Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

Diabetes is the key critical issue needs to be concerned for various problems in our body. Increase in glucose and fructose content in our body results in diabetes mellitus. When a body generates higher insulin level than the required, it results in increased urination and excessive thirstiness which in turn results in kidney failure and other cardio-related issues. Many research agencies invested their funds on defining the predictive methodology and finding the root cause of those results in mellitus. Mellitus results in the highest mortality rate compared to any other disease reported by the health organizations across the globe. In this, the predictive methodologies, various classification techniques are discussed, and the results are analyzed. The classification methodology could be on medications, food habits, personal behaviors, age factors and so on. The datasets are processed and analyzed with the neural network algorithms, and the results are compared with one another. The datasets are taken from the National Family Health Survey results published during the period of 2016–2017. The result implies that men between ages 15–49 among 1 billion people have reported with diabetes mellitus. Diagnose and forecast on this disease are done by recognizing the pattern formation and grouping the similar structures. Various algorithmic techniques like M-layer perceptron, nearest neighbor, vector machines, data regressions, binary regression and their accuracy of forecast, speed and sensitivity are calculated, analyzed and compared to define the accurate prediction methodology over a short span of time. The forecast methodologies are focussed to provide solutions to avoid the intensive care system provided proper medications with a long duration when it is been predicted to be a risk factor. A statistical method of analyzing is performed for the comparative analysis. The learning and training methodologies are discussed in this system. Accuracy, specificity, sensitivity are the key parameters to define the best forecast methodology. Classification on association, regression techniques and neural algorithmic techniques is analyzed and compared to refine the best predictive forecast methodology by processing 30 samples across the states of India with focus on determining the type of mellitus along with the accuracy on definition. The forecast data utilized to define the type of mellitus and the prediction on critical measures over a period of time. © Springer Nature Singapore Pte Ltd 2021.

Department of Database Systems

School of Computer Science and Engineering

Vellore Campus

Department of Commerce

School of Social Sciences & Languages

World University root node

Lecture Notes in Networks and Systems

Supervised learning method and neural network algorithm for the analysis of diabetic mellius and its comparitive analysis

According to the survey 17.5 million deaths are happened due to the cardiovascular disease that leads to create heart attack, chest pain and stroke. Based on the survey it clearly concludes that most of the people affected by heart problem that need to be identified in the earlier stage for eliminating the future risk in patient health. The importance of the heart disease detection process helps to create the earlier detection system for identifying heart problem by using machine learning and optimized techniques but the developed forecasting systems are difficult to predict the heart problems in an accurate manner with minimum time. So, hybridized Ruzzo–Tompa memetic based deep trained Neocognitron neural network is introduced to analyze the heart disease related features and predict the heart disease in earlier stage. First, heart disease data has been collected from UCI repository, dimensionality of the data is minimized by hybridized Ruzzo–Tompa memetic approach. After reducing the number of features, that are trained by deep learning approach which analyze the features using maximum number of hidden layers that used to predict heart disease features successfully while making the Neocognitron neural network classification. Further efficiency of the system is evaluated using MATLAB based simulation results. © 2019, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature.

Department of Software Systems

Health and Technology

Heart disease classification using hybridized Ruzzo-Tompa memetic based deep trained Neocognitron neural network

Abstract: Machine learning is used as an effective support system in health diagnosis which contains large volume of data. More commonly, analyzing such a large volume of data consumes more resources and execution time. In addition, all the features present in the dataset do not support in achieving the solution of the given problem. Hence, there is a need to use an effective feature selection algorithm for finding the more important features that contribute more in diagnosing the diseases. The Particle Swarm Optimization (PSO) is one of the metaheuristic algorithms to find the best solution with less time. Nowadays, PSO algorithm is not only used to select the more significant features but also removes the irrelevant and redundant features present in the dataset. However, the traditional PSO algorithm has an issue in selecting the optimal weight to update the velocity and position of the particles. To overcome this issue, this paper presents a novel function for identifying optimal weights on the basis of population diversity function and tuning function. We have also proposed a novel fitness function for PSO with the help of Support Vector Machine (SVM). The objective of the fitness function is to minimize the number of attributes and increase the accuracy. The performance of the proposed PSO-SVM is compared with the various existing feature selection algorithms such as Info gain, Chi-squared, One attribute based, Consistency subset, Relief, CFS, Filtered subset, Filtered attribute, Gain ratio and PSO algorithm. The SVM classifier is also compared with several classifiers such as Naive Bayes, Random forest and MLP. © 2018, Pleiades Publishing, Ltd.

Programming and Computer Software

A Machine Learning Framework for Feature Selection in Heart Disease Classification Using Improved Particle Swarm Optimization with Support Vector Machine Classifier

Here has been rising enthusiasm for secure structure that must be attempted and genuine and smart respond for the organizations and companions. RFID (Radio Frequency Identification) is one of the consistent and speedy techniques for perceive the material dissent. In the long-back the institutionalized distinguishing pieces of proof are more perfect when stood out from RFID because of their cost yet now day by day's RFID are viably open and are more beneficial to use. Research has revealed some exceptional enhancements which make its programming significantly shorter and less requesting is an immediate consequence of supplanting microcontroller with Arduino. Arduino makes the circuit and programming a significant measure easier to get it. Paper relies upon security access and control system using RFID and Arduino with GSM module. A segment of the sensors are used like PIR, LPG for spillage area. Security get to system is astoundingly beneficial to use at home, office and business structures. © 2006-2018 Asian Research Publishing Network (ARPN).

