Sri Ramakrishna Engineering College

About: Sri Ramakrishna Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Control theory. The organization has 1030 authors who have published 843 publications receiving 3822 citations.

Simulation-based ATPG for low power testing of crosstalk delay faults in asynchronous circuits

Abstract: A new multi-objective genetic algorithm has been proposed for testing crosstalk delay faults in asynchronous sequential circuits that reduces average power dissipation during test application. The proposed Elitist Non-dominated Sorting Genetic Algorithm ENGA-based Automatic Test Pattern Generation ATPG for crosstalk induced delay faults generates test pattern set that has high fault coverage and low switching activity. Redundancy is introduced in ENGA-based ATPG by modifying the fault dropping phase and hence a very good reduction in transition activity is achieved. Tests are generated for several asynchronous SIS benchmark circuits. Experimental results demonstrate that ENGA gives higher fault coverage, reduced transitions and compact test vectors for most of the asynchronous benchmark circuits when compared with those generated by Weighted Sum Genetic Algorithm WSGA.

A comparative analysis of feature selection algorithms on classification of gene microarray dataset

Abstract: Analysis of gene expression is important in many fields of biological research in order to retrieve the required information. As the time advances, the illness in general and cancer in particular have become more and more complex and complicated, in detecting, analyzing and curing. Cancer research is one of the major research areas in the medical field. Accurate prediction of different tumor types has great value in providing better treatment and toxicity minimization on the patients. To minimize it, the data mining algorithms are important tool and the most extensively used approach to classify gene expression data and plays an important role for cancer classification. One of the major challenges is to discover how to extract useful information from datasets. This research is based on recent advances in the machine learning based microarray gene expression data analysis with three feature selection algorithms.