scholarly journals Online Doctor Recommendation with Convolutional Neural Network and Sparse Inputs

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yongjie Yan ◽  
Guang Yu ◽  
Xiangbin Yan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Health Resources ◽  
Feature Representation ◽  
Medical Consultation ◽  
Internet Technology ◽  
Utilization Efficiency ◽  
Medical Community

The recommendation system in the online medical consultation website is a system to assist patients to find appropriate doctors. Based on the analysis of the current situation of the development of an online medical community (Haodf.com) in China, this paper puts forward recommendation suggestions of finding the right hospital and doctor to promote the rapid integration of Internet technology and traditional medical services. A new recommendation model called Probabilistic Matrix Factorization integrated with Convolutional Neural Network (PMF-CNN) is proposed in the paper. Doctors’ data in Haodf.com were used to evaluate the performance of our system. The model improves the performance of medical consultation recommendations by fusing review text and doctor information based on CNN (Convolutional Neural Network). Specifically, CNN is used to learn the feature representation of the review text and the doctors’ information. Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the doctors’ information for recommendation. As is shown in the experimental results on Haodf.com datasets, the proposed PMF-CNN achieves better recommendation performances than the other state-of-the-art recommendation algorithms. And the recommendation system in an online medical website improves the utilization efficiency of doctors and the balance of public health resources allocation.

scholarly journals Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Huazhen Liu ◽  
Wei Wang ◽  
Yihan Zhang ◽  
Renqian Gu ◽  
Yaqi Hao
Keyword(s):  
Neural Network ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Heterogeneous Data ◽  
User Preference ◽  
Personalized Recommendation ◽  
Implicit Feedback ◽  
Network Training ◽  
Feedback Data ◽  
Explicit Feedback

Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation algorithm (EINMF) based on explicit-implicit feedback. First, neural network is used to learn nonlinear feature of explicit-implicit feedback of user-item interaction. Second, combined with the traditional matrix factorization, explicit feedback is used to accurately reflect the explicit preference and the potential preferences of users to build a recommendation model; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according to prediction results, personalized recommendation list is pushed to the user. The feasibility, validity, and robustness are fully demonstrated in comparison with multiple baseline models on two real datasets.

scholarly journals Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7731 ◽  
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Guohao Shen ◽  
Feng Hong
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Feature Representation ◽  
Training Data ◽  
Ecg Signal ◽  
Automatic Feature Extraction ◽  
Adjacent Segments ◽  
The Impact

Sleep apnea (SA) is the most common respiratory sleep disorder, leading to some serious neurological and cardiovascular diseases if left untreated. The diagnosis of SA is traditionally made using Polysomnography (PSG). However, this method requires many electrodes and wires, as well as an expert to monitor the test. Several researchers have proposed instead using a single channel signal for SA diagnosis. Among these options, the ECG signal is one of the most physiologically relevant signals of SA occurrence, and one that can be easily recorded using a wearable device. However, existing ECG signal-based methods mainly use features (i.e. frequency domain, time domain, and other nonlinear features) acquired from ECG and its derived signals in order to construct the model. This requires researchers to have rich experience in ECG, which is not common. A convolutional neural network (CNN) is a kind of deep neural network that can automatically learn effective feature representation from training data and has been successfully applied in many fields. Meanwhile, most studies have not considered the impact of adjacent segments on SA detection. Therefore, in this study, we propose a modified LeNet-5 convolutional neural network with adjacent segments for SA detection. Our experimental results show that our proposed method is useful for SA detection, and achieves better or comparable results when compared with traditional machine learning methods.

scholarly journals Design of the Piano Score Recommendation Image Analysis System Based on the Big Data and Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuanyuan Zhang
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Big Data ◽  
Convolutional Neural Network ◽  
Recommendation System ◽  
Piano Music ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Data Environment ◽  
Music Recommendation System

In the era of big data, the problem of information overload is becoming more and more obvious. A piano music image analysis and recommendation system based on the CNN classifier and user preference is designed by using the convolutional neural network (CNN), which can realize accurate piano music recommendation for users in the big data environment. The piano music recommendation system based on the CNN is mainly composed of user modeling, music feature extraction, recommendation algorithm, and so on. In the recommendation algorithm module, the potential characteristics of music are predicted by the regression model, and the matching degree between users and music is calculated according to user preferences. Then, music that users may be interested in is generated and sorted in order to recommend new piano music to relevant users. The image analysis model contains four “convolution + pooling” layers. The classification accuracy and gradient change law of the CNN under RMSProp and Adam optimal controllers are compared. The image analysis results show that the Adam optimal controller can quickly find the direction, and the gradient decreases greatly. In addition, the accuracy of the recommendation system is 55.84%. Compared with the traditional CNN algorithm, this paper uses the convolutional neural network (CNN) to analyze and recommend piano music images according to users’ preferences, which can realize more accurate piano music recommendation for users in the big data environment. Therefore, the piano music recommendation system based on the CNN has strong feature learning ability and good prediction and recommendation ability.

