Informatics Publishing Limited

M. K. Verma ¹, G. K. Sachdeva ¹, S. Gautam ², M. M. Ali ³, R. Kumar ⁴, S. Bindal ³

Affiliations
1 Division of Dairy Cattle Breeding, National Dairy Research Institute, KARNAL (HARYANA), IN
2 Division of Dairy Cattle Nutrition, National Dairy Research Institute, KARNAL (HARYANA), IN
3 Livestock Production and Management Section, National Dairy Research Institute, KARNAL (HARYANA), IN
4 Bihar Veterinary College, PATNA (BIHAR), IN

Abstract

The present study pertained to records on milk production and milk constituents of 259 Sahiwal cattle with 600 lactations spread over a period of 10 years from 2001 to 2010, collected from Dairy Cattle Breeding division of National Dairy Research Institute, Karnal, Haryana. To study the effect of various genetic and non-genetic factors on milk yield and milk constituents traits, mixed model least square analysis was used for analysis of data. Overall least square mean for all lactation traits of total milk yield (TMY), milk yield in 305 days or less (305MY), lactational average fat per cent (LFA) and lactational average solid not fat per cent (LSA) were 1880.39 ± 73.82 kg, 1782.97 ± 68.37 kg, 4.71 ± 0.01 per cent and 8.81 ± 0.01 per cent, respectively. Analysis of variance showed that the differences were statistically significant for the effect of sire on TMY and 305MY; period on all the traits except TMY; parity on 305MY. However, the effect of season of calving was not found significant on all the traits.

Keywords

Genetic and Non-Genetic Factors, Mixed Model, TMY, 305MY, LFA, LSA.

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K. S. Sivaraman ¹, S. Gautam ¹, S. Sarvesh ¹, Archit Khullar ¹, A. Baskar ¹, Shriram K. Vasudevan ¹

Affiliations
1 Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham (University), Coimbatore, IN

Abstract

Objective: This research aims at formulating a method to categorise a given class of objects by obtaining a weighted matrix computed as explained below. Methods/Analysis: The method deployed can be branched into two phases: Training and Testing. In the first phase, a set of images of the concerned objects are taken. By set of images, one can refer to images of different objects, or different positions of the same object. The features are then, extracted for these input images and stored in the database as vectors. Any computation hence forth, is performed using these vectors. In testing stage, the algorithm uses its knowledge to identify the input image to a specified class. Findings: Our method is computationally inexpensive since all the calculations are performed on the basic grounds of matrix operations. This method is not just limited to the domain of object recognition alone. Any real-time entity that can be statistically represented in a vector form can be deployed. All that is required of the application is that the range of vectors is defined so as to obtain the minimum components and maximum components, individually. Once this is obtained, the algorithm will be sufficient to identify any input and will accordingly determine the category to which it belongs. The only challenge identified is that the range of vectors obtained from the input data for various categories must not overlap. That being the case will result in multiple hits or in simpler words, will give an incorrect result. Further work can be implemented on how to make the algorithm independent of this dependency. Also, the algorithm improves the results through various illumination and scaling conditions and this has been discussed in results and analysis section.Even with the existing methods to recognize an object, this algorithm can be combined to categorize or classify objects. Conclusion/Application: The proposed algorithm successfully classifies the input image into one of the trained categories by identifying the features followed by computing these obtained features as prescribed the given algorithm.

Keywords

Geometrical Modelling, Object Detection, Object Identification, Object Recognition, Weighted Matrix.

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S. Gautam ¹, G. Saxena ¹

Affiliations
1 Department of Home Science, The IIS University, Jaipur (Rajasthan), IN

Abstract

Wheat grains germinated for over a period of 2-3 days is usually called as the wheatgrass. Wheat grass being rich in various nutrients and phytochemicals makes it effective in many chronic diseases like atherosclerosis, anaemia, diabetes, thalassemia, leukaemia, breast cancer, and ulcerative colitis. The present study was conducted with an objective to cultivate wheat grass, to analyse proximate composition of wheat grass powder and chlorophyll content in fresh wheat grass and to develop value added products by incorporating wheat grass juice and wheat grass powder for cancer patients. Four commonly consumed recipes were standardized and 4 variations of each recipe were developed for cancer patients and the recipes were also evaluated for sensory characteristics. The estimated values of proximate composition of wheat grass powder per 100g are 14.3±0.13 (moisture), 4.52±0.33 (Total ash), 1.74±0.08 (Crude fibre), 18.55±0.195 (Protein), 40.29±0.229 (Carbohydrate), 293.26±1.18 (Energy) and estimated value of chlorophyll in 100 g fresh wheat grass was found to be 484.43±12.27.

Keywords

Wheat Grass (Triticum aestivum) Juice and Powder, Proximate Composition, Chlorophyll, Cucumber Cooler, Orange Blast, Idli, Khaman.

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