scholarly journals An English Teaching Resource Recommendation System Based on Network Behavior Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jing Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recommendation System ◽  
User Behavior ◽  
Teaching Resources ◽  
English Teaching ◽  
Collaborative Recommendation ◽  
Proposed Model ◽  
Deep Learning Model ◽  
Local User

The sharing of English teaching resources has always been a concern. In order to further improve the value of different English teaching resources, this paper proposes a resource management system based on an improved collaborative recommendation algorithm. The proposed model can predict user behavior based on deep learning models of graph neural network (GNN) and recurrent neural network (RNN). The graph neural network can capture the hidden state of local user behavior and be used as a preprocessing step. Recurrent neural networks can capture time series information. Therefore, the model is constructed by combining GNN and RNN to obtain the advantages of both. In order to prove the effectiveness of the model, we used CNGrid’s real user behavior dataset in the experiment and finally compared the results with other methods. The different deep learning-based models achieved a precision of up to 88% and outperformed other traditional models. The experimental results show that this new deep learning model has good sharing value.

scholarly journals Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

2021 ◽  
Vol 11 (6) ◽  
pp. 5467
Author(s):  
Surenthiran Krishnan ◽  
Pritheega Magalingam ◽  
Roslina Ibrahim
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Learning Model ◽  
Disease Prediction ◽  
The Neural Network ◽  
Proposed Model ◽  
Deep Learning Model

<span>This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.</span>

Genes expression classification through Histone Modification using Temporal Neural Network

2019 ◽  
Vol 12 ◽  
Author(s):  
Rajit Nair ◽  
Amit Bhagat
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Deep Learning ◽  
Histone Modification ◽  
Convolution Neural Network ◽  
Proposed Model ◽  
Genes Expression ◽  
Expression Classification ◽  
Improved Accuracy ◽  
Deep Learning Model

Histones are one of the important proteins present in our body. So histone modification will regulate the genes expression. Many computational methods are based on histone modification that predicts gene expression. The prediction of genes expression helps us in discovering epigenetic drugs for different types of diseases like cancer. Some of the methods are already developed to show the relation between histone and genes expression but failed in many aspects. This paper presents a work based on deep learning method that is namely Temporal Convolution Neural Network (TCN) and it classifies genes expression using histones as input. Temporal Convolution Neural Network basically extract features from input histones and predict the gene expression as on or off through temporal convolution neural network. This work also shows how proposed model generates improved accuracy than other machine learning and deep learning model working in this area. The comparison of the proposed work is done with other algorithms which are SVM, Logistic Regression, Convolutional Neural Network and Deep Chrome Neural Network.

scholarly journals Applying Internet information technology combined with deep learning to tourism collaborative recommendation system

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0240656
Author(s):  
Meng Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recommendation System ◽  
Product Information ◽  
Tourism Industry ◽  
Word Embedding ◽  
Personalized Recommendation ◽  
Collaborative Recommendation ◽  
Factorization Machine ◽  
Tourism Service

Recently, more personalized travel methods have emerged in the tourism industry, such as individual travel and self-guided travel. The service models of traditional tourism limit the diversity of service options and cannot fully meet the individual needs of tourists anymore. The aim is to integrate sparse tourism information on the Internet, thereby providing more convenient, faster, and more personalized tourism services. Based on the shortcomings of the traditional tourism recommendation system, a deep learning-based classification processing method of tourism product information is proposed. This method uses word embedding in the data preprocessing stage. The Convolutional Neural Network (CNN) is used to process review information of users and tourism service items. The Deep Neural Network (DNN) is used to process the necessary information of users and tourism service items. Also, factorization machine technology is used to learn the interaction between the extracted features to improve the prediction model. The results show that the proposed model can maintain an excellent precision of 64.2% when generating personalized recommendation lists for users. The sensitivity and accuracy of the recommendation list are better than other algorithms. By adding DNN, the word embedding method, and the factorization machine model, the precision is improved by 30%, 33.3%, and 40%, respectively. The model accuracy is the highest with 40 hidden factors, 100 convolutions, and a 100+50 combination hidden layer. Compared with traditional methods, the proposed algorithm can provide users with personalized travel products more accurately in personalized travel recommendations. The results have enriched and developed the theory of tourism service supply chain, providing a reference for constructing a personalized tourism service system.

scholarly journals A Deep-Learning Model with Task-Specific Bounding Box Regressors and Conditional Back-Propagation for Moving Object Detection in ADAS Applications

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5269
Author(s):  
Guan-Ting Lin ◽  
Vinay Malligere Shivanna ◽  
Jiun-In Guo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Receptive Field ◽  
Object Detection ◽  
Back Propagation ◽  
Learning Model ◽  
Aspect Ratios ◽  
Bounding Box ◽  
Proposed Model ◽  
Deep Learning Model

