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Journal articles on the topic 'Forecasting ; Predictive Analytics'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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1

Paukova, Yulia V., and Konstantin V. Popov. "FORECASTING MIGRATION FLOWS USING PREDICTIVE ANALYTICS." SCIENTIFIC REVIEW. SERIES 1. ECONOMICS AND LAW, no. 1-2 (2020): 45–54. http://dx.doi.org/10.26653/2076-4650-2020-1-2-04.

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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 gro

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Holthoff, Gero, and René Decher. "Implementierung von Predictive Analytics im Forecasting." Controlling 32, no. 6 (2020): 53–59. http://dx.doi.org/10.15358/0935-0381-2020-6-53.

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Der Einsatz von Predictive Analytics im Forecasting hat sich trotz seiner Vorteile noch nicht umfänglich in der Praxis etabliert. Dies ist u. a. verschiedenen Herausforderungen bei der Implementierung geschuldet, wie z. B. der menschlichen Abneigung gegenüber algorithmusbasierten Prognosen (Algorithm Aversion). Diese Herausforderungen werden näher beleuchtet und Lösungsmöglichkeiten aufgezeigt.

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Maciejewski, R., R. Hafen, S. Rudolph, et al. "Forecasting Hotspots—A Predictive Analytics Approach." IEEE Transactions on Visualization and Computer Graphics 17, no. 4 (2011): 440–53. http://dx.doi.org/10.1109/tvcg.2010.82.

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Fitzpatrick, Dylan J., Wilpen L. Gorr, and Daniel B. Neill. "Keeping Score: Predictive Analytics in Policing." Annual Review of Criminology 2, no. 1 (2019): 473–91. http://dx.doi.org/10.1146/annurev-criminol-011518-024534.

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Predictive analytics in policing is a data-driven approach to ( a) characterizing crime patterns across time and space and ( b) leveraging this knowledge for the prevention of crime and disorder. This article outlines the current state of the field, providing a review of forecasting tools that have been successfully applied by police to the task of crime prediction. We then discuss options for structured design and evaluation of a predictive policing program so that the benefits of proactive intervention efforts are maximized given fixed resource constraints. We highlight examples of predictiv

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Furmanchuk, Al'ona, Ankit Agrawal, and Alok Choudhary. "Predictive analytics for crystalline materials: bulk modulus." RSC Advances 6, no. 97 (2016): 95246–51. http://dx.doi.org/10.1039/c6ra19284j.

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The machine learning-based generalized model developed for forecasting bulk moduli of various types of stoichiometric and non-stoichiometric crystalline materials. The web application (ThermoEl) deploying the developed predictive model is available for public use.

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Sharma, Aastha, and Vijayakumar V. "Predictive Analytics In Weather Forecasting Using Machine Learning Algorithms." EAI Endorsed Transactions on Cloud Systems 5, no. 14 (2019): 159405. http://dx.doi.org/10.4108/eai.7-12-2018.159405.

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Iftikhar, Rehan, and Mohammad Saud Khan. "Social Media Big Data Analytics for Demand Forecasting." Journal of Global Information Management 28, no. 1 (2020): 103–20. http://dx.doi.org/10.4018/jgim.2020010106.

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Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is conc

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Reddy, Pundru, and Alladi Sureshbabu. "An Adaptive Model for Forecasting Seasonal Rainfall Using Predictive Analytics." International Journal of Intelligent Engineering and Systems 12, no. 5 (2019): 22–32. http://dx.doi.org/10.22266/ijies2019.1031.03.

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Dutt, Raman, and Vinita Krishna. "Forecasting the Grant Duration of a Patent using Predictive Analytics." International Journal of Computer Applications 178, no. 51 (2019): 1–7. http://dx.doi.org/10.5120/ijca2019919398.

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Sabu, Kiran M., and T. K. Manoj Kumar. "Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala." Procedia Computer Science 171 (2020): 699–708. http://dx.doi.org/10.1016/j.procs.2020.04.076.

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Kaluzhina, Marina, Tamara Makarenko, Marina Spasennikova, and Tatyana Vedernikova. "The Methods of Digital Forecasting of Inmate Misconduct in Penal Institutions." Russian Journal of Criminology 13, no. 5 (2019): 747–56. http://dx.doi.org/10.17150/2500-4255.2019.13(5).747-756.

