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ISSN 2063-5346
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IMPROVISATION OF MUSHROOM TOXICITY BASED ON FEATURES EXTRACTED FROM IMAGES BY USING K-NEAREST NEIGHBOUR ALGORITHM COMPARING GCFOREST ALGORITHM

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Sk Yaseen Ahmed,T. P. Anithaashri
» doi: 10.31838/ecb/2023.12.sa1.472

Abstract

Aim:The aim is to improvise the detection of Mushroom Toxicity using novel K-Nearest Neighbor algorithm compared to the GCforest algorithm. Materials and methods: By using novel K-Nearest Neighbor algorithm and GCforest both were identified and performed with the sample size of 45 each and the software tools that were used in this project are jupyter notebook. Accuracy values for identification of toxicity in mushrooms are calculated to quantify the performance of the GCforest algorithm against novel K-Nearest Neighbors algorithm. Results : The analysis on train dataset and test dataset were successfully performed using SPSS and acquired accuracy for the GCforest algorithm and novel k-nearest neighbor algorithm method which gave the accuracy with the level of significance (p>0.05) the resultant data depicts the reliability in independent sample tests. Conclusion: On the whole process of prediction of accuracy the K-Nearest Neighbor method gives significantly better performance compared with GCforest algorithm. By extracting images from image processing.

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