000			a
999			_c9962 _d9962
003			OSt
005			20191104144907.0
008			191104b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_910304 _aGaurav Raj
245			_aImputing Missing Data Analysis in Web Service Quality Dataset for Improving QoS Prediction
250			_aVol.6(2), May-Aug
260			_aNew Delhi _bSTM Journals _c2019
300			_a8-22p.
520			_aThe web services at present have countless options for similar tasks. This wide range in web services induce challenge to choose the best service among all available. QoS prediction is a key of the selection but it is very time-consuming affair. Any prediction strategy relies on accuracy and completeness of available data, especially in case of QOS Prediction. Feedback, throughput and response time are the major attribute that should not be missed and incorrect. So, it's important to identify the missing value in the web service datasets. Therefore, a study of three missing value prediction approaches was undertaken to investigate their performance for missing values in datasets for web service. Benchmarked WS Dream dataset include response time and throughput matrices of web services is selected to analyze the performance of selected approaches. An extensive experiment is performed, and results are collected, which conclude the superiority of MICE approach over other approaches.
650		0	_94622 _aComputer Engineering
700			_910305 _aMahajan, Manish
773	0		_dNoida STM Journals _tRecent trends in programming languages
856			_uhttp://computers.stmjournals.com/index.php?journal=RTPL&page=article&op=view&path%5B%5D=2278 _yClick here
942			_2ddc _cAR