Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score

Kim, Hyun-Jin; Baek, Jiwon; Chung, Kyungyong

doi:10.3390/app10134590

Cited by 20 publications

(11 citation statements)

References 32 publications

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“…The data used in this study are eleven pothole road images and eleven normal road images, both of which have 512x512 in size. For the detection of the structural features of potholes, the brightness and contrast of these images, which are the features used in SSIM, were adjusted and preprocessed [16,17]. As for eleven normal road images, a total of twenty-one images were compared, and therefore comparative tests were conducted two hundreds forty-two times.…”

Section: Discussionmentioning

confidence: 99%

Pothole Region Extraction Based On Similarity Evaluation Scale Classification Using Image Processing

et.al¹

2021

TURCOMAT

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Pavement deterioration and abnormal climate induced by global warming lead to a constant rise in the number of potholes. Accordingly, the loss cost for maintenance and accidents also increases. Therefore, it is necessary to develop a method of classifying pavement potholes and detecting their locations. This study proposes the pothole region extraction based on similarity evaluation scale classification using image processing. The proposed technique sets up a classification threshold appropriately by considering the structure, brightness, and other factors of the grayscale-converted image through SSIM (Structural Similarity Index Measure). It binarizes porthole images classified according to the threshold, and then extracts pothole regions through the threshold based segmentation. A conventional image classification method utilizes the rules found in objects or the label selected by a user. The proposed method can take into account detailed factors by comparing image similarity in the unit of pixel. According to the performance evaluation, the proposed classification method’s F1-score is 0.83, and its accuracy of pothole region extraction is 0.851. Therefore, with the proposed technique, it is possible to make classification in consideration of similarity between images. In addition, the proposed method makes it possible to detect the regions similar to actual potholes accurately.

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Section: Discussionmentioning

confidence: 99%

Pothole Region Extraction Based On Similarity Evaluation Scale Classification Using Image Processing

et.al¹

2021

TURCOMAT

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“…In IDF, the less frequent words appear in the document, the greater the value. To determine the amount of the IDF value, we use the formula [28]:…”

Section: Materials and Methods 21 Tf-idfmentioning

confidence: 99%

The effect of the TF-IDF algorithm in times series in forecasting word on social media

Lubis

Nasution

Sitompul

et al. 2021

IJEECS

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Forecasting is one of the main topics in data mining or machine learning in which forecasting, a group of data used, has a label class or target. Thus, many algorithms for solving forecasting problems are categorized as supervised learning with the aim of conducting training. In this case, the things that were supervised were the label or target data playing a role as a 'supervisor' who supervise the training process in achieving a certain level of accuracy or precision. Time series is a method that is generally used to forecast based on time and can forecast words in social media. In this study had conducted the word forecasting on twitter with 1734 tweets which were interpreted as weighted documents using the TF-IDF algorithm with a frequency that often comes out in tweets so the TF-IDF value is getting smaller and vice versa. After getting the word weight value of the tweets, a time series forecast was performed with the test data of 1734 tweets that the results referred to 1203 categories of Slack words and 531 verb tweets as training data resulting in good accuracy. The division of word forecasting was classified into two groups i.e. inactive users and active users. The results obtained were processed with a MAPE calculation process of 50% for inactive users and 0.1980198% for active users.

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“…As for general transactions used for association rule generation, Term Frequency is not taken into account. For this reason, object frequency in video content is not considered [24]. To solve the problem, the ranking table generated on the basis of TF-IDF is used.…”

Section: B Pre-processing and Normalization Using Shift And Reversed Min-maxmentioning

confidence: 99%

Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents

2021

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Video content data have a variety of objects that could be associated with each other. Although content data contains similar objects or themes, their associations can become ambiguous. Accordingly, if associations between video content data are found in general association rules, their accuracy and confidence are low. Therefore, this study proposes the associative knowledge graph using fuzzy clustering and min-max normalization in video contents. With the use of the objects of video content, the proposed method finds clear and accurate associations between video content data and generates a knowledge graph. In the first step, the streaming video content data massively generated are collected, and objects in each image video are classified by an object detection algorithm. In the second step, normalization is executed in consideration of the different length of each video content and generate transaction data based on object frequency. In this way, it is possible to consider all the collected video content in the same condition. Additionally, it is possible to find unnecessary objects and significant objects in video content. Lastly, the degree of ambiguity is analyzed through fuzzy clustering using a probability that each object is involved in a group. Associations between fuzzy-clustered objects are extracted and an association knowledge graph is created. In this way, the accuracy and confidence of associations between different video content data are improved. As for performance, the association knowledge graph generated in the proposed method was better than a conventional association rule method in terms of the count of generated rules, support, and confidence.

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Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score

Cited by 20 publications

References 32 publications

Pothole Region Extraction Based On Similarity Evaluation Scale Classification Using Image Processing

Pothole Region Extraction Based On Similarity Evaluation Scale Classification Using Image Processing

The effect of the TF-IDF algorithm in times series in forecasting word on social media

Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents

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