Predictive Analytics in Soil for Agriculture Using Kendall Normalized Feature Selection Based Jaccarized Rocchio Boyer-Moore Bootstrap Aggregative Mapreduce Classifier for Predictive Analytics with Big data
How to Cite?
Anita M, Dr. Shakila S, "Predictive Analytics in Soil for Agriculture Using Kendall Normalized Feature Selection Based Jaccarized Rocchio Boyer-Moore Bootstrap Aggregative Mapreduce Classifier for Predictive Analytics with Big data," International Journal of Engineering Trends and Technology, vol. 69, no. 9, pp. 80-91, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I9P211
Big Data investigation is the method of collecting, arranging, and examining a huge amount of raw data, which extracts useful information. Big data investigation is a very difficult practice of investigating large datasets for taking future decisions. The conventional techniques failed to look up the prediction accurateness and also diminishes the prediction time while processing the large volumes of data. A novel Kendall Normalized feature selection based Jaccarized Rocchio Boyer-Moore Bootstrap Aggregative Mapreduce classifier (KNFS-JRBMBAMC) method is the preamble for advance predicting the potential outcomes with elevated prediction accurateness and get with smaller time. The KNFSJRBMBAMC methods encompass two techniques, namely data-based feature selection and its related classification for prediction. In the KNFS-JRBMBAMC method, Kendall Ranking Correlative Normalized Discriminant feature selection is agreed to identify the linear combination of features and select the relevant features for performing the classification task. After feature selection, the Jaccarized Rocchio Boyer-Moore Bootstrap Aggregative Mapreduce classification method is applied for classifying the raw input data into dissimilar classes with higher classification accuracy using the Boyer-Moore voting scheme. Then, the map () and reduce () function is used for the classifier result to perform an accurate prediction. Exploratory assessment is completed utilizing agricultural soil data collection set on factors like expectation exactness, bogus positive rate, forecast time, and space intricacy regarding various information. The talked about outcomes investigation shows that the KNFS-JRBMBAMC strategy gives better execution as far as accomplishing higher expectation exactness and lesser time just as space intricacy when contrasted with the cutting edge works
Intelligent Data Prediction(IDP), Extreme Learning Machine (ELM) Kendall Normalized feature selection (KNFS), Jaccarized Rocchio Boyer- MooreBootstrap aggregation, Jaccarized similarity MapReduce, (JRBMBAMC), Cuckoo–Grey wolf-based Correlative Naive Bayes classifier and MapReduce Model (CGCNB-MRM)
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