Srimani P.K.,Bangalore University |
Patil M.M.,JSSATE |
Patil M.M.,Bhartiyaar University
Advances in Intelligent Systems and Computing | Year: 2014
Mining educational data is an emerging interdisciplinary research area that mainly deals with the development of methods to explore the data stored in educational institutions which is referred to as Edu-Data. Data mining is concerned with the analysis of data for finding patterns which are previously unknown and are presently useful for future analysis. The technique of mining Edu-data is referred to as Edu-mining. On the other hand statistics is a mathematical science concerned with the collection, analysis, interpretation or explanation, and presentation of data which plays a very important role in the process of data mining. The paper aims at developing a simple linear regression model for Edu-data using the statistical approach. The results obtained helps the management to predict the semester results and also helps in proper decision making processes in Technical Education System. It is also found that the predictions were almost nearing to the actual values. The present work is first of its kind in literature. © Springer International Publishing Switzerland 2014.
Sinha S.,JSSATE |
Mehfiiz S.,Jamia Millia Islamia University |
Urooj S.,Gautam Buddha University
Proceedings of the 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2014 | Year: 2014
Cognitive Radio offers a solution by utilizing the spectrum holes that represent the potential opportunities for non-interfering use of spectrum. This paper focuses on spectrum sensing using energy detection techniques of an unknown signal over a multipath channel. The paper consider with out-diversity case, and explain some alternative closed-form expressions for the probability of miss detection and probability of false alarm to those recently reported in the literature. Probabilities are calculated based on Rayleigh fading channel. © 2014 IEEE.
International Journal of Applied Engineering Research | Year: 2014
Finishing of surfaces without sub surface damage is very difficult and is high in demand. Dimensional accuracy and precision is highly required in industries such as automotive, etc. and also, it is of utmost importance in such industries. Surface finishing in nanometer range and providing a defect free surface is a challenging task for already developed fine finishing processes. Also few advanced finishing processes have been developed but their applications are limited to few geometries only, due to the restricted movements of work piece and the finishing medium. To overcome such limitations a new fine finishing process Ball End Magnetorheological (MR) Finishing process is developed for flat as well as 3D surfaces, for ferromagnetic and non-ferromagnetic materials. In this process the smart behavior of the magnetorheological polishing (MRP) fluid, by controlling the finishing forces, is utilized to achieve the final surface finish. An experimental setup, computer controlled, is designed and developed for the study of process performance and characteristics. In the present study the simulations of magnetostatic force using the Ball end magnetorheological tool on a nonferromagnetic copper workpiece is been done by increasing or decreasing the number of carbonyl iron particles. © Research India Publications.
Chayadevi M.L.,JSSATE |
Smart Innovation, Systems and Technologies | Year: 2015
Malaria is an endemic, global and life threatening disease. Technically skilled person or an expert is needed to analyze the microscopic blood smears for long hours. This paper presents an efficient approach to segment the parasites of malaria. Three different colour space namely LAB, HSI and gray has been used effectively to pre-process and segment the parasite in digital images with noise, debris and stain. L and B plane of LAB, S plane of HSI of input image is extracted with convolution and DCT. Fuzzy based segmentation has been proposed to segment the malaria parasite. Colour features, fractal features are extracted and feature vectors are prepared as a result of segmentation. Adaptive Resonance Theory Neural Network (ARTNN), Back Propagation Network (BPN) and SVM classifiers are used with Fuzzy segmentation and fractal feature extraction methods. Automated segmentation with ARTNN has recorded an accuracy of 95 % compared to other classifiers. © Springer India 2015.
AIP Conference Proceedings | Year: 2011
Lot of work is being accomplished in the national and international standards communities to reach a consensus on standardizing metadata and repositories for organizing the metadata. Descriptions of several metadata standards and their importance to statistical agencies are provided in this paper. Existing repositories based on these standards help to promote interoperability between organizations, systems, and people. Repositories are vehicles for collecting, managing, comparing, reusing, and disseminating the designs, specifications, procedures, and outputs of systems, e.g. statistical surveys. © 2011 American Institute of Physics.