Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types

Feyzioğlu, Ahmet; Taşpınar, Yavuz Selim

doi:10.3390/s23042222

Cited by 21 publications

(8 citation statements)

References 47 publications

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“…Many studies demonstrated the widespread use of e-nose in assessing food quality-related properties, including the detection of Salmonella (Gonçalves et al, 2023), the quality status of fruits (Buratti et al, 2018;Qiu et al, 2014Qiu et al, , 2015, the quality of oils (Hosseini et al, 2023), and the detection of contaminated foods (Feyzioglu & Taspinar, 2023;Putri et al, 2023;Tian et al, 2023Tian et al, ). et al, 2019.…”

Section: Figurementioning

confidence: 99%

An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification

Wan Azman,

Ku Azir,

Mohd Khairuddin

2024

JICT

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Food waste’s negative environmental repercussions are causing it to become a global concern. Several studies have examined thefactors influencing food waste behaviour and management. This work was motivated by the lack of previous research on machinelearning and electronic noses to detect contamination from leftover cooked food. This work proposes using machine learning algorithms and electronic nose technology to recognise and forecast the contamination in leftover cooked food. After five days of storage, the freshness of cooked leftovers was evaluated using an electronic nose combined with machine learning algorithms. Most food samples used in this work were from Malaysian’s leftover lunch and dinner dishes. Four (4) gas sensors—MQ-2, MQ-136, MQ-137, and MQ-138—are used in developing the electronic nose to identify the presence of gas in the food sample. The data from the gas sensors was analysed using machine learning methods, namely Random Forest, k-nearest Neighbors, Support Vector Machine, and Linear Discriminant Analysis. Based on the results, a multi-classification technique yielded a greater accuracy rate in classifying and identifying the level of contamination in the cooked food leftovers, with average accuracy ranging from 90 percent to 100 percent. In conclusion, the work demonstrates a novel method for using machine learning algorithms to classify, identify, and predict the contamination level of leftover cooked food, contributing to reducing food waste generated primarily by Malaysians.

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Section: Figurementioning

confidence: 99%

An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification

Wan Azman,

Ku Azir,

Mohd Khairuddin

2024

JICT

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“…Matrixes to differentiate between the actual and expected values of the model's constituent parts in Java applications were classified into faulty and non-faulty classes using four confusion matrix measures: TP, FP, TN, and FN. The figure shows the confusion matrix of binary classification [41]- [44]. Figure 6 shows the confusion matrix of binary classification [45].…”

Section: Confusion Matrixmentioning

confidence: 99%

Assessing the effectiveness of data mining tools in classifying and predicting road traffic congestion

Arabiat,

Altayeb

2024

IJEECS

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Traffic congestion is a significant issue in cities, impacting the environment, commuters, and the economy. Predicting congestion is crucial for efficient network operation, but high-quality data and computational techniques are challenging for scientists and engineers. The revolution of data mining and machine learning has enabled the development of effective prediction methods. Machine learning (ML) approaches have shown potential in predicting traffic congestion, with classification being a key area of study. Open-source software tools WEKA and Orange are used to predict and classify traffic congestion. However, there is no single best strategy for every situation. This study compared the effectiveness of both data mining tools for predicting congestion in one of the areas of the capital of the Hashemite Kingdom of Jordan, Amman, by testing several classifiers including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF) classifications. The results showed that the Orange mining tool was superior in predicting traffic congestion, with a prediction accuracy of 100% for Random forest, logistic regression, and 99.8% for KNN. On the other hand, results were better in WEKA for the SVM classifier with an accuracy of 99.7%.

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“…A confusion matrix created for two classes and the parameters it contains are given in Table 2. True Positive (TP); correctly classified positive samples, True Negative (TN); correctly classified negative samples, False Positive (FP); falsely classified positive samples and False Negative (FN); refers to negative samples that were incorrectly classified [28,29].…”

Section: Performance Metrics and Confusion Matrixmentioning

confidence: 99%

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

Feyzioğlu

Taşpınar

2023

International Journal of Applied Mathematics Electronics and Computers

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Iron metal is the most widely used metal type. This metal, which is used in countless sectors, is processed in different ways and turned into steel. Since steel has a brittle structure compared to iron, defects may occur in the plates during the rolling process. Detection of these defects at the production stage is of great importance in terms of commercial and safety. Machine learning methods can be used in such problems for fast and high accuracy detection. For this purpose, using a dataset obtained from stainless steel surface defects in this study, classification processes were carried out to detect defects with four different machine learning methods. Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for classification processes. The highest classification accuracy was obtained from the 79.44% RF model. Correlation analysis was performed in order to analyze the effects of the features in the dataset on the classification results. It is thought that the classification accuracy of the proposed models is satisfactory for this challenging problem, but needs to be upgraded.

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Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types

Cited by 21 publications

References 47 publications

An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification

An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification

Assessing the effectiveness of data mining tools in classifying and predicting road traffic congestion

Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models

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