A Method for Detecting Document Orientation by Using Na&amp;#x00CF;ve Bayes Classifier

Deng, Xue; Guo, Jun; Chen, Youguang; Li, Xiaoping

doi:10.1109/icicee.2012.120

Cited by 4 publications

(2 citation statements)

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“…Where X = attribute value in the training data used, n = the value of the total training data for each class Based on the data in the training database and equations (9) and (10) Furthermore, the calculation is performed on all the attributes of the training data based on each class. As for the overall results of the calculation of μ and σ, each attribute of the training data based on rotten, mature and raw classes can be seen in tables 1, 2, and 3.…”

Section: Resultsmentioning

confidence: 99%

“…Along with the development of information technology, it is possible to identify fruit maturity with the computers help [3] [4]. Identification can be done by classifying the image of tomatoes with various methods such as K-Nearest Neighbor (KNN) [4] [5], Random Forest [6], Support Vector Machine(SVM) [7] [8], Naïve Bayes [7] [8] [9] [10] [11] [12] [13], etc. In this study, identification of maturity in a set of tomatoes using the Naïve Bayes algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Tomato Maturity Classification using Naive Bayes Algorithm and Histogram Feature Extraction

Kusuma

Setiadi

Putra

2018

JAIS

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Tomatoes have nutritional content that is very beneficial for human health and is one source of vitamins and minerals. Tomato classification plays an important role in many ways related to the distribution and sales of tomatoes. Classification can be done on images by extracting features and then classifying them with certain methods. This research proposes a classification technique using feature histogram extraction and Naïve Bayes Classifier. Histogram feature extractions are widely used and play a role in the classification results. Naïve Bayes is proposed because it has high accuracy and high computational speed when applied to a large number of databases, is robust to isolated noise points, and only requires small training data to estimate the parameters needed for classification. The proposed classification is divided into three classes, namely raw, mature and rotten. Based on the results of the experiment using 75 training data and 25 testing data obtained 76% accuracy

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Section: Resultsmentioning

confidence: 99%