Data Mining Practical Machine Learning Tools and Techniques

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A Break in the Clouds: Towards a Cloud Definition

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Communication without words

Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques

Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease. The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease. Keywords—CANFIS, Genetic Algorithms (GA), Heart disease, Membership Function (MF).

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Intelligent Heart Disease Prediction System Using CANFIS and Genetic Algorithm

In cloud computing (CC), load balancing tasks remain a critical problem in distributing resources from a data center. Ensure that every virtual machine (VM) has a balanced load to maximize capacity utilization. In the CC world, load balancing is a Non-Polynomial (NP) problem resolved with metaheuristic algorithms. A new Quasi-Oppositional Dragonfly Algorithm for Load Balancing (QODA-LB) has been developed to obtain optimum resource scheduling in a CC configuration. The proposed QODA-LB algorithm uses three variables to calculate an objective function: execution time, execution cost, and charge. The QODA-LB algorithm assigns tasks to VM according to its potential and the resulting objective function. Also, the QODA-LB algorithm employs the Quasi-Oppositional Based Learning principle to increase the standard convergence rate of the Dragonfly (DA) algorithm. A complete series of experiments were conducted, and the results were analyzed in various ways to ensure the increased efficiency of the QODA-LB algorithm. Simulation results demonstrated an optimal load balancing efficiency and outperformed key approaches.

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Quasi Oppositional Dragonfly Algorithm for Load Balancing in Cloud Computing Environment

A Novel Texture Extraction Technique with T1 Weighted MRI for the Classification of Alzheimer’s Disease

Cloud computing is an emerging, revenue generating and internet based technology, where computing power, storage and other resources are provided to the stakeholders in a single package. The traditional online banking systems can make use of the cloud framework for providing economical and high-speed online service to the consumers. This paper first describes a systematic Multi-factor bio-metric Fingerprint Authentication (MFA) approach which provides a high-secure identity verification process for validating the legitimacy of the remote users. The significance of this approach is that the authentication credentials of the users are not revealed to the bank and cloud authentication servers, but allows the servers to perform remote users' authentication. We then extend this investigated framework to develop a privacy protection gateway for obscuring and desensitizing the customers' account details using tokenization and data anonymization techniques. This approach retains the original format of data fields at various levels of the database management systems and makes the data worthless to others except the owner. In addition to designing an efficient MFA, through extensive experimental results we illustrate our privacy protection gateway is practical and effective.

Trusted framework for online banking in public cloud using multi-factor authentication and privacy protection gateway

In this paper, a new hybrid classification approach, which uses Weighted-Particle Swarm Optimization (WPSO) for data clustering in sequence with Smooth Support Vector Machine (SSVM) for classification is proposed. The performance of WPSO clustering is compared with K means and fuzzy methods using intercluster, intracluster and validity index. The accuracy of proposed WPSO-SSVM classification methodology are 83.76% for liver disorder, 98.42% for WBCD, 95.21% for mammographic mass data which are better than in existing literature.

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Latha Parthiban

Papers

Intelligent Heart Disease Prediction System Using CANFIS and Genetic Algorithm

Quasi Oppositional Dragonfly Algorithm for Load Balancing in Cloud Computing Environment

A Novel Texture Extraction Technique with T1 Weighted MRI for the Classification of Alzheimer’s Disease

Trusted framework for online banking in public cloud using multi-factor authentication and privacy protection gateway

Abnormality detection using weighed particle swarm optimization and smooth support vector machine