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The authors use the analysis of existing research ideas regarding the structure and content of the criminological prediction methodology to examine modern approaches to predicting illegal activities in penitentiary institutions. They analyze and classify the objects of prevention — those inmates in places of confinement who need to be controlled while serving their sentence because they have a range of unlawful behavior. In the diagnostic sub-task the object is viewed as a source of information whose attributes and features are studied as they manifest its essence and condition. The authors pr

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Doyle, Andy, Graham Katz, Kristen Summers, et al. "Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System." Big Data 2, no. 4 (2014): 185–95. http://dx.doi.org/10.1089/big.2014.0046.

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Blackburn, Robert, Kristina Lurz, Benjamin Priese, Rainer Göb, and Inga-Lena Darkow. "A predictive analytics approach for demand forecasting in the process industry." International Transactions in Operational Research 22, no. 3 (2014): 407–28. http://dx.doi.org/10.1111/itor.12122.

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Mathew, Elezabeth, and Sherief Abdulla. "The LSTM technique for demand forecasting of e-procurement in the hospitality industry in the UAE." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (2020): 757. http://dx.doi.org/10.11591/ijai.v9.i4.pp757-765.

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The hospitality industry is growing at a faster pace across the world which has resulted in the accumulation of a huge amount of data in terms of employee details, property details, purchase details, vendor details, and so on. The industry is yet to fully benefit from these big data by applying ML and AI. The data has not been fully investigated for decision-making or revenue/budget forecasting. In this research data is collected from a chain hotel for advanced predictive analytics. Descriptive and diagnostic analytics is done to an extent across the hotel industry, whereas predictive and pres

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Pavlyshenko, Bohdan. "Machine-Learning Models for Sales Time Series Forecasting." Data 4, no. 1 (2019): 15. http://dx.doi.org/10.3390/data4010015.

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In this paper, we study the usage of machine-learning models for sales predictive analytics. The main goal of this paper is to consider main approaches and case studies of using machine learning for sales forecasting. The effect of machine-learning generalization has been considered. This effect can be used to make sales predictions when there is a small amount of historical data for specific sales time series in the case when a new product or store is launched. A stacking approach for building regression ensemble of single models has been studied. The results show that using stacking techniqu

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N.A., Myat Cho Mon Oo. "Real-Time Predictive Big Data Analytics System: Forecasting Stock Trend Using Technical Indicators." International Journal of Business Intelligence and Data Mining 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijbidm.2022.10041467.

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N, Ahish, Shashikala H K, and Bharath N. "Automated Modular Data Analysis and Visualization System with Predictive Analytics Using Machine Learning for Agriculture field." International Journal of Research in Science 5, no. 1 (2019): 1. http://dx.doi.org/10.24178/ijrs.2019.5.1.01.

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Economy of an India is majorly depending on growth of agricultural yields, and its allied agro industry products. Prediction of agricultural yield growth is a most difficult for the agriculture departments across iglobe. 1The agricultural yields growth is depending on several factors. In this paper historical data is analyzed and a predictive model was designed. 1Several Regression models such as linear model, multiple linear model and nonlinear models were tested for an effective prediction, or for forecasting the agricultural yield for a variety of crops. Along with this the crop trade for l

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Piccialli, Francesco, Fabio Giampaolo, Edoardo Prezioso, Danilo Crisci, and Salvatore Cuomo. "Predictive Analytics for Smart Parking: A Deep Learning Approach in Forecasting of IoT Data." ACM Transactions on Internet Technology 21, no. 3 (2021): 1–21. http://dx.doi.org/10.1145/3412842.

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Nowadays, a sustainable and smart city focuses on energy efficiency and the reduction of polluting emissions through smart mobility projects and initiatives to “sensitize” infrastructure. Smart parking is one of the building blocks of intelligent mobility, innovative mobility that aims to be flexible, integrated, and sustainable and consequently integrated into a Smart City. By using the Internet of Things (IoT) sensors located in the parking areas or the underground car parks in combination with a mobile application, which indicates to citizens the free places in the different areas of the ci

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Jeon, Jun-Woo, Okan Duru, Ziaul Haque Munim, and Naima Saeed. "System Dynamics in the Predictive Analytics of Container Freight Rates." Transportation Science 55, no. 4 (2021): 946–67. http://dx.doi.org/10.1287/trsc.2021.1046.

