Nickolas Savarimuthu

Bio: Nickolas Savarimuthu is an academic researcher from National Institute of Technology, Tiruchirappalli. The author has contributed to research in topics: Cloud computing & Cloud storage. The author has an hindex of 7, co-authored 24 publications receiving 161 citations.

SVM ranking with backward search for feature selection in type II diabetes databases

Abstract: Clinical databases have accumulated large quantities of information about patients and their clinical history. Data mining is the search for relationships and patterns within this data that could provide useful knowledge for effective decision-making. Classification analysis is one of the widely adopted data mining techniques for healthcare applications to support medical diagnosis, improving quality of patient care, etc. Usually medical databases are high dimensional in nature. If a training dataset contains irrelevant features (i.e., attributes), classification analysis may produce less accurate results. Data pre-processing is required to prepare the data for data mining and machine learning to increase the predictive accuracy. Feature selection is a preprocessing technique commonly used on high-dimensional data and its purposes include reducing dimensionality, removing irrelevant and redundant features, reducing the amount of data needed for learning, improving algorithms' predictive accuracy, and increasing the constructed models' comprehensibility. Much research work in data mining has gone into improving the predictive accuracy of the classifiers by applying the techniques of feature selection. The importance of feature selection in medical data mining is appreciable as the diagnosis of the disease could be done in this patient-care activity with minimum number of features. Feature selection may provide us with the means to reduce the number of clinical measures made while still maintaining or even enhancing accuracy and reducing false negative rates. In medical diagnosis, reduction in false negative rate can, literally, be the difference between life and death. In this paper we propose a feature selection approach for finding an optimum feature subset that enhances the classification accuracy of Naive .Bayes classifier. Experiments were conducted on the Pima Indian Diabetes Dataset to assess the effectiveness of our approach. The results confirm that SVM Ranking with Backward Search approach leads to promising improvement on feature selection and enhances classification accuracy.

Metaheuristic algorithms and probabilistic behaviour: a comprehensive analysis of Ant Colony Optimization and its variants

Abstract: The application of metaheuristic algorithms to combinatorial optimization problems is on the rise and is growing rapidly now than ever before. In this paper the historical context and the conducive environment that accelerated this particular trend of inspiring analogies or metaphors from various natural phenomena are analysed. We have implemented the Ant System Model and the other variants of ACO including the 3-Opt, Max---Min, Elitist and the Rank Based Systems as mentioned in their original works and we converse the missing pieces of Dorigo's Ant System Model. Extensive analysis of the variants on Travelling Salesman Problem and Job Shop Scheduling Problem shows how much they really contribute towards obtaining better solutions. The stochastic nature of these algorithms has been preserved to the maximum extent to keep the implementations as generic as possible. We observe that stochastic implementations show greater resistance to changes in parameter values, still obtaining near optimal solutions. We report how Polynomial Turing Reduction helps us to solve Job Shop Scheduling Problem without making considerable changes in the implementation of Travelling Salesman Problem, which could be extended to solve other NP-Hard problems. We elaborate on the various parallelization options based on the constraints enforced by strong scaling (fixed size problem) and weak scaling (fixed time problem). Also we elaborate on how probabilistic behaviour helps us to strike a balance between intensification and diversification of the search space.

Cloud Service Evaluation and Selection Using Fuzzy Hybrid MCDM Approach in Marketplace

Abstract: Cloud services are offered independently or combining two or more services to satisfy consumer requirements. Different types of cloud service providers such as direct sellers, resellers and aggregators provide services with different level of service features and quality. The selection of best suitable services involves multi-criteria nature of services to be compared with the presence of both qualitative and quantitative factors, which make it considerably more complex. To overcome this complexity, a fuzzy hybrid multi-criteria decision making approach has been proposed, which includes both qualitative and quantitative factors. Triangular fuzzy numbers are used in all pairwise comparison matrices in the Fuzzy ANP and the criteria weights are utilized by Fuzzy TOPSIS and Fuzzy ELECTRE methods to rank the alternatives. This strategy is demonstrated with selection of cloud based collaboration tool for designers. Finally, sensitivity analysis is performed to prove the robustness of the proposed approach.

Application based brokering algorithm for optimal resource provisioning in multiple heterogeneous clouds

Abstract: In recent years, adoption of cloud computing for computational needs is growing significantly due to various factors such as no upfront cost and access to latest service. In general, cloud infrastructure providers offer a wide range of services with different pricing models, instance types and a host of value-added features. Efficient selection of cloud services constitutes significant management challenges for cloud consumer, which is tedious and involves large information processing. To overcome this, the cloud brokers provide resource provisioning options that ease the task of choosing the best services based on consumers requirements and also provide a uniform management interface to access cloud services. This paper proposes a novel cloud brokering architecture that provides an optimal deployment plan for placement of virtual resources in multiple clouds. The objective of the deployment plan is to select the best cloud services with optimal cost, taking into account various attributes defined in service measurement index (SMI) with additional physical and logical constraints. The proposed cloud brokering architecture has been modeled using mixed integer programming formulation and Benders decomposition algorithm to solve efficiently. Efficacy of the proposed algorithm has been verified by extensive numerical studies and sensitivity analysis.

ESKEA: Enhanced Symmetric Key Encryption Algorithm Based Secure Data Storage in Cloud Networks with Data Deduplication

Abstract: Data deduplication approach is utilized in cloud storage to decrease the bandwidth of communication and storage space by eliminating the data copies from the cloud service provider (CSP). However, one of the main problems of cloud storage is data deduplication with secure data storage. To overcome this issue, the researchers presented symmetric data storage methods based on an encryption algorithm. Nonetheless, the Enhanced Symmetric Key Encryption Algorithm (ESKEA) based on secure data storage with data deduplication is proposed in this research to further improve data confidentiality. In this approach, the block-level deduplication of data is performed using the Convergent Encryption (CE) algorithm to check the CSP duplicate copies of data. Then, ESKEA algorithm is presented for secure storage of data. In ESKEA, Spider Monkey Optimization Algorithm (SMOA) optimally selects the secret key. The results of this work showed that the execution of the proposed ESKEA is more effective than that of the SKEA in terms of upload time and download time.

Statistical Pattern Recognition

Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

The Health Insurance Portability and Accountability Act.

Abstract: The Health Insurance Portability and Accountability Act, also known as HIPAA, was first delivered to congress in 1996 and consisted of just two Titles. It was designed to protect health insurance coverage for workers and their families while between jobs. It establishes standards for electronic health care transactions and addresses the issues of privacy and security when dealing with Protected Health Information (PHI). HIPAA is applicable only in the United States of America.

An ImproVed Ant Colony Algorithm for Robot Path Planning

Abstract: An improved ant colony algorithm is proposed for robot path planning under a static environment.Grid method is used to establish workspace model of the robot.Furthermore,the strategy of backspace from traps and the meeting strategy were applied to path planning of mobile robot,so it avoided path-locked situation as well as improved the efficiency of planning optimal path.The simulation results show that the performance of path planning can be obviously improved by the proposed ant colony algorithm,and the algorithm is very simple and efficient.

A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems

Abstract: In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments.