Nitte Meenakshi Institute of Technology

About: Nitte Meenakshi Institute of Technology is a based out in . It is known for research contribution in the topics: Computer science & Ultimate tensile strength. The organization has 846 authors who have published 644 publications receiving 2702 citations.

KMMRAE structure and Sawada lines of Co, Ni, Cu, Zn and Ga

Abstract: Kβ spectrum of Co, Ni, Cu, Zn and Ga generated by photoexcitation is studied using a WDXRF spectrometer. KMM RAE structure is de-convoluted into six components. The energy shift of the peaks of these components relative to the diagram line is determined and compared with various theoretical estimates available. The integrated relative intensity of KMM structure is measured. The energy and relative intensity of Sawada lines and Kβ5 peaks are also measured.

Fault Tolerant Cloud Software Systems Using Software Configurations

Abstract: Cloud computing software systems are customizable using various configurations based on users needs. Performance of such software systems vary depending on selected configurations and interaction among these configurations. Most of the users of a software system, that consists of several hundreds of configurations, use only very few configurations that are common. Failure of such important or common configurations of any software system brings down its performance severely. However we can improve the performance, reliability and fault tolerance of cloud computing software systems by identifying the important or commonly used configurations and enabling them with suitable fault tolerant schemes. In this research paper we propose a technique to identify very important and frequently used configurations that play vital role in software systems then provide them fault tolerance to improve performance and fault tolerance of configurable cloud software systems.

WNPWR: Web navigation prediction framework for webpage recommendation

Abstract: Huge amount of user request data is generated in web-log. Predicting users' future requests based on previously visited pages is important for web page recommendation, reduction of latency, on-line advertising etc. These applications compromise with prediction accuracy and modelling complexity. we propose a Web Navigation Prediction Framework for webpage Recommendation(WNPWR) which creates and generates a classifier based on sessions as training examples. As sessions are used as training examples, they are created by calculating average time on visiting web pages rather than traditional method which uses 30 minutes as default timeout. This paper uses standard benchmark datasets to analyze and compare our framework with two-tier prediction framework. Simulation results shows that our generated classifier framework WNPWR outperforms two-tier prediction framework in prediction accuracy and time.

Failure Analysis of Additive Manufactured Fiber-Reinforced Thermoplastics

Abstract: In the present work, a comprehensive study is undertaken to analyze the mechanical strength and failure of different polymers reinforced with adaptable fibers. For the purpose of analyzing tensile strength, specimens as per ASTM standards with different permutations and combinations of polymers with fibers were additively manufactured and tested. Three different sets of samples, namely ABS (acrylonitrile butadiene styrene), PLA (polylactic acid) and onyx in combination with other synthetic fibers such as carbon and Kevlar, were used. These materials, i.e., ABS, PLA and onyx in combination with carbon fiber, have generated a healthy response to tensile loading and exhibited much higher peak load with larger plastic phase. Also, these additive manufactured fiber-reinforced thermoplastics’ failure was analyzed using von Mises criterion and is found to be within the recommended limit. These reinforced thermoplastics can be used for manufacturing of tiny, intricate and complex parts for medical applications.

Prediction of Diabetes Mellitus Type-2 Using Machine Learning

Abstract: Around 400 million people suffer from diabetes around the world. Diabetes prediction is challenging as it involves complex interactions or interdependencies between various human organs like eye, kidney, heart, etc. The machine learning (ML) algorithms provide an efficient way of predicting the diabetes. The objective of this work is to build a system using ML techniques for the accurate forecast of diabetes in a patient. The decision tree (DT) algorithms are well suited for this. In this work, we have applied the DT algorithm to forecast type 2 diabetes mellitus (T2DM). Extensive experiments were performed on the Pima Indian Diabetes Dataset (PIDD) obtained from the UCI machine learning repository. Based on the results, we observed that the decision tree was able to forecast accurately when compared to the SVM algorithm on the diabetes data.