TRACE: Travel Reinforcement Recommendation Based on Location-Aware Context Extraction

2022 ◽  
Vol 16 (4) ◽  
pp. 1-22
Author(s):  
Zhe Fu ◽  
Li Yu ◽  
Xi Niu
Keyword(s):  
Real Time ◽  
Recommender System ◽  
Ad Hoc ◽  
Learning Model ◽  
User Preference ◽  
Context Learning ◽  
User Context ◽  
Location Aware ◽  
Proposed Model ◽  
Online Travel

As the popularity of online travel platforms increases, users tend to make ad-hoc decisions on places to visit rather than preparing the detailed tour plans in advance. Under the situation of timeliness and uncertainty of users’ demand, how to integrate real-time context into dynamic and personalized recommendations have become a key issue in travel recommender system. In this article, by integrating the users’ historical preferences and real-time context, a location-aware recommender system called TRACE ( T ravel R einforcement Recommendations Based on Location- A ware C ontext E xtraction) is proposed. It captures users’ features based on location-aware context learning model, and makes dynamic recommendations based on reinforcement learning. Specifically, this research: (1) designs a travel reinforcing recommender system based on an Actor-Critic framework, which can dynamically track the user preference shifts and optimize the recommender system performance; (2) proposes a location-aware context learning model, which aims at extracting user context from real-time location and then calculating the impacts of nearby attractions on users’ preferences; and (3) conducts both offline and online experiments. Our proposed model achieves the best performance in both of the two experiments, which demonstrates that tracking the users’ preference shifts based on real-time location is valuable for improving the recommendation results.

Location-Aware Caching for Semantic-Based Image Queries in Mobile AD HOC Networks

2012 ◽  
Vol 3 (1) ◽  
pp. 17-35
Author(s):  
Bo Yang ◽  
Manohar Mareboyana
Keyword(s):  
Information Retrieval ◽  
Ad Hoc ◽  
Effective Means ◽  
Image Data ◽  
Research Attention ◽  
Mobile Data ◽  
Location Aware ◽  
Technological Advances ◽  
Proposed Model ◽  
Hoc Networks

Mobile image information retrieval, i.e., the processing of mobile image queries, has attracted research attention due to the recent technological advances in mobile and ubiquitous computing, network infrastructures, and multimedia streaming. The previous research focuses on data delivery, while few works have reported on content-based mobile information retrieval. Therefore, it is important to devise effective means to describe the semantics as well as content distribution of mobile data. Caching is an attractive solution that helps reveal semantic relationships among mobile data sources. However, traditional caching techniques rely on exact match of fixed values and are not efficient in dealing with imprecisely described image data. To address these issues, the authors propose a location-aware caching model which reflects the distribution of images based on the analysis of earlier queries. Through extensive simulations, the authors show that the proposed model can perform search with less cost for voluminous data.

scholarly journals Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

2021 ◽  
Vol 13 (10) ◽  
pp. 1919
Author(s):  
Deqi Chen ◽  
Xuedong Yan ◽  
Xiaobing Liu ◽  
Liwei Wang ◽  
Fengxiao Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Sampling Rate ◽  
Learning Model ◽  
Operation Performance ◽  
Floating Car Data ◽  
Proposed Model ◽  
Intersection Operation ◽  
Spatio Temporal ◽  
Deep Learning Model

Urban road intersection bottleneck has become an important factor in causing traffic delay and restricting traffic efficiency. It is essential to explore the prediction of the operating performance at intersections in real-time and formulate corresponding strategies to alleviate intersection delay. However, because of the sophisticated intersection traffic condition, it is difficult to capture the intersection traffic Spatio-temporal features by the traditional data and prediction methods. The development of big data technology and the deep learning model provides us a good chance to address this challenge. Therefore, this paper proposes a multi-task fusion deep learning (MFDL) model based on massive floating car data to effectively predict the passing time and speed at intersections over different estimation time granularity. Moreover, the grid model and the fuzzy C-means (FCM) clustering method are developed to identify the intersection area and derive a set of key Spatio-temporal traffic parameters from floating car data. In order to validate the effectiveness of the proposed model, the floating car data from ten intersections of Beijing with a sampling rate of 3s are adopted for the training and test process. The experiment result shows that the MFDL model enables us to capture the Spatio-temporal and topology feature of the traffic state efficiently. Compared with the traditional prediction method, the proposed model has the best prediction performance. The interplay between these two targeted prediction variables can significantly improve prediction accuracy and efficiency. Thereby, this method predicts the intersection operation performance in real-time and can provide valuable insights for traffic managers to improve the intersection’s operation efficiency.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

scholarly journals Adaptive Real-Time Routing Protocol for (m,k)-Firm in Industrial Wireless Multimedia Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1633 ◽  
Author(s):  
Beom-Su Kim ◽  
Sangdae Kim ◽  
Kyong Hoon Kim ◽  
Tae-Eung Sung ◽  
Babar Shah ◽  
...  
Keyword(s):  
Real Time ◽  
Routing Protocol ◽  
Traffic Management ◽  
Research Area ◽  
Multimedia Data ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Proposed Model ◽  
Event Information ◽  
Firm Model

