Ramesh C. Joshi

Bio: Ramesh C. Joshi is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Cache & Denial-of-service attack. The author has an hindex of 22, co-authored 102 publications receiving 1669 citations. Previous affiliations of Ramesh C. Joshi include Indian Institutes of Technology.

Hybrid prediction model for Type-2 diabetic patients

Abstract: A wide range of computational methods and tools for data analysis are available. In this study we took advantage of those available technological advancements to develop prediction models for the prediction of a Type-2 Diabetic Patient. We aim to investigate how the diabetes incidents are affected by patients' characteristics and measurements. Efficient predictive modeling is required for medical researchers and practitioners. This study proposes Hybrid Prediction Model (HPM) which uses Simple K-means clustering algorithm aimed at validating chosen class label of given data (incorrectly classified instances are removed, i.e. pattern extracted from original data) and subsequently applying the classification algorithm to the result set. C4.5 algorithm is used to build the final classifier model by using the k-fold cross-validation method. The Pima Indians diabetes data was obtained from the University of California at Irvine (UCI) machine learning repository datasets. A wide range of different classification methods have been applied previously by various researchers in order to find the best performing algorithm on this dataset. The accuracies achieved have been in the range of 59.4-84.05%. However the proposed HPM obtained a classification accuracy of 92.38%. In order to evaluate the performance of the proposed method, sensitivity and specificity performance measures that are used commonly in medical classification studies were used.

Association Rule for Classification of Type-2 Diabetic Patients

Abstract: The discovery of knowledge from medical databases is important in order to make effective medical diagnosis. The aim of data mining is extract the information from database and generate clear and understandable description of patterns. In this study we have introduced a new approach to generate association rules on numeric data. We propose a modified equal width binning interval approach to discretizing continuous valued attributes. The approximate width of the desired intervals is chosen based on the opinion of medical expert and is provided as an input parameter to the model. First we have converted numeric attributes into categorical form based on above techniques. Apriori algorithm is usually used for the market basket analysis was used to generate rules on Pima Indian diabetes data. The data set was taken from UCI machine learning repository containing total instances 768 and 8 numeric attributes.We discover that the often neglected pre-processing steps in knowledge discovery are the most critical elements in determining the success of a data mining application. Lastly we have generated the association rules which are useful to identify general associations in the data, to understand the relationship between the measured fields whether the patient goes on to develop diabetes or not. We are presented step-by-step approach to help the health doctors to explore their data and to understand the discovered rules better.

Defending against Distributed Denial of Service Attacks: Issues and Challenges

Abstract: Distributed Denial of Service (DDoS) attacks on user machines, organizations, and infrastructures of the Internet have become highly publicized incidents and call for immediate solution. It is a complex and difficult problem characterized by an explicit attempt of the attackers to prevent access to resources by legitimate users for which they have authorization. Several schemes have been proposed on how to defend against these attacks, yet the problem still lacks a complete solution. The main purpose of this paper is therefore twofold. First is to present a comprehensive study of a wide range of DDoS attacks and defense methods proposed to combat them. This provides better understanding of the problem, current solution space, and future research scope to defend against DDoS attacks. Second is to propose an integrated solution for completely defending against flooding DDoS attacks at the Internet Service Provider (ISP) level.

Cooperative Caching Strategy in Mobile Ad Hoc Networks Based on Clusters

Abstract: In this paper, we present a scheme, called Cluster Cooperative (CC) for caching in mobile ad hoc networks. In CC scheme, the network topology is partitioned into non-overlapping clusters based on the physical network proximity. For a local cache miss, each client looks for data item in the cluster. If no client inside the cluster has cached the requested item, the request is forwarded to the next client on the routing path towards server. A cache replacement policy, called Least Utility Value with Migration (LUV-Mi) is developed. The LUV-Mi policy is suitable for cooperation in clustered ad hoc environment because it considers the performance of an entire cluster along with the performance of local client. Simulation experiments show that CC caching mechanism achieves significant improvements in cache hit ratio and average query latency in comparison with other caching strategies.

A survey on Data Mining approaches for Healthcare

Abstract: Data Mining is one of the most motivating area of research that is become increasingly popular in health organization. Data Mining plays an important role for uncovering new trends in healthcare organization which in turn helpful for all the parties associated with this field. This survey explores the utility of various Data Mining techniques such as classification, clustering, association, regression in health domain. In this paper, we present a brief introduction of these techniques and their advantages and disadvantages. This survey also highlights applications, challenges and future issues of Data Mining in healthcare. Recommendation regarding the suitable choice of available Data Mining technique is also discussed in this paper.