Convolutional neural network - Support vector machine based approach for classification of cyanobacteria and chlorophyta microalgae groups

Sönmez, Mesut Ersin; Eczacıoglu, Numan; Gümüş, Numan Emre; Aslan, Muhammet Fatih; Sabancı, Kadir; Aşıkkutlu, Baran

doi:10.1016/j.algal.2021.102568

Cited by 39 publications

(15 citation statements)

References 20 publications

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“…Compared to SVM, CNN has overcome inaccuracy problems and is easy to develop in an actual application. The SVM method in several works of literature, such as [29,30], has good accuracy in a small number of classes. The research conducted by Lamberti (2021) [29] and Sonmez (2022) [30] uses two-three classes in order to classify tasks.…”

Section: -Discussionmentioning

confidence: 99%

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Comparison of Machine Learning Approach for Waste Bottle Classification

et al. 2022

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The use of machine learning for the image classification process is growing all the time. Many methods can be used to classify an image with good accuracy. Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are popular methods for this case. The two approaches have differences in the data training process to achieve classification objectives. Although there are some differences between these approaches, there are some advantages to both of them. This research explores the comparison of the two CNN and SVM methods by comparing the training process carried out and the accuracy results of the classification. The process stages are divided into pre-processing, training, and testing. The objects used are ten waste plastic bottles with different brands of medium size with a total data of 1100 images. Based on the observations, both methods have advantages and disadvantages in the data training and classification process. However, from the results, CNN's accuracy is better than SVM. The accuracy of both networks is 99% for CNN and 74% for SVM, respectively. So, from the results of experiments that have been carried out in the study, it was found that CNN was still better than SVM. Doi: 10.28991/ESJ-2022-06-05-011 Full Text: PDF

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Section: -Discussionmentioning

confidence: 99%

“…The comparison of CNN and SVM methods in the last few kinds of literature gives dynamic results. In the use of two classes of databases, SVM provides a high level of accuracy compared to CNN [29,30]. Meanwhile, for data that uses multiple classes of databases, CNN provides a higher level of performance compared to SVM [31].…”

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Comparison of Machine Learning Approach for Waste Bottle Classification

et al. 2022

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“…Confusion matrices were used to determine the performance of the machine learning algorithms [23]. To obtain the confusion matrices values the formulas are shown in Eqs 1-5 [24], [25]were employed with the python SciKitlearn library.…”

Section: Classification Methodsmentioning

confidence: 99%

Classification of Wheat Rootstock and Their Hybrids According to Color Features by Machine Learning Algorithms

Sönmez

Sabancı

Aydın

2022

International Journal of Applied Mathematics Electronics and Computers

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Accurate classification of wheat varieties has a large economic market in the world is enabled both high income in the market and the development of new fertile hybrids for changing weather conditions due to global warming. In this study, instead of using the conventional classification method, we extracted color features of the 1400 durum wheat grain samples, consisting of Ahmetbugdayi, Cesare and their hybrids BC1F6 and BC2F5, by using image processing techniques. For the color features, every twelve channels of four different color spaces were used and square-shaped samples were taken from the center of all the grains in these channels of images. the averages of the channel pixels values were used as color features. Then six different machine learning algorithms were employed for the classification task. ANN, SVM and DT models achieved more than 0.99 accuracies. On the other hand, k-NN and RF model reached approximately 0.99 accuracies. According to our results, in addition to different wheat varieties, also sibling hybrid seeds can be classified with high accuracy according to their color characteristics by the methods we proposed.

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“…The model could achieve 96% accuracy in training and 93.5% accuracy even in actual tests. Mesut Ersin Sonmez et al used multiple structured convolutional neural networks and a coupled support vector machine algorithm to classify microalgae, all of which yielded excellent results (Sonmez et al, 2022). All microalgae images were obtained from an inverted microscope, with only 20 images of each microalgae in the initial dataset.…”

Section: Microalgae Detection and Classification With Machine Learningmentioning

confidence: 99%

Machine learning for microalgae detection and utilization

Ning

Zhou

2022

Front. Mar. Sci.

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Microalgae are essential parts of marine ecology, and they play a key role in species balance. Microalgae also have significant economic value. However, microalgae are too tiny, and there are many different kinds of microalgae in a single drop of seawater. It is challenging to identify microalgae species and monitor microalgae changes. Machine learning techniques have achieved massive success in object recognition and classification, and have attracted a wide range of attention. Many researchers have introduced machine learning algorithms into microalgae applications, and similarly significant effects are gained. The paper summarizes recent advances based on various machine learning algorithms in microalgae applications, such as microalgae classification, bioenergy generation from microalgae, environment purification with microalgae, and microalgae growth monitor. Finally, we prospect development of machine learning algorithms in microalgae treatment in the future.

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Convolutional neural network - Support vector machine based approach for classification of cyanobacteria and chlorophyta microalgae groups

Cited by 39 publications

References 20 publications

Comparison of Machine Learning Approach for Waste Bottle Classification

Comparison of Machine Learning Approach for Waste Bottle Classification

Classification of Wheat Rootstock and Their Hybrids According to Color Features by Machine Learning Algorithms

Machine learning for microalgae detection and utilization

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