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CBML: CLASSIFICATION – THAI RED DRAGON FRUIT
(2022-11-01)
Yusamran, Naruwan
;
Hiransakolwong, Nualsawat
A dragon fruit is nutritious having low sugar levels suitable for diabetics. Its shape is spherical with petals surrounding the bark. In Thailand, 7 species of dragon fruit are grown. Its appearance can be divided into red bark (6 species) or yellow bark (1 species). People can separate between yellow and red bark. Since the red-skinned dragon fruit may have its fruit pulp in one of three possible colors: white, red or pink, it is difficult to guess from the outside what color of the fruit pulp will be. The big problem is that people cannot classify its species of red bark. Some people may be allergic or dislike dragon fruit in some species. Therefore, this research is trying to find the best way to automatically classify the species of Thai red dragon fruit from its image. The CBML stands for content base and Machine Learning. This method uses content base to extract key features (34 attributes), and uses Machine Learning for giving optimization results with the Support Vector Machine (SVM), setting kernel for polynomial in 8 degree. The results showed that the CBML method was able to identify any species of dragon fruits in the red bark group with an accuracy of 98.47%.
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DIPDEEP: Classification for Thai dragon fruit
(2022-02-22)
Yusamran, Naruwan
;
Hiransakolwong, Nualsawat
Thai dragon fruit is an interesting fruit with beautiful colors and high nutritional value, which can be used for food and pharmaceutical. In Thailand, there are 7 species of dragon fruits. Its skin can be divided into red or yellow groups, but inside can be divided into white, red or pink groups. If farmers know the species of dragon fruits, they can export fruits at a good price. Most people are unable to distinguish species of the dragon fruits. This paper is focus on classifying species of dragon fruits from images using digital image processing and deep learning called DIPDEEP. The DIPDEEP method has three steps; color space transformation, calculation ratio of the yellow and red color, classification using the deep learning method. First, the Thai dragon fruit images were classified into yellow (1 species) out from red (6 species). The Thai red dragon fruit was resized into 100x100 pixel resolutions in the pre-processing step only 6 species. Then, the red class was sent to classify again using the deep learning method. The experiments were processed in a dataset with 9,754 dragon fruit images on a black background (laboratory), and 10,072 images of dragon fruits at outdoor environment (outdoor). The results showed that accuracy of classification between the red and yellow dragon fruit for laboratory and outdoor datasets was 100% and 95.26%, respectively. The red dragon fruit is classified its species with accuracy 98.80%. The DIPDEEP has the smallest file size, and can save workload time because of separating yellow skin out at first step.
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A novel technique for feature subset selection based on Cosine similarity
(2012-11-27)
Suebsing, Anirut
;
Hiransakolwong, Nualsawat
Nowadays, data mining has been playing an important role in the various disciplines of sciences and technologies. Data mining is composed of many tasks but one of the essential procedures of data mining is feature selection, which is the technique mostly based on the machine learning for selecting a subset consisted of significant features, building a stronger learning model, and enhancing the efficiency of prediction rate. Normally, a processing of building a learning model from the huge amount of collected data needs high computation cost. Therefore, with feature selection, the computation cost can be reduced by selecting relevant features. In the previous researches on feature selection, the criteria and algorithms for selecting the features from the raw data are mostly complicated and difficult to implement. Therefore, this paper presents a novel method by applied Cosine similarity to feature selection method. The proposed algorithm begins with selecting a robust feature subset using the Cosine similarity. This method is the simple algorithm using smallerstorage space, reducing computation time and gaining higher predictive performance. During the evaluation phase, the tendata sets from UCI benchmark data sets are used to evaluate the performance of proposed approach by using the C5.0, CARTand Neural Networks classifiers. Experimental results show that the method based on the Cosine similarity can improve the performance of accuracy detection rate with less error rate.

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