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This study proposes a two-tier cross-validation and backtesting procedure, including expanding and rolling-window test metrics in predictive analytics of container freight rates by utilizing the system dynamics approach. The study utilized system dynamics to represent the nonlinear complex structure of container freight rates for predictive analytics and performed univariate and multivariate time-series analysis as benchmarks of the conventional approach. In particular, the China containerized freight index (CCFI) has been investigated through various parametric methodologies (both conventiona

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Watson, Linda, Siwei Qi, Andrea DeIure, et al. "Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes." International Journal of Environmental Research and Public Health 18, no. 16 (2021): 8365. http://dx.doi.org/10.3390/ijerph18168365.

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An increasing incidence of cancer has led to high patient volumes and time challenges in ambulatory oncology clinics. By knowing how many patients are experiencing complex care needs in advance, clinic scheduling and staff allocation adjustments could be made to provide patients with longer or shorter timeslots to address symptom complexity. In this study, we used predictive analytics to forecast the percentage of patients with high symptom complexity in one clinic population in a given time period. Autoregressive integrated moving average (ARIMA) modelling was utilized with patient-reported o

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Коновалова and Valyeriya Konovalova. "HR-ANALYST: ACHIEVEMENTS, CAPABILITIES AND CONDITIONS OF USE." Management of the Personnel and Intellectual Resources in Russia 6, no. 1 (2017): 5–11. http://dx.doi.org/10.12737/24683.

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The article deals with problems of HR analytics and its practical application to improve management decisions of individual and organizational performance. The results of Russian and foreign research on the current state of and prospects for the use of HR analysts are summarizes. The levels of HR analysts (from the drafting of HR-metrics to predictive modeling) are allocated, their characteristics are disclosed. The examples of the successful use of HR analysts in modern practice are given, special attention is paid to predictive analysts, the author highlights the potential benefits of organi

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Baneres, David, Ana Elena Guerrero-Roldán, M. Elena Rodríguez-González, and Abdulkadir Karadeniz. "A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System." Applied Sciences 11, no. 13 (2021): 5781. http://dx.doi.org/10.3390/app11135781.

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Learning analytics is quickly evolving. Old fashioned dashboards with descriptive information and trends about what happened in the past are slightly substituted by new dashboards with forecasting information and predicting relevant outcomes about learning. Artificial intelligence is aiding this revolution. The accessibility to computational resources has increased, and specific tools and packages for integrating artificial intelligence techniques leverage such new analytical tools. However, it is crucial to develop trustworthy systems, especially in education where skepticism about their appl

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Lam, Max W. Y. "One-Match-Ahead Forecasting in Two-Team Sports with Stacked Bayesian Regressions." Journal of Artificial Intelligence and Soft Computing Research 8, no. 3 (2018): 159–71. http://dx.doi.org/10.1515/jaiscr-2018-0011.

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AbstractThere is a growing interest in applying machine learning algorithms to real-world examples by explicitly deriving models based on probabilistic reasoning. Sports analytics, being favoured mostly by the statistics community and less discussed in the machine learning community, becomes our focus in this paper. Specifically, we model two-team sports for the sake of one-match-ahead forecasting. We present a pioneering modeling approach based on stacked Bayesian regressions, in a way that winning probability can be calculated analytically. Benefiting from regression flexibility and high sta

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Kyriazi, Foteini, and Dimitrios D. Thomakos. "Distance-based nearest neighbour forecasting with application to exchange rate predictability." IMA Journal of Management Mathematics 31, no. 4 (2020): 469–90. http://dx.doi.org/10.1093/imaman/dpz016.

