Development of Digital Repository and Retrieval System for Rose Germplasm Management

Live repository of rose consisting of different genotypes and species of roses available across the globe has been established at ICAR-IIHR. All these genotypes have been characterized for 60 morphological characters for description of these varieties. Along with the live repository of plants, efforts have been made to develop digital repository of all these genotypes. The digital repository consists of description of characters, quantitative measurement for selected important characters and images for all the descriptors. A web-enabled interface has been developed for the selective retrieval of accessions with desired characters, and also for retrieval of all the information for the selected genotype. The information system will be useful across the germplasm collection centers, for the breeders and other end users by enabling them to select the appropriate germplasm andavoid duplicates

ANTI INFLAMMATORY ACTIVITY OF ERYTHRINA VARIEGATA

Objective: Erythrina variegata had been widely used for its reported biological activity in indigenous system of medicine. The present investigation was carried out to find the anti-inflammatory effect of ethanolic extract of Erythrina variegata in albino rats.Methods: The anti-inflammatory activity was evaluated using acute inflammatory model like carrageenan induced paw edema and chronic inflammatory model like cotton pellet induced granuloma respectively.Results: The ethanolic extract in different doses (200, and 400mg/kg, p. o) exhibited dose dependent and significant antiinflammatory activity in acute and chronic model of inflammation.Conclusion: The alcoholic extract of Erythrina variegata has anti-inflammatory activity. This activity was related to the dose and these results corroborate the potential traditional use of the plant in folk medicine

Wireless Data Gloves Controlled Virtual Musical Instrument

In this paper we describe the Data Glove Controlled Virtual Musical Instruments. Traditionally musical instruments are very bulky and expensive to carrying it, whenever user wants to play it. Now the mew system is generated which helps user to play musical instruments virtually, using data glove and computer system. The data glove is input device to the system which is made with the help of flex based register and which helps to tracking gesture made by user. These gestures are playing various musical instruments. Data glove produces the triggered signal which is receives by music generation system. This triggered signal used to play the predefined musical notes of instruments. DOI: 10.17762/ijritcc2321-8169.15035

Iris Recognition Approach for Preserving Privacy in Cloud Computing

Biometric identification systems involve securing biometric traits by encrypting them using an encryption algorithm and storing them in the cloud. In recent decades, iris recognition schemes have been considered one of the most effective biometric models for identifying humans based on iris texture, due to their relevance and distinctiveness. The proposed system focuses on encrypting biometric traits. The user’s iris feature vector is encrypted and stored in the cloud. During the matching process, the user’s iris feature vector is compared with the one stored in the cloud. If it meets the threshold conditions, the user is authenticated. Iris identification in cloud computing involves several steps. First, the iris image is pre-processed to remove noise using the Hough transform. Then, the pixel values are normalized, Gabor filters are applied to extract iris features. The features are then encrypted using the AES 128-bit algorithm. Finally, the features of the test image are matched with the stored features on the cloud to verify authenticity. The process ensures the privacy and security of the iris data in cloud storage by utilizing encryption and efficient image processing techniques. The matching is performed by setting an appropriate threshold for comparison. Overall, the approach offers a significant level of safety, effectiveness, and accuracy

Friendbook: An Advanced Friend Recommendation System For Social Networking sites

A couple of years ago, people had a limited social circle. In this century of evolving technology, it has become easier for people to socialize on a large scale via social media. There are several methods in the existing social networking sites where friend recommendation is based on pre-existing relationships like mutual friends, geographical distances, etc. This is not best way to suggest friends based on recent social findings. Hence we have developed a friend recommendation system that recommends friends to the user based on the lifestyle vector. Inspired by Data Mining, this method uses Apriori algorithm to match the websites visited by the users, which is the similarity metric used in this method

Growth Response of Amaranthus gangeticus to Azotobacter chroococcum Isolated from Different Agroclimatic Zones of Karnataka

In the present study Azotobacter chroococcum was isolated from various agro climatic zones of Karnataka. The effect of A. chroococcum isolates on seed germination of Amaranthus.gangeticus was studied and also the effect of A.chroococcum isolates on growth, biomass and nutrient content of Amaranthus gangeticus was studied under green house conditions. In seed germination studies the length of plumule and radicle was higher with inoculation of A. chroococcum isolates than uninoculated control plants. Treatments of A.chroococcum isolates from ten different zones of Karnataka were given to seedlings of Amaranthus gangeticus to study plant growth parameters such as plant height, number of leaves, number of branches, root length, shoot and root fresh and dry weight and nutrient uptake. Plants inoculated with Azotobacter isolates performed well when compared to uninoculated control plants. In Biochemical analysis chlorophyll content, nitrogen, phosphorous and potassium content was higher when compared to uninoculated control plants. The results of these experiments concluded that plants inoculated with Azotobacter isolates showed better growth response, biomass yield and nutrient content when compared with uninoculated control plants. Hence plants inoculated with A.chroococcum isolates were found to enhance the plant growth, biomass and nutrient content

Study and Survey of Social Networking and Facebook

Online social networking provides the user with the facilities love sharing, organizing and finding content and contacts. The utility and speedy development of those sites offers rise to review the characteristics and also the utilization of on-line social networks on giant scale. Understanding and analysis of social networking is incredibly vital to boost this system and to style new applications for on-line social networks. This text presents a user’s study and analysis of social networks love Facebook

MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning

Training deep networks and tuning hyperparameters on large datasets is computationally intensive. One of the primary research directions for efficient training is to reduce training costs by selecting well-generalizable subsets of training data. Compared to simple adaptive random subset selection baselines, existing intelligent subset selection approaches are not competitive due to the time-consuming subset selection step, which involves computing model-dependent gradients and feature embeddings and applies greedy maximization of submodular objectives. Our key insight is that removing the reliance on downstream model parameters enables subset selection as a pre-processing step and enables one to train multiple models at no additional cost. In this work, we propose MILO, a model-agnostic subset selection framework that decouples the subset selection from model training while enabling superior model convergence and performance by using an easy-to-hard curriculum. Our empirical results indicate that MILO can train models $3\times - 10 \times$ faster and tune hyperparameters $20\times - 75 \times$ faster than full-dataset training or tuning without compromising performance