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.

Fault tolerant software systems using software configurations for cloud computing

Abstract: Customizable software systems consist of a large number of different, critical, non-critical and interdependent configurations Reliability and performance of configurable system depend on successful completion of communication or interactions among its configurations Most of the time users of configurable systems very often use critical configurations than non-critical configurations Failure of critical configurations will have severe impact on system reliability and performance We can overcome this problem by identifying critical configurations that play a vital role, then provide a suitable fault tolerant candidate to each critical configuration In this article we have proposed an algorithm that identifies optimal fault tolerant candidate for every critical configuration of a software system We have also proposed two schemes to classify configurations into critical and non-critical configurations based on: 1) Frequency of configuration interactions (IFrFT), 2) Characteristics and frequency of interactions (ChIFrFT) These schemes have played very important role in achieving reliability and fault tolerance of a software system in a cost effective manner The percentage of successful interactions of IFrFT and ChIFrFT are 25 and 40% higher than that of the NoFT scheme In NoFT scheme none of the configurations are supported by fault tolerance candidates Performance of IFrFT, ChIFrFT, and NoFT schemes are tested using a file structure system

Analysis and Detection of Diabetes Using Data Mining Techniques—A Big Data Application in Health Care

Abstract: In digitized world, data is growing exponentially and Big Data Analytics is an emerging trend and a dominant research field. Data mining techniques play an energetic role in the application of Big Data in healthcare sector. Data mining algorithms give an exposure to analyse, detect and predict the presence of disease and help doctors in decision-making by early detection and right management. The main objective of data mining techniques in healthcare systems is to design an automated tool which diagnoses the medical data and intimates the patients and doctors about the intensity of the disease and the type of treatment to be best practiced based on the symptoms, patient record and treatment history. This paper emphasises on diabetes medical data where classification and clustering algorithms are implemented and the efficiency of the same is examined.

Evolution of novel activation functions in neural network training for astronomy data: habitability classification of exoplanets.

Abstract: Quantification of habitability is a complex task. Previous attempts at measuring habitability are well documented. Classification of exoplanets, on the other hand, is a different approach and depends on quality of training data available in habitable exoplanet catalogs. Classification is the task of predicting labels of newly discovered planets based on available class labels in the catalog. We present analytical exploration of novel activation functions as consequence of integration of several ideas leading to implementation and subsequent use in habitability classification of exoplanets. Neural networks, although a powerful engine in supervised methods, often require expensive tuning efforts for optimized performance. Habitability classes are hard to discriminate, especially when attributes used as hard markers of separation are removed from the data set. The solution is approached from the point of investigating analytical properties of the proposed activation functions. The theory of ordinary differential equations and fixed point are exploited to justify the “lack of tuning efforts” to achieve optimal performance compared to traditional activation functions. Additionally, the relationship between the proposed activation functions and the more popular ones is established through extensive analytical and empirical evidence. Finally, the activation functions have been implemented in plain vanilla feed-forward neural network to classify exoplanets. The mathematical exercise supplements the grand idea of classifying exoplanets, computing habitability scores/indices and automatic grouping of the exoplanets converging at some level.