Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

A Novel Sanitization Approach for Privacy Preserving Utility Itemset Mining

Abstract: Data mining plays a vital role in today’s information world wherein it has been widely applied in various business organizations. The current trend in business collaboration demands the need to share data or mined results to gain mutual benefit. However it has also raised a potential threat of revealing sensitive information when releasing data. Data sanitization is the process to conceal the sensitive itemsets present in the source database with appropriate modifications and release the modified database. The problem of finding an optimal solution for the sanitization process which minimizes the non-sensitive patterns lost is NP-hard. Recent researches in data sanitization approaches hide the sensitive itemsets by reducing the support of the itemsets which considers only the presence or absence of itemsets. However in real world scenario the transactions contain the purchased quantities of the items with their unit price. Hence it is essential to consider the utility of itemsets in the source database. In order to address this utility mining model was introduced to find high utility itemsets. In this paper, we focus primarily on protecting privacy in utility mining. Here we consider the utility of the itemsets and propose a novel approach for sanitization such that minimal changes are made to the database with minimum number of non-sensitive itemsets removed from the database.

Synthesis and Characterization of PANI/Ferric Chloride Composite for Fabrication of Electrodes in Supercapacitor

Abstract: The main objective of this work is to synthesize polyaniline/ferric chloride composite and to measure the conductivity of the as prepared composite. And also, to analyze the ability of using Ferric Chloride doped polyaniline as an electrode material for the fabrication of supercapacitor application. By in-situ polymerization method, polyaniline in pure form and doped form with the ferric chloride were synthesized using ammonium persulfate as initiator in HCl medium. Polyaniline nanoparticles and its composite are characterized by UV-Vis spectroscopy, FTIR, XRD and Conductivity meter.

A sequential learning method for detection and classification of exudates in retinal images to assess diabetic retinopathy

Abstract: One of the greatest concerns to the personnel in the current health care sector is the severe progression of diabetes. People can often have diabetes and be completely unaware as the symptoms seem harmless when they are seen on their own. Diabetic retinopathy (DR) is an eye disease that is associated with long-standing diabetes. Retinopathy can occur with all types of diabetes and can lead to blindness if left untreated. The conventional method followed by ophthalmologists is the regular testing of the retina. As this method takes time and energy of the ophthalmologists, a new feature-based automated technique for classification and detection of exudates in color fundus image is proposed in this paper. This method reduces the work of the professionals while examining every fundus image rather than only on abnormal image. The exudates are detected from the color fundus image by applying a few pre-processing techniques that remove the optic disk and similar blood vessels using morphological operations. The pre-processed image was then applied for feature extraction and these features were utilized for classification purpose. In this paper, a novel classification technique such as self-adaptive resource allocation network (SRAN) and meta-cognitive neural network (McNN) classifier is employed for classification of images as exudates, their severity and nonexudates. SRAN classifier makes use of self-adaptive thresholds to choose the appropriate training samples and removes the redundant samples to prevent over-training. These selected samples are availed to improve the classification performance. McNN classifier employs human-like meta-cognition to regulate the sequential learning process. The meta-cognitive component controls the learning process in the cognitive component by deciding what-to-learn, when-to-learn and how-to-learn. It is therefore evident that the implementation of human meta-cognitive learning principle improves efficient learning.

Bigdata in the Management of Diabetes Mellitus Treatment

Abstract: Due to technological advancement and digitalization within the medical field, there is tremendous growth in the medical data on a daily basis The data includes but not limited to, personal data of patients and other frequent clinical data generated through health centers, government and private hospitals, etc This data is represented in the form of electronic health records, registers, and patient wearable sensors and stored in a secure cloud Eventually, the storage will have heterogeneous data A large amount of information stored on cloud space is required to extract the data by applying the efficient big data analytic techniques, to assemble and handle the analysis The various applications of big data analytics tools and techniques are growing quickly in the domain of management of diabetes mellitus treatment as it is considered one of the most common deficiencies of the current era Applying machine learning and deep learning techniques to produce a prediction of disease risk and collect the various hospital’s performance related to diabetes It provides a large number of benefits such as available treatments, costs of treatment, outbreaks prediction of epidemics, and to recommend the best health care system In this chapter, we are going to discuss the various types of diabetes and its management, sustainable healthcare systems, and health care information exchanges using big data, decrease medical errors and supporting collaboration, etc Also, the big data technology plays vital role to manage the diabetic mellitus treatment in the trustworthy and security The identification of fraud in medical information and misuse of medical resources are discussed briefly in this chapter A deep study is carried out on key issues and the actual use of big data analytics in diabetes healthcare

Modified PID controller for avoiding overshoot in temperature of barrel heating system

Abstract: The control of temperature in the different zones of barrel heating system in plastic molding machine is a challenging task, as it requires quick rise time without overshoot. The conventional PID controller does not produce the desired performance for this application. This paper ends in presenting the idea of using PI-PD controller-a modified structure of PID controller for controlling the temperature of the melt in the barrel. Fuzzy logic technique is used for obtaining the optimum values of the controller parameters. The controller is modeled in SIMULINK. From the simulation results, it is observed that the fuzzy tuned PI-PD controller outperforms the other controllers namely Ziegler-Nichols tuned PID controller, Fuzzy Logic controller and Fuzzy tuned PID controller.