scholarly journals EEG Signal and Feature Interaction Modeling-Based Eye Behavior Prediction Research

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Pengcheng Ma ◽  
Qian Gao
Keyword(s):  
Recommendation System ◽  
Rapid Development ◽  
User Preferences ◽  
Feature Interaction ◽  
Eeg Signal ◽  
Machine Model ◽  
Data Set ◽  
Eeg Data ◽  
Factorization Machine

In recent years, with the development of brain science and biomedical engineering, as well as the rapid development of electroencephalogram (EEG) signal analysis methods, using EEG signals to monitor human health has become a very popular research field. The innovation of this paper is to analyze the EEG signal for the first time by building a depth factorization machine model, so that on the basis of analyzing the characteristics of user interaction, we can use EEG data to predict the binomial state of eyes (open eyes and closed eyes). The significance of the research is that we can diagnose the fatigue and the health of the human body by detecting the state of eyes for a long time. On the basis of this inference, the proposed method can make a further useful auxiliary support for improving the accuracy of the recommendation system recommendation results. In this paper, we first extract the features of EEG data by wavelet transform technology and then build a depth factorization machine model (FM+LSTM) which combines factorization machine (FM) and Long Short-Term Memory (LSTM) in parallel. Through the test of real data set, the proposed model gets more efficient prediction results than other classifier models. In addition, the model proposed in this paper is suitable not only for the determination of eye features but also for the acquisition of interactive features (user fatigue) in the recommendation system. The conclusion obtained in this paper will be an important factor in the determination of user preferences in the recommendation system, which will be used in the analysis of interactive features by the graph neural network in the future work.

scholarly journals Graph Neural Network and Context-Aware Based User Behavior Prediction and Recommendation System Research

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Qian Gao ◽  
Pengcheng Ma
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
User Behavior ◽  
Absolute Error ◽  
User Preferences ◽  
Feature Interaction ◽  
Context Aware ◽  
Behavior Prediction ◽  
Complex Interactions ◽  
System Maps

Due to the influence of context information on user behavior, context-aware recommendation system (CARS) has attracted extensive attention in recent years. The most advanced context-aware recommendation system maps the original multi-field features into a shared hidden space and then simply connects it to a deep neural network (DNN) or other specially designed networks. However, for different areas, the ability of modeling complex interactions in a sufficiently flexible and explicit way is limited by the simple unstructured combination of feature fields. Therefore, it is hard to get the accurate results of the user behavior prediction. In this paper, a graph structure is used to establish the interaction between context and users/items. Through modeling user behavior, we can explore user preferences in different context environments, so as to make personalized recommendations for users. In particular, we construct a context-user and context-item interactions graph separately. In the interactions graph, each node is composed of a user feature field, an item feature field, and a feature field of different contexts. Different feature fields can interact through edges. Therefore, the task of modeling feature interaction can be transformed into modeling the node interaction on the corresponding graph. To this end, an innovative model called context-aware graph neural network (CA-GNN) model is designed. Furthermore, in order to obtain more accurate and efficient recommendation results, first, we innovatively use the attention mechanism to improve the interpretability of CA-GNN; second, we innovatively use the degree of physical fatigue features which has never been used in traditional CARS as critical contextual feature information into our CA-GNN. We simulated the Food and Yelp datasets. The experimental results show that CA-GNN is better than other methods in terms of root mean square error (RMSE) and mean absolute error (MAE).

ARTIFICIAL NEURAL NETWORK BASED APPROACH TO EEG SIGNAL SIMULATION

2012 ◽  
Vol 22 (03) ◽  
pp. 1250008 ◽  
Author(s):  
NIKOLA M. TOMASEVIC ◽  
ALEKSANDAR M. NESKOVIC ◽  
NATASA J. NESKOVIC
Keyword(s):  
Moving Average ◽  
Training Data ◽  
Autoregressive Moving Average ◽  
Eeg Signal ◽  
Data Set ◽  
Eeg Data ◽  
Simulation Based ◽  
Signal Simulation ◽  
Artificial Neural ◽  
The One