This paper proposes a deep-learning model with task-specific bounding box regressors (TSBBRs) and conditional back-propagation mechanisms for detection of objects in motion for advanced driver assistance system (ADAS) applications. The proposed model separates the object detection networks for objects of different sizes and applies the proposed algorithm to achieve better detection results for both larger and tinier objects. For larger objects, a neural network with a larger visual receptive field is used to acquire information from larger areas. For the detection of tinier objects, the network of a smaller receptive field utilizes fine grain features. A conditional back-propagation mechanism yields different types of TSBBRs to perform data-driven learning for the set criterion and learn the representation of different object sizes without degrading each other. The design of dual-path object bounding box regressors can simultaneously detect objects in various kinds of dissimilar scales and aspect ratios. Only a single inference of neural network is needed for each frame to support the detection of multiple types of object, such as bicycles, motorbikes, cars, buses, trucks, and pedestrians, and to locate their exact positions. The proposed model was developed and implemented on different NVIDIA devices such as 1080 Ti, DRIVE-PX2 and Jetson TX-2 with the respective processing performance of 67 frames per second (fps), 19.4 fps, and 8.9 fps for the video input of 448 × 448 resolution, respectively. The proposed model can detect objects as small as 13 × 13 pixels and achieves 86.54% accuracy on a publicly available Pascal Visual Object Class (VOC) car database and 82.4% mean average precision (mAP) on a large collection of common road real scenes database (iVS database).

scholarly journals Coronavirus 2019 (COVID-19) Detection Based on Deep Learning

2020 ◽  
Vol 23 (4) ◽  
pp. 408-415
Author(s):  
Toqa Abd Ul-Mohsen Sadoon ◽  
Mohammed Hussein Ali
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Imaging Techniques ◽  
Treatment Decision ◽  
High Accuracy ◽  
The Other ◽  
Test Accuracy ◽  
Proposed Model ◽  
Deep Learning Model

Deep learning modeling could provide to detected Corona Virus 2019 (COVID-19) which is a critical task these days to make a treatment decision according to the diagnostic results. On the other hand, advances in the areas of artificial intelligence, machine learning, deep learning, and medical imaging techniques allow demonstrating impressive performance, especially in problems of detection, classification, and segmentation. These innovations enabled physicians to see the human body with high accuracy, which led to an increase in the accuracy of diagnosis and non-surgical examination of patients. There are many imaging models used to detect COVID-19, but we use computerized tomography (CT) because is commonly used. Moreover, we use for detection a deep learning model based on convolutional neural network (CNN) for COVID-19 detection. The dataset has been used is 544 slice of CT scan which is not sufficient for high accuracy, but we can say that it is acceptable because of the few datasets available in these days. The proposed model achieves validation and test accuracy 84.4% and 90.09%, respectively. The proposed model has been compared with other models to prove superiority of our model over the other models.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

scholarly journals Ensemble Empirical Mode Decomposition with Adaptive Noise with Convolution Based Gated Recurrent Neural Network: A New Deep Learning Model for South Asian High Intensity Forecasting

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 931
Author(s):  
Kecheng Peng ◽  
Xiaoqun Cao ◽  
Bainian Liu ◽  
Yanan Guo ◽  
Wenlong Tian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
South Asian ◽  
Recurrent Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
South Asian High ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Deep Learning Model

The intensity variation of the South Asian high (SAH) plays an important role in the formation and extinction of many kinds of mesoscale systems, including tropical cyclones, southwest vortices in the Asian summer monsoon (ASM) region, and the precipitation in the whole Asia Europe region, and the SAH has a vortex symmetrical structure; its dynamic field also has the symmetry form. Not enough previous studies focus on the variation of SAH daily intensity. The purpose of this study is to establish a day-to-day prediction model of the SAH intensity, which can accurately predict not only the interannual variation but also the day-to-day variation of the SAH. Focusing on the summer period when the SAH is the strongest, this paper selects the geopotential height data between 1948 and 2020 from NCEP to construct the SAH intensity datasets. Compared with the classical deep learning methods of various kinds of efficient time series prediction model, we ultimately combine the Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which has the ability to deal with the nonlinear and unstable single system, with the Permutation Entropy (PE) method, which can extract the SAH intensity feature of IMF decomposed by CEEMDAN, and the Convolution-based Gated Recurrent Neural Network (ConvGRU) model is used to train, test, and predict the intensity of the SAH. The prediction results show that the combination of CEEMDAN and ConvGRU can have a higher accuracy and more stable prediction ability than the traditional deep learning model. After removing the redundant features in the time series, the prediction accuracy of the SAH intensity is higher than that of the classical model, which proves that the method has good applicability for the prediction of nonlinear systems in the atmosphere.

scholarly journals Comparison Study on Improved Movie Recommender Systems

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1470-1478
Author(s):  
R. Lavanya ◽  
Ebani Gogia ◽  
Nihal Rai
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Recommendation System ◽  
Singular Value ◽  
Experimental Result ◽  
Comparison Study ◽  
Deep Learning Neural Network ◽  
Value Decomposition ◽  
Crucial Part

Recommendation system is a crucial part of offering items especially in services that offer streaming. For streaming movie services on OTT, RS are a helping hand for users in finding new movies for leisure. In this paper, we propose a machine learning an approach based on auto encoders to produce a CF system which outputs movie rating for a user based on a huge DB of ratings from other users. Utilising Movie Lens dataset, we explore the use of deep learning neural network based Stacked Auto encoders to predict user s ratings on new movies, thereby enabling movie recommendations. We consequently implement Singular Value Decomposition (SVD) to recommend movies to users. The experimental result showcase that our R S out performs a user-based neighbourhood baseline in terms of MSE on predicted ratings and in a survey in which user judge between recommendation s from both systems.

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