Development Planning at the State Level in India - A Case Study with Reference to Kerala, 1957-'84

This thesis entitled “Development planning at the state level in india a case study with reference to kerala1957-84.Planning in India is a concurrent subject with the Centre and the States having well-defined domains of jurisdiction with regard to planning functions and sources of resource mobilisation.The genesis of the lack of academic interest in state level planning is in the widely held belief that in the extent scheme of Centre-State economic relations, the states have little scope for initiative in planning.Both at the theoretical and empirical levels, Kerala has attached very great importance to planning.It has been the localeof wide and deep discussions on the various dimensions of planning.In Kerala's development process, the leading sector consists of social services such as education and public healthOne point that needs special emphasis in this regard is that the high demand for education in Kerala cannot be attributed to the Keralites' ‘unique urge‘ for education. Rather, it is related to the very high level of unemployment in the state (Kerala has the highest level of unemployment in the country.In resource allocation under the Five Year Plans, Kerala attached the highest weightage to power generation, hydro-electric projects being the major source of power in the state. Nearly one-fourth of the plan resources has been claimed by hydro-electric projects.In the agricultural sector, Kera1a's level of productive use of electric power is one of the lowest.As is evident.from above, planning in Kerala has not enabled us to solve the basic problems of the state. More 'scientific' planning in the sense of applying mre sophisticated planning techniques is obviously not the answer. It, on the contrary, consists of more fundamental changes some of which can be brought about through an effective use of measures well within the power of the State Government.

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.