Many applications are able to obtain enriched information by employing a wireless multimedia sensor network (WMSN) in industrial environments, which consists of nodes that are capable of processing multimedia data. However, as many aspects of WMSNs still need to be refined, this remains a potential research area. An efficient application needs the ability to capture and store the latest information about an object or event, which requires real-time multimedia data to be delivered to the sink timely. Motivated to achieve this goal, we developed a new adaptive QoS routing protocol based on the (m,k)-firm model. The proposed model processes captured information by employing a multimedia stream in the (m,k)-firm format. In addition, the model includes a new adaptive real-time protocol and traffic handling scheme to transmit event information by selecting the next hop according to the flow status as well as the requirement of the (m,k)-firm model. Different from the previous approach, two level adjustment in routing protocol and traffic management are able to increase the number of successful packets within the deadline as well as path setup schemes along the previous route is able to reduce the packet loss until a new path is established. Our simulation results demonstrate that the proposed schemes are able to improve the stream dynamic success ratio and network lifetime compared to previous work by meeting the requirement of the (m,k)-firm model regardless of the amount of traffic.

MRTP: a multiflow real-time transport protocol for ad hoc networks

2006 ◽  
Vol 8 (2) ◽  
pp. 356-369 ◽  
Author(s):  
Shiwen Mao ◽  
D. Bushmitch ◽  
S. Narayanan ◽  
S.S. Panwar
Keyword(s):  
Ad Hoc Networks ◽  
Real Time ◽  
Ad Hoc ◽  
Transport Protocol ◽  
Hoc Networks

Research on the Distributed Characteristics of the Delay for Network Coding Based Real-Time Multicast in MANETs

2014 ◽  
Vol 530-531 ◽  
pp. 768-772
Author(s):  
Guo Ping Tan ◽  
Lin Feng Tan ◽  
Lei Cao ◽  
Mei Yan Ju
Keyword(s):  
Maximum Likelihood ◽  
Probability Distribution ◽  
Network Coding ◽  
Real Time ◽  
Lognormal Distribution ◽  
Ad Hoc ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Actual Distribution ◽  
Delay Distribution

For the study of the applications of partial network coding based real-time multicast protocol (PNCRM) in Mobile Ad hoc networks, the researches should be developed in the probability distribution of delay. In this paper, NS2 is used to obtain the delay of data packets through simulations. Because the delay does not obey the strict normal distribution, the maximum likelihood estimate method based on the lognormal distribution is used to process the data. Using MATLAB to obtain the actual distribution of the natural logarithm of delay, then drawing the delay distribution with the maximum likelihood estimation method based on the lognormal distribution, the conclusion that the distributions obtained by the above mentioned methods are basically consistent can be obtained. So the delay distribution of PNCRM meets the lognormal distribution and the characteristic of delay probability distribution can be estimated.

Power Reserve Classification and Control for Peaking Balance with Intermittent Generation Grid

2014 ◽  
Vol 521 ◽  
pp. 252-255
Author(s):  
Jian Yuan Xu ◽  
Jia Jue Li ◽  
Jie Jun Zhang ◽  
Yu Zhu
Keyword(s):  
Real Time ◽  
Balance Control ◽  
Control Model ◽  
Intermediate Variable ◽  
Proposed Model ◽  
Grid Operators ◽  
Period Constant ◽  
Grid Operation ◽  
And Control ◽  
Time Reserve

The problem of intermittent generation peaking is highly concerned by the grid operator. To build control model for solving unbalance of peaking is great necessary. In this paper, we propose reserve classification control model which contain constant reserve control model with real-time reserve control model to guide the peaking balance of the grid with intermittent generation. The proposed model associate time-period constant reserve control model with real-time reserve control model to calculate, and use the peaking margin as intermediate variable. Therefore, the model solutions which are the capacity of reserve classification are obtained. The grid operators use the solution to achieve the peaking balance control. The proposed model was examined by real grid operation case, and the results of the case demonstrate the validity of the proposed model.

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