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Rosenberg S.,CardioDx | Elashoff M.R.,CardioDx | Beineke P.,CardioDx | Daniels S.E.,CardioDx | And 17 more authors.
Annals of Internal Medicine | Year: 2010

Background: Diagnosing obstructive coronary artery disease (CAD) in at-risk patients can be challenging and typically requires both noninvasive imaging methods and coronary angiography, the gold standard. Previous studies have suggested that peripheral blood gene expression can indicate the presence of CAD. Objective: To validate a previously developed 23-gene, expression-based classification test for diagnosis of obstructive CAD in nondiabetic patients. Design: Multicenter prospective trial with blood samples obtained before coronary angiography. (ClinicalTrials.gov registration number: NCT00500617) Setting: 39 centers in the United States. Patients: An independent validation cohort of 526 nondiabetic patients with a clinical indication for coronary angiography. Measurements: Receiver-operating characteristic (ROC) analysis of classifier score measured by real-time polymerase chain reaction, additivity to clinical factors, and reclassification of patient disease likelihood versus disease status defined by quantitative coronary angiography. Obstructive CAD was defined as 50% or greater stenosis in 1 or more major coronary arteries by quantitative coronary angiography. Results: The area under the ROC curve (AUC) was 0.70 ± 0.02 (P < 0.001); the test added to clinical variables (Diamond-Forrester method) (AUC, 0.72 with the test vs. 0.66 without; P = 0.003) and added somewhat to an expanded clinical model (AUC, 0.745 with the test vs. 0.732 without; P = 0.089). The test improved net reclassification over both the Diamond-Forrester method and the expanded clinical model (P < 0.001). At a score threshold that corresponded to a 20% likelihood of obstructive CAD (14.75), the sensitivity and specificity were 85% and 43% (yielding a negative predictive value of 83% and a positive predictive value of 46%), with 33% of patient scores below this threshold. Limitation: Patients with chronic inflammatory disorders, elevated levels of leukocytes or cardiac protein markers, or diabetes were excluded. Conclusion: A noninvasive whole-blood test based on gene expression and demographic characteristics may be useful for assessing obstructive CAD in nondiabetic patients without known CAD. Primary Funding Source: CardioDx. © 2010 American College of Physicians.


Rao N.G.,MCE | Srikanth P.C.,MCE | Sharan P.,TOCE
Optik | Year: 2016

Quantum-dot cellular automata is a promising successor of CMOS technology. QCA proposed by Lent et al. is an emerging technology that offers an innovative approach for computing at nano-scale by monitoring the position of a single electron. This technology allows the implementation of logic devices using quantum dots instead of transistors, diodes. QCA technology has large potential in terms of high space density and power, possible to achieve miniaturization of circuits and high speed processing. The paper provides an efficient design and layout of code converters based on quantum-dot cellular automata using QCADesigner tool. In this paper a number of new results on binary to gray and gray to binary code converters and detailed simulation using QCAD designer tool is presented. We have performed a comparative study of proposed design with recent previous designs and proved that proposed design is efficient in terms of complexity, cell count, area usage and clocking. © 2015 Elsevier GmbH. All rights reserved.


Chandrika J.,MCE | Ananda Kumar K.R.,SJBIT
ICBEIA 2011 - 2011 International Conference on Business, Engineering and Industrial Applications | Year: 2011

Many scenarios, such as network analysis, real time surveillance systems, sensor networks and financial applications generate massive streams of data. These streams consist of millions or billions of updates and must be processed to extract the useful information to enable timely strategic decisions. Mining data streams have many inherent challenges among which the most important challenges are adapting to available resources and assuring quality of the output result. Recent work in the area of data stream mining addresses these two challenges separately. Algorithms in the area of stream mining lack the combination of resource and quality awareness. That means, although they deal with resource adaptation, they do not take quality aspects into consideration. The purpose of this paper is to discuss the importance of resource adaptation and quality awareness with respect to data stream mining and then propose a novel framework that accounts for both quality awareness and resource adaptation. The proposed framework can be generalized for any stream mining technique. © 2011 IEEE.


Chandrika,MCE | Kumar K.R.A.,SJBIT
Communications in Computer and Information Science | Year: 2011

In recent years, advances in hardware technology have facilitated the ability to collect data continuously. Simple transactions of everyday life such as using a credit card, a phone or browsing the web lead to automated data storage thus generating massive streams of data. These streams consist of millions or billions of updates and must be processed to extract the useful information to enable timely strategic decisions. Mining data streams have many inherent challenges among which the most important challenges are adapting to available resources and assuring quality of the output result. The purpose of this paper is to use a novel framework that accounts for both quality awareness and resource adaptation for clustering data streams. © 2011 Springer-Verlag.


Shivakumar G.,MCE | Vijaya P.A.,BNMIT
Lecture Notes in Electrical Engineering | Year: 2013

Developing systems and devices that can recognize, interpret, and process human emotions are an interdisciplinary field involving computer science, psychology, and cognitive science. A system has been developed in order to formally categorize the emotions depending on facial expressions. The feature selection is done based on facial action coding system which is basically a contraction or relaxation of one or more face muscles. Our goal is to categorize the facial expression using image into six basic emotional states: Happy, Sad, Anger, Fear, Disgust, and Surprise. Extraction of facial features from eye, mouth, eyebrow, and nose is performed by employing an iterative search algorithm, on the edge information of the localized face region in binary scale. Finally, emotion class assignment is done by applying the extracted blocks as inputs to a feed-forward neural network trained by back-propagation algorithm. © 2013 Springer.

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