Browsing by Author "Prasad, Om Kumar"

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A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion
(2016-12-01)
Singh, Jagendra
;
Prasad, Mukesh
;
Prasad, Om Kumar
;
Joo, Er Meng
;
Saxena, Amit Kumar
In this paper, a novel fuzzy logic-based expansion approach considering the relevance score produced by different rank aggregation approaches is proposed. It is well known that different rank aggregation approaches yield different relevance scores for each term. The proposed fuzzy logic approach combines different weights of each term by using fuzzy rules to infer the weights of the additional query terms. Experimental results demonstrate that the proposed approach achieves significant improvement over individual expansion, aggregated and other related state-of-the-arts methods.
A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion
(2016-12)
Singh, Jagendra
;
Prasad, Mukesh
;
Prasad, Om Kumar
;
Joo, Er Meng
;
Saxena, Amit Kumar
In this paper, a novel fuzzy logic-based expansion approach considering the relevance score produced by different rank aggregation approaches is proposed. It is well known that different rank aggregation approaches yield different relevance scores for each term. The proposed fuzzy logic approach combines different weights of each term by using fuzzy rules to infer the weights of the additional query terms. Experimental results demonstrate that the proposed approach achieves significant improvement over individual expansion, aggregated and other related state-of-the-arts methods.
Impacts of Co doping on ZnO transparent switching memory device characteristics
(2016-05-02)
Simanjuntak, Firman Mangasa
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Prasad, Om Kumar
;
Panda, Debashis
;
Lin, Chun-An
;
Tsai, Tsung-Ling
The resistive switching characteristics of indium tin oxide (ITO)/Zn1-xCoxO/ITO transparent resistive memory devices were investigated. An appropriate amount of cobalt dopant in ZnO resistive layer demonstrated sufficient memory window and switching stability. In contrast, pure ZnO devices demonstrated a poor memory window, and using an excessive dopant concentration led to switching instability. To achieve suitable memory performance, relying only on controlling defect concentrations is insufficient; the grain growth orientation of the resistive layer must also be considered. Stable endurance with an ON/OFF ratio of more than one order of magnitude during 5000 cycles confirmed that the Co-doped ZnO device is a suitable candidate for resistive random access memory application. Additionally, fully transparent devices with a high transmittance of up to 90% at wavelength of 550 nm have been fabricated. Published by AIP Publishing.
Novel Data Knowledge Representation with TSK-type Preprocessed Collaborative Fuzzy Rule based System
(2015)
Prasad, Mukesh
;
Er, Meng Joo
;
Lin, Chin-Teng
;
Prasad, Om Kumar
;
Mohanty, Manoranjan
A novel data knowledge representation with the combination of structure learning ability of preprocessed collaborative fuzzy clustering and fuzzy expert knowledge of Takagi-Sugeno-Kang type model is presented in this paper. The proposed method divides a huge dataset into two or more subsets of dataset. The subsets of dataset interact with each other through a collaborative mechanism in order to find some similar properties within each-other. The proposed method is useful in dealing with big data issues since it divides a huge dataset into subsets of dataset and finds common features among the subsets. The salient feature of the proposed method is that it uses a small subset of dataset and some common features instead of using the entire dataset and all the features. Before interactions among subsets of the dataset, the proposed method applies a mapping technique for granules of data and centroid of clusters. The proposed method uses information of only halve or less/more than the halve of the data patterns for the training process, and it provides an accurate and robust model, whereas the other existing methods use the entire information of the data patterns. Simulation results show that proposed method performs better than existing methods on some benchmark problems.