YMCA University of Science and Technology

About: YMCA University of Science and Technology is a education organization based out in Faridabad, India. It is known for research contribution in the topics: Web page & Web crawler. The organization has 299 authors who have published 568 publications receiving 4547 citations.

A fuzzy multi attribute decision making approach for evaluating effectiveness of advanced manufacturing technology – in Indian context

Abstract: Globalisation has increased opportunities for the manufacturers, as it has increased the customer size but at the same time it has brought competition. Customers are enjoying the variety of products at the minimum cost. Because of this, manufacturers are striving to improve their flexibility, quality of product, delivery time, etc. Manufacturers are adopting advanced manufacturing technologies (AMT), so as to meet these requirements. Adoption of (AMT) is a colossal investment and decision should be taken with proper evaluation. From the literature survey, it is proved that traditional financial methods for evaluating the effectiveness of AMT are not enough as it also enhances many intangible factors like flexibility, quality, employees’ satisfaction etc. Therefore, in this paper an endeavour has been made to develop a model for the evaluation of AMT investments by using fuzzy graph theoretic approach (FGTA). FGTA quantifies the intangible factors and based upon these factors gives a single numerical index which is useful for managers to evaluate the effectiveness of AMT.

Selection of cutting-fluids using a novel, decision-making method: preference selection index method

Abstract: This paper presents a simple and systematic multi-criteria decision making methodology to select a cutting fluid for the given application using the preference selection index (PSI) method. In this methodology, cutting fluid is selected for a given machining application without considering relative importance between the selection attributes. Two real time examples are cited from the literature in order to demonstrate and validate the applicability and potentiality of PSI method in solving the cutting-fluid selection problem. It is observed that the relative rankings of the alternative cutting fluid as obtained using PSI method match quite well with those as derived by the past researchers.

Leveraging machine learning for optimize predictive classification and scheduling E-Health traffic

Abstract: Wireless Body Area Network (WBAN) is a special kind of autonomous sensor network evolved to provide wide variety of services. Nowadays WABN becomes an integral component of healthcare management system where a patient needs to be monitors both inside and outside home or hospital. These applications are responsible for gathering and managing heterogeneous data in terms of both for real time and non-real time traffic. Heterogeneous traffic classification plays an important role in various application of WBAN. Due to the ineffectiveness of traditional port-based and payload-based methods, recent work were proposed using machine learning methods to classify flows based on statistical characteristics. In this paper, we evaluate the effectiveness of integral concept of machine learning in terms of binary decision tree and genetic algorithm for classification of heterogeneous traffic flow according to rules. We have also designed an Earliest Deadline based flexible dynamic scheduling algorithm, which has been proven to be an optimal prioritized scheduling for problem like starvation.

Framework for Distributed Semantic Web Crawler

Abstract: Relevant information retrieval from the www mainly depends on the technique and efficiency of a crawler. So crawlers must be capable enough to understand the text and context of a link which they are going to crawl. Anchor text contains a very useful information to know about the target web page. Because knowledge about the target web page content helps the crawlers to decide their preferences of crawling the particular page. In this paper we have presented a design of distributed semantic web crawler capable of crawling both HTML and semantic web pages written using owl/RDf. In our crawler a component called page analyser is used to understand the theme of content of page and context of anchor tag in the page. The output of the page analyser is used to make crawling decisions. Our approach have revealed the great improvement in extracting the information from the links and guide the crawler for more relevant domain specific crawling.

Evaluating key performance indicators of leagile manufacturing using fuzzy TISM approach

Abstract: Leagile manufacturing strategy has emerged as one of the important strategy adopted by most of manufacturing organizations now a days. It has advantages of both lean as well as agile manufacturing system. Lean manufacturing tries to eliminate all different types of wastages like overproduction, inventory, unnecessary motion etc., while agile manufacturing focus on changing the production system as per the requirements of the customer and provide customized products within short span of time. Lean manufacturing focuses on no inventory and try to implement Just in Time methodology but for the system to be agile, there should be at least some inventory in store so that production can be started as soon as customer order is achieved. In this paper, key performance indicators (KPI) of leagile manufacturing are found by literature review and in consultation of experts and academicians working in the concerned field. Fuzzy TISM approach has been applied to find levels of different KPI’S. MICMAC analysis has been made to analyze the KPI’S and categorize them as autonomous, dependent, linkage, independent etc. on the basis of driving and dependence power. Finally, digraph is drawn to show relationship between various KPI’s.