An Efficient Multi-Layer Encryption Framework with Authentication for EHR in Mobile Crowd Computing

레티나쿠마르,Gopinath Ganapathy,강건욱 한국인터넷방송통신학회 2019 Journal of Advanced Smart Convergence Vol.8 No.2

Mobile Crowd Computing is one of the most efficient and effective way to collect the Electronic health records and they are very intelligent in processing them. Mobile Crowd Computing can handle, analyze and process the huge volumes of Electronic Health Records (EHR) from the high-performance Cloud Environment. Electronic Health Records are very sensitive, so they need to be secured, authenticated and processed efficiently. However, security ,privacy and authentication of Electronic health records(EHR) and Patient health records(PHR) in the Mobile Crowd Computing Environment have become a critical issue that restricts many healthcare services from using Crowd Computing services .Our proposed Efficient Multi-layer Encryption Framework(MLEF) applies a set of multiple security Algorithms to provide access control over integrity, confidentiality ,privacy and authentication with cost efficient to the Electronic health records(HER)and Patient health records(PHR). Our system provides the efficient way to create an environment that is capable of capturing, storing, searching, sharing, analyzing and authenticating electronic healthcare records efficiently to provide right intervention to the right patient at the right time in the Mobile Crowd Computing Environment.

A Novel Architecture for Mobile Crowd and Cloud computing for Health care

레티나쿠마르,Gopinath Ganapathy,강정진 국제문화기술진흥원 2018 International Journal of Advanced Culture Technolo Vol.6 No.4

The rapid pace of growth in internet usage and rich mobile applications and with the advantage of incredible usage of internet enabled mobile devices the Green Mobile Crowd Computing will be the suitable area to research combining with cloud services architecture. Our proposed Framework will deploy the eHealth among various health care sectors and pave a way to create a Green Mobile Application to provide a better and secured way to access the Products/ Information/ Knowledge, eHealth services, experts / doctors globally. This green mobile crowd computing and cloud architecture for healthcare information systems are expected to lower costs, improve efficiency and reduce error by also providing better consumer care and service with great transparency to the patient universally in the field of medical health information technology. Here we introduced novel architecture to use of cloud services with crowd sourcing.

A Novel Architecture for Mobile Crowd and Cloud computing for Health care

kumar, Rethina,Ganapathy, Gopinath,Kang, Jeong-Jin The International Promotion Agency of Culture Tech 2018 International Journal of Advanced Culture Technolo Vol.6 No.4

The rapid pace of growth in internet usage and rich mobile applications and with the advantage of incredible usage of internet enabled mobile devices the Green Mobile Crowd Computing will be the suitable area to research combining with cloud services architecture. Our proposed Framework will deploy the eHealth among various health care sectors and pave a way to create a Green Mobile Application to provide a better and secured way to access the Products/ Information/ Knowledge, eHealth services, experts / doctors globally. This green mobile crowd computing and cloud architecture for healthcare information systems are expected to lower costs, improve efficiency and reduce error by also providing better consumer care and service with great transparency to the patient universally in the field of medical health information technology. Here we introduced novel architecture to use of cloud services with crowd sourcing.

An Efficient Multi-Layer Encryption Framework with Authentication for EHR in Mobile Crowd Computing

kumar, Rethina,Ganapathy, Gopinath,Kang, GeonUk The Institute of Internet 2019 International journal of advanced smart convergenc Vol.8 No.2

Mobile Crowd Computing is one of the most efficient and effective way to collect the Electronic health records and they are very intelligent in processing them. Mobile Crowd Computing can handle, analyze and process the huge volumes of Electronic Health Records (EHR) from the high-performance Cloud Environment. Electronic Health Records are very sensitive, so they need to be secured, authenticated and processed efficiently. However, security, privacy and authentication of Electronic health records(EHR) and Patient health records(PHR) in the Mobile Crowd Computing Environment have become a critical issue that restricts many healthcare services from using Crowd Computing services .Our proposed Efficient Multi-layer Encryption Framework(MLEF) applies a set of multiple security Algorithms to provide access control over integrity, confidentiality, privacy and authentication with cost efficient to the Electronic health records(HER)and Patient health records(PHR). Our system provides the efficient way to create an environment that is capable of capturing, storing, searching, sharing, analyzing and authenticating electronic healthcare records efficiently to provide right intervention to the right patient at the right time in the Mobile Crowd Computing Environment.

