Informatics Publishing Limited

B. Kavitha ¹, G. Hemalatha ¹, S. Kanchana ¹, S. P. Sundaram ², K. Sivasubramaniam ³

Affiliations
1 Department of Food Science and Nutrition, Home Science College and Research Institute, Tamil Nadu Agricultural University, Madurai, IN
2 Dean, Department of Agricultural Microbiology, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai, India, IN
3 Department of Seed Science and Technology, Agricultural College and Research Institute, Theni, IN

Abstract

The present work is designed to study the physico chemical, functional, pasting properties, and nutritional composition of selected black gram varieties. (viz., VBN 3, VBN 4, VBN 5, VBN 6, ADT 3, T9, MV, TMV, VBg010 025, VBg010 024, VBg09 005 and CO 6). Thousand grain weight of the selected black gram varieties was recorded to be 33.20 to 40.45 g, seed volume 38.66 to 40.2 ml, seed colour ranged as black, dull black and black and dull black, bulk density 0.06 to 1.07 g/ml, water absorption index 151.00 to 155.10 g/100g, water solubility index 13.0 to 15.6 g %, water absorption 36.6 to 56.6 ml/100g and oil absorption 40.1 to 66.2 ml/100g. The moisture values were in the range of 9.6 to 11.6 g/100g, ash 6.1 to 6.7 g/100g, protein 25.5 to 28.5 g/100g, fat 4.4 to 5.6 g/100g, starch 51.3 to 47.7 g/100g, calcium 106.66 to 134.00 mg/100g, iron 3.0 to 4.4 mg/100g and phosphorus 376.00 to 416 mg/100g. It was observed that black gram varieties, VBN 5, VBg 010 025, CO 6 and T9 had high 1000 grain weight and bulk density. Variety VBN 5 and T9 had higher foaming stability, foaming capacity, peak viscosity, final viscosity, hold viscosity and set back value. Also higher levels of protein, starch, calcium, iron and phosphorus was observed in VBN 5 and low amount of fat and ash .Vamban 5, VBg010 025 and T9 were observed to have good physicochemical characteristics and are hence suitable for further breeding and processing as value added products.

Keywords

Black Gram Varieties, Vamban, Tindivanam, Coimbatore, Aduthurai, Market Variety, Physicochemical, Pasting Properties, Arabinose

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B. Kavitha ¹, M. Uma Gowri ², R. Sangeetha ²

Affiliations
1 PGP College of Agricultural Sciences, NAMAKKAL (T.N.), IN
2 Department of Agricultural Economics, Tamil Nadu Agricultural University, COIMBATORE (T.N.), IN

Abstract

Water is a critical input for success of agriculture and its ever widening technology creation. Water scarcity as a risk has always been a part of agriculture and farm business. Hence, management of available irrigation water assumes great importance in the field of agriculture. The present study was attempted in Namakkal district of Tamil Nadu, India, because of the availability of exclusive dry farms. In Namakkal district, blocks namely Namakkal, Mohanur, Pudhuchatram, Tiruchengode for each risky crop were purposively selected on the basis of area, production and productivity of those crops. Linear programming model was applied to derive optimal crop plan for the sample farms under water stress conditions. Optimization of crop portfolio for the selected farm is a type of risk management strategy. The decline in gross cropped area in Namakkal block would imply relatively less pressure on land that would again indicate the sign of bringing in sustainability of the productive capacity of the land. Even though the decline trend in net income, it showed that the resources which were used in the cultivation as efficiency factor in Pudhuchatram. The results indicated that water and other resources were used efficiently to get optimum pattern in the cultivation of different crops.

Keywords

Water Stress, Resource Use, Farm Size, Linear Programming, Optimization.

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R. Sangeetha ¹, B. Kavitha ¹, T. R. Shanmugam ¹

Affiliations
1 Department of Agricultural Economics, Tamil Nadu Agricultural University, COIMBATORE (T.N.), IN

