The paper describes a system dynamics model developed for dynamic analysis of human resource development for the agricultural sector in different sources of employment, viz., government, private (including corporate), academic, financial institutes, nongovernmental organizations, self employment, and others in India. Besides projecting an overall scenario for continuation of current agricultural education policy and trends, the paper analyses simulated results from the model for the current curriculum with 80:20 proportion of technical to soft skills. The analysis shows that in the coming years the private sector will emerge as a major employer for the graduates of agriculture and allied sciences. Keywords-Human Resource Development, Agricultural, India. AGRICULTURAL SECTOR Agricultural growth is critical for sustainable and inclusive economic growth in India, as the vast majority of the population depends on the agricultural sector for their livelihood. Close to 60 percent of India's labor force is employed in agriculture, according to the 2011 census. The majority of landholdings are small. Some 82 percent were classified as small scale in 2006; and farms less than two hectares occupied 40 percent of India's agricultural land (GoI 2011). Since the Green Revolution era, India has achieved impressive growth in agricultural production, boosting national food security and reducing poverty (Fan, Gulati, and Thorat 2008). But the agricultural sector still faces crucial challenges. Growth in agricultural production continues to lag behind the targeted 4 percent, and poverty and malnutrition remain widespread. Key development challenges for the coming decades are meeting the growing and diversifying food demand, especially for livestock and horticultural products, managing natural resources sustainably, and raising the productivity of rain fed agriculture. I.

No abstract

The Aim of Association Rule Mining(ARM) is to find Frequent itemsets. Apriori Algorithm is one of the most efficient Frequent itemset mining Algorithm. However Frequent itemset mining does not includes interestingness or utility. Utility mining is a new area in data mining which considers all external utility factors. A specialized form of Association Rule Mining is utility-frequent itemset mining, here both utility factors and itemset frequencies are considered. Fast utility frequent itemset mining (FUFM) is one of the efficient algorithm to find utilityfrequent itemsets. The performance of an Algorithm depends on several factors like space(memory), computing time, cyclomatic complexity, external data dependency and so on. The proposed system aims in reducing the computing time of existing FUFM by implementing a Parallel computing strategy, the proposed Algorithm is Parallel implementation of Fast Utility Frequent itemset Mining algorithm(P-FUFM). Utility-frequent itemset mining algorithm consists of two phases, candidates generation and utilities generation. Utility generation is just a product function whereas candidate generation is a iterative selection process, hence the proposed algorithm is to implement parallel generation of candidate keys and standalone strategy for utilities generation. The proposed implementation and results shows that P-FUFM computes utility-frequent itemsets in very less computing time and is more suitable for Business Development.

No abstract

No abstract