The article examines the relationship between public investment in infrastructure in India and regional incomes and whether the character of this relationship is affected both by the nature of investment and the level of socio-economic development of the different regions. The possibility of using regional distribution of public investment as a policy instrument to reduce regional disparities is evaluated, with the analysis demonstrating that while there were prospects of concentrating on the varying forms of infrastructure in different categories of states at earlier points in time, the current situation calls for each region receiving investment in all of the different forms of infrastructure.Regional, Planning, Infrastructure, Public, Investment, Impact, Policy, Analysis,

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Systems for web information mining can be isolated into a few classifications as indicated by a sort of mined data and objectives that specif-ic classifications set: Web structure mining, Web utilization mining, and Web Content Mining. This paper proposes another Web Content Mining system for page significance positioning taking into account the page content investigation. The strategy, we call it Page Content Rank (PCR) in the paper, consolidates various heuristics that appear to be critical for breaking down the substance of Web pages. The page significance is resolved on the base of the significance of terms which the page contains. The significance of a term is determined concern-ing a given inquiry q and it depends on its measurable and linguistic elements. As a source set of pages for mining we utilize an arrangement of pages reacted by a web search tool to the question q. PCR utilizes a neural system as its inward order structure. We depict a usage of the proposed strategy and an examination of its outcomes with the other existing characterization framework -page rank algorithm.

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System reconfiguration which is compelled non linear enhancement issue has been tackled for loss minimization, load balancing, and so on. Another factor of equivalent significance is of DG plays a critical responsibility in the management of distribution system. It's important to optimize its size and location in order extract maximum benefits of its placement. There are number reasons for optimizing the location and sizing, chief among them being reduce power loss and enhancement of voltage profile. Here a hybrid algorithm is proposed for reconfiguration and DG siting is employed to enhance the benefits of DG placement. Non Sorted Genetic Algorithm (NSGA) is employed to reconfigure the distribution system prior to the placement of DG. Once the system is reconfigured, Particle Swarm Optimization (PSO) is employed to recognize the ideal size and placement of DG. The results exhibit the suitability of this combined algorithm in terms reduced power losses and enhanced voltage profile. Results are compared and analyzed for DG placement with and without reconfiguration for IEEE 69 bus distribution system.

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