Abstract

Investment plays a vital role in the agricultural production process. It induces production and savings, further investment helps in the development of the economy. It plays an equally important role in farm economy especially in the era of technological revolution. The study was attempted to assess the impact of farm finance and investment on profitability of farms in Annur block of Coimbatore district. This block was purposively selected for the study since it has the highest number of borrowers for agriculture purpose from commercial banks and it also has the highest loan amount given for agricultural purposes. From the selected block, four villages were selected randomly for the study. The tools of analysis included conventional percentage analysis, returns to investment, net cash income. The borrower farms depend more on the Commercial banks (55.00 %) to meet their farm investment and it was followed by Regional Rural Banks (15.00 %) and Co-operative Banks (15.00 %). The contribution of relatives and friends to the total farm investment was 12.50 per cent and the money lenders contributed about 2.50 per cent to the total farm investment in the borrower farm firms. The gross income was calculated for the borrower and non-borrower farms, the results revealed that borrowers had higher gross income (Rs. 314301.07) than that of the non-borrowers (Rs. 208116.40). The major contributor of income for the borrowers was crop income (45.04 %), which was followed by livestock income (34.90 %) and then by non-farm and off farm income (20.06 %). In the non-borrowers crop income contributed (57.82 %) of the total income, followed by livestock income (25.62 %) and then by non-farm and off farm income (16.56 %). Returns from the investment were higher in case of the borrower farms as the returns from investment ratio was 1.61, whereas in the non-borrowers it was 1.15. The net cash income obtained was also higher in the borrower farms (Rs. 154818.75) than that of the non- borrower farms (Rs. 92980.67).

Keywords

Conventional Percentage Analysis, Returns to Investment, Net Cash Income.

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B. Kavitha ¹, T. Sathiyavathi ¹

Affiliations
1 Computer Applications Dept, Karpagam University, Coimbatore-21, IN

Abstract

E-commerce and m-commerce transactions are growing at an explosive rate. The success of these depends on how transactions are carried out in the most secured manner. As the popularity of the Web increases, the Web will continue to evolve from a means of providing an easy way of accessing (and publishing) information on the Internet to a virtual marketplace where everything can be bought or sold, just like in the physical world. In order to ensure privacy, there needs to be security. The market already provides several security tools, such as the Secure Socket Layer (SSL) protocol developed by Netscape. Another example is Pretty Good Privacy. Such security tools can help protect privacy by preventing access to the information for non-authorized parties. But privacy requires more than that. There also need to be ways of controlling the access to and the distribution of information. This paper presents how message level security is achieved in web services interactions and evaluated a number of commonly used cryptographic algorithms to determine which are most suitable for the task. The implementation of a hybrid algorithm is performed by combining both the symmetric key algorithm of AES and the asymmetric key algorithm of Elliptic Curve Cryptography (ECC). This hybrid algorithm that has been implemented also considers takes care of the integrity of data using MD5 algorithm.

Keywords

Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC), Internet Security, MD5 Algorithm Secure Socket Layer (SSL).

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B. Kavitha ¹, D. Shanmuga Priyaa ¹, B. Chitra ²

Affiliations
1 Department of Computer Science, Karpagam University, Coimbatore-21, IN
2 Department of Computer Applications, Shri Nehru Maha Vidyalaya College of Arts and Science, Coimbatore-21, IN

Abstract

Data Mining is the process of extracting patterns from data. Machine Learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn based on data, such as from sensor data or databases. A major focus of machine learning research is automatically learn to recognize complex patterns and make intelligent decisions based on data. Engineers and researchers in the automobile industry have tried to design and build safer automobiles, but traffic accidents are unavoidable. Patterns involved in dangerous crashes could be detected if we develop a prediction model that automatically classifies the type of injury severity of various traffic accidents. These behavioral and roadway patterns are useful in the development of traffic safety control policy. We believe that to obtain the greatest possible accident reduction effects with limited budgetary resources, it is important that measures be based on scientific and objective surveys of the causes of accidents and severity of injuries. This paper deals about some classification models to predict the severity of injury that occurred during traffic accidents using two machine-learning approaches. We compared Naive Bayesian classifier and J48 decision tree Classifier for classifying the type of injury severity of various traffic accidents and the result shows that J48 outperforms Naive Bayesian.

Keywords

Data Mining, J48 Decision Tree Classifier, Machine Learning, Naive Bayesian Classifier, Prediction.

