Fixing rules for data cleaning based on conditional functional dependency

Salem, Rashed; Abdo, Asmaa S.

doi:10.1016/j.fcij.2017.03.002

Cited by 17 publications

(6 citation statements)

References 13 publications

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“…They conducted the study for effectiveness comparison of existing miner techniques. They observed the response time, storage space and database scability (Salem and Abdo, 2016). Authors discussed about efficient and effective method for feature subset selection.…”

Section: To Generate An Ensemble Model For Women Thyroid Prediction Umentioning

confidence: 99%

To Generate an Ensemble Model for Women Thyroid Prediction Using Data Mining Techniques

Yadav

Pal

2019

Asian Pac J Cancer Prev

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Objective: The main objective of this paper is to easily identify thyroid symptom for treatment. Methods: In this paper two main techniques are proposed for mining the hidden pattern in the dataset. Ensemble-I and Ensemble-II both are machine learning techniques. Ensemble-I generated from decision tree, over fitting and neural network and Ensemble-II generated from combinations of Bagging and Boosting techniques. Finally proposed experiment is conducted by Ensemble-I vs. Ensemble-II. Results: In the entire experimental setup find an ensemble-II generated model is the higher compare to other ensemble-I model. In each experiment observe and compare the value of all the performance of ROC, MAE, RMSE, RAE and RRSE. Stacking (ensemble-I) ensemble model estimate the weights for input with output model by thyroid dataset. After the measurement find out the results ROC=(98.80), MAE= (0.89), 6RMSE=(0.21), RAE= (52.78), RRSE=(83.71)and in the ensemble-II observe thyroid dataset and measure all performance of the model ROC=(98.79), MAE= (0.31), RMSE=(0.05) and RAE= (35.89) and RRSE=(52.67). Finally concluded that (Bagging+ Boosting) ensemble-II model is the best compare to other.

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Section: To Generate An Ensemble Model For Women Thyroid Prediction Umentioning

confidence: 99%

To Generate an Ensemble Model for Women Thyroid Prediction Using Data Mining Techniques

Yadav

Pal

2019

Asian Pac J Cancer Prev

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“…Data cleaning is mainly to solve the inconsistency problem by filling in missing values, smooth noise data, identifying or deleting outliers and other methods. 22 There are three commonly used data cleaning methods, namely binning method, clustering method, and regression method. 23 The Kalman filter method in the regression method used in this article.…”

Section: Big Data Quality Improvementmentioning

confidence: 99%

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

Liu,

Pan,

Cao

et al. 2024

Concurrency and Computation

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SummaryAs the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real‐time (RT) load forecasting. In this article, an edge‐cloud computing analysis energy system is proposed to collect and analyze power load data, and a combination of graph convolutional network (GCN) with LSTM, called KGLSTM is used to achieve mid‐long term mixed sequential mode RT forecasting. In the proposed edge‐cloud framework, distributed intelligent sockets are regarded as edge nodes to collect, analyze and upload data to cloud services for further processing. The proposed KGLSTM network adopts a double branch structure. One branch extracts the data characteristics of mid‐short term time‐series data through an encoding–decoding LSTM module; the other branch extracts the data features of long term timing data through an adapted GCN. GCN is used to extract spatial correlations between different nodes. In addition, by combining a dynamic weighted loss function, the accuracy of peak forecasting is effectively improved. Finally, through various experimental indicators, this article shows that KGLSTM and weighted KGLSTM have achieved significant performance improvement over recent methods in mid‐long term time‐series forecasting and peak forecasting.

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“…Data quality is an essential characteristic that determines data reliability for organizational decisionmaking. Specifically, guaranteeing high-quality, reliable data is a competitive advantage for all industries (Salem & Abdo, 2016).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Dashboard for the Management and Acceptance of Customer Orders

Nascimento

Teixeira

Ribeiro

2022

Business Systems Research Journal

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Background: This paper focuses on activities related to Customer Orders Management withing an auto components plant in the Automotive Industry. The main challenge was highlighted: customers don’t always regard the flexibility rules agreed with the company. Hence, planners must decide if variation in ordered quantity can be accepted in the forecast period or if adjusting is necessary. Objectives: The purpose was not only to streamline the decision-making process in the planning team but also to provide essential tools for the execution of their daily tasks – a visual and interactive dashboard to assess whether variations in customer orders were within the limits agreed with the company. Methods/Approach: Following Lean information management and business intelligence principles, a thorough process analysis was carried out, centralized and standardized reports were created that served as databases, and the dashboard was developed. Results: The proposed tool allowed reductions from 3,5h per week, spent mainly on collecting data, calculating variations, and selecting and adjusting the flexibility limits, to 0,2h a day per planner. Conclusions: Besides streamlining planners’ daily activities, main contributions regard the promotion of digital transformation, data-driven decision-making, and an automated record of customer order variations that could easily be adapted to suppliers.

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Fixing rules for data cleaning based on conditional functional dependency

Cited by 17 publications

References 13 publications

To Generate an Ensemble Model for Women Thyroid Prediction Using Data Mining Techniques

To Generate an Ensemble Model for Women Thyroid Prediction Using Data Mining Techniques

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

Dashboard for the Management and Acceptance of Customer Orders

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