Predictive Analytics Approach to Evaluate Wage Inequality in Engineering Organizations

2020 ◽  
Vol 36 (6) ◽  
pp. 04020072
Author(s):  
Amirhosein Jafari ◽  
Behzad Rouhanizadeh ◽  
Sharareh Kermanshachi ◽  
Munahil Murrieum
Keyword(s):  
Wage Inequality ◽  
Predictive Analytics ◽  
Engineering Organizations

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.

Econometric Analysis of Returns to Education of Rural and Urban Labour Markets Workers in Tamil Nadu

2020 ◽  
pp. 336-344
Keyword(s):  
Wage Inequality ◽  
Tamil Nadu ◽  
Returns To Education ◽  
Labour Markets ◽  
Wage Distribution ◽  
Rural Market ◽  
Rural And Urban ◽  
Rate Of Returns ◽  
Education Groups ◽  
Quantile Regression Method

The objective of this study was to empirically evaluate the returns to education of rural and urban labour markets workers in Tamil Nadu using the IHDS data with appropriate Econometric models. First, the present study estimated the earning functions of the rural and urban market's workers by OLS technique and standard Mincerian earning functions. Secondly, the quantile regression method was also used to examine the evolution of wage inequality. The findings of the study showed that the effects of education and experience on the log of hourly wages were positive, and these coefficients were statistically significant. The returns to education increased with the level of education and differed among the workers of rural and urban labour markets. The results showed that the rates of returns to primary, middle and higher secondary were higher in the urban market, whereas those of secondary and graduation were higher in the rural market. The study revealed that the effect of education was not the same across the rural and urban wage distribution. The rate of returns differed considerably within education groups across different quantiles of the wage distribution.

MONITORING AND PREDICTIVE ANALYTICS OF TECHNOLOGICAL EQUIPMENT ON THE BASED OF INDUSTRIAL INTERNET OF THINGS

2020 ◽  
pp. 7-12
Author(s):  
С.Л. Добрынин ◽  
В.Л. Бурковский
Keyword(s):  
Internet Of Things ◽  
Data Acquisition ◽  
Predictive Analytics ◽  
Actual State ◽  
Monitoring And Control ◽  
Industrial Internet Of Things ◽  
Machining Time ◽  
Industrial Internet ◽  
Acquisition Device ◽  
Data Acquisition Device

Произведен обзор технологий в рамках концепции четвертой промышленной революции, рассмотрены примеры реализации новых моделей управления технологическими процессами на базе промышленного интернета вещей. Описано техническое устройство основных подсистем системы мониторинга и контроля, служащей для повышения осведомленности о фактическом состоянии производственных ресурсов в особенности станков и аддитивного оборудования в режиме реального времени. Архитектура предлагаемой системы состоит из устройства сбора данных (УСД), реализующего быстрый и эффективный сбор данных от станков и шлюза, передающего ликвидную часть информации в облачное хранилище для дальнейшей обработки и анализа. Передача данных выполняется на двух уровнях: локально в цехе, с использованием беспроводной сенсорной сети (WSN) на базе стека протоколов ZigBee от устройства сбора данных к шлюзам и от шлюзов в облако с использованием интернет-протоколов. Разработан алгоритм инициализации протоколов связи между устройством сбора данных и шлюзом, а также алгоритм выявления неисправностей в сети. Расчет фактического времени обработки станочных подсистем позволяет более эффективно планировать профилактическое обслуживание вместо того, чтобы выполнять задачи обслуживания в фиксированные интервалы без учета времени использования оборудования We carried out a review of technologies within the framework of the concept of the fourth industrial revolution; we considered examples of the implementation of new models of process control based on the industrial Internet of things. We described the technical structure of the main subsystems of the monitoring and control system to increase awareness of the actual state of production resources in particular machine tools and additive equipment in real time. The architecture of the proposed system consists of a data acquisition device (DAD) that implements fast and efficient data collection from machines and a gateway that transfers the liquid part of information to the cloud storage for further processing and analysis. We carried out the data transmission at two levels, locally in the workshop, using a wireless sensor network (WSN) based on ZigBee protocol stack from the data acquisition device to the gateways and from the gateways to the cloud using Internet protocols. An algorithm was developed for initializing communication protocols between a data acquisition device and a gateway, as well as an algorithm for detecting network malfunctions. Calculating the actual machining time of machine subsystems allows us to more efficiently scheduling preventive maintenance rather than performing maintenance tasks at fixed intervals without considering equipment usage

Sign in / Sign up

Export Citation Format

Share Document