Hybrid clustering based health decision-making for improving dietary habits

Baek, Jiwon; Kim, Joo-Chang; Chun, Junchul; Chung, Kyungyong

doi:10.3233/thc-191730

Cited by 41 publications

(21 citation statements)

References 24 publications

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“…are developed. In addition, the IoT and big data processing technology helps to provide diversified and personalized healthcare services by finding user health information and dietary habit patterns [27,28].…”

Section: Machine Learning and Artificial Neural Network Modelsmentioning

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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Section: Machine Learning and Artificial Neural Network Modelsmentioning

confidence: 99%

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

2020

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“…When data is integrated in smart health and used for learning or analysis, problems such as feature extraction and absence of variables arise [23]. To overcome this problem, health data representing different characteristics is configured as each single-modal.…”

Section: B Deep Learning-based Smart Healthcare Modelmentioning

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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“…Since these features are a base classifier and a candidate for a weak classifier, it was necessary to decide how many times the process of weak classifier selection should be repeated [32,33]. In other words, it was necessary to determine how many weak classifiers should be combined into one stronger classifier, and to select one feature having the best performance in classifying training samples by class and to calculate a weak classifier for the corresponding iteration [34]. Therefore, we used a weighted linear combination of T weak classifiers, as shown in Equation 1.…”

Section: Image Multi-block Process For Face Main Point Extractionmentioning

confidence: 99%

Detection of Emotion Using Multi-Block Deep Learning in a Self-Management Interview App

2019

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Recently, domestic universities have constructed and operated online mock interview systems for students’ preparation for employment. Students can have a mock interview anywhere and at any time through the online mock interview system, and can improve any problems during the interviews via images stored in real time. For such practice, it is necessary to analyze the emotional state of the student based on the situation, and to provide coaching through accurate analysis of the interview. In this paper, we propose detection of user emotions using multi-block deep learning in a self-management interview application. Unlike the basic structure for learning about whole-face images, the multi-block deep learning method helps the user learn after sampling the core facial areas (eyes, nose, mouth, etc.), which are important factors for emotion analysis from face detection. Through the multi-block process, sampling is carried out using multiple AdaBoost learning. For optimal block image screening and verification, similarity measurement is also performed during this process. A performance evaluation of the proposed model compares the proposed system with AlexNet, which has mainly been used for facial recognition in the past. As comparison items, the recognition rate and extraction time of the specific area are compared. The extraction time of the specific area decreased by 2.61%, and the recognition rate increased by 3.75%, indicating that the proposed facial recognition method is excellent. It is expected to provide good-quality, customized interview education for job seekers by establishing a systematic interview system using the proposed deep learning method.

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Hybrid clustering based health decision-making for improving dietary habits

Cited by 41 publications

References 24 publications

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

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

Detection of Emotion Using Multi-Block Deep Learning in a Self-Management Interview App

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