Advertisement Click-Through Rate Prediction Based on the Weighted-ELM and Adaboost Algorithm

Accurate click-through rate (CTR) prediction can not only improve the advertisement company’s reputation and revenue, but also help the advertisers to optimize the advertising performance. There are two main unsolved problems of the CTR prediction: low prediction accuracy due to the imbalanced distribution of the advertising data and the lack of the real-time advertisement bidding implementation. In this paper, we will develop a novel online CTR prediction approach by incorporating the real-time bidding (RTB) advertising by the following strategies: user profile system is constructed from the historical data of the RTB advertising to describe the user features, the historical CTR features, the ID features, and the other numerical features. A novel CTR prediction approach is presented to address the imbalanced learning sample distribution by integrating the Weighted-ELM (WELM) and the Adaboost algorithm. Compared to the commonly used algorithms, the proposed approach can improve the CTR significantly.

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On Estimate of Real Accuracy of EOP Prediction

International Astronomical Union Colloquium ◽

10.1017/s0252921100061674 ◽

2000 ◽

Vol 178 ◽

pp. 505-510

Author(s):

Zinovy Malkin

Keyword(s):

Real Time ◽

Prediction Accuracy ◽

The Real

AbstractTo estimate the real accuracy of EOP predictions, real-time predictions made by the IERS Subbureau for Rapid Service and Prediction (USNO) and at the IAA EOP Service are analyzed. Methods of estimating prediction accuracy are discussed.

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Real Time Data Integration Based on the GIS Pipeline Infrastructure for the Management of the Gas and Power Business

Volume 3: Materials and Joining; Pipeline Automation and Measurement; Risk and Reliability, Parts A and B ◽

10.1115/ipc2006-10506 ◽

2006 ◽

Author(s):

Fernando Jose´ de Carvalho Salcedo ◽

Ronaldo Jose´ Seixas de Carvalho

Keyword(s):

Data Integration ◽

Real Time ◽

Performance Indicators ◽

Historical Data ◽

Geographical Information ◽

Main Process ◽

Business Unit ◽

Time Data ◽

The Real ◽

Real Time Data

The Strategic Data and Information Management (GEDI, as per Portuguese initials) in PETROBRAS (Brazilian oil company - Gas & Power Business Unit), has as its main process to turn available the most correct and updated information to the related user, using the adequate means to access and capture of data, coming from a variety of sources, in order to add strategic value to business. The SCADA system (Supervisory Control And Data Acquisition) integrates the facilities of thermo electrical plant and pipeline with the field, including operational stations, measurements and energy deliveries. The Geographical Information Systems (GIS) allows the use of maps to visualize the geopolitics aspects, gas pipeline infrastructure and satellite images. The historical data systems has as its requirements, the interface among many SCADA systems, through the tracking of historical data, common process variables real time data (flow, pressure, temperature, etc.) and KPI’s visualization (typical performance indicators of energy systems such as unavailability, generation efficiency, distribution, etc.). Based on the business systemic vision, the Real-Time Enterprise Architecture (real time data integration and performance indicators based on the GIS software platform ) was developed for PETROBRAS, Gas & Power Business Unit (GPBU) enterprise scenario. The present work has its focus in the real time visualization of integrated data, coming from gas pipelines and thermo electrical plants GIS infra-structure, guaranteeing the integrity, the audit trail of information and a proactive vision for the GPBU management.

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Vehicle Location Prediction System Based on Historical Data

10.36227/techrxiv.12866978.v1 ◽

2020 ◽

Author(s):

Tarak Nandy ◽

Rafidah Md Noor ◽

Mohd Yamani Idna Idris ◽

Sananda Bhattacharyya

Keyword(s):

Real Time ◽

Historical Data ◽

Positioning System ◽

Location Prediction ◽

Prediction System ◽

The Real ◽

Vehicle Location ◽

Statistical Approaches ◽

Networking Technologies ◽

Vehicle Positioning

This work is based on the vehicle positioning system using the statistical approaches. Moreover, the method does not depend on the real time devices such as GPS, or networking technologies.

