M. Durairaj

Bio: M. Durairaj is an academic researcher from Bharathidasan University. The author has contributed to research in topics: Cloud computing & Spoofing attack. The author has an hindex of 10, co-authored 36 publications receiving 381 citations.

Data Mining Applications In Healthcare Sector: A Study

Abstract: In this paper, we have focused to compare a variety of techniques, approaches and different tools and its impact on the healthcare sector. The goal of data mining application is to turn that data are facts, numbers, or text which can be processed by a computer into knowledge or information. The main purpose of data mining application in healthcare systems is to develop an automated tool for identifying and disseminating relevant healthcare information. This paper aims to make a detailed study report of different types of data mining applications in the healthcare sector and to reduce the complexity of the study of the healthcare data transactions. Also presents a comparative study of different data mining applications, techniques and different methodologies applied for extracting knowledge from database generated in the healthcare industry. Finally, the existing data mining techniques with data mining algorithms and its application tools which are more valuable for healthcare services are discussed in detail.

Prediction Of Heart Disease Using Back Propagation MLP Algorithm

Abstract: Diagnosing the presence of heart disease is actually tedious process,as it requires depth knowledge and rich experience. In general, the prediction of heart disease lies upon the traditional way of examining medical report such as ECG (The Electrocardiogram), MRI (Magnetic Resonance Imaging), Blood Pressure, Stress tests by a medical practitioner. Now days, a large volume of medical data is available in medical industry and acts as a great source of predicting useful and hidden facts in almost all medical problems. These facts would really in turn, help the practitioners to make accurate predictions. The novel techniques of Artificial Neural Network concepts have also been contributing themselves in yielding highest prediction accuracy over medical data. This paper aims to predict the existence of heart disease using Back Propagation MLP (Multilayer Perceptron) of Artificial Nerual Network. The results are compared with the existing works carried out in the same domain.

A Study on Security Issues in Cloud based E-Learning

Abstract: Cloud based E-Learning is the method to reduce cost and complexity of data accessing, which are controlled by third party services. Traditional E-Learning methods are incorporated with cloud computing technology to provide massive advantages to the academic users but it compromises in security aspects. Proposed methodology ensures data availability and provides solution to protect indispensable data from the attackers. This study identifies different security issues in cloud service delivery model with an aim to suggest a solution in the form of security measures related to the cloud based e-learning. Different types of attacks in service delivery models of e-learning proposed by different researchers are discussed. Threats, security requirements, and challenges involved are also taken into consideration. This study of e-Learning models advocates users to access their data in the cloud through a secured layer using the internet.

A Study On Virtualization Techniques And Challenges In Cloud Computing

Abstract: Cloud computing is a modern technology that increase application potentialities in terms of functioning, elastic resource management and collaborative execution approach. The central part of cloud computing is virtualization which enables industry or academic IT resources through ondemand allocation dynamically. The resources have different forms such as network, server, storage, application and client. This paper focus as on how virtualization helps to improve elasticity of the resources in cloud computing environment. In addition to, this paper gives a detailed review on open source virtualization techniques, challenges and future research direction.

An empirical study on applying data mining techniques for the analysis and prediction of heart disease

Abstract: The health care environment is found to be rich in information, but poor in extracting knowledge from the information This is because of the lack of effective analysis tool to discover hidden relationships and trends in them By applying the data mining techniques, valuable knowledge can be extracted from the health care system Heart disease is a group of condition affecting the structure and functions of heart and has many root causes Heart disease is the leading cause of death in the world over past ten years Researches have been made with many hybrid techniques for diagnosing heart disease This paper deals with an overall review of application of data mining in heart disease prediction

Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

Abstract: Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

Applications of artificial neural networks in health care organizational decision-making: A scoping review

Abstract: Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. We provide a seminal review of the applications of ANN to health care organizational decision-making. We screened 3,397 articles from six databases with coverage of Health Administration, Computer Science and Business Administration. We extracted study characteristics, aim, methodology and context (including level of analysis) from 80 articles meeting inclusion criteria. Articles were published from 1997–2018 and originated from 24 countries, with a plurality of papers (26 articles) published by authors from the United States. Types of ANN used included ANN (36 articles), feed-forward networks (25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%. The majority of ANN informed decision-making at the micro level (61 articles), between patients and health care providers. Fewer ANN were deployed for intra-organizational (meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 articles) decision-making. Our review identifies key characteristics and drivers for market uptake of ANN for health care organizational decision-making to guide further adoption of this technique.

A Survey of Internet of Things (IoT) Authentication Schemes

Abstract: The Internet of Things (IoT) is the ability to provide everyday devices with a way of identification and another way for communication with each other. The spectrum of IoT application domains is very large including smart homes, smart cities, wearables, e-health, etc. Consequently, tens and even hundreds of billions of devices will be connected. Such devices will have smart capabilities to collect, analyze and even make decisions without any human interaction. Security is a supreme requirement in such circumstances, and in particular authentication is of high interest given the damage that could happen from a malicious unauthenticated device in an IoT system. This paper gives a near complete and up-to-date view of the IoT authentication field. It provides a summary of a large range of authentication protocols proposed in the literature. Using a multi-criteria classification previously introduced in our work, it compares and evaluates the proposed authentication protocols, showing their strengths and weaknesses, which constitutes a fundamental first step for researchers and developers addressing this domain.

Big data analytics for preventive medicine

Abstract: Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.