In this paper a new approach to the electroencephalogram (EEG) signal simulation based on the artificial neural networks (ANN) is proposed. The aim was to simulate the spontaneous human EEG background activity based solely on the experimentally acquired EEG data. Therefore, an EEG measurement campaign was conducted on a healthy awake adult in order to obtain an adequate ANN training data set. As demonstration of the performance of the ANN based approach, comparisons were made against autoregressive moving average (ARMA) filtering based method. Comprehensive quantitative and qualitative statistical analysis showed clearly that the EEG process obtained by the proposed method was in satisfactory agreement with the one obtained by measurements.

scholarly journals Attention neural collaboration filtering based on GRU for recommender systems

2021 ◽  
Author(s):  
Hongbin Xia ◽  
Yang Luo ◽  
Yuan Liu
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Auxiliary Information ◽  
User Preferences ◽  
Feature Representation ◽  
Long Distance ◽  
Data Set ◽  
Filtering Method ◽  
The Impact

AbstractThe collaborative filtering method is widely used in the traditional recommendation system. The collaborative filtering method based on matrix factorization treats the user’s preference for the item as a linear combination of the user and the item latent vectors, and cannot learn a deeper feature representation. In addition, the cold start and data sparsity remain major problems for collaborative filtering. To tackle these problems, some scholars have proposed to use deep neural network to extract text information, but did not consider the impact of long-distance dependent information and key information on their models. In this paper, we propose a neural collaborative filtering recommender method that integrates user and item auxiliary information. This method fully integrates user-item rating information, user assistance information and item text assistance information for feature extraction. First, Stacked Denoising Auto Encoder is used to extract user features, and Gated Recurrent Unit with auxiliary information is used to extract items’ latent vectors, respectively. The attention mechanism is used to learn key information when extracting text features. Second, the latent vectors learned by deep learning techniques are used in multi-layer nonlinear networks to learn more abstract and deeper feature representations to predict user preferences. According to the verification results on the MovieLens data set, the proposed model outperforms other traditional approaches and deep learning models making it state of the art.

scholarly journals Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data

2021 ◽  
Vol 11 (21) ◽  
pp. 10502
Author(s):  
Ling Dai ◽  
Guangyun Zhang ◽  
Jinqi Gong ◽  
Rongting Zhang
Keyword(s):  
Remote Sensing ◽  
Logistic Regression ◽  
Expert Knowledge ◽  
Rapid Development ◽  
Remotely Sensed ◽  
Laboratory Research ◽  
Feature Interaction ◽  
Statistical Regression ◽  
Remotely Sensed Data ◽  
Data Set

In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data.

Semantic Tags and Neutral Network Based Personalized Advertisement Recommendation System for Movies

2012 ◽  
Vol 488-489 ◽  
pp. 1727-1731
Author(s):  
Wei Du ◽  
Jun Liang Chen
Keyword(s):  
Recommendation System ◽  
Rapid Development ◽  
Internet Technology ◽  
Neutral Network ◽  
User Preference ◽  
Simulation Experiments ◽  
Data Set ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Personalized Advertisement

With the rapid development of information especially internet technology, people have to choose the most suitable goods without any experience, so the recommendation system is seriously required. Yet no research on advertisement recommendation system for movie play is presented. Regarding this problem, the paper introduces the theory of semantic computing and annotates the semantic tags from the movie slices and the candidate advertisements, the potential preferences on them are predicted with neutral network model trained by some data set predefined. The user preference model and the predicting workflow are described in detail. Finally, the MovieLens dataset is employed to validate the validity of the system designed. The results of simulation experiments prove that the technology proposed can not only satisfy the requirement of matched advertisement recommendation but also outperform the traditional collaborative filtering algorithm.

