41 Papers
228 Citations
S. Nickolas is an academic researcher from National Institute of Technology, Tiruchirappalli. The author has contributed to research in topics: Computer science & Job shop scheduling. The author has an hindex of 10, co-authored 38 publications.
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Papers
A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems
TL;DR: A hybrid discrete firefly algorithm HDFA is proposed to solve the multi-objective flexible job shop scheduling problem FJSP and is combined with local search LS method to enhance the searching accuracy and information sharing among fireflies.
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A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problem with limited resource constraints
TL;DR: In this article, a hybrid discrete firefly algorithm is presented to solve the multi-objective flexible job shop scheduling problem with limited resource constraints, where three minimisation objectives are simultaneously considered: the maximum completion time, the workload of the critical machine and the total workload of all machines.
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Feature Selection Using Decision Tree Induction in Class level Metrics Dataset for Software Defect Predictions
N. Gayatri,S. Nickolas,A. V. Reddy +2 more
- 01 Jan 2010
TL;DR: A new method for feature selection based on Decision Tree Induction is proposed which has the advantage of easy interpretability and comprehensibility and outperforms the other two feature selection techniques.
Fetal Health State Monitoring Using Decision Tree Classifier from Cardiotocography Measurements
M. Ramla,S. Sangeetha,S. Nickolas +2 more
- 14 Jun 2018
TL;DR: An efficient method to predict the high-risk pregnancy based on the fetal health status using CART is proposed and was quantified using precision, recall and F-score.
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Performance Analysis of Datamining Algorithms for Software Quality Prediction
N. Gayatri,S. Nickolas,A. V. Reddy,R. Chitra +3 more
- 27 Oct 2009
TL;DR: Experimental results using KC2 NASA software metrics dataset demonstrates that decision trees are much useful for fault predictions and based on rules generated only some measurement attributes in the given set of the metrics play an important role in establishing final rules and for improving the software quality by giving correct predictions.
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