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Ishaq Adeyanju Raji
Researcher at King Fahd University of Petroleum and Minerals
Publications - 8
Citations - 79
Ishaq Adeyanju Raji is an academic researcher from King Fahd University of Petroleum and Minerals. The author has contributed to research in topics: Control chart & Chart. The author has an hindex of 4, co-authored 7 publications receiving 38 citations. Previous affiliations of Ishaq Adeyanju Raji include Universiti Teknologi Malaysia.
Papers
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Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach
TL;DR: This study underscores the better predictive performance and successful application of the least square support vector machine (LSSVM), a machine learning model for predicting the compressive strength of ternary-blend concrete.
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On designing a robust double exponentially weighted moving average control chart for process monitoring
TL;DR: This investigation was motivated in exploring the scheme with some robust statistic, as the mean estimator performs woefully, and pronounced the trimean estimator to be the best of all the five estimators used, including the mean.
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On Designing Mixed EWMA Dual-CUSUM Chart With Applications in Petro-Chemical Industry
TL;DR: This paper proposes a new charting scheme called mixed exponentially weighted moving average (EWMA) dual-cumulative sum (CUSUM) to monitor the location parameter to enhance the sensitivity of the control scheme used by analyzers in the petro-chemical industry.
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Robust dual-CUSUM control charts for contaminated processes
TL;DR: This article improves the efficiency of the Dual CUSUM chart by focusing on its robustness, ability to resist some disturbances in the process environment and violation of basic assumptions by proposing some robust estimators for constructing the chart for both contaminated and uncontaminated environments.
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Outliers Detection Models in Shewhart Control Charts; an Application in Photolithography: A Semiconductor Manufacturing Industry
TL;DR: This study proposes models based on Tukey and median absolute deviation outlier detectors for detecting the errors in phase-I and implements the findings in the semiconductor manufacturing industry, where a real dataset is extracted from a photolithography process.