Model Selection of Sea Clutter Using Cross Validation Method

Kerbaa, Taha Hocine; Mezache, Amar; Oudira, Houcine

doi:10.1016/j.procs.2019.09.067

Cited by 16 publications

(10 citation statements)

References 14 publications

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“…To use cross-validation, the data are separated into equal sections based on a certain ratio. 35,36 In this study, a fivefold cross-validation technique is employed, which means that the data are divided into five equal parts. This ensures that the model's performance is evaluated on all parts of the dataset, increasing the reliability of the model's overall performance.…”

Section: Cross-validationmentioning

confidence: 99%

Classification of cucumber leaf diseases on images using innovative ensembles of deep neural networks

Ulutas,

Emin Sahin

2023

J. Electron. Imag.

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.Plant diseases are a major concern for farmers worldwide as they can cause significant economic losses. Among vegetable crops, cucumber is particularly susceptible to various diseases, including fusarium and mildew. We present a unique dataset of cucumber leaf images collected from the Carsamba and Silifke districts of Turkey. This dataset is used in both newly proposed convolutional neural networks and pre-trained models available in the literature in addition to new ensemble models for disease prediction and classification. To optimize the classification accuracy, the grid search method is employed to identify the best combinations of base models and ensemble learning methods. The classification process is carried out using newly designed triple ensemble models and utilized fivefold cross-validation, and the results are evaluated using accuracy, F1-score, and receiver operating characteristic curves. The highest accuracy rate achieved in our study is 93.79%, which demonstrates the effectiveness of our approach for accurately identifying cucumber leaf diseases. Our proposed approach is not only accurate but also effective, making it a practical solution for farmers to use to quickly identify and control diseases in their crops. Our study provides valuable insights into the application of deep learning techniques for the classification of cucumber plant diseases and can serve as a useful model for further research in this field.

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Section: Cross-validationmentioning

confidence: 99%

Classification of cucumber leaf diseases on images using innovative ensembles of deep neural networks

Ulutas,

Emin Sahin

2023

J. Electron. Imag.

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“…It constitutes a very popular method for applied machine learning when comparing and choosing a model for a given problem of prognostic modeling, because it is simple and can be easily implemented, yields the estimates of abilities that have a remittent bias unremarkably than other distinct approaches. In a resampling process that is used to analyze the machine learning approaches on a small record set, the cross-validation method is counted [9]. It is a commonly used technique because it is straightforward in understanding and because its outcomes can usually be less biased than those obtained from other alternative approaches.…”

Section: K-fold Cross-validation Techniquementioning

confidence: 99%

“…3. SVM class dividing boundry an upright place, frontal position with tolerance for some side movement [9,17]. These images can be viewed with the 'xv' program which is very handy and the format of all these images is in the PGM, each image-file is 92x112 size, 8-bit grey levels, and all these images are structured in 40 directories (one for each subject) named as sX where X shows the subject number (between 1 and 40).…”

Section: Data Setmentioning

confidence: 99%

Face Recognition Comparative Analysis Using Different Machine Learning Approaches

Ahmed¹,

Khan²,

Ullah³

et al. 2021

Adv. Sci. Technol. Res. J.

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The problem of a facial biometrics system was discussed in this research, in which different classifiers were used within the framework of face recognition. Different similarity measures exist to solve the performance of facial recognition problems. Here, four machine learning approaches were considered, namely, K-nearest neighbor (KNN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Principal Component Analysis (PCA). The usefulness of multiple classification systems was also seen and evaluated in terms of their ability to correctly classify a face. A combination of multiple algorithms such as PCA+1NN, LDA+1NN, PCA+ LDA+1NN, SVM, and SVM+PCA was used. All of them performed with exceptional values of above 90% but PCA+LDA+1N scored the highest average accuracy, i.e. 98%.

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“…Its ends in skills estimates unremarkably that have a remittent bias than extraordinary methods. Cross-validation can be a resampling manner wont to assess the system approximately to understand fashions on a restricted records sample [12].…”

Section: Proposed Workmentioning

confidence: 99%

A Comparative Analysis Using Different Machine Learning: An Efficient Approach for Measuring Accuracy of Face Recognition

Faridi¹,

Zia²,

Javed³

et al. 2021

IJMLC

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Feature extracting and training module can be done by using face recognition neural learning techniques. Moreover, these techniques are widely employed to extract features from human images. Some detection systems are capable to scan the full body, iris detection, and finger print detection systems. These systems have deployed for safety and security intension. In this research work, we compare different machine learning algorithms for face recognition. Four supervised face recognition machine-learning classifiers such as Principal Component Analysis (PCA), 1-nearest neighbor (1-NN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are considered. The efficiency of multiple classification systems is also demonstrated and tested in terms of their ability to identify a face correctly. Face Recognition is a technique to identify faces of people whose images are stored in some databases and available in the form of datasets. Extensive experiments conducted on these datasets. The comparative analysis clearly shows that which machine-learning algorithm is the best in terms of accuracy of image detection. Despite the fact, other identification methods are also very effective; face recognition has remained a major focus of research due to its non-meddling nature and being the easy method of personal identification for people. The findings of this work would be useful identification of a suitable machine-learning algorithm in order to achieve better face recognition accuracy.

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Model Selection of Sea Clutter Using Cross Validation Method

Cited by 16 publications

References 14 publications

Classification of cucumber leaf diseases on images using innovative ensembles of deep neural networks

Classification of cucumber leaf diseases on images using innovative ensembles of deep neural networks

Face Recognition Comparative Analysis Using Different Machine Learning Approaches

A Comparative Analysis Using Different Machine Learning: An Efficient Approach for Measuring Accuracy of Face Recognition

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