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We provide electrical projects based on power electronics, MATLAB Simulink and SIM Power
For Electronics Engineering Students we support technologies like ARM, GSM, GPS, RFID, Robotics, VLSI, NSL, NS3, OMNet++, OPNet, QUALNET, PeerSim
Forecast of Malaria in rural area using satellite imagery and GIS Malaria is a vector-borne disease which has high mortality in the world. Various spatial environmental factors have an effect on this disease. Several attempts have been made to review the use of GIS and remote sensing to predict vector-borne disease transmission. The analysis of the correlation between the environment and malaria are rare. The present study gives us information about the past and helps us to determine the future role of GIS and remote sensing for the control of vector-borne diseases. Remote sensing technology allows the user to extract measurements on a local level and thus generate spatial patterns which would otherwise not be visible. This system helps us to find a relation between malaria, different land types and the vegetation index. The normalized difference vegetation index (NDVI) is an indicator used to predict the live vegetation that is present in an area and the land type of the location selected. Environmental information for monitoring malaria and thus providing an early warning will be possible through this study. It will provide information as to not only where, but also when malaria is most likely to occur. Thus, decision makers can select various procedures such as nets treated with insecticides, drugs and draining of stagnant water by observing the graph generated by Spearman’s rank correlation coefficient which will depict which area needs more treatment against the disease.