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Abstract Forecasting non-stationary time series, especially when the data generating processes contains a random walk component, is a difficult and sometimes impossible task. In this paper we suggest an intuitive, computationally fast and expedient way of forecasting time series of the above type using distance-based nearest neighbours (NN). We exploit to advantage the path and scale dependence present in a random walk model and so we provide a number of theoretical results (a) on the distances used for selecting the NN, (b) on a number of new forecasting models that use these distances and (c

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Venayagamoorthy, Ganesh Kumar, Kurt Rohrig, and Istvan Erlich. "One Step Ahead: Short-Term Wind Power Forecasting and Intelligent Predictive Control Based on Data Analytics." IEEE Power and Energy Magazine 10, no. 5 (2012): 70–78. http://dx.doi.org/10.1109/mpe.2012.2205322.

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Panda, Bijayakumar. "Need of Business Analytics and Prediction Modeling in Retail Marketing in Indian Context." Asian Journal of Managerial Science 9, no. 1 (2020): 18–24. http://dx.doi.org/10.51983/ajms-2020.9.1.1635.

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In the digital era, Indian Retail market growing fast than ever and creating stiff competitive business environment. So, analyzing customer behavior, buying pattern and the ability to customize the products to meet demand targeted customer in time has become more important. Therefore, analyzing, diagnosing and channelizing customer data for the benefits of customer as well as the business growth is important to survive in industry for long run. International retail players are already using effective customer analytics systems or big data analytics software’s for each and every stage of the re

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Ugulava, Gocha. "USE OF ARTIFICIAL NEURAL NETWORKS TO PREDICT TERRITORIAL ECONOMIC INDICATORS." Globalization and Business 4, no. 8 (2019): 143–46. http://dx.doi.org/10.35945/gb.2019.08.019.

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Modern economic science is unthinkable without predicting and planning the prospects for economic life development. There are many different mathematical and statistical tools in the arsenal of scientists as well as practitioners and economists today in purpose of forecasting. To date, one of the most prominent effective tools for data analytics is artificial neural networks. Artificial Neural Network - is a mathematical mod- el created in the likeness of a human neural network, and its software and hardware implementation. We carried out modeling and forecasting of regional economic indicator

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Gonçalves, João M. C., Filipe Portela, Manuel F. Santos, et al. "Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine." International Journal of Healthcare Information Systems and Informatics 9, no. 3 (2014): 36–54. http://dx.doi.org/10.4018/ijhisi.2014070103.

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Optimal treatments for patients with microbiological problems depend significantly on the ability of the attending physicians to predict sepsis level. A set of Data Mining (DM) models has been developed using forecasting techniques and classification models to aid decision making by physicians about the appropriate, and most effective, therapeutic plan to adopt in specific situations. A combination of Decision Trees, Support Vector Machines and Naïve Bayes classifier were being used to generate the DM models. Confusion Matrix, including associated metrics, and Cross-validation were used to eva

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Ferrero Bermejo, Jesús, Juan Francisco Gómez Fernández, Rafael Pino, Adolfo Crespo Márquez, and Antonio Jesús Guillén López. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants." Energies 12, no. 21 (2019): 4163. http://dx.doi.org/10.3390/en12214163.

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Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure di

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Stepanyan (Gevorkyan), Anna. "Predictive analytics in the prognostic activity of the police of modern states." Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia 2019, no. 4 (2019): 43–50. http://dx.doi.org/10.35750/2071-8284-2019-4-43-50.

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The relevance of the topic of the study is due to the need to find an optimal model of police activity that meets the requirements and challenges of the present. The transformation of the public organization requires the comprehension of the current situation, new forms of theorizing and the development of new conceptual ideas. The growing importance of police systems in the context of globalization has been determined by a variety of law enforcement practices that requires critical analysis. A historiographic analysis of the problem of the use of predictive analytics in police activities show

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Dias, Tiago, Rodolfo Oliveira, Pedro Saraiva, and Marco S. Reis. "Predictive analytics in the petrochemical industry: Research Octane Number (RON) forecasting and analysis in an industrial catalytic reforming unit." Computers & Chemical Engineering 139 (August 2020): 106912. http://dx.doi.org/10.1016/j.compchemeng.2020.106912.

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Behl, Ramesh, and Manit Mishra. "COVID-19 Lifecycle: Predictive Modelling of States in India." Global Business Review 21, no. 4 (2020): 883–91. http://dx.doi.org/10.1177/0972150920934642.

