Enhanced Hybrid Data Preprocessing Technique for Eliminating Inconsistencies in the Diabetic Dataset to Improve Mining Results

2018 ◽  
Vol 15 (6) ◽  
pp. 1999-2002
Author(s):  
S Sathya ◽  
A Rajesh
Keyword(s):  
Data Preprocessing ◽  
Hybrid Data ◽  
Preprocessing Technique

scholarly journals Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1931 ◽  
Author(s):  
Yechi Zhang ◽  
Jianzhou Wang ◽  
Haiyan Lu
Keyword(s):  
Power Distribution ◽  
Data Preprocessing ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
Power Stations ◽  
South Wales ◽  
Model Combining ◽  
Forecasting Performance ◽  
Preprocessing Technique

Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model’s effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models.

scholarly journals A Highly Effective Data Preprocessing in Side-Channel Attack Using Empirical Mode Decomposition

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
ShuaiWei Zhang ◽  
XiaoYuan Yang ◽  
Lin Chen ◽  
Weidong Zhong
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Data Preprocessing ◽  
Side Channel ◽  
Intrinsic Mode Functions ◽  
Side Channel Attack ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Highly Effective ◽  
Preprocessing Technique

Side-channel attacks on cryptographic chips in embedded systems have been attracting considerable interest from the field of information security in recent years. Many research studies have contributed to improve the side-channel attack efficiency, in which most of the works assume the noise of the encryption signal has a linear stable Gaussian distribution. However, their performances of noise reduction were moderate. Thus, in this paper, we describe a highly effective data-preprocessing technique for noise reduction based on empirical mode decomposition (EMD) and demonstrate its application for a side-channel attack. EMD is a time-frequency analysis method for nonlinear unstable signal processing, which requires no prior knowledge about the cryptographic chip. During the procedure of data preprocessing, the collected traces will be self-adaptably decomposed into sum of several intrinsic mode functions (IMF) based on their own characteristics. And then, meaningful IMF will be reorganized to reduce its noise and increase the efficiency of key recovering through correlation power analysis attack. This technique decreases the total number of traces for key recovering by 17.7%, compared to traditional attack methods, which is verified by attack efficiency analysis of the SM4 block cipher algorithm on the FPGA power consumption analysis platform.

Discussion on Preprocessing Technique of Geophysical Logging Data

2014 ◽  
Vol 496-500 ◽  
pp. 2178-2181
Author(s):  
Hua Ping Yu ◽  
Huan Wu
Keyword(s):  
Standard Procedure ◽  
Data Preprocessing ◽  
Reservoir Evaluation ◽  
Geophysical Logging ◽  
Correction Technique ◽  
Curve Interpolation ◽  
Interpolation Technique ◽  
Data Pretreatment ◽  
Reservoir Description ◽  
Preprocessing Technique

Logging data preprocessing is the basis of reservoir description and reservoir evaluation with logging data, which is a technique that can reduce the influence generated by the nongeological factors (such as the logging environment, logging instrument precision and calibration, the artificial operation etc.) as much as possible by some mathematic methods. This paper analyzes the regular methods of logging data preprocessing such as well logging curve environment correction technique, logging curve normalization technique and logging curve interpolation technique, and presents standard procedure of logging data pretreatment and application results. The result shows that logging preprocessing will get better result.

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