scholarly journals Research and Analysis of Recommendation Algorithm Based on Convolutional Neural Network

2021 ◽  
Vol 2132 (1) ◽  
pp. 012011
Author(s):  
Yuhang Ding ◽  
Wenrong Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recommendation System ◽  
Development Trend ◽  
Research Progress ◽  
Data Types ◽  
Research Directions ◽  
Continuous Growth ◽  
Main Research ◽  
Recommendation Algorithms

Abstract This paper first introduces the situation that the traditional recommendation algorithms cannot meet the needs of users for accurate and efficient recommendation results because of the continuous growth of data and the increasingly diversified data types. Then it introduces the Deep Learning (DL) algorithm which is getting more and more attention, organizes the research progress of recommendation system based on Convolutional Neural Networks (CNN) in recent years, analyzes its advantages compared with traditional recommendation algorithms. The main research directions and application progress are classified, compared and summarized. Finally, the future development trend of recommendation system based on convolutional neural network is summarized and analyzed.

scholarly journals Indigenous Food Recognition Model Based on Various Convolutional Neural Network Architectures for Gastronomic Tourism Business Analytics

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 322
Author(s):  
Mohd Norhisham Razali ◽  
Ervin Gubin Moung ◽  
Farashazillah Yahya ◽  
Chong Joon Hou ◽  
Rozita Hanapi ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Web Application ◽  
Empirical Evaluation ◽  
Feature Representation ◽  
Business Analytics ◽  
Memory Space ◽  
Recognition Model ◽  
Food Recognition ◽  
Web Application System

In gastronomic tourism, food is viewed as the central tourist attraction. Specifically, indigenous food is known to represent the expression of local culture and identity. To promote gastronomic tourism, it is critical to have a model for the food business analytics system. This research undertakes an empirical evaluation of recent transfer learning models for deep learning feature extraction for a food recognition model. The VIREO-Food172 Dataset and a newly established Sabah Food Dataset are used to evaluate the food recognition model. Afterwards, the model is implemented into a web application system as an attempt to automate food recognition. In this model, a fully connected layer with 11 and 10 Softmax neurons is used as the classifier for food categories in both datasets. Six pre-trained Convolutional Neural Network (CNN) models are evaluated as the feature extractors to extract essential features from food images. From the evaluation, the research found that the EfficientNet feature extractor-based and CNN classifier achieved the highest classification accuracy of 94.01% on the Sabah Food Dataset and 86.57% on VIREO-Food172 Dataset. EFFNet as a feature representation outperformed Xception in terms of overall performance. However, Xception can be considered despite some accuracy performance drawback if computational speed and memory space usage are more important than performance.

scholarly journals Semi-Supervised Convolutional Neural Network for Law Advice Online

2019 ◽  
Vol 9 (17) ◽  
pp. 3617 ◽  
Author(s):  
Fen Zhao ◽  
Penghua Li ◽  
Yuanyuan Li ◽  
Jie Hou ◽  
Yinguo Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Text Classification ◽  
Learning Task ◽  
Automatic Classification ◽  
Labor Cost ◽  
Internet Technology ◽  
Data Annotation

With the rapid developments of Internet technology, a mass of law cases is constantly occurring and needs to be dealt with in time. Automatic classification of law text is the most basic and critical process in the online law advice platform. Deep neural network-based natural language processing (DNN-NLP) is one of the most promising approaches to implement text classification. Meanwhile, as the convolutional neural network-based (CNN-based) methods developed, CNN-based text classification has already achieved impressive results. However, previous work applied amounts of manually-annotated data, which increased the labor cost and reduced the adaptability of the approach. Hence, we present a new semi-supervised model to solve the problem of data annotation. Our method learns the embedding of small text regions from unlabeled data and then integrates the learned embedding into the supervised training. More specifically, the learned embedding regions with the two-view-embedding model are used as an additional input to the CNN’s convolution layer. In addition, to implement the multi-task learning task, we propose the multi-label classification algorithm to assign multiple labels to an instance. The proposed method is evaluated experimentally subject to a law case description dataset and English standard dataset RCV1 . On Chinese data, the simulation results demonstrate that, compared with the existing methods such as linear SVM, our scheme respectively improves by 7.76%, 7.86%, 9.19%, and 2.96% the precision, recall, F-1, and Hamming loss. Analogously, the results suggest that compared to CNN, our scheme respectively improves by 4.46%, 5.76%, 5.14% and 0.87% in terms of precision, recall, F-1, and Hamming loss. It is worth mentioning that the robustness of this method makes it suitable and effective for automatic classification of law text. Furthermore, the design concept proposed is promising, which can be utilized in other real-world applications such as news classification and public opinion monitoring.

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