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The study captures the COVID-19 lifecycle in different states of India using predictive analytics. Drawing upon the seminal susceptible–infected–removed (SIR) model of capturing the spread of viral diseases, this study models the spread of COVID-19 in the ten most infected states of India (as on 30 April 2020). Using publicly available state-wise time series data of COVID-19 patients during the period 1–30 April 2020, the study uses the forecasting technique of auto-regressive integrated moving averages (ARIMA) to predict the likely population susceptible to COVID-19 in each state. Thereafter,

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Díaz-Domínguez, Alejandro. "How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence." World Futures Review 12, no. 2 (2019): 169–80. http://dx.doi.org/10.1177/1946756719894602.

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Drawing from ethical concerns raised by communities of machine learning developers and considering predictive analytics’ very short-term predictions, several futures studies techniques are examined to offer some insights about possible bridges between machine learning and foresight. This review develops three main sections: (1) a brief explanation of central concepts, such as big data, machine learning, and artificial intelligence, hopefully not too simplistic but readable for larger audiences; (2) a discussion about ethical issues, such as bias, discrimination, and dilemmas in research; and (

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Fayoumi, Ayman G., and Amjad Fuad Hajjar. "Advanced Learning Analytics in Academic Education." International Journal on Semantic Web and Information Systems 16, no. 3 (2020): 70–87. http://dx.doi.org/10.4018/ijswis.2020070105.

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The integration of innovative data mining and decision-making techniques in the context of higher education is a bold initiative towards enhanced performance. Predictive and descriptive analytics add interesting insights for significant aspects the education. The purpose of this article is to summarize a novel approach for the adoption of artificial intelligence (AI) techniques towards forecasting of academic performance. The added value of applying AI techniques for advanced decision making in education is the realization that the scientific approach to standard problems in academia, like the

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Huikku, Jari, Timo Hyvönen, and Janne Järvinen. "The role of a predictive analytics project initiator in the integration of financial and operational forecasts." Baltic Journal of Management 12, no. 4 (2017): 427–46. http://dx.doi.org/10.1108/bjm-05-2017-0164.

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Purpose The purpose of this paper is to investigate the initiation of accounting information system projects. Specifically, it examines the role of the predictive analytics (PA) project initiator in the integration of financial and operational sales forecasts. Design/methodology/approach The study uses a field study method to address the studied phenomenon in eight Finnish companies that have recently adopted PA systems. The data are primarily based on 19 interviews in the companies and five interviews with the PA consultants. Findings The authors found that initiators appear to play a major r

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Ashouri, Mahsa, Kate Cai, Furen Lin, and Galit Shmueli. "Assessing the Value of an Information System for Developing Predictive Analytics: The Case of Forecasting School-Level Demand in Taiwan." Service Science 10, no. 1 (2018): 58–75. http://dx.doi.org/10.1287/serv.2017.0200.

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D'Arco, Mario, Letizia Lo Presti, Vittoria Marino, and Riccardo Resciniti. "Embracing AI and Big Data in customer journey mapping: from literature review to a theoretical framework." Innovative Marketing 15, no. 4 (2019): 102–15. http://dx.doi.org/10.21511/im.15(4).2019.09.

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Nowadays, Big Data and Artificial Intelligence (AI) play an important role in different functional areas of marketing. Starting from this assumption, the main objective of this theoretical paper is to better understand the relationship between Big Data, AI, and customer journey mapping. For this purpose, the authors revised the extant literature on the impact of Big Data and AI on marketing practices to illustrate how such data analytics tools can increase the marketing performance and reduce the complexity of the pattern of consumer activity. The results of this research offer some interestin

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Sanyal, Manas K., Indranil Ghosh, and R. K. Jana. "Characterization and Predictive Analysis of Volatile Financial Markets Using Detrended Fluctuation Analysis, Wavelet Decomposition, and Machine Learning." International Journal of Data Analytics 2, no. 1 (2021): 1–31. http://dx.doi.org/10.4018/ijda.2021010101.

