COMPARATIVE ANALYSIS OF PVM AND MPI FOR THE DEVELOPMENT OF PHYSICAL APPLICATIONS IN PARALLEL AND DISTRIBUTED SYSTEMS

This research is aimed to explore each of these two Parallel Virtual Machine (PVM) and Message Passing Interface(MPI) vehicles for DPP (Distributed Parallel Processing) considering capability, ease of use, and availability, and compares their distinguishing features and also explores programmer interface and their utilization for solving real world parallel processing applications. This work recommends a potential research issue, that is, to study the feasibility of creating a programming environment that allows access to the virtual machine features of PVM and the message passing features of MPI. PVM and MPI, two systems for programming clusters, are often compared. Each system has its unique strengths and this will remain so in to the foreseeable future. The comparisons usually start with the unspoken assumption that PVM and MPI represent different solutions to the same problem. In this paper we show that, in fact, the two systems often are solving different problems. In cases where the problems do match but the solutions chosen by PVM and MPI are different, we explain the reasons for the differences. Usually such differences can be traced to explicit differences in the goals of the two systems, their origins, or the relationship between their specifications and their implementations. This paper also compares PVM and MPI features, pointing out the situations where one may be favored over the other; it explains the deference’s between these systems and the reasons for such deference’s

Using Web-Referral Architectures to Mitigate Denial-of-Service Threats

Abstract The web is a complicated graph, with millions of websites interlinked together. To use this web site-graph structure to mitigate flooding attacks on a website, using new web referral architecture for privileged service ("WRAPS"). WRAPS allows a legitimate client to obtain a privilege URL through a simple click on a referral hyperlink, from a website trusted by the target website. Using that URL, the client can get privileged access to the target website in a manner that is far less vulnerable to a distributed denial-of-service (DDOS) flooding attack than normal access would be. WRAPS does not require changes to web client software and is extremely lightweight for referrer websites, which makes its deployment easy. The massive scale of the web sitegraph could deter attempts to isolate a website through blocking all referrers. WRAPS enables legitimate clients to connect to a website smoothly in spite of a very intensive flooding attack, at the cost of small overheads on the website's ISP's edge routers. The security properties of WRAPS and a simple approach to encourage many small websites to help protect an important site during DOS attacks

Importance of supervised learning in prediction analysis

Counterfeit medicines are fake medicines which are either contaminated or contain the wrong or no active ingredient. Up to 30% of medicines in developing countries are counterfeit. Using Supervised Machine learning techniques we build a predictive model for predicting sales figures given other information related to counterfeit medicine selling operations. Thus, by predicting the values we can identify these illegal operations and counter them. In this paper we have also mentioned the importance of Data mining and Machine Learning algorithms with some comparison analysis

Data science: Identifying influencers in social networks

Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data. The common use of Online Social Networks (OSN)[2] for networking communication which authorizes real-time multimedia capturing and sharing, have led to enormous amounts of user-generated content in online, and made publicly available for analysis and mining. The efforts have been made for more privacy awareness to protect personal data against privacy threats. The principal idea in designing different marketing strategies is to identify the influencers in the network communication. The individuals influential induce “word-of-mouth” that effects in the network are responsible for causing particular action of influence that convinces their peers (followers) to perform a similar action in buying a product. Targeting these influencers usually leads to a vast spread of the information across the network. Hence it is important to identify such individuals in a network, we use centrality measures to identify assign an influence score to each user. The user with higher score is considered as a better influencer

DATA MINING AND TEXT MINING: EFFICIENT TEXT CLASSIFICATION USING SVMS FOR LARGE DATASETS

The Text mining and Data mining supports different kinds of algorithms for classification of large data sets. The Text Categorization is traditionally done by using the Term Frequency and Inverse Document Frequency. This method does not satisfy elimination of unimportant words in the document. For reducing the error classifying of documents in wrong category, efficient classification algorithms are needed. Support Vector Machines (SVM) is used based on the large margin data sets for classification algorithms that give good generalization, compactness and performance. Support Vector Machines (SVM) provides low accuracy and to solve large data sets, it typically needs large number of support vectors. We introduce a new learning algorithm, which is comfortable to solve the dual problem, by adding the support vectors incrementally. It majorly involves a classification algorithm by solving the primal problem instead of the dual problem. By using this, we are able to reduce the resultant classifier complexity by comparing with the existing works. Experimental results done and produce comparable classification accuracy with existing works