Classification of deep image features of lentil varieties with machine learning techniques

Bütüner, Resul; Çinar, İ̇lkay; Taşpınar, Yavuz Selim; Kursun, Ramazan; Calp, M. Hanefi; Köklü, Murat

doi:10.1007/s00217-023-04214-z

Cited by 40 publications

(18 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The random forest algorithm is a machine learning method and is used to solve problems such as classification and regression analysis [21]. Random forest is an ensemble learning method by combining multiple decision trees.…”

Section: Random Forest (Rf)mentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Random Forest (Rf)mentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…During the creation of individual trees, a random subset of attributes is drawn, and the optimal attribute for a split is determined from this subset, hence the term "Random." The final model is established based on a majority vote from the independently developed trees in the forest, making Random Forest effective for both classification and regression tasks (Butuner et al, 2023). The fundamental properties include specifying the number of trees in the forest and determining the number of attributes considered at each split.…”

Section: Random Forest Classificationmentioning

confidence: 99%

Utilizing Random Forests for the Classification of Pudina Leaves through Feature Extraction with InceptionV3 and VGG19

Yasin,

KOKLU

2023

icontas

View full text Add to dashboard Cite

An analysis of the "Pudina Leaf Dataset: Freshness Analysis" reveals distinct classes of dried, fresh, and spoiled mint leaves. Convolutional neural networks, InceptionV3 and VGG19, were used to extract features from the dataset using advanced image processing techniques. The classification task was then performed using a Random Forest machine learning algorithm. In this study, notable results were obtained, proving the effectiveness of the selected methodologies. Mint (Pudina) leaves were classified accurately using InceptionV3-extracted features at 94.8%, demonstrating robust performance in distinguishing freshness states. This deep learning architecture was further shown to be able to capture meaningful patterns within the dataset by utilizing VGG19-extracted features, resulting in an improved accuracy of 96.8%.

show abstract

“…Convolution and pooling layers, along with activation functions and fully connected layers, form the core components of CNN. This enables the extraction of features from visual data and the creation of higher-level representations [30,31,[33][34][35][36][37][38]. In the study, the ResNet50 architecture, commonly used and stable in literature, was chosen for training the machine learning methods.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Predicting Future Demand Analysis in the Logistics Sector Using Machine Learning Methods

HAYTA,

GENCTURK,

ERGEN

et al. 2023

imiens

View full text Add to dashboard Cite

In this study, the potential of machine learning methods for analyzing future needs in the logistics sector was investigated. The research is conducted using the MATLAB platform. Numeric pallet demand data obtained from a logistics company are employed to train MLP, LSTM, and CNN models. Data security and confidentiality take priority during the data collection process. This dataset, comprising a total of 3,062 daily records, serves as the primary data source for the study. In the data preprocessing phase, missing or erroneous data is rectified, and outliers are detected and corrected. The models are tested to predict pallet quantities over periods of 25 days and 4 weeks. The results are evaluated by comparing the model predictions with actual data. Model performances are assessed using metrics such as MSE, RMSE, NRMSE, MAE, ESD, and RC. The outcomes of the last 25 days demonstrate that the LSTM model exhibits the lowest MSE (6,410.5571) and RMSE (80.0660) values. For the MLP model, the MSE value is calculated as 20,536.5564, and the RMSE value is 143.3058. Performance evaluations for the CNN model yield an MSE of 8,492.4297 and an RMSE of 92.1544. Furthermore, it is observed that the MLP model provides the best results for the 4-week forecasts. The results of this study indicate the success of the models used for predicting pallet transportation quantities in the logistics sector. In addition to this study, a contribution is made toward enabling logistics companies to make more informed and strategic decisions.

show abstract

Classification of deep image features of lentil varieties with machine learning techniques

Cited by 40 publications

References 35 publications

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

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

Utilizing Random Forests for the Classification of Pudina Leaves through Feature Extraction with InceptionV3 and VGG19

Predicting Future Demand Analysis in the Logistics Sector Using Machine Learning Methods

Contact Info

Product

Resources

About