Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Computer science & Cloud computing. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Nanocrystalline K–CaO for the transesterification of a variety of feedstocks: Structure, kinetics and catalytic properties

Abstract: The work presented in current manuscript demonstrated the preparation of potassium ion impregnated calcium oxide in nano particle form and its application as solid catalyst for the transesterification of a variety of triglycerides. The catalyst was characterized by powder X-ray diffraction, scanning electron and transmission electron microscopic, BET surface area measurement, and Hammett indicator studies in order to establish the effect of K+ impregnation on catalyst structure, particle size, surface morphology, and basic strength. The catalyst prepared by impregnating a mass fraction of 3.5% K+ in CaO was found to exist as similar to 40 nm sized particles, and same was employed in present study as solid catalyst for the transesterification of a variety of feedstocks viz., mutton fat, soybean, virgin cotton seed, waste cotton seed, castor, karanja and jatropha oil. Under optimized conditions, K-CaO was found to yield 98 +/- 2% fatty acid methyl esters (FAMEs) from the employed feedstocks, and showed a high tolerance to the free fatty acid and moisture contents. A pseudo first order kinetic model was applied to evaluate the kinetic parameters and under optimized conditions first order rate constant and activation energy was found to be 0.062 mm(-1) and 54 kJ mol(-1), respectively. The Koros-Nowak criterion test has been employed to demonstrate that measured catalytic activity was independent of the influence of transport phenomenon. Finally, few physicochemical properties of the FAMEs prepared from waste cotton seed oil, karanj a oil and jatropha oils have been studied and compared with European standards. (C) 2012 Elsevier Ltd. All rights reserved.

A package-SFERCB-Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors

Abstract: An interactive computer aided dignostic (CAD) system for assisting inexperience young radiologists is developed. The difficulty in brain tumors classification is due to similar size, shape, location, hetrogeniety, presence of oedema, cystic and isointense regions has been the key feature of this research. Genetic Algorithm is employed as it is an easy concept and is well understood by radiologists without going in much depth of engineering.Display Omitted Brain tumors as segmented regions of interests (SROIs) by content based active contour model (CBAC).Feature extraction-intensity and texture based features.Feature reduction by Genetic Algorithm.Classification by Hybrid Models-GA-SVM and GA-ANN. The objective of this experimentation is to develop an interactive CAD system for assisting radiologists in multiclass brain tumor classification. The study is performed on a diversified dataset of 428 post contrast T1-weighted MR images of 55 patients and publically available dataset of 260 post contrast T1-weighted MR images of 10 patients. The first dataset includes primary brain tumors such as Astrocytoma (AS), Glioblastoma Multiforme (GBM), childhood tumor-Medulloblastoma (MED) and Meningioma (MEN), along with secondary tumor-Metastatic (MET). The second dataset consists of Astrocytoma (AS), Low Grade Glioma (LGL) and Meningioma (MEN). The tumor regions are marked by content based active contour (CBAC) model. The regions are than saved as segmented regions of interest (SROIs). 71 intensity and texture feature set is extracted from these SROIs. The features are specifically selected based on the pathological details of brain tumors provided by the radiologist. Genetic Algorithm (GA) selects the set of optimal features from this input set. Two hybrid machine learning models are implemented using GA with support vector machine (SVM) and artificial neural network (ANN) (GA-SVM and GA-ANN) and are tested on two different datasets. GA-SVM is proposed for finding preliminary probability in identifying tumor class and GA-ANN is used for confirmation of accuracy. Test results of the first dataset show that the GA optimization technique has enhanced the overall accuracy of SVM from 79.3% to 91.7% and of ANN from 75.6% to 94.9%. Individual class accuracies delivered by GA-SVM are: AS-89.8%, GBM-83.3%, MED-95.6%, MEN-91.8%, and MET-97.1%. Individual class accuracies delivered by GA-ANN classifier are: AS-96.6%, GBM-86.6%, MED-93.3%, MEN-96%, MET-100%. Similar results are obtained for the second dataset. The overall accuracy of SVM has increased from 80.8% to 89% and that of ANN has increased from 77.5% to 94.1%. Individual class accuracies delivered by GA-SVM are: AS-85.3%, LGL-88.8%, MEN-93%. Individual class accuracies delivered by GA-ANN classifier are: AS-92.6%, LGL-94.4%, MED-95.3%. It is observed from the experiments that GA-ANN classifier has provided better results than GA-SVM. Further, it is observed that along with providing finer results, GA-SVM provides advantage in speed whereas GA-ANN provides advantage in accuracy. The combined results from both the classifiers will benefit the radiologists in forming a better decision for classifying brain tumors.

Generalized intuitionistic fuzzy multiplicative interactive geometric operators and their application to multiple criteria decision making

Abstract: In this paper, group decision making methods based on intuitionistic fuzzy multiplicative preference relations has been developed. For it, firstly some new operational laws on intuitionistic multiplicative numbers have been defined and then by using these operations some new intuitionistic fuzzy multiplicative interactive weighted geometric, intuitionistic fuzzy multiplicative interactive ordered weighted geometric and intuitionistic fuzzy multiplicative interactive hybrid weighted geometric operators have been developed. Some desirable properties of these operators, such as idempotency, boundedness, monotonicity etc., are studied in the paper. The major advantage of the proposed operators as compared to existing ones are that it consider the proper interaction between the membership and non-membership functions and proposed operators are more pessimistic than existing ones. Furthermore, these operators are applied to decision making problems in which experts provide theory preference relation by intuitionistic fuzzy multiplicative intuitionistic fuzzy environment to show the validity, practicality and effectiveness of the new approach. Finally, a systematic comparison between the existing work and the proposed work has been given.

Longitudinal Guided Waves for Monitoring Chloride Corrosion in Reinforcing Bars in Concrete

Abstract: >> Corrosion of reinforcing steel in concrete is one of the major durability problems faced by civil engineers as they maintain an aging infrastructure. The problem accelerates since steel is embedded inside concrete. If it remains unnoticed inside concrete, it further accelerates and can cause loss of life and property. This article discusses a nonintrusive corrosion monitoring technique for early detection of damages in steel embedded in concrete. Corrosion manifests itself in debond and pitting steel bars. Guided ultrasonic waves offer a potentially attractive solution for this problem. But it is imperative to excite the right mode for detection of a particular type of corrosion. In the present work, longitudinal guided ultrasonic waves have been utilized to monitor notch and debond defects in steel bars in concrete simulating pitting and delamination phenomena caused by corrosion. Two ultrasonic techniques of pulse transmission and pulse echo were used to monitor the healthy and damaged specimens. The developed methodology is successfully applied for real time monitoring of RC beam specimens undergoing accelerated chloride corrosion. The ultrasonic signals effectively relate to the state of reinforcing bars.