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A Unified Platform for Crisis Mapping Using Web Enabled Crowdsourcing Powered by Knowledge Managememuch lesser time and provides means to collaborate with various Organizations. Here we collect the problems through forums, mails, social networks, and mobile messages, based on the data we receive the platform supervises the problem eradication manifesto supported by real time collaboration.Abnormal 发表于 2025-3-22 15:51:25
Conference proceedings 2020Engineering and Technology, Tehri, India, from 20 to 21 April 2019. The book is divided into two major sections – Intelligent Computing and Smart Communication. Some of the areas covered are Parallel and Distributed Systems, Web Services, Databases and Data Mining Applications, Feature Selection andGleason-score 发表于 2025-3-22 17:56:16
2524-7565 tworking and mobile computing.Serves as a reference guide fo.This book gathers high-quality research papers presented at the First International Conference, ICSC 2019, organised by THDC Institute of Hydropower Engineering and Technology, Tehri, India, from 20 to 21 April 2019. The book is divided in欢乐东方 发表于 2025-3-22 22:52:11
Conference proceedings 2020, High-Voltage Insulation Technologies, Fault Detection and Protection, Power System Analysis, Embedded Systems, Architectures, Electronics in Renewable Energy, CAD for VLSI, Green Electronics, Signal and Image Processing, Pattern Recognition and Analysis, Multi-Resolution Analysis and Wavelets, 3D and Stereo Imaging, and Neural Networks... .GROUP 发表于 2025-3-23 01:22:33
A Survey of Fuzzy Logic Inference System and Other Computing Techniques for Agricultural Diseases,se of the fuzzy logic inference system or expert system for precision agriculture by making use of rule-based approach. This will help farmers to control and manage the diseases and increase crop productivity.澄清 发表于 2025-3-23 08:22:33
A Model for Predicting Occurrence of Leaf Blast Disease in Rice Crop by Using Fuzzy Logic Techniqueariable and prediction percent as the dependent variable. At last this database is applied to ANFIS to provide a model that directly shows the level grading and predicted the severity of the disease. Our dataset has 83% accuracy and both variables are 99% correlated to each other.