PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

Kang, JiSoo; Baek, Jiwon; Chung, Kyungyong

doi:10.1109/access.2020.3007485

Cited by 15 publications

(13 citation statements)

References 36 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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“…Apache Tomcat 9.0 was used as an HTTP web server for communications and web service. To establish a reliable neural network model, the code of Java Deep Learning Essentials was used as a reference [38,39]. In the code, each layer is set as a class.…”

Section: Configuration and Processing Of Heart Condition Classificatimentioning

confidence: 99%

A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

2020

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Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

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“…Health examination information is periodically collected from members of the National Health Insurance. It is a data set that includes variables such as height, weight, vision, blood pressure, blood sugar, and cholesterol [32,33]. The National Health Examination Blood Pressure Blood Sugar Data is a data set that includes age, blood pressure, pre-meal blood sugar, diabetes and hypertension prevalence.…”

Section: A Deep Learning Model For Experimentsmentioning

confidence: 99%

Hybrid Multi-Modal Deep Learning using Collaborative Concat Layer in Health Bigdata

Kim

Chung

2020

IEEE Access

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A health model based on data has various missing values depending on the user situation, and the accuracy of a health model requiring variables that the user cannot collect appears low. A deep learning health model is fitted by learning weights to increase accuracy. This implies that additional learning is required when the environment changes, such as when new variables are added or deleted depending on the user situation. In the process of applying a deep-learning-based health model to the user situation, accuracy may be degraded if learning is omitted. In this paper, we propose hybrid multimodal deep learning using a collaborative concat layer in health big data. The proposed method uses a machine learning technique to alleviate the issue caused by the change in the data observation range according to a change in the user situation, and occurring in multimodal health deep learning. It is a layer composed of the connection, input, and output of the model of the collaborative node (CN). A CN is a node that predicts absent variables through filtering using the similarity of input values. With CN, a collaborative concat layer (CCL) that handles missing values from the input of the health model can be configured, and the issue related to missing values occurring in the health model can be resolved. With the proposed CCL, it is possible to reuse existing models or construct new models through the concatenation of several single-modal deep learning models. By evaluating the effect on the input and output of the model according to the structural position of the CCL, various networks can be configured, and the performance of the single-modal model can be maintained. In particular, the accuracy of a deep learning model is more stable when the CCL is used, suggesting the experiment progress based on the assumption that a specific variable is absent depending on the user situation.

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PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

Cited by 15 publications

References 36 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

A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Hybrid Multi-Modal Deep Learning using Collaborative Concat Layer in Health Bigdata

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