A Hybrid Mod K-Means Clustering with Mod SVM Algorithm to Enhance the Cancer Prediction

Kumar, Rethina,Ganapathy, Gopinath,Kang, Jeong-Jin The Institute of Internet 2021 International Journal of Internet, Broadcasting an Vol.13 No.2

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

K-Means Clustering with Content Based Doctor Recommendation for Cancer

Rethina kumar,Gopinath Ganapathy,Jeong-Jin Kang 국제문화기술진흥원 2020 International Journal of Advanced Culture Technolo Vol.8 No.4

Recommendation Systems is the top requirements for many people and researchers for the need required by them with the proper suggestion with their personal indeed, sorting and suggesting doctor to the patient. Most of the rating prediction in recommendation systems are based on patient’s feedback with their information regarding their treatment. Patient’s preferences will be based on the historical behaviour of similar patients. The similarity between the patients is generally measured by the patient’s feedback with the information about the doctor with the treatment methods with their success rate. This paper presents a new method of predicting Top Ranked Doctor’s in recommendation systems. The proposed Recommendation system starts by identifying the similar doctor based on the patients’ health requirements and cluster them using K-Means Efficient Clustering. Our proposed K-Means Clustering with Content Based Doctor Recommendation for Cancer (KMC-CBD) helps users to find an optimal solution. The core component of KMC-CBD Recommended system suggests patients with top recommended doctors similar to the other patients who already treated with that doctor and supports the choice of the doctor and the hospital for the patient requirements and their health condition. The recommendation System first computes K-Means Clustering is an unsupervised learning among Doctors according to their profile and list the Doctors according to their Medical profile. Then the Content based doctor recommendation System generates a Top rated list of doctors for the given patient profile by exploiting health data shared by the crowd internet community. Patients can find the most similar patients, so that they can analyze how they are treated for the similar diseases, and they can send and receive suggestions to solve their health issues. In order to the improve Recommendation system efficiency, the patient can express their health information by a natural-language sentence. The Recommendation system analyze and identifies the most relevant medical area for that specific case and uses this information for the recommendation task. Provided by users as well as the recommended system to suggest the right doctors for a specific health problem. Our proposed system is implemented in Python with necessary functions and dataset.

A Preliminary Exploration on Component Based Software Engineering

Basha, N Md Jubair,Ganapathy, Gopinath,Moulana, Mohammed International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.9

Component-based software development (CBD) is a methodology that has been embraced by the software industry to accelerate development, save costs and timelines, minimize testing requirements, and boost quality and output. Compared to the conventional software development approach, this led to the system's development being completed more quickly. By choosing components, identifying systems, and evaluating those systems, CBSE contributes significantly to the software development process. The objective of CBSE is to codify and standardize all disciplines that support CBD-related operations. Analysis of the comparison between component-based and scripting technologies reveals that, in terms of qualitative performance, component-based technologies scale more effectively. Further study and application of CBSE are directly related to the CBD approach's success. This paper explores the introductory concepts and comparative analysis related to component-based software engineering which have been around for a while, but proper adaption of CBSE are still lacking issues are also focused.

Chaotic Theory based Defensive Mechanism against Distributed Denial of Service Attack in Cloud Computing Environment

N. Ch. S. N. Iyengar,Gopinath Ganapathy 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.9

Cloud computing is an advantageous technology, which allows any enterprises to shift their data towards Cloud Service Provider (CSP) end. This shift poses an essential necessity for data being available all the time with a considerable level of security. Availability is an important concern for any subscribers as their sensitive data are prone to attack threats. Resource and data availability are most important security measure. So, blocking the attack traffic towards Data Center (DC) improves availability, but passive outwitting leads to high false positive and negative rate. This affects the legitimate requestors being outwitted. So, the proposed chaotic theory based defense mechanism considers the stability state of traffic and detects the anomaly traffic condition. The anomaly traffic condition is just the passive way of diminishing the effect of overload, but classifying them appropriately and allowing the non-attack case of overload improves the availability and utilization and reduces the false case rates. Considering several cases of overload threats and allowing the legitimate overload case improves efficiency. The simulation results proved that the mechanism proposed is deployable at an attack-prone DC for resource protection, which would eventually benefit the DC economically as well.

An Effective Layered Load Balance Defensive Mechanism against DDoS Attacks in Cloud Computing Environment

N. Ch. S. N. Iyengar,Gopinath Ganapathy 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.7

Cloud computing is a technology which completely shifts the data to unaware Datacenter (DC) where the cloud service provider (CSP) is responsible for the subscribers’ data and its protection. Distributed Denial of Service (DDoS) is a kind of overload threat aims to subvert DC and their resources which leads to resource unavailable to legitimate requestors. In this paper we proposed an effective layered load balancing mechanism which scrutinizes the incoming requestors’ traffic at various layers and each layer outwits some kind of attack traffic. The early network traffic condition prediction paves the way to detect the threats earlier which in turn improves the availability. The significance of the proposed mechanism is detecting the higher rate of overload threats at earlier layers. Constant monitoring and stringent protocol setup for incoming traffic strengthens the proposed mechanism against several kinds of overload threats. Based on the traffic pattern of incoming requestors, the vulnerability is observed and outwitted at various layers. The simulation proved that the mechanism proposed is deployable at an attack-prone DC for resource protection, which would eventually benefit the DC economically as well.