ARPN Journal of Engineering and Applied Sciences

Rfid based automated gate security system

The most common cause of visual impairment which affects about twenty five thousand patients is diabetic retinopathy. Adaptive neuro fuzzy system was used to create a rule base for fuzzy system. The fuzzy system is then used to find out the risk level of the diabetic retinopathy that the patient has. The dataset was taken from Diabetic Retinopathy Study (DRS). The accuracy, specificity and sensitivity found in this study were 92.9%, 71.4% and 97% respectively. © IAEME Publication.

International Journal of Civil Engineering and Technology

Risk level prediction of diabetic retinopathy using adaptive neuro fuzzy inference system

The aim of this study is to develop an expert system for the diagnosis of diabetic patients. PIMA Indian Diabetes Dataset is used by the proposed system. The proposed system uses feature reduction techniques like Recursive feature elimination and principal component analysis for enhancing the performance. The expert system is built using Deep Neural Networks and Artificial Neural Networks. This study also compares the performance of proposed deep neural network with artificial neural networks using various performance measures: accuracy, sensitivity, specificity. © IAEME Publication.

An expert system for the diagnosis of diabetic patients using deep neural networks and recursive feature elimination

A standout amongst the most genuine complexities of diabetes is Diabetic foot. The frequency of diabetic foot increments similarly on an ascent of diabetic's increments and it is regularly disregarded by individuals prompting evacuation of foot. A Diabetic foot expert system which constructs large scale knowledge framework is proposed using fuzzy. Fuzzification is used to build knowledge. © IAEME Publication.

Anticipating diabetic foot ulcer using generative fuzzy expert system framework

VIT Unitversity

2016 1st India International Conference on Information Processing (IICIP)

Fuzzy logic based decision support system

The conventional operating system usually works on a particular workstation but the same when implemented on a distributed environment like cloud faces many problems. This paper analyzes and explains how does the cloud operating system works in order to resolve these problems. It also differentiates the working mechanism of both based on basic storage management service provided by the cloud. At the end it provides the issues and the future challenges faced in the cloud operating system. © 2016 SERSC.

Fulltext

International Journal of Grid and Distributed Computing

Analysis and Issues in Cloud Operating System

Mobile devices are playing a vital role in information processing systems in day to day life. Current trend suggests that smart phones are going to be the first-most computing devices which many people will carry with them for maximum time everywhere. However, smart phones are still lacking behind the essential resources. This demands the need for Mobile Cloud Computing, where the key elements of data storage and data processing are carried outside the mobile devices. So this provides flexibility to mobile users as, the application and the data stored remotely can be accessed on-the-fly with high speed networks. Variation among the mobile network on basis of elements such as, bandwidth, unstable network and its uncertain availability are barriers for Quality Of Services in the performance. Thus the solution is to connect the mobile devices heterogeneously where they work collaboratively as resource providers. Our mechanism suggest for "Mobile to Hub to Cloud" model which can tackles the network disparity problem. A scenario gives the perfect example for the need of such model, mobile users in areas with infrastructure where the costs of accessing the network is too high, such as traveler who doesn't wished to pay high roaming charges. Thus the suggested model can evaluate the following options: storing data in mobile hubs and improving the message delivery performance. So as in our example, traveler now can push the data onto the mobile buffers or hubs which in turn will send it to the cloud server. © 2016 SERSC.

International Journal of Software Engineering and Its Applications

Architecture for Mobile Cloud Computing via Middle-Ware Technologies

Access to school continues to be challenging to most learners with visual and hearing impairments,irrespective of the government’s commitment to scale up efforts to support them at various levels of education. Deaf blindness affects students’ progress and well-being. This paper presents a methodology for assisting the inclusion system in education. In this context,the present work focuses the development of a Text-to-Braille application for the blind and deafand its ultimate purpose is the development of a mobile application that allows a blind user to read text that is written on the classroom board along with regular sighted-hearing students. The proposed system improves the deaf and blind student’s ability to access and connect with people and the environment. © 2016,International Journal of Pharmacy and Technology. All rights reserved.

International Journal of Pharmacy and Technology

A smart system for supporting blind people in education

Typically visually impaired individuals use white sticks which are exceptionally constrained in its capacity to give route help to the client also,can’t undoubtedly recognize complications. Portability of outwardly disabled individuals is restricted by their powerlessness to see their environment. This paper presents a review on frameworks and techniques that can guide individuals with vision impair. © 2016,International Journal of Pharmacy and Technology. All rights reserved.

Technical aids for outdoor assistive navigation for visually impaired people

Heart disease is one of the main sources of demise around the world and it is imperative to predict the disease at a premature phase. The computer aided systems help the doctor as a tool for predicting and diagnosing heart disease. The objective of this review is to widespread about Heart related cardiovascular disease and to brief about existing decision support systems for the prediction and diagnosis of heart disease supported by data mining and hybrid intelligent techniques. © 2016 SERSC.

International Journal of Bio-Science and Bio-Technology

Heart Disease Prediction System Using Data Mining and Hybrid Intelligent Techniques: A Review

Cloud computing is a very powerful concept that can be used to enhance the next generation data center and allow service provider to use data center capability provided by cloud and develop the application based on user requirement. This work aims to develop an android based web application namely Cloud based Virtual Reporting Application (VRA) wherein the database is stored on the cloud which resembles large storage facility. VRA is basically an android application which can be used by managers of the organization to monitor their employees. It is one kind of employee monitoring application where the manager can monitor the location of the employee and keep track of the his location whether the employee has done the allocated work or not. The app makes use of GPS technology available in the mobile phone to monitor the location. In addition to this, OPENCV library is used for face recognition to verify the authentication of the employee. © 2016 SERSC.