An Application of Intelligent Data Analysis Techniques to a Large Software Engineering Dataset

2009 ◽  
pp. 261-272 ◽  
Author(s):  
James Cain ◽  
Steve Counsell ◽  
Stephen Swift ◽  
Allan Tucker
Keyword(s):  
Data Analysis ◽  
Software Engineering ◽  
Intelligent Data Analysis ◽  
Analysis Techniques ◽  
Large Software

Intelligent Data Analysis Techniques

2019 ◽  
pp. 129-196
Author(s):  
Arvind Kumar Bansal ◽  
Javed Iqbal Khan ◽  
S. Kaisar Alam
Keyword(s):  
Data Analysis ◽  
Intelligent Data Analysis ◽  
Analysis Techniques

Large-Scale Renewable Energy Monitoring and Forecast Based on Intelligent Data Analysis

2021 ◽  
pp. 645-669
Author(s):  
Mehmet Baris Ozkan ◽  
Dilek Küçük ◽  
Serkan Buhan ◽  
Turan Demirci ◽  
Pinar Karagoz
Keyword(s):  
Renewable Energy ◽  
Data Analysis ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Intelligent Data Analysis ◽  
Statistical Machine Learning ◽  
Energy Monitoring ◽  
Analysis Techniques ◽  
Energy Informatics ◽  
Energy Domain

Intelligent data analysis techniques such as data mining or statistical/machine learning algorithms are applied to diverse domains, including energy informatics. These techniques have been successfully employed in order to solve different problems within the energy domain, particularly forecasting problems such as renewable energy and energy consumption forecasts. This chapter elaborates the use of intelligent data analysis techniques for the facilitation of renewable energy monitoring and forecast. First, a review of the literature is presented on systems and forecasting approaches applied to the renewable energy domain. Next, a generic and large-scale renewable energy monitoring and forecast system based on intelligent data analysis is described. Finally, a genuine implementation of this system for wind energy is presented as a case study, together with its performance analysis results. This chapter stands as a significant reference for renewable energy informatics, considering the provided conceptual and applied system descriptions, heavily based on smart computing techniques.

Large-Scale Renewable Energy Monitoring and Forecast Based on Intelligent Data Analysis

2020 ◽  
pp. 53-77 ◽  
Author(s):  
Mehmet Baris Ozkan ◽  
Dilek Küçük ◽  
Serkan Buhan ◽  
Turan Demirci ◽  
Pinar Karagoz
Keyword(s):  
Renewable Energy ◽  
Data Analysis ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Intelligent Data Analysis ◽  
Statistical Machine Learning ◽  
Energy Monitoring ◽  
Analysis Techniques ◽  
Energy Informatics ◽  
Energy Domain

Intelligent data analysis techniques such as data mining or statistical/machine learning algorithms are applied to diverse domains, including energy informatics. These techniques have been successfully employed in order to solve different problems within the energy domain, particularly forecasting problems such as renewable energy and energy consumption forecasts. This chapter elaborates the use of intelligent data analysis techniques for the facilitation of renewable energy monitoring and forecast. First, a review of the literature is presented on systems and forecasting approaches applied to the renewable energy domain. Next, a generic and large-scale renewable energy monitoring and forecast system based on intelligent data analysis is described. Finally, a genuine implementation of this system for wind energy is presented as a case study, together with its performance analysis results. This chapter stands as a significant reference for renewable energy informatics, considering the provided conceptual and applied system descriptions, heavily based on smart computing techniques.

PENGARUH PERBAIKAN BERKESINAMBUNGAN TERHADAP KINERJA KARYAWAN (STUDI KASUS DI PT.RENTANG BUANA NIAGAMAKMUR TASIKMALAYA)

2019 ◽  
Vol 3 (1) ◽  
pp. 31-41
Author(s):  
Sri Sudiarti
Keyword(s):  
Data Analysis ◽  
Continuous Improvement ◽  
Research Method ◽  
Employee Performance ◽  
Sampling Technique ◽  
Positive Influence ◽  
Performance Criteria ◽  
Coefficient Of Determination ◽  
Analysis Techniques ◽  
Simple Regression

The objectives of this research are to know and to analyze about the effect of Continuous Improvement on the performance of employees at PT. Rentang Buana Niagamakmur (PT.RBN) Tasikmalaya. Research method which applied in this research is survey research method, while data collecting technique is done by through questionaire. Sampling technique applies sample is accidental sampling technique and the size sample is 55 respondents. Data analysis techniques used in the study is simple regression technique, analysis of the coefficient of determination  and t test. The results showed that the Continuous Improvement  including both criteria, including employee performance criteria, as well as Continuous Improvement  has a positive influence on employee performance of 76,4% in PT . Rentang Buana Niagamakmur (PT.RBN) Tasikmalaya.

INDONESIA LANGUAGE INTERFERENCE FORM IN BASIC PPL LEARNING THAILAND UNHASY COLLEGE SYUDENTS

2020 ◽  
Vol 8 (2) ◽  
pp. 38
Author(s):  
Siti Mariana Ulfa
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Data Collection ◽  
Teaching Practice ◽  
Data Presentation ◽  
Analysis Techniques ◽  
Thai Students ◽  
The Impact ◽  
Collection Methods ◽  
Recording Techniques

AbstractHumans on earth need social interaction with others. Humans can use more than one language in communication. Thus, the impact that arises when the use of one or more languages is the contact between languages. One obvious form of contact between languages is interference. Interference can occur at all levels of life. As in this study, namely Indonesian Language Interference in Learning PPL Basic Thailand Unhasy Students. This study contains the form of interference that occurs in Thai students who are conducting teaching practices in the classroom. This type of research is descriptive qualitative research that seeks to describe any interference that occurs in the speech of Thai students when teaching practice. Data collection methods in this study are (1) observation techniques, (2) audio-visual recording techniques using CCTV and (3) recording techniques, by recording all data that has been obtained. Whereas the data wetness uses, (1) data triangulation, (2) improvement in perseverance and (3) peer review through discussion. Data analysis techniques in this study are (1) data collection, (2) data reduction, (3) data presentation and (4) conclusions. It can be seen that the interference that occurs includes (1) interference in phonological systems, (2) interference in morphological systems and (3) interference in syntactic systems. 

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