Growth in Agricultural Resource USE: An Application of Exponential Growth Curve.
Agriculture is the means of livelihood for around two thirds of the work force of India. Agriculture is the production, processing, marketing and use of foods, fibers and bye prducts from plant crops and animals. It was the key development that led to the rise of human civilization with the husbandry of domesticated animals plants creating food surpluses that enabled the development of more densely populated and stratified societies. At the time of independence, the revenue from the agriculture sector was quite low compared to what it is today. The main reason for the increase in the revenue is the in crease in agricultural production that was brought about by the Green revolution, over the years, agriculture has emerged as one of the top priorities of the Central & state governments. In 2000, the government announced the first over “National Agriculture Policy”. The resources taken were consumption of fertilizers, consumption of electricity in agricultural sector, short term and long term credit, number of tractors, area under high yielding varieties, net irrigated and gross irrigated area and total cropped area. It was computed using the exponential trend equation i.e.: Y = abt Log Y = log a + t log b taking ^ b = (1+r) r = (b-1) x 100 Where Y = study variable; area, production, yield or Resource variables a = constant ^ b = regression coefficient t = time, t = 1 ……………n r = compound growth rate in percent To test the significance of the compound growth rates, t-test applied was t* = r/S.E( r) Where t* = calculated t-ratio, distributed with (n-2) degrees of freedom r = compound growth rate S.E. (r) = standard error of the compound growth rate, S.E. was calculated by fitting the following formula. 100 x b S.E. (r) = Σ (log y)2 (Σ log y)2 (log b )2 Σ ( t – t )2 Log 10e n (n-2) Σ (t – t )2 where the limit for Σ is, i = 1,2……n
Published by: Prince Singh, Dr Manjeet Jakha
Author: Prince Singh
Paper ID: V2I6-1177
Paper Status: published
Published: December 2, 2016
Data Mining : Text Classification System for Classifying Abstracts of Research Papers
Text classification is the process of classifying documents into predefined categories based on their content.Text classification is the primary requirement of text retrieval systems,which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data.We have proposed a Text Classification system for classifying abstract of different research papers. In this System we have extracted keywords using Porter Stemmer and Tokenizer. The word set is formed from the derived keywords using Association Rule and Apriori algorithm. The Probability of the word set is calculated using naive bayes classifier and then the new abstract inserted by the user is classified as belonging to one of the various classes. The accuracy of the system is found satisfactory. It requires less training data as compared to other classification system.
Published by: Shirdi Wazeed Baba, Reddi Sanjeev Kumar
Author: Shirdi Wazeed Baba
Paper ID: V2I6-1175
Paper Status: published
Published: December 1, 2016
Multivariate Indoor Scene Recognition using the Object Level Analysis with SVM Classification
The research area of the indoor scene recognition has attracted the various scientists and engineers across the globe, which includes the neuroscientists, electronics engineers, robotic engineers, digital image experts, camera developers and manufacturers for the purpose of application designing in the fields of the computer vision, vision based communications and the access control systems. The indoor scene recognition methods require the inclusion of the various methods in the computer vision, image processing and feature recognition for the scene recognition by identifying the category of the input image by comparing it against the given training databases by the means of the feature descriptor (popularly based upon the color or low level features) and the classification algorithm. The indoor scene classification algorithms require the number of the computations and feature transformations along with the normalization and automatic categorization. In this thesis, the multi-category dataset has been incorporated with the robust feature descriptor using the scale invariant feature transform (SIFT) along with the multi-category enabled support vector machine (mSVM).
Published by: Neetu Dhingra
Author: Neetu Dhingra
Paper ID: V2I6-1174
Paper Status: published
Published: November 30, 2016
Microwave Welding using Stainless Steel Grades – A Short Survey
This paper deals with Microwave welding using stainless steel grades.The field of microwave joining had taken a great leap in the past decade, Due to its special and exceptional characteristics like selective heating, volumetric heating and inverted heating profile its has been introduce in the field of the microwave joining of materials ,previously which was limited only to the processing of food. It also has an edge over conventional methods due to these characteristics. Microwave energy is generally new area of topic in material welding or material joining even though it has been introduced already in many industries like medical, food processing, drying. This process with the help of microwave radiations deals with joining of similar and dissimilar materials. These joints were characterized using various techniques.
Published by: Valsaraj T.S, Mr. Sathish Kumar
Author: Valsaraj T.S
Paper ID: V2I6-1172
Paper Status: published
Published: November 29, 2016
On the Principle of Exchange of Stabilities in the Magnetohydro Dynamic Benard Problem with Variable Gravity by Positive Operator Method.
In the present paper, the problem of Benard for the magneto hydrodynamic field heated from below with variable gravity is analyzed and it is established by the method of positive operator of Weinberger and by using the properties of Green’s function that principle of exchange of stabilities is valid for this general problem, when g(z) is non-negative throughout the fluid layer.
Published by: Pushap Lata Sharma
Author: Pushap Lata Sharma
Paper ID: V2I6-1171
Paper Status: published
Published: November 29, 2016
Development of Multi Objective Techniques-A Case Study of Punjab and Haryana
In India and abroad, the commonly used decision modelling in real life rests on the assumption that the decision maker seeks to optimize a well-defined single objective using traditional mathematics programming approach. A farmer may be interested in maximizing his cash income, with certain emphasis on risk minimization. On the other at county level especially in a developing country a planner may aspire for a plan while maximizes food grains production and also to some extent considers employment maximization etc as the goals. Keeping in view the objectives of the study, state-wise secondary data on different variables for the period 1980-81 to 2014-15 were collected from Statistical Abstracts of Punjab, Fertilizer Statistics, Agricultural Statistics at a glance and the reports of the Commission for Agricultural Costs and Prices, published by Ministry of Agriculture By taking its deviations of observed Yt from its estimated value we got the error or the risk coefficients for each year for each crop. These risk coefficients were taken in the matrix formulation in the MOTAD format suggested by Hazell (1971 a and b). To give a meaningful explanation to the level of risk, total mean absolute deviations in gross returns were derived as under: Min A = 1/S Σ│ (chj-gj) xj│ Where A is the minimum average absolute deviation defined as the mean over (h=1………s) years, of the sum of the deviations of gross returns (chj) from the trend in gross returns (gj) multiplied by activity levels x j (j = 1………n). Where A is an unbiased estimator of the population mean absolute income deviation Where A = estimated mean absolute deviation S = no. of years chj = gross returns of the jth activity in hth year gj = sample mean of gross returns of jth activity x j = activity level This was minimized subject to the following constraints: Σaij xj ≤ bi (for all i = 1………….m, j =1……..n) Total activity requirements for the i th constraint, the sum of the unit activity requirements aij for the constraint i times the activity levels ‘xj‘do not exceed the level of the i th constraint bi for all ‘i’ and x j 0 all activity levels are non negative. Where a ij = per unit technical requirement for the jth activity of the ith resource. bi = the ith resource constraint level m = no. of constraints n = no. of activities
Published by: Prince Singh, Dr Manjeet Jakha
Author: Prince Singh
Paper ID: V2I6-1170
Paper Status: published
Published: November 29, 2016
