Spatial-Temporal Analysis of LU/LC Classification in Nirmal Mandal, Adilabad, Telangana State, India, by Using Remote Sensing and GIS

In this project, we studied land use and land cover classification of Nirmal Mandal, Adilabad district, Telenagna state by using Geographical Information system and Remote sensing techniques. LISS-IV satellite image resolution 5 m × 5 m provides quality information for identification of Land use/Land cover characteristics. The image accuracy shows 45.70% of Agricultural land, 9.10% Built-up land, Forest area is 7.90%, Barren land have 7.60% and Uncultivated land occupied 29.70%. National land use and land cover mapping report based on 5 divisions classified in the study area. The area land use and land cover classification provide reliable data to understand land, water, soil, forests, urban sprawl. This socio economic survey significantly shows the changes that so far have taken place. This will help the people/farmers for the future land use and land cover change detection. Regular monitoring of agriculture, forest and greening efforts for plantation at suitable area, schemes and limitations. Free ware browsing of land cover gives sufficient development to plenty resource.

Routing with Cooperative Nodes Using Improved Learning Approaches

In IoT,routing among the cooperative nodes plays an incredible role in fulfilling the network requirements and enhancing system performance.The eva-luation of optimal routing and related routing parameters over the deployed net-work environment is challenging.This research concentrates on modelling a memory-based routing model with Stacked Long Short Term Memory(s-LSTM)and Bi-directional Long Short Term Memory(b-LSTM).It is used to hold the routing information and random routing to attain superior performance.The pro-posed model is trained based on the searching and detection mechanisms to com-pute the packet delivery ratio(PDR),end-to-end(E2E)delay,throughput,etc.The anticipated s-LSTM and b-LSTM model intends to ensure Quality of Service(QoS)even in changing network topology.The performance of the proposed b-LSTM and s-LSTM is measured by comparing the significance of the model with various prevailing approaches.Sometimes,the performance is measured with Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)for mea-suring the error rate of the model.The prediction of error rate is made with Learn-ing-based Stochastic Gradient Descent(L-SGD).This gradual gradient descent intends to predict the maximal or minimal error through successive iterations.The simulation is performed in a MATLAB 2020a environment,and the model performance is evaluated with diverse approaches.The anticipated model intends to give superior performance in contrast to prevailing approaches.