Optimizing Machine Learning Parameters for Classifying the Sweetness of Pineapple Aroma Using Electronic Nose

Hasan, Mhd Arief; Sarno, Riyanarto; Sabilla, Shoffi Izza

doi:10.22266/ijies2020.1031.12

Cited by 17 publications

(5 citation statements)

References 23 publications

(24 reference statements)

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“…The results of this feature selection will assist us in choosing the most relevant variables for further analysis in this study [4]- [7]. Here is the general formula for calculating the Chi-Square test statistic (χ²) [8]:…”

Section: Feature Selection Dengan Chi Squarementioning

confidence: 99%

Evaluation Study of the Chi-Square Method for Feature Selection in Stroke Prediction with Random Forest Regression

Nasution,

Erlin

et al. 2024

Proceedings of the 2nd International Conference on Environmental, Energy, and Earth Science, ICEEES 2023, 30 October 2023, Peka

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This study aims to develop a more accurate classification model for diagnosing Stroke cases based on various clinical features. Stroke is a serious global health issue, and early detection has a positive impact on prognosis and the prevention of complications. In this research, we combine two main approaches, feature selection using the Chi-Square statistical test and the implementation of Random Forest Regression, to enhance the accuracy of Stroke diagnosis.First, we use the Chi-Square test to evaluate the relationship between categorical variables (such as gender, smoking history, marital status, and others) and Stroke status. The results of this test are used to select variables that have a significant association with Stroke. We then map categorical variables into numerical values so that the model can process them, overcoming errors that previously arose due to categorical data.Subsequently, we apply Random Forest Regression to the pre-processed dataset. The resulting model achieves an accuracy of 95%, indicating a significant improvement compared to the previous model. In addition to accuracy, we also observe improvements in precision, recall, and F1-Score, which indicate the model's ability to identify Stroke cases and avoid misdiagnoses.The findings of this research hold significant potential in clinical practice, particularly in the early detection and management of Stroke. Early Stroke detection can lead to faster intervention, ultimately reducing the negative impact of Stroke on patients. We hope that this study will serve as a foundation for the development of more advanced and accurate models for Stroke diagnosis, contributing to overall public healthcare improvement..

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Section: Feature Selection Dengan Chi Squarementioning

confidence: 99%

Evaluation Study of the Chi-Square Method for Feature Selection in Stroke Prediction with Random Forest Regression

Nasution,

Erlin

et al. 2024

Proceedings of the 2nd International Conference on Environmental, Energy, and Earth Science, ICEEES 2023, 30 October 2023, Peka

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“…Noise from the E-nose signals can be removed using signal processing techniques such as PCA, LDA, QDA, mother wavelet, etc. The methods employed for signal processing aim to determine the optimal parameters that accurately identify the properties of each signal [15]. In order to overcome the noise contamination of the E-nose signals when monitoring beef quality, the discrete wavelet transform and long shortterm memory signal processing technique was proposed by Wijaya et al [16] and obtained a performance of 94.83% accuracy.…”

Section: Introductionmentioning

confidence: 99%

A deep neural network with electronic nose for water stress prediction in Khasi Mandarin Orange plants

Sharma,

Barkataki,

Sarma

2023

Meas. Sci. Technol.

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Water stress is a significant environmental factor that hampers plant productivity and leads to various physiological and biological changes in plants. These include modifications in stomatal conductance and distribution, alteration of leaf water potential & turgor loss, altered chlorophyll content, and reduced cell expansion and growth. Additionally, water stress induces changes in the emission of volatile organic compounds across different parts of the plants. This study presents the development of an electronic nose (E-nose) system integrated with a deep neural network (DNN) to detect the presence and levels of water stress induced in Khasi Mandarin Orange plants. The proposed approach offers an alternative to conventional analytical methods that demand expensive and complex laboratory facilities. The investigation employs the leaf relative water content (RWC) estimation, a conventional technique, to evaluate water stress induction in the leaves of 20 plants collected over a span of 9 days after stopping irrigation. Supervised pattern recognition algorithms are trained using the results of RWC measurement, categorising leaves into non-stressed or one of four stress levels based on their water content. The dataset used for training and optimising the DNN model consists of 27 940 samples. The performance of the DNN model is compared to traditional machine learning methods, including linear and radial basis function support vector machines, k-nearest neighbours, decision tree, and random forest. From the results, it is seen that the optimised DNN model achieves the highest accuracy of 97.59% in comparison to other methods. Furthermore, the model is validated on an unseen dataset, exhibiting an accuracy of 97.32%. The proposed model holds the potential to enhance agricultural practices by enabling the detection and classification of water stress in crops, thereby aiding in water management improvements and increased productivity.

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“…Electronic nose has been widely used for identification of sweetness of pineapple [9], detection of toxic substances [10], monitoring fish quality [11], detection of civet and non-civet coffee [12], and identification of asthma [13].…”

Section: Introductionmentioning

confidence: 99%

Identification of Diabetes through Urine Using Gas Sensor and Convolutional Neural Network

2022

IJIES

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Currently, blood glucose disease detection devices are administered invasively by inserting a needle into a finger or a blood vessel. This can cause trauma to the patient, especially if the frequency of examinations is very frequent. Therefore, we need a more comfortable and effective device for early recognition of the signs of diabetes mellitus. Blood sugar monitoring can also be done by smelling the patient's urine. Electronic nose is a system that can be used to identify someone with diseases including diabetes. The electronic nose system used in this study consists of several semiconductor gas sensors. The pattern recognition algorithms utilize convolutional neural network (CNN), learning vector quantization (LVQ), multilayer Perceptron (MLP), Naïve Bayes, k-Nearest Neighbors (k-NN), SVM, PCA-Logistic Regression (PCA-LR), PCA -LVQ, and PCA-MLP. The experimental results show that the CNN has the highest accuracy of 100% in the identification of two classes, namely healthy, and diabetic.

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Optimizing Machine Learning Parameters for Classifying the Sweetness of Pineapple Aroma Using Electronic Nose

Cited by 17 publications

References 23 publications

Evaluation Study of the Chi-Square Method for Feature Selection in Stroke Prediction with Random Forest Regression

Evaluation Study of the Chi-Square Method for Feature Selection in Stroke Prediction with Random Forest Regression

A deep neural network with electronic nose for water stress prediction in Khasi Mandarin Orange plants

Identification of Diabetes through Urine Using Gas Sensor and Convolutional Neural Network

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