driven ◽  
Incident Management ◽  
Cascading Effects ◽  
Data Mining Approach ◽  
Industry Standard ◽  
Data Driven Approach

Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better.

scholarly journals Predictive Modelling for Quality Prediction and Assurance of Extrusion Blow Molding

2019 ◽  
Vol 8 (11) ◽  
pp. 1364-1368
Keyword(s):  
Data Mining ◽  
Predictive Modelling ◽  
Molding Process ◽  
K Nearest Neighbors ◽  
Blow Molding ◽  
Data Mining Approach ◽  
Complex Relationships ◽  
Using Data ◽  
Extrusion Blow Molding

Extrusion Blow Molding process plays an important role in manufacturing of hollow products with wide variety of materials like polyethylene (PE), polypropylene (PP), polyvinylchloride (PVC). Extrusion blow molded products are rejected due to the occurrence of defects such as die lines, blowouts, shrinkage, over weight of part. The complex relationships that exist between the process variables, and causes of defects are investigated for 1 litre container made of highdensity polyethylene (HDPE) using data mining techniques in order to reduce scrap. In this paper Data Mining approach is implemented by applying Decision Tree, k-Nearest Neighbors, Rule Induction and Vote techniques in RapidMiner for quality assurance and prediction of the quality of the extrusion blow molded product

scholarly journals A Machine Learning Approach to Predictive Modelling of Student Performance

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1144
Author(s):  
Hu Ng ◽  
Azmin Alias bin Mohd Azha ◽  
Timothy Tzen Vun Yap ◽  
Vik Tor Goh
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Secondary Schools ◽  
Student Performance ◽  
Performance Metrics ◽  
Binary Classification ◽  
Predictive Modelling ◽  
Support Vector ◽  
Data Mining Approach ◽  
History Of

Background - Many factors affect student performance such as the individual’s background, habits, absenteeism and social activities. Using these factors, corrective actions can be determined to improve their performance. This study looks into the effects of these factors in predicting student performance from a data mining approach. This study presents a data mining approach in identify significant factors and predict student performance, based on two datasets collected from two secondary schools in Portugal. Methods – In this study, two datasets collected from two secondary schools in Portugal. First, the data used in the study is augmented to increase the sample size by merging the two datasets. Following that, data pre-processing is performed and the features are normalized with linear scaling to avoid bias on heavy weighted attributes.  The selected features are then assigned into four groups comprising of student background, lifestyle, history of grades and all features. Next, Boruta feature selection is performed to remove irrelevant features. Finally, the classification models of Support Vector Machine (SVM), Naïve Bayes (NB), and Multilayer Perceptron (MLP) origins are designed and their performances evaluated. Results - The models were trained and evaluated on an integrated dataset comprising 1044 student records with 33 features, after feature selection. The classification was performed with SVM, NB and MLP with 60-40 and 50-50 train-test splits and 10-fold cross validation. GridSearchCV was applied to perform hyperparameter tuning. The performance metrics were accuracy, precision, recall and F1-Score. SVM obtained the highest accuracy with scores of 77%, 80%, 91% and 90% on background, lifestyle, history of grades and all features respectively in 50-50 train-test splits for binary classification (pass or fail). SVM also obtained highest accuracy for five class classification (grade A, B, C, D and F) with 39%, 38%, 73% and 71% for the four categories respectively.

Data mining approach to construction productivity prediction for cutter suction dredgers

2019 ◽  
Vol 105 ◽  
pp. 102833 ◽  
Author(s):  
Shuo Bai ◽  
Mingchao Li ◽  
Rui Kong ◽  
Shuai Han ◽  
Heng Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Construction Productivity ◽  
Data Mining Approach ◽  
Productivity Prediction
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