Browsing by Author "Sharath, S."

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Experimental investigations on performance of concrete incorporating precious slag balls (PS Balls) as fine aggregates
(Techno Press technop2@chollian.net, 2019) Sharath, S.; Gayana, G.B.; Reddy, K.R.; Ram Chandar, R.C.
Substitution of natural fine aggregates with industrial by-products like precious slag balls (PS Balls) offers various advantages like technical, economic and environmental which are very important in the present era of sustainability in construction industry. PS balls are manufactured by subjecting steel slag to slag atomizing Technology (SAT) which imparts them the desirable characteristics of fine aggregates. The main objective of this research paper is to assess the feasibility of producing good quality concrete by using PS balls, to identify the potential benefits by their incorporation and to provide solution for increasing their utilization in concrete applications. The study investigates the effect of PS balls as partial replacement of fine aggregates in various percentages (20%, 40%, 60%, 80% and 100%) on mechanical properties of concrete such as compressive strength, splitting tensile strength, and flexural strength. The optimum mix was found to be at 40% replacement of PS balls with maximum strength of 62.89 MPa at 28 days curing. Permeability of concrete was performed and it resulted in a more durable concrete with replacement of PS balls at 40% and 100% as fine aggregates. These two specific values were considered as optimum replacement is 40% and also the maximum possible replacement is 100%. Scanning electron microscope (SEM) analysis was done and it was found that the PS balls in concrete were unaffected and with optimum percentage of PS balls as fine aggregates in concrete resulted in good strength and less cracks. Hence, it is possible to produce good workable concrete with low water to cement ratio and higher strength concrete by incorporating PS balls. © 2019 Techno-Press, Ltd.
Memetic NSGA - A multi-objective genetic algorithm for classification of microarray data
(2007) Kumar, K, P.; Sharath, S.; D'Souza, G, R.; Sekaran, K.C.
In Gene Expression studies, the identification of gene subsets responsible for classifying available samples to two or more classes is an important task. One major difficulty in identifying these gene subsets is the availability of only a few samples compared to the number of genes in the samples. Here we treat this problem as a Multi-objective optimization problem of minimizing the gene subset size and minimizing the number ofmisclassified samples. We present a new elitist Non-dominated Sorting-based Genetic Algorithm (NSGA) called Memetic-NSGA which uses the concept of Memes. Memes are a group of genes which have a particular functionality at the phenotype level. We have chosen a 50 gene Leukemia dataset to evaluate our algorithm. A comparative study between Memetic-NSGA and another Non-dominated Sorting Genetic Algorithm, called NSGA-II, is presented. Memetic-NSGA is found to perform better in terms of execution time and gene-subset length identified. � 2007 IEEE.
Memetic NSGA - A multi-objective genetic algorithm for classification of microarray data
(Institute of Electrical and Electronics Engineers Inc., 2007) Kumar K, P.; Sharath, S.; D'Souza G, R.; Chandra Sekaran, K.C.
In Gene Expression studies, the identification of gene subsets responsible for classifying available samples to two or more classes is an important task. One major difficulty in identifying these gene subsets is the availability of only a few samples compared to the number of genes in the samples. Here we treat this problem as a Multi-objective optimization problem of minimizing the gene subset size and minimizing the number ofmisclassified samples. We present a new elitist Non-dominated Sorting-based Genetic Algorithm (NSGA) called Memetic-NSGA which uses the concept of Memes. Memes are a group of genes which have a particular functionality at the phenotype level. We have chosen a 50 gene Leukemia dataset to evaluate our algorithm. A comparative study between Memetic-NSGA and another Non-dominated Sorting Genetic Algorithm, called NSGA-II, is presented. Memetic-NSGA is found to perform better in terms of execution time and gene-subset length identified. Â© 2007 IEEE.