Timestamp Feature Variation based Weather Prediction Using Multi-Perception Neural Classification for Successive Crop Recommendation in Big Data Analysis

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Proposed architecture diagram TFV-MPNC
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Published: Jan 31, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i2s.6030
Keywords:
Deep learning, Mutual invariance, feature selection, classification, weather prediction forecasting model, successive weather rate

Main Article Content

K. Bommi
Research Scholar, Department of Computer Science, Rajah Serfoji Govt. College (Autonomous) Thanjavur. Affiliated to Bharathidasan University, Tiruchirapalli 620 024
D. J. Evanjaline
Asst. Professor, Department of Computer Science, Rajah Serfoji Govt. College (Autonomous) Thanjavur. Affiliated to Bharathidasan University, Tiruchirapalli 620 024

Abstract

The recent generation has a lot of information for analysing growth in future prediction. Especially India is an extensive agricultural resource for the world's expansive economic growth. But in extensive data analysis, a problem for the recommendation of the seasonal crop is tedious because of improper feature analysis due to varying periods in weather conditions. So time variation-based big data analysis is essential for research improvement. To resolve this problem, we propose a Timestamp feature variation-based weather prediction using multi-perception neural classification (TFV-MPNC) for successive crop recommendation in big data analysis. Initially, the pre-processing was carried out to prepare the redundant noise dataset for fast prediction. Initially, the Preprocessing ensures the Contemporary Forecasting rate (CFR) for predicting the previous deficiency rate. Based on that Time stamp feature analysis (TSFA). The Dense region harvest rate (DRHR) was evaluated, and features were decision using Fuzzy intensive decision Function (FIDF), selected the scaled features and trained with multi-perception neural classification (MPNN). The proposed system produces higher forecasting by prediction features as well supportive to the weather dependences related to higher classification rate in precision, and recall has the best classification result.

Article Details

How to Cite
Bommi, K. ., & Evanjaline, D. J. . (2023). Timestamp Feature Variation based Weather Prediction Using Multi-Perception Neural Classification for Successive Crop Recommendation in Big Data Analysis. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 68–76. https://doi.org/10.17762/ijritcc.v11i2s.6030
Issue
Vol. 11 No. 2s (2023)
Section
Articles

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