Performance Comparison of Mushroom Types Classification Using K-Nearest Neighbor Method and Decision Tree Method

Chitayae, Nadya; Sunyoto, Andi

doi:10.1109/icoiact50329.2020.9332148

Cited by 17 publications

(6 citation statements)

References 11 publications

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“…Chitayae and Sunyoto used K-Nearest Neighbor (KNN) and Decision Tree methods to classify mushroom using UCI mushroom dataset and compared performance of these two algorithms. The analysis of results in the research indicate the Decision Tree based CART algorithm classifies types of mushroom with an accuracy of 91.93% while the KNN with 89.61% accuracy [3].…”

Section: Literature Reviewmentioning

confidence: 95%

Mushroom Classification using Random Forest and REP Tree Classifiers

Paudel¹,

Bhatta²

2022

Nep J. Math. Sci.

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Mushroom is a popular fruit of a much larger fungus that has a high level of protein and a rich source of vitamin B. It aids in the prevention of cancer, weight loss, and immune system enhancement. There are numerous thousands of mushroom species within the world and a few are eatable and a few are noxious due to noteworthy poisons on them. Hence, it is a vital errand to distinguish between eatable and harmful mushrooms. This paper focuses on comparing the performance of Random Forest and Reduced Error Pruning (REP) Tree classification algorithms for the classification of edible and poisonous mushrooms. In this paper, mushroom dataset from UCI machine learning repository has been classified using Random Forest and REP Tree classifiers. The result based on accuracy, precision, recall and F-measure showed that the Random Forest outperformed REP Tree algorithm as it had highest accuracy value of 100%, precision value of 100%, recall value of 100% and F- measure value of 100%. The performance is 100% by using Random Forest, which is found better with respect to REP Tree classifier.

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Section: Literature Reviewmentioning

confidence: 95%

Mushroom Classification using Random Forest and REP Tree Classifiers

Paudel¹,

Bhatta²

2022

Nep J. Math. Sci.

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“…Then after getting good accuracy in classification of type of exercise, we tried to identify the person doing the exercise and obtained good results [26]. conducted to identify the person comforters of the activity [27]. The third experiment was performed using both the IMU sensors.…”

Section: Methodsmentioning

confidence: 99%

Classification of lower limb rehabilitation exercises with multiple and individual inertial measurement units

Tannna¹,

Vithalani

2022

IJEECS

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Straight leg raise rehabilitation exercises (for both lying and seated position) for lower limb injuries play a critical role in terms of stress on joints after the injury. The primary objective of the paper is to find how accurately and efficiently a single and a two IMU sensor-based system could classify SSLR (Seated straight leg raise) and LSLR (Lying straight leg raise) exercises using machine learning. Inertial Measurement Units (IMUs) that include accelerometer and gyroscope were calibrated and tested, individual and combined, for classified seating as well as lying exercise and for different demanded personalities. Individual IMUs achieved about 96 % accuracy in binary classification. However, the combined (two) IMUs achieved about 96.8 % accuracy. The merits of the proposed IMU based sensor system are that it is easy to install, cost effective and very useful for telemedical operations in pandemic situations like COVID19. On the basis of these results, it could be concluded that the accuracy of a single IMU sensor system and a two IMU sensor-based system is approximately 96% and both were efficiently able to classify SSLR and LSLR exercises as well as identify the individual performing the exercise.

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“…It includes operations like rescaling pixel values, horizontal flipping, and zooming. Data augmentation helps in increasing the diversity of the training data, which can improve model generalization [3].…”

Section: Data Augmentationmentioning

confidence: 99%

Mushroom Decay Detection using Deep Learning

2023

IJETER

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This project aims to develop a system for detecting decay in mushrooms using image processing techniques and Deep Learning. Decay in mushrooms is a significant issue in the food industry, as it can lead to quality deterioration and potentially harmful consumption. The proposed system involves capturing images of mushrooms using a camera and processing these images to detect any signs of decay. The image processing techniques will include pre- processing, feature extraction, and classification using deep learning algorithms. The dataset used for training and testing the system will consist of images of both healthy and decayed mushrooms. The system's performance will be evaluated based on accuracy. The outcome of this project will be a tool that can assist in early detection of decay in mushrooms, thus reducing food waste and improving food safety.

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Performance Comparison of Mushroom Types Classification Using K-Nearest Neighbor Method and Decision Tree Method

Cited by 17 publications

References 11 publications

Mushroom Classification using Random Forest and REP Tree Classifiers

Mushroom Classification using Random Forest and REP Tree Classifiers

Classification of lower limb rehabilitation exercises with multiple and individual inertial measurement units

Mushroom Decay Detection using Deep Learning

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