http://10.9.150.37:8080/dspace//handle/atmiyauni/921 Mon, 27 Apr 2026 18:44:24 GMT 2026-04-27T18:44:24Z http://10.9.150.37:8080/dspace//handle/atmiyauni/924 Title: Comparison of Cloud Data Centre Services and Cost Authors: Rayjada, H.; Shukla, P. Abstract: Comparing with the traditional data centre, cloud computing makes it easier for enterprises to scale their services and lowers the cost of access for smaller companies. The cost comparison between cloud computing and the old-style data centre is an important issue of concern. In this paper, the cost roles traditional cloud data centre and server room are settled up, the performance of the data centre and cloud computing were tested based on the compared with the data centre with low workload intensity strength. More than a few data centre have a primary backup of data and servers. The cloud data centre provider like Amazon, Google Facebook data centre are called Availability zones. The availability zones are a within the same region, distinct colocation centre is connected to Virtual Private Cloud network. This research discovering new network architectures for the data centre. In this paper, we considerate the among different data centre charges and network architectures. the comparison on cost using current and predicted trends in data centre cost and power consumption. Fri, 01 Jan 2021 00:00:00 GMT http://10.9.150.37:8080/dspace//handle/atmiyauni/924 2021-01-01T00:00:00Z http://10.9.150.37:8080/dspace//handle/atmiyauni/922 Title: Creation and Segmentation of image dataset of Mung bean plant leaf Authors: Naik, Akruti; Thaker, Hetal; Desai, Nirav Abstract: Automated plant disease identification is an enduring research subject. Leaves are available for most of the season, and they have a flat (2d) surface that is why practically, it is physible to detect disease symptoms using image analysis. Data collection and pre-processing are the most significant and crucial stages to obtain the data that can be taken as accurate and appropriate for further processing. Machine learning techniques require a large amount of data for training. The present paper focuses on process standardization for the creation of an image dataset of Mung bean plant leaves and pre-processing steps to enhanced captured images. The diseases in leaves result in loss of economic and production status in the agricultural industry worldwide. The identification of disease in leaves using image processing reduces the reliance on the farmers for the safeguard of agricultural crops. In this paper, creation and segmentation process of Mung bean plant leaf is performed. Present dataset will be available to be used by researchers to save their time, efforts, and cost related to dataset creation. Segmentation of images will intensify the accuracy of the identification of various diseases. Tue, 01 Feb 2022 00:00:00 GMT http://10.9.150.37:8080/dspace//handle/atmiyauni/922 2022-02-01T00:00:00Z