Data Mining Algorithms for Water Main Condition Prediction—Comparative Analysis

2022 ◽  
Vol 148 (2) ◽  
Author(s):  
Ahmed Assad ◽  
Ahmed Bouferguene
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Main Condition ◽  
Data Mining Algorithms ◽  
Water Main ◽  
Mining Algorithms

scholarly journals AUMENTANDODESEMPENHO DEALGORITMOSDEMINERAÇÃODEDADOSUTILIZANDOAPLATAFORMACUDA

2018 ◽  
pp. 90-102
Author(s):  
Matheus Varela Ferreira ◽  
Francisco Assis da Silva ◽  
Leandro Luiz de Almeida ◽  
Danillo Roberto Pereira
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Data Processing ◽  
Processing Time ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Short Term ◽  
Data Mining Algorithms ◽  
Mining Algorithms

With the increasing need to make decisions in the short term, industry (pharmaceutical, petrochemical, aeronautics and etc.) has been seeking new ways to reduce the time of the data mining process to obtain knowledge. In recent years, many technological resources are being used to mitigate this need, an example is CUDA. CUDA is a platform that enables the use of GeForce GPUs in conjunction with CPUs for data processing, significantly reducing processing time. This work proposes to perform a comparative analysis of the processing time between two versions of some data mining algorithms (Apriori, AprioriAll, Naïve Bayes and K-Means), one running on CPU only and one on CPU in conjunction with GPU through platform CUDA. Through the experiments performed, it was observed that using the CUDA platform it is possible to obtain satisfactory results.

scholarly journals Comparative Analysis of Kohonen-SOM and K-Means data mining algorithms based on Academic Activities

2013 ◽  
Vol 6 (1) ◽  
pp. 237-241
Author(s):  
Shaina Dhingra ◽  
Rimple Gilhotra ◽  
Ravishanker Ravishanker
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Large Scale ◽  
Decision Makers ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Academic Activities ◽  
Increasing Demand ◽  
Kohonen Som ◽  
Mining Algorithms

With the increasing demand of IT and subsequent growth in this sector, the high- dimensional data came into existence. Data Mining plays an important role in analyzing and extracting the useful information. The key information which is extracted from a huge pool of data is useful for decision makers. Clustering, one of the techniques of data mining is the mostly used methods of analyzing the data. In this paper, the approach of Kohonen SOM and K-Means and HAC are discussed. After that these three methods are used for analyzing the academic data set of the faculty members of particular university. Finally a comparative analysis of these algorithms are done against some parameters like number of clusters, error rate and accessing rate, etc.  This work will present new and improved results from large-scale datasets.

scholarly journals Comparative analysis of classification models for diagnosis Type 2 Diabetes

2021 ◽  
Author(s):  
Daniah Almadni
Keyword(s):  
Data Mining ◽  
Type 2 Diabetes ◽  
Comparative Analysis ◽  
Expert System ◽  
Support Vector ◽  
Fuzzy Expert System ◽  
Inference System ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Diabetes mellitus type 2 has become one of the major causes of premature diseases and death in many countries. It accounts for the majority of diabetes cases around the world. Thus, we need to develop a system that diagnoses type 2 diabetes. In this thesis, a fuzzy expert system is proposed using the Mamdani fuzzy inference system to diagnose type 2 diabetes effectively. In order to evaluate the performance of our system, a comparative study has been initiated, and will contrast the proposed system with data mining algorithms, namely J48 Decision tree, multilayer perceptron, support vector machine, and Naïve Bayes. The developed fuzzy expert system and the data mining algorithms are validated with real data from the UCI machine learning datasets. Moreover, the performance of the fuzzy expert system is evaluated by comparing it to related work that used the Mamdani inference system to diagnose the incidence of type 2 diabetes. Alternate title: Comparative analysis of data mining algorithms for diagnosis Type 2 Diabetes

scholarly journals Comparative analysis of classification models for diagnosis Type 2 Diabetes

2021 ◽  
Author(s):  
Daniah Almadni
Keyword(s):  
Data Mining ◽  
Type 2 Diabetes ◽  
Comparative Analysis ◽  
Expert System ◽  
Support Vector ◽  
Fuzzy Expert System ◽  
Inference System ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Diabetes mellitus type 2 has become one of the major causes of premature diseases and death in many countries. It accounts for the majority of diabetes cases around the world. Thus, we need to develop a system that diagnoses type 2 diabetes. In this thesis, a fuzzy expert system is proposed using the Mamdani fuzzy inference system to diagnose type 2 diabetes effectively. In order to evaluate the performance of our system, a comparative study has been initiated, and will contrast the proposed system with data mining algorithms, namely J48 Decision tree, multilayer perceptron, support vector machine, and Naïve Bayes. The developed fuzzy expert system and the data mining algorithms are validated with real data from the UCI machine learning datasets. Moreover, the performance of the fuzzy expert system is evaluated by comparing it to related work that used the Mamdani inference system to diagnose the incidence of type 2 diabetes. Alternate title: Comparative analysis of data mining algorithms for diagnosis Type 2 Diabetes

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

2019 ◽  
Vol 14 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Viswam Subeesh ◽  
Eswaran Maheswari ◽  
Hemendra Singh ◽  
Thomas Elsa Beulah ◽  
Ann Mary Swaroop
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Disproportionality Analysis ◽  
Positive Signal ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

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