K. Uma ¹, G. Murugusundaramoorthy ¹, K. Vijaya ¹

Affiliations
1 School of Advanced Sciences, VIT University, Vellore - 632014, Tamil Nadu, IN

Abstract

We introduced certain new subclasses of analytic functions of complex order defined by fractional integrals involving Fox’s H-functions in the unit disc and investigate the various properties and characteristics of analytic functions belonging to the subclasses Sn(l,b,g). We also defined an another subclass Rn(l,b,g) involving Fox’s H- functions. Apart from deriving a set of coefficient bounds for each of these function classes, we establish several inclusion relationships involving the (n, d) — neighbourhoods of analytic functions with negative coefficients belonging to these subclasses.

Keywords

Convex, Generalized Hypergeometric Functions, Hadamard Product, Inclusion Relations, (n, S)-Neighborhood, Starlike, Univalent

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K. Uma ¹, K. Suhasini ¹, M. Vijayakumar ²

Affiliations
1 Department of IT, VIT University, Vellore - 632014, Tamil Nadu, IN
2 Department of English, VIT University,Vellore-632014, IN

Abstract

Brain tumor is one among the conscientious diseases in medical discipline. A brain tumor is an assembly of anomalous cells that develops in or around the brain area. These Tumors can candidly wreck firm brain cells. They can likewise by allusion harm sound cells by swarming different parts of the brain and bringing on irritation, brain swelling and weight inside the skull. Brain tumors are either dangerous or harmless. Brain tumor identification and segmentation is one of the trickiest and tedious undertaking in restorative image handling. MRI (Magnetic Resonance Imaging) is a therapeutic system, fundamentally utilized by the radiologist for representation of the inward structure of the human body without any surgery. MRI gives copious data about the delicate human tissue, which helps in the analysis of brain tumor. The exact segmentation of MRI image is essential for the analysis of brain tumor by system supported clinical apparatus. This paper focuses on various technologies and implementations for segmenting brain tumor images. Besides summarizing those classification techniques, this paper also provides a basic evaluation of these facets of segmenting tumor images.

Keywords

Fuzzy Clustering, K-Means Clustering, Linear Svm, Morphological Filtering, Mri Brain Tumor Segmentation, Neutrosophic Set Approach, Pnn and Grnn.

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K. Uma ¹, D. Kannan ¹, S. Vikas ¹, S. Sree Dharinya ¹

Affiliations
1 Department of IT, VIT University, Vellore – 632014, Tamil Nadu, IN

Abstract

To construct a student healthcare website which is termed as education world and reducing the answer time while bountiful any query towards the database wherever the data are stored. By using this website the consumer can acquire educational related material by queries. In this mission, the website is working to find the available frequent stuff using the procedure called APRIORI. This mission will be valuable in applications wherever the users consuming a set of stuff repeatedly. In order to separate these things from the databank, this process uses APRIORI algorithm. These are the areas where spending the time period to decrease the time using search. The disadvantage of existing scheme is it aimed at each and every demand given through the consumer. It will examine for all records which are not at altogether used and wanted by the consumer. So the total amount of the stuff escalates, the determined time period also escalates which will dynamically escalate the response time which will shrink the concert of the scheme. So the knowledge in which the system is working to the instrument will reduce the reply time in such a condition where the common data are used through the consumers. The system is also refining performance through parallelizing the actions in finding common data items. To shrink the response time period the system is spending the time period to catch the records in the databank. If the penetrating time reduces, mechanically the response period of the consumer query will also reduces. It clues to perform development of the scheme.

Keywords

Apriori, Biological Data, Candidate Item Set, Eclat, FP-Growth.

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K. Uma ¹, S. Srilatha ¹, D. Kushal ¹, A. R. Pallavi ¹, V. Nanda Kumar ¹

Affiliations
1 Department of IT, VIT University, Vellore – 632 014, Tamil Nadu, IN

Abstract

Objectives: Face recognition has arisen as a smart solution to discourse many present-day needs for empathy and the confirmation of identity claims. It fetches together the capacity of other biometric methods, which endeavor to tie uniqueness for the individual characteristic features of the body, and the further familiar functionality of visual reconnaissance systems. Face acknowledgment is a vital field for authentication purpose particularly in the case of student's attendance. This paper is intended at applying a digitized system for attendance recording. Methods/Statistical Analysis: In this process two methods are used to determine the face recognition attendance system- Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA). PCA is a statistical procedure that uses an orthogonal modification to transform a set of observations of probably correlated variables into a set of values of linearly uncorrelated variables. LDA is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear permutation of features that shows or separates two or more classes of objects or events. Findings: However the current system of tracking attendance via records is difficult to manage. Face recognition based attendance system deals with the maintenance of the student's daily attendance details. Biometric appreciation comprises alike, within an open-mindedness of calculation, of pragmatic biometric behaviors in contradiction of formerly poised data for a focus. Estimated identical is obligatory due to the dissimilarities in biological characteristics and deeds both within and among persons. It generates the attendance for the student on basis of presence in the class. Application/Improvements: Project can be modernized in nearby future as, when a responsibility for the same arises, as it is very flexible in positions of growth. And the enrichment approach of camera formation based on the result of the position valuation in order to progress the face detection effectiveness.

Keywords

Biometric Features, Computer Vision Communities, Machine Learning, Pattern Recognition.

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K. Uma ¹, M. Swetha ¹, M. Manisha ¹, S. V. Revathi ¹, Anand Kannan ²

Affiliations
1 School of Information Technology and Engineering, VIT University, Vellore – 632014, Tamil Nadu, IN
2 Department of Electricals and Computers, KTH Technical University, Swedan

Abstract

Objectives: The Embedded Technology is currently in its primary and the affluence of Knowledge offered is amazing. Embedded System is a permutation of hardware and software. Embedded technology plays a most important role in integrating the variety of functions coupled with it. Methods: This desire to bind up the variety of sources of the Department in a closed loop structure. This proposal significantly reduces the manpower, saves time and operates efficiently without individual interfering. This project puts out the first action in achieving the desired target. With the advent in technology, the existing systems are developed to contain inbuilt intelligence. This system will automatically broadcast the real time surroundings data. Findings: In this project we are going to observe the environment circumstances using the smart sensors in embedded technology, using this project we can analyze the climate and pollution state of our surrounding, using this data we can recover our surroundings from pollution. Applications: The Arduino as the prime controller which uses ATMEGA328 microcontroller, temperature, humidity, gas, sound sensors are used to sense the environment condition and provide the data to the Microcontroller which is used to observe the level and send the data to the cloud server via IOT module.

Keywords

Arduino, Embedded Technology, Internet of Things, Sensors

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