Leveraging Cloud-Based Predictive Analytics to Strengthen Audience Engagement

2015 ◽  
Author(s):  
U. Shakeel ◽  
M. Limcaco
Keyword(s):  
Predictive Analytics ◽  
Audience Engagement

Understanding the link between audience engagement metrics and the perceived quality of online news using machine learning

2021 ◽  
pp. 1-24
Author(s):  
Catherine Sotirakou ◽  
Damian Trilling ◽  
Panagiotis Germanakos ◽  
Dimitrios Alexandros Sinis ◽  
Constantinos Mourlas
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Perceived Quality ◽  
Online News ◽  
Machine Learning Algorithms ◽  
News Story ◽  
Extended Model ◽  
Audience Engagement ◽  
Data Set

This article aims to explain the perceived quality of online news articles. Discovering which elements of a news story influence readers’ perceptions could drive online popularity, which is the paramount factor of digital news readership. This work explores an approach to use tree-based machine learning algorithms to address this problem based on selected characteristics, which measure engagement, drawn from prior research mostly developed by communication scientists. A proposed extended model is used to examine the association between the engagement features and perceived quality concerning all the articles depending mainly on their genre. To demonstrate the capacity of using predictive analytics to facilitate journalistic news writing the proposed methodology is applied on a novel data set with 200K articles in total constructed from a blog site. The results of phase A, indicate interesting correlations between the features and the perceived quality of the articles. In stage B, the paper seeks to extract a set of rules that can be used as guidelines for authors in the writing of their next articles, indicating the probability of popularity that their articles may gain if these rules are taken into consideration.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

Predictive Analytics erfolgreich implementieren

Controlling ◽  
2020 ◽  
Vol 32 (1) ◽  
pp. 58-64
Author(s):  
Daniel Schlatter ◽  
Christopher Stoll ◽  
Klaus Möller
Keyword(s):  
Predictive Analytics

Trotz deutlicher technologischer Fortschritte wird Predictive Analytics in der Praxis noch immer nur selten für die finanzielle Prognose eingesetzt. Notwendig für eine erfolgreiche Anwendung ist ein ganzheitlicher Ansatz bei der Implementierung, der über die rein technisch „richtige“ Anwendung hinausgeht. Aus der Analyse verschiedener Implementierungsprojekte wurden daher die Erfolgsfaktoren für Predictive Analytics Projekte abgeleitet und in einem ganzheitlichen Konzept zusammengefasst. Damit können Verbesserungen in den Bereichen Prognosegenauigkeit, Ressourceneinsatz und Steuerungswirkung realisiert werden.

Implementaon of digital economy elements in electric power industry

2018 ◽  
Vol 11 (2) ◽  
pp. 94-102 ◽  
Author(s):  
A. G. Filimonov ◽  
N. D. Chichirova ◽  
A. A. Chichirov ◽  
A. A. Filimonovа
Keyword(s):  
Electric Power ◽  
Energy Production ◽  
Predictive Analytics ◽  
Digital Economy ◽  
Digital Transformation ◽  
Cloud Services ◽  
Small Scale ◽  
Economic Activities ◽  
Industrial Enterprises ◽  
Automation Systems

Energy generation, along with other sectors of Russia’s economy, is on the cusp of the era of digital transformation. Modern IT solutions ensure the transition of industrial enterprises from automation and computerization, which used to be the targets of the second half of the last century, to digital enterprise concept 4.0. The international record of technological and structural solutions in digitization may be used in Russia’s energy sector to the full extent. Specifics of implementation of such systems in different countries are only determined by the level of economic development of each particular state and the attitude of public authorities as related to the necessity of creating conditions for implementation of the same. It is shown that a strong legislative framework is created in Russia for transition to the digital economy, with research and applied developments available that are up to the international level. The following digital economy elements may be used today at enterprises for production of electrical and thermal energy: — dealing with large amounts of data (including operations exercised via cloud services and distributed data bases); — development of small scale distributed generation and its dispatching; — implementation of smart elements in both electric power and heat supply networks; — development of production process automation systems, remote monitoring and predictive analytics; 3D-modeling of parts and elements; real time mathematic simulation with feedback in the form of control actions; — creating centres for analytical processing of statistic data and accounting in financial and economic activities with business analytics functions, with expansion of communication networks and computing capacities. Examples are presented for implementation of smart systems in energy production and distribution. It is stated in the paper that state-of art information technologies are currently being implemented in Russia, new unique digital transformation projects are being launched in major energy companies. Yet, what is required is large-scale and thorough digitization and controllable energy production system as a multi-factor business process will provide the optimum combination of efficient economic activities, reliability and safety of power supply.

Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 869-888 ◽  
Author(s):  
David Martens ◽  
◽  
Foster Provost ◽  
Jessica Clark ◽  
Enric Junqué de Fortuny ◽  
...  
Keyword(s):  
Predictive Analytics ◽  
Fine Grained

FORECASTING MIGRATION FLOWS USING PREDICTIVE ANALYTICS

2020 ◽  
pp. 45-54
Author(s):  
Yulia V. Paukova ◽  
◽  
Konstantin V. Popov ◽  
Keyword(s):  
Information Technologies ◽  
Predictive Analytics ◽  
Migration Policy ◽  
Modern World ◽  
Migration Activity ◽  
Wide Range ◽  
Future Events ◽  
The Russian Federation ◽  
Migration Flows ◽  
Develop State

The present article considers the need to predict migration flows using Predictive Analytics. The Russian Federation is a center of migration activity. The modern world is changing rapidly. An effective migration policy requires effective monitoring of migration flows, assessing the current situation in our and other countries and forecasting migration processes. There are information systems in Russia that contain a wide range of information about foreign citizens and stateless persons that provide the requested information about specific foreign citizens, including grouping it on various grounds. However, it is not possible to analyze and predict it automatically using thousands of parameters. Special attention in Russia is paid to digitalization. Using information technologies (artificial intelligence, machine learning and big data analysis) to forecast migration flows in conditions of variability of future events will allow to take into account a number of events and most accurately predict the quantitative and so-called "qualitative" structure of arrivals. The received information will help to develop state policy and to take appropriate measures in the field of migration regulation. The authors come to the conclusion that it is necessary to amend existing legal acts in order to implement information technologies of Predictive Analytics into the practice of migration authorities.

Sign in / Sign up

Export Citation Format

Share Document