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This paper proposes a granular framework for examining the dynamics of stock indexes that exhibit nonparametric and highly volatile behavior, and subsequently carries out the predictive analytics task by integrating detrended fluctuation analysis (DFA), maximal overlap discrete wavelet transformation (MODWT), and machine learning algorithms. DFA test ascertains the key temporal characteristics of the daily closing prices. MODWT decomposes the time series into granular components. Four pattern recognition algorithms—adaptive neuro fuzzy inference system (ANFIS), dynamic evolving neural-fuzzy in

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Henning, Sören, Wilhelm Hasselbring, Heinz Burmester, Armin Möbius, and Maik Wojcieszak. "Goals and measures for analyzing power consumption data in manufacturing enterprises." Journal of Data, Information and Management 3, no. 1 (2021): 65–82. http://dx.doi.org/10.1007/s42488-021-00043-5.

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AbstractThe Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. In two industrial pilot cases, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. Accompanied by a literature review, we propose to implement the measures real

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Babushkin, Vitaly Mikhailovich, Ilgizar Shayheevich Sharafeev, Rafik Zuferovich Valiullin, and Gaziz Fuatovich Mingaleev. "ADAPTIVE PLANNING OF THE PRODUCTION ORGANIZATION INDUSTRIAL ENTERPRISE." Computational nanotechnology 6, no. 3 (2019): 47–53. http://dx.doi.org/10.33693/2313-223x-2019-6-3-47-53.

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The article presents an approach to planning the organization of production, in which the increase in productivity can be considered as a result of the implementation of predictive models of data Analytics and forecasting of events at each level of planning. Considered and justified mathematical model of optimization of resources of the enterprise, implemented in a three- tier system adaptive planning with the formation of indicators, optimization of production and allowing it to form the most effective scenarios of solving the problem maximize performance subject to qualitative changes in the

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Bravo, César, Luigi Saputelli, Francklin Rivas, et al. "State of the Art of Artificial Intelligence and Predictive Analytics in the E&P Industry: A Technology Survey." SPE Journal 19, no. 04 (2013): 547–63. http://dx.doi.org/10.2118/150314-pa.

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Summary Artificial intelligence (AI) has been used for more than 2 decades as a development tool for solutions in several areas of the exploration and production (E&P) industry: virtual sensing, production control and optimization, forecasting, and simulation, among many others. Nevertheless, AI applications have not been consolidated as standard solutions in the industry, and most common applications of AI still are case studies and pilot projects. In this work, an analysis of a survey conducted on a broad group of professionals related to several E&P operations and service companies

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Garcia, Diego, Vicenç Puig, and Joseba Quevedo. "Prognosis of Water Quality Sensors Using Advanced Data Analytics: Application to the Barcelona Drinking Water Network." Sensors 20, no. 5 (2020): 1342. http://dx.doi.org/10.3390/s20051342.

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Water Utilities (WU) are responsible for supplying water for residential, commercial and industrial use guaranteeing the sanitary and quality standards established by different regulations. To assure the satisfaction of such standards a set of quality sensors that monitor continuously the Water Distribution System (WDS) are used. Unfortunately, those sensors require continuous maintenance in order to guarantee their right and reliable operation. In order to program the maintenance of those sensors taking into account the health state of the sensor, a prognosis system should be deployed. Moreov

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Abdullah, Nur Syahidah Wong, Sylvia @. Nabila Azwa Ambad, Sakka Nordin, Jasmine Vivienne Andrew, and Karen Esther Tan. "Justifying Business Intelligence Systems Adoption: A Literature Review on Healthcare Supply Chain Perspective." Jurnal Intelek 16, no. 1 (2021): 108–14. http://dx.doi.org/10.24191/ji.v16i1.370.

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Business Intelligence (BI) systems have played an essential position in facilitating information sharing, strategic cost-cutting, and improvement in business process management through data-driven decision-making analytics. The technological enablers of Industry 4.0 have empowered the clinician to attain accurate information in formulating predictive and data-driven diagnoses based on artificial intelligence-enabled medical devices resulting in an efficient and quality clinical pathway for patients. However, there is a noticeable distinction between the hospital's technological aptitude betwee

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Ghosh, Shounak, and Tapomoy Koley. "A Canvas of Data & Indian Card Industry." International Journal of Economics and Finance 9, no. 7 (2017): 39. http://dx.doi.org/10.5539/ijef.v9n7p39.

