Environmental impact of sand mining

This thesis Entitled Environmental impact of Sand Mining :A case Study in the river catchments of vembanad lake southwest india.The entire study is addressed in nine chapters. Chapter l deals with the general introduction about rivers, problems of river sand mining, objectives, location of the study area and scope of the study. A detailed review on river classification, classic concepts in riverine studies, geological work of rivers and channel processes, importance of river ecosystems and its need for management are dealt in Chapter 2. Chapter 3 gives a comprehensive account of the study area - its location, administrative divisions, physiography, soil, geology, land use and living and non-living resources. The various methods adopted in the study are dealt in Chapter 4. Chapter 5 contains river characteristics like drainage, environmental and geologic setting, channel characteristics, river discharge and water quality of the study area. Chapter 6 gives an account of river sand mining (instream and floodplain mining) from the study area. The various environmental problems of river sand mining on the land adjoining the river banks, river channel, water, biotic and social / human environments of the area and data interpretation are presented in Chapter 7. Chapter 8 deals with the Environmental Impact Assessment (EIA) and Environmental Management Plan (EMP) of sand mining from the river catchments of Vembanad lake.

Management of Newspaper Industry in Kerala with Special Reference to Marketing

The general objective of the study is to examine in depth the organisation and management practices of newspaper industry in Kerala with particular reference to the marketing aspects, with a View to suggesting measures for improving the economics and the managerial efficiency of the industry. The detailed investigation into the management aspects of the industry is done with particular reference to the two most popular Malayalam dailies in Kerala, namely, the Malayala Manorama and the Mathrubhumi. The purposeful selection of these two papers for the study is amply justified as these two dailies together account for about 80 percent of the total circulation of the newspapers in Kerala. Technically speaking, both these papers are owned by organisations registered as public limited companies and are, to a large extent professionally managed. The Malayala Manorama, though a public limited company in principle, functions, however, more or less as a private company or a family concern. These two papers therefore provide a scope for studying the management of newspaper industry practically under two different organisational set up, namely private limited company and public limited company The study has been divided into eight chapters. Chapter-I spells out an introduction about the newspaper industry and its unique features.Chapter-II, deals with a review of literature, objective, scope, methodology and limitations of the study. Chapter-III deals with origin, growth and status of newspaper industry. Chapter—IV examines the cost, revenue and profitability of the Malayala Manorama and Mathrubhumi. Chapter-V deals with the Organisation and Management. Chapter-VI examines the Marketing Management of Newspapers. Chapter-VII deals with the Marketing Strategy and Performance of Malayala Manorama and Mathrubhumi: An Assessment. Chapter-VIII presents the main findings of the study.

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining