http://10.9.150.37:8080/dspace//handle/atmiyauni/919 2026-04-27T18:58:59Z http://10.9.150.37:8080/dspace//handle/atmiyauni/929 Title: Accurate Identification of complex Land use and Land Cover Features using IRS (LISS III) Multispectral Image Authors: Desai, Nirav; Shukla, Parag Abstract: Land Use and Land Cover (LULC) is an assortment of activities executed by humans on to the land. The present study was carried out to evaluate supervised classification mechanisms for classification complex Land use and Land cover features using India Remote Sensing System-IRS (Linear Imaging Self-Scanning Sensor 3- LISS III) multispectral data. It showed that Artificial neural networks (ANN) fared better across all the land use and land cover classes with an overall accuracy of 88%. It also revealed that Maximum Likelihood (ML) and Support Vector Machine (SVM) classifier is prone to miss classification of pixels in one or more classes. Outcomes of the present study are comforting the competence of IRS (LISS III) multispectral data for the accurate mapping of complex land use and land cover features. Additionally, the ability of an ANN classifier in the classification of complex features using multispectral data was re-established in the present study. 2022-07-07T00:00:00Z http://10.9.150.37:8080/dspace//handle/atmiyauni/928 Title: A Land Use/Land Cover Classification Of Irs (Liss – Iii) Multispectral Data Using Decision Tree And Svm Classification Mechanism Authors: Desai, Nirav; Shukla, Parag Abstract: Identification of the effect of human activities on our planet concerns over worldwide land use and land cover change is very complex. Land Use and Land Cover refer to the utilization of land through events like agriculture, different types of cultivation areas, residential areas, and the physical features on the earth’s surface like the sea, mangroves forest, vegetation cover, and water bodies. However, empirical techniques used for this type of classification are cost-effective and laborious. This paper is focused on remote sensing images and various supervised classifications to identify various Land Use/Land Cover. This research work aims to use images taken from IRS (LISS III) platform to perform supervised classification. The study was performed to compare the performance of Supervised classifiers Decision Tree and SVM to classify different land use land cover classes. The Decision tree classifier gives better results than SVM for the study area. The decision tree classifier achieved 89.97 %. and SVM 81.90 %. It revealed that Decision Tree did better across different levels of occupancy of Land use/Land Cover. 2022-10-04T00:00:00Z http://10.9.150.37:8080/dspace//handle/atmiyauni/927 Title: Land Cover Land Use Mapping & Classification Model Authors: Desai, Nirav; Dr. Shukla, Parag Abstract: Measurement of land use and land cover is costly and time-consuming by imperial technique. Remote sensing plays important role in the mappings and classification of land cover features. Indigenous space-born images can be used for identification and mapping. Remote sensed imagery is most popular method to capture data on Land Use Land Cover. Multispectral imaging is one of the most widely used technologies for LULC mapping and monitoring.This paper proposes a model which will help to classify and map land cover land use using remote sensing imagery. It also increase accuracy in the mapping and classification of land area land cover. 2022-01-01T00:00:00Z http://10.9.150.37:8080/dspace//handle/atmiyauni/926 Title: A Study for Challenges and Site Selection Criteria of the Data Center Authors: Rayjada, Hardiksinh H.; Shukla, Parag C. Abstract: In this study paper for Data Center Site selection is very important and effect to data center Budget, future expansion and upgradation. Today the data center word has been a topic of discussion, research, study, analysis expected on the preceding period. in the upcoming years it will increase importance and new aspects will be discovered. The services of cloud services provided via different Data Centers. The data center Service Providers build their own data centers and get local government support. A possible geographical location to build data center planning to selection of selection process. Data center security mentions to the physical and virtual data security to protect a data center from external threats and cyberattacks. The data center moves in Malty-Story (Vertical) Data center. Data Center challenges is providing higher speed and trusted network connectivity to customers and Environment, cooling system, land cost and Data center site selection criteria. This paper focuses on what is a suitable location for building data centers and a challenge that is an essential part of data centers. 2020-01-01T00:00:00Z