IRDO: Iris Recognition by Fusion of DTCWT and OLBP

Iris Biometric is a physiological trait of human beings. In this paper, we propose Iris an Recognition using Fusion of Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP) Features. An eye is preprocessed to extract the iris part and obtain the Region of Interest (ROI) area from an iris. The complex wavelet features are extracted for region from the Iris DTCWT. OLBP is further applied on ROI to generate features of magnitude coefficients. The resultant features are generated by fusing DTCWT and OLBP using arithmetic addition. The Euclidean Distance (ED) is used to compare test iris with database iris features to identify a person. It is observed that the values of Total Success Rate (TSR) and Equal Error Rate (EER) are better in the case of proposed IRDO compared to the state-of-the art technique

Automatic Discovery and Ranking of Synonyms for Search Keywords in the Web

Search engines are an indispensable part of a web user's life. A vast majority of these web users experience difficulties caused by the keyword-based search engines such as inaccurate results for queries and irrelevant URLs even though the given keyword is present in them. Also, relevant URLs may be lost as they may have the synonym of the keyword and not the original one. This condition is known as the polysemy problem. To alleviate these problems, we propose an algorithm called automatic discovery and ranking of synonyms for search keywords in the web (ADRS). The proposed method generates a list of candidate synonyms for individual keywords by employing the relevance factor of the URLs associated with the synonyms. Then, ranking of these candidate synonyms is done using co-occurrence frequencies and various page count-based measures. One of the major advantages of our algorithm is that it is highly scalable which makes it applicable to online data on the dynamic, domain-independent and unstructured World Wide Web. The experimental results show that the best results are obtained using the proposed algorithm with WebJaccard

Managing IT Operations in a Cloud-driven Enterprise: Case Studies

Enterprise IT needs a new approach to manage processes, applications and infrastructure which are distributed across a mix of environments. In an Enterprise traditionally a request to deliver an application to business could take weeks or months due to decision-making functions, multiple approval bodies and processes that exist within IT departments. These delays in delivering a requested service can lead to dissatisfaction, with the result that the line-of-business group may seek alternative sources of IT capabilities. Also the complex IT infrastructure of these enterprises cannot keep up with the demand of new applications and services from an increasingly dispersed and mobile workforce which results in slower rollout of critical applications and services, limited resources, poor operation visibility and control. In such scenarios, it’s better to adopt cloud services to substitute for new application deployment otherwise most Enterprise IT organizations face the risk of losing 'market share' to the Public Cloud. Using Cloud Model the organizations should increase ROI, lower TCO and operate with seamless IT operations. It also helps to beat shadow IT and the practice of resource over-or under provisioning. In this research paper we have given two case studies where we migrated two Enterprise IT application to public clouds for the purpose of lower TCO and higher ROI. By migrating, the IT organizations improved IT agility, enterprise-class software for performance, security and control. In this paper, we also focus on the advantages and challenges while adopting cloud services

Assimilating sense into disaster recovery databases and judgement framing proceedings for the fastest recovery

The replication between the primary and secondary (standby) databases can be configured in either synchronous or asynchronous mode. It is referred to as out-of-sync in either mode if there is any lag between the primary and standby databases. In the previous research, the advantages of the asynchronous method were demonstrated over the synchronous method on highly transactional databases. The asynchronous method requires human intervention and a great deal of manual effort to configure disaster recovery database setups. Moreover, in existing setups there was no accurate calculation process for estimating the lag between the primary and standby databases in terms of sequences and time factors with intelligence. To address these research gaps, the current work has implemented a self-image looping database link process and provided decision-making capabilities at standby databases. Those decisions from standby are always in favor of selecting the most efficient data retrieval method and being in sync with the primary database. The purpose of this paper is to add intelligence and automation to the standby database to begin taking decisions based on the rate of concurrency in transactions at primary and out-of-sync status at standby

Security and Privacy Issues in Cloud Computing

Cloud computing transforming the way of information technology (IT) for consuming and managing, promising improving cost efficiencies, accelerate innovations, faster time-to-market and the ability to scale applications on demand (Leighton, 2009). According to Gartner, while the hype grew ex-ponentially during 2008 and continued since, it is clear that there is a major shift towards the cloud computing model and that the benefits may be substantial (Gartner Hype-Cycle, 2012). However, as the shape of the cloud computing is emerging and developing rapidly both conceptually and in reality, the legal/contractual, economic, service quality, interoperability, security and privacy issues still pose significant challenges. In this chapter, we describe various service and deployment models of cloud computing and identify major challenges. In particular, we discuss three critical challenges: regulatory, security and privacy issues in cloud computing. Some solutions to mitigate these challenges are also proposed along with a brief presentation on the future trends in cloud computing deployment

Systematic survey on evolution of cloud architectures

Cloud architectures are becoming an active area of research. The quality and durability of a software system are defined by its architecture. The architecture approaches that are used to build cloud-based systems are not available in a blended fashion to achieve an effective universal architecture solution. The paper aims to contribute to the systematic literature review (SLR) to assist researchers who are striving to contribute in this area. The main objective of this review is to systematically identify and analyse the recently published research topics related to software architecture for cloud with regard to research activity, used tools and techniques, proposed approaches, domains. The applied method is SLR based on four selected electronic databases proposed by (Kitchenham and Charters, 2007). Out of 400 classified publications, we regard 121 as relevant for our research domain. We outline taxonomy of their topics and domains, provide lists of used methods and proposed approaches. At present, there is little research coverage on software architectures for cloud, while other disciplines have become more active. The future work is to develop a secure architecture to achieve quality of service and service level agreements

Concept Based Dynamic Ontology Creation for Job Recommendation System

AbstractThe basis of our research is to construct a job recommendation system to the job seekers by collecting the job portals data. Due to huge amounts of the data in job portals the employers are facing difficulty in the identification of right candidate for the required skill and experience. The job seekers are also facing the problem of getting the suitability of the job based on their skill and experience. The knowledge acquisition based on the requirements is very difficult in case of huge amounts of the data sources. In fact classical development of domain ontology is typically entirely based on strong human participation. It does not adequately fit new applications requirements, because they need a more dynamic ontology and the possibility to manage a considerable quantity of concepts that human cannot achieve alone. The main focus of our work is to generate a job recommendation system with the details of job by taking account into the data posted in the web sites and data from the job seekers by the creation of dynamic ontology. We strongly believe that our system will give the best outcome in case of suitable job recommendation for both employers and job seekers without spending much time. To achieve this first we have extracted the data from various web pages and stored the collected data into .csv files. In the second stage the stored input files are used by the similarity measure and ontology creation module by generating the corresponding Web Ontology Language (.owl) file. The third stage is creating the ontology with the generated .owl by using protégé tool