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Dhananjay Kumar ¹, B. Kavitha ², M. Padmavathy ¹, B. Harshini ¹, E. Preethi ¹, P. Varalakshmi ²

Affiliations
1 Department of Information Technology, Anna University, MIT Campus, Chennai, IN
2 Department of Computer Technology, Anna University, MIT Campus, Chennai, IN

Abstract

Cloud Computing is a dominant way of sharing of computing resources that can be configured and provisioned easily. Task scheduling in Hybrid cloud is a challenge as it suffers from producing the best QoS (Quality of Service) when there is a high demand. In this paper a new resource allocation algorithm, to find the best External Cloud provider when the intermediate provider's resources aren't enough to satisfy the customer's demand is proposed. The proposed algorithm called Optimized Particle Swarm Optimization (OPSO) combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO). These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even when the number of tasks submitted for execution is very high. This optimization is performed to allocate job requests to internal and external cloud providers to obtain maximum profit. It helps to improve the system performance by improving the CPU utilization, and handle multiple requests at the same time. The simulation result shows that an OPSO yields 0.1% - 5% profit to the intermediate cloud provider compared with standard PSO and ACO algorithms and it also increases the CPU utilization by 0.1%.

Keywords

Hybrid Cloud, Particle Swarm Optimization, Ant Colony Optimization, Task Scheduling.

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M. Uma Gowri ¹, B. Kavitha ²

Affiliations
1 Centre for Agricultural and Rural Development Studies, Tamil Nadu Agricultural University, Coimbatore (T.N.), IN
2 Department of Agricultural Economics, P.G.P. College of Agricultural Sciences, Namakkal (T.N.), IN

Abstract

The banana is an edible fruit, botanically a berry, produced by several kinds of large her baceous flowering plants in the genus Musa. Banana is a globally important fruit crop with 97.5 million tones of production. In India it supports livelihood of millions of people with total annual production of 16.91 million tones from 490.70 thousand ha. with national average of 33.5 T/ ha. Banana contributes 37 per cent to total fruit production in India. Forecasting tools was used study of banana in Tamil Nadu. According to the MAPE value ARIMA method is most appropriate method for forecasting in banana. The cost and returns analysis reveals that higher net returns was realized in Nendran variety.

Keywords

Banana, Forecating Tools, Exponential Smoothing, ARIMA Model.

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M. Chinnadurai ¹, R. Sangeetha ¹, A. Anbarassan ¹, B. Kavitha ¹

Affiliations
1 Centre for Agriculture and Rural Development Studies, Tamil Nadu Agricultural University, Coimbatore (T.N.), IN

Abstract

The present study examines the performance of major cotton domestic markets viz., Andhra Pradesh (Khammam), Gujarat (Gondal), Karnataka (Raichur), Maharashtra (Akola) and Tamil Nadu (Konganapuram) of India using monthly wholesale prices of cotton in terms of market integration by using co-integration test and Johansen multivariate co-integration test. Unit ischolar_main test indicated that the price series in each location are non -stationary at their levels, and stationary at their first differences. Co-integration results showed that the regional markets have price linkages and thus, these markets are spatially integrated. The findings revealed that bidirectional relationships exist within domestic markets which indicated the price transmission happening in short run adjustments and the presence of long run equilibrium existed among the cotton markets in Andhra Pradesh, Gujarat, Karnataka, Maharashtra and Tamil Nadu. In case of Tamil Nadu cotton market, the speed of adjustment towards equilibrium was almost 0.34 per cent and Karnataka market was the key determinant of shocks in the cotton market of Tamil Nadu. Overall, the results imply effective price transmission mechanism in the domestic markets and any further boost to the existing infrastructure will help in improving both producer’s and consumer’s surpluses.

Keywords

Cotton, Time Series, Spatial Market Integration, Price Transmission.

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B. Kavitha ¹, M. Uma Gowri ¹, K. Muthulakshmi ²

Affiliations
1 Department of Agricultural Economics, Centre for Agriculture and Rural Development Studies, Tamil Nadu Agricultural University, Coimbatore 641 003, IN
2 Kalasalingam School of Agriculture and Horticulture, Kalasalingam Academy for Research and Education, Krishnan Kovil, Virudhunagar 626 126, IN

Abstract

Good agricultural practices (GAPs) in mango production are essential to enable farm produce to be internationally competitive with sufficient institutional credit. Economic efficiency of 0.45 and 0.68 respectively for conventional and GAP farms in Krishnagiri district of Tamil Nadu, India implies that there is scope to increase mango output by 55% and 32% respectively, by optimum allocation of resources. The highest return invested by GAP borrowers might be due to efficient use of resource and GAPs. The extension workers should develop strategies to increase income through adoption of GAPs, efficient use of resources and strengthening the loan delivery mechanism to enhance mango production.

Keywords

Data Envelopment Analysis, Economic Efficiency, Farm Credit, Good Agricultural Practices, Mango Cultivation.

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