Design and Application of Handicraft Recommendation System Based on Improved Hybrid Algorithm

2022 ◽  
Author(s):  
Yong Yang ◽  
Young Chun ko
Keyword(s):  
Collaborative Filtering ◽  
Hybrid Algorithm ◽  
Recommendation System ◽  
Sparse Matrix ◽  
Rapid Development ◽  
Data Set ◽  
Improve Accuracy ◽  
Collaborative Filtering Algorithm ◽  
Candidate Set ◽  
Design And Application

With the rapid development of online e-commerce, traditional collaborative filtering algorithms have the disadvantages of data set reduction and sparse matrix filling cannot meet the requirements of users. This paper takes handicrafts as an example to propose the design and application of handicraft recommendation system based on an improved hybrid algorithm. Based on the theory of e-commerce system, through the traditional collaborative filtering algorithm of users, the personalized e-commerce system of hybrid algorithm is designed and analyzed. The personalized e-commerce system based on hybrid algorithm is further proposed. The component model of the business recommendation system and the specific steps of the improved hybrid algorithm based on user information are given. Finally, an experimental analysis of the improved hybrid algorithm is carried out. The results show that the algorithm can effectively improve the effectiveness and exemption of recommending handicrafts. What’s more, it can reduce the user item ratings of candidate set and improve accuracy of the forecast recommendation.

scholarly journals IMPLEMENTATION OF A HYBRID METHOD OF SEARCHING FOR CLOSE OBJECTS, TAKING INTO ACCOUNT THE GENERAL AND ACOUSTIC CHARACTERISTICS

2021 ◽  
pp. 59-68
Author(s):  
Olga Malyeyeva ◽  
Vadym Yesipov ◽  
Roman Artiukh ◽  
Viktor Kosenko
Keyword(s):  
Collaborative Filtering ◽  
Hybrid Method ◽  
Recommendation System ◽  
Hybrid Methods ◽  
Similarity Theory ◽  
User Preferences ◽  
Musical Compositions ◽  
Analysis Of Similarity ◽  
Calculation Algorithms

The subject of research in the article is the methods of finding close objects and technologies of forming recommendations. The aim of the article is to develop a recommendation system based on a hybrid method of searching for objects, taking into account both user preferences and audio characteristics of objects. The following tasks are solved: analysis of methods and algorithms used in recommendation systems; development of a hybrid method of forming recommendations on the principle of double organization; determination of the main functions and architecture of the system of formation of musical recommendations; testing of calculation algorithms and search methods in the system for analysis of similarity of musical recommendations. The following research methods are used: methods of correlation analysis, methods of similarity theory, algorithms of collaborative filtering and content analysis, hybrid methods, methods of analysis of audio characteristics, programming technologies. The following results were obtained: A study of collaborative filtering, content-based filtering and hybrid methods. Algorithms and calculation formulas of the considered methods are given. The main audio characteristics of musical compositions are considered. The method of formation of recommendations on the principle of double organization is developed. The main functions of the system of formation of musical recommendations are listed and the diagram of components is formed. An example of calculating the characteristics of user preferences and similarity of musical compositions by audio characteristics is given. Conclusions: According to the results of testing the system by three methods, we can conclude that the proposed hybrid method was the most effective among the studied recommendation methods with the lowest standard error rate.  In addition, the hybrid method on the principle of double organization solves such problems of existing recommendation methods as excessive similarity of recommendations, potentially small number or no proposals at all by compensating data from one block of data from another.

scholarly journals Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Feifeng Huang
Keyword(s):  
Neural Network ◽  
Social Media ◽  
New Media ◽  
Recommendation System ◽  
Rapid Development ◽  
Mobile Internet ◽  
Data Set ◽  
Data Volume ◽  
Short Video ◽  
Specific Implementation

With the rapid development of mobile Internet, short video has become another darling after traditional webcast in recent years. How to make full use of short video for effective marketing has become a hot issue that academia and industry are paying close attention to. This article is mainly aimed at exploring practical new media through in-depth research and exploration of the specific implementation methods and strategies of short video marketing in social media, based on the advantages and characteristic models of short video marketing in social media. The strategy of short video marketing in social media, and the use of highly in-depth neural network analysis technology for the personalized marketing recommendation system of new media short videos, so as to better promote the use of social media short videos by enterprises or individuals. We have to learn from marketing activities. The experimental results of this article show that when the data volume reaches 80%, the performance of the VRBCH algorithm steadily improves, so the performance of the main F of the VRBCH algorithm is still relatively ideal when the data volume changes. Due to the high dilution of the experimental data set, the amount of data in the VRBCH algorithm has increased sharply by 30% to 35%, but the purchase rate of the marketing recommendation system is as high as 98%. Therefore, the system has high feasibility.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

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