Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors
Volume-3 | Issue-2
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
Machine Learning Algorithms Performance Analysis for VLSI IC Design
Volume-3 | Issue-2
A Review on Data Securing Techniques using Internet of Medical Things
Volume-3 | Issue-3
Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
Volume-3 | Issue-4
Maximizing the Prediction Accuracy in Tweet Sentiment Extraction using Tensor Flow based Deep Neural Networks
Volume-3 | Issue-2
DDOS ATTACK DETECTION IN TELECOMMUNICATION NETWORK USING MACHINE LEARNING
Volume-1 | Issue-1
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
Construction of a Framework for Selecting an Effective Learning Procedure in the School-Level Sector of Online Teaching Informatics
Volume-3 | Issue-4
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Machine Learning Algorithms Performance Analysis for VLSI IC Design
Volume-3 | Issue-2
Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Characterizing WDT subsystem of a Wi-Fi controller in an Automobile based on MIPS32 CPU platform across PVT
Volume-2 | Issue-4
Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
Volume-3 | Issue-4
LoRa-IoT Focused System of Defense for Equipped Troops [LIFE]
Volume-2 | Issue-3
Design of Data Mining Techniques for Online Blood Bank Management by CNN Model
Volume-3 | Issue-3
Volume - 2 | Issue - 2 | june 2020
Published
26 May, 2020
The paper emphasis on an instinctual and efficacious method to forecast and analyze the condition of different atmospheric determinants all over the world. The difficulty in the extant remedies or the apparatus is its incapability to provide a comprehensive information regarding the evaluation of the attributes of the atmosphere. The proposed methodology in the paper gathers the actual information about the atmospheric attributes such as the water, air, the forest and the tree cover etc. from the government bases and processes the collective information. The methodology does the extrication transformation load over the original collective data's that are in its raw format. The converted information sets are imported into the database to develop a dash boards with the multiple information's displayed on it. This allows to have an evaluated data about the various atmospheric factors. To forecast the deteriorations and the conditions of the atmospheric attributes the methodology proffered utilizes the Fuzzy C means clustering, R-studio, and the ARIMA frame work. The dash board assists the NLP enabling the end users to post their queries as input and get back the desired output. The developed deterioration forecasting and evaluation can be used in the evaluation of the conditions of atmospheric attributes for the different countries in the world.
KeywordsAtmospheric Deterioration Data Mining Business Intelligence Evaluation Forecasting
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