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Vehicle Location Prediction System Based on Historical Data

10.36227/techrxiv.12866978 ◽

2020 ◽

Author(s):

Tarak Nandy ◽

Rafidah Md Noor ◽

Mohd Yamani Idna Idris ◽

Sananda Bhattacharyya

Keyword(s):

Real Time ◽

Historical Data ◽

Positioning System ◽

Location Prediction ◽

Prediction System ◽

The Real ◽

Vehicle Location ◽

Statistical Approaches ◽

Networking Technologies ◽

Vehicle Positioning

This work is based on the vehicle positioning system using the statistical approaches. Moreover, the method does not depend on the real time devices such as GPS, or networking technologies.

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Comparative Study of Real Time Machine Learning Models for Stock Prediction through Streaming Data

JUCS - Journal of Universal Computer Science ◽

10.3897/jucs.2020.059 ◽

2020 ◽

Vol 26 (9) ◽

pp. 1128-1147

Author(s):

Ranjan Behera ◽

Sushree Das ◽

Santanu Rath ◽

Sanjay Misra ◽

Robertas Damasevicius

Keyword(s):

Machine Learning ◽

Real Time ◽

Historical Data ◽

Streaming Data ◽

Support Vector ◽

Learning Models ◽

Stock Prediction ◽

The Real ◽

Lambda Architecture ◽

Machine Learning Models

Stock prediction is one of the emerging applications in the field of data science which help the companies to make better decision strategy. Machine learning models play a vital role in the field of prediction. In this paper, we have proposed various machine learning models which predicts the stock price from the real-time streaming data. Streaming data has been a potential source for real-time prediction which deals with continuous ow of data having information from various sources like social networking websites, server logs, mobile phone applications, trading oors etc. We have adopted the distributed platform, Spark to analyze the streaming data collected from two different sources as represented in two case studies in this paper. The first case study is based on stock prediction from the historical data collected from Google finance websites through NodeJs and the second one is based on the sentiment analysis of Twitter collected through Twitter API available in Stanford NLP package. Several researches have been made in developing models for stock prediction based on static data. In this work, an effort has been made to develop scalable, fault tolerant models for stock prediction from the real-time streaming data. The Proposed model is based on a distributed architecture known as Lambda architecture. The extensive comparison is made between actual and predicted output for different machine learning models. Support vector regression is found to have better accuracy as compared to other models. The historical data is considered as a ground truth data for validation.

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Syndrome and Outbreak Detection Using Chief-Complaint Data - Experience of the Real-Time Outbreak and Disease Surveillance Project

PsycEXTRA Dataset ◽

10.1037/e307182005-005 ◽

2004 ◽

Cited By ~ 3

Author(s):

Michael M. Wagner ◽

J. Espino ◽

F-C. Tsui ◽

P. Gesteland ◽

W. Chapman ◽

...

Keyword(s):

Real Time ◽

Disease Surveillance ◽

Chief Complaint ◽

Outbreak Detection ◽

The Real

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The Real-Time Neural Genesis of a Punchline

PsycEXTRA Dataset ◽

10.1037/e528942014-019 ◽

2014 ◽

Author(s):

Irving Biederman ◽

Ori Amir

Keyword(s):

Real Time ◽

The Real

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Real-Time System for Sending Data from Three Clients to One Server with MATLAB

ACMIT Proceedings ◽

10.33555/acmit.v2i1.7 ◽

2015 ◽

Vol 2 (1) ◽

pp. 35-41

Author(s):

Rivan Risdaryanto ◽

Houtman P. Siregar ◽

Dedy Loebis

Keyword(s):

Information System ◽

Real Time ◽

Time System ◽

Real Time System ◽

The Real ◽

Performance Time ◽

Data Process ◽

Effectiveness And Efficiency

The real-time system is now used on many fields, such as telecommunication, military, information system, evenmedical to get information quickly, on time and accurate. Needless to say, a real-time system will always considerthe performance time. In our application, we define the time target/deadline, so that the system should execute thewhole tasks under predefined deadline. However, if the system failed to finish the tasks, it will lead to fatal failure.In other words, if the system cannot be executed on time, it will affect the subsequent tasks. In this paper, wepropose a real-time system for sending data to find effectiveness and efficiency. Sending data process will beconstructed in MATLAB and sending data process has a time target as when data will send.

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