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Indian card industry has gone through several interesting changes in the recent past. We wanted to explore this fascinating world both from card issuing and merchant acquiring perspective within our chosen study period of around 6 years - from Q2, 2011 to Q4, 2016. However rather than hunting the data to prove any pre-defined notion we wanted to listen to the data to capture the story it wants to tell us. We took a canvas of raw primary data from a no. of sources ranging from Reserve Bank of India to World Bank, from Ministry of Finance to Ministry of Statistics & Implementation (of Govt.

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Moro Visconti, Roberto, Giuseppe Montesi, and Giovanni Papiro. "Big data-driven stochastic business planning and corporate valuation." Corporate Ownership and Control 15, no. 3-1 (2018): 189–204. http://dx.doi.org/10.22495/cocv15i3c1p4.

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The research question of this paper is concerned with the investigation of the links between Internet of Things and related big data as input parameters for stochastic estimates in business planning and corporate evaluation analytics. Financial forecasts and company appraisals represent a core corporate ownership and control issue, impacting on stakeholder remuneration, information asymmetries, and other aspects. Optimal business planning and related corporate evaluations derive from an equilibrated mix of top-down and bottom-up approaches. While the former follows a traditional dirigistic met

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Kavitha, K., K. Rohini, and G. Suseendran. "A Comparative Evaluation of Meta Classification Algorithms with Smokers Lung Data." International Journal of Engineering & Technology 7, no. 4.36 (2018): 845. http://dx.doi.org/10.14419/ijet.v7i4.36.24543.

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Data mining is the course of process during which knowledge is extracted through interesting patterns recognized from large amount of data. It is one of the knowledge exploring areas which is widely used in the field of computer science. Data mining is an inter-disciplinary area which has great impact on various other fields such as data analytics in business organizations, medical forecasting and diagnosis, market analysis, statistical analysis and forecasting, predictive analysis in various other fields. Data mining has multiple forms such as text mining, web mining, visual mining, spatial m

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Ovchinnikova, Svetlana, Aleksandr Borovkov, Galina Kukinova, and Nina Markina. "Environmental substantiation for the use of alternative energy sources." E3S Web of Conferences 244 (2021): 01007. http://dx.doi.org/10.1051/e3sconf/202124401007.

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An overview of the substantiation of the relevance of the transition to mass ecological housing construction, which is determined by an acute shortage of housing and a high increase in the cost of electricity, is given. The development of an ecological substantiation of an energy-independent house using the example of a one-story house, taking into account natural and climatic conditions, is presented. The characteristics of the work of all utilized engineering systems are considered. It has been established that the housing and utilities sector, being one of the main sources of air and ground

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Macheret, D. A. "Uncertainty of the Future as a Fundamental Problem of the Long-Term Development of Transport." World of Transport and Transportation 17, no. 6 (2020): 6–19. http://dx.doi.org/10.30932/1992-3252-2019-17-06-19.

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The article is devoted to consideration of the problem of long-term development of transport under the conditions of uncertainty, which is a fundamental feature of the conditions of human activity. The objectives are to reveal the specifics of the «path dependence» in the field of transport, to show the fundamental nature of the problem of uncertainty of human activity and its special significance for development of transport, to propose a methodological basis for mitigating the problem of uncertainty in the long-term development of transport.While achieving the objectives, it is shown using h

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Chou, Jui-Sheng, Trang Thi Phuong Pham, and Chia-Chun Ho. "Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management." Applied Sciences 11, no. 12 (2021): 5533. http://dx.doi.org/10.3390/app11125533.

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Multi-class classification is one of the major challenges in machine learning and an ongoing research issue. Classification algorithms are generally binary, but they must be extended to multi-class problems for real-world application. Multi-class classification is more complex than binary classification. In binary classification, only the decision boundaries of one class are to be known, whereas in multiclass classification, several boundaries are involved. The objective of this investigation is to propose a metaheuristic, optimized, multi-level classification learning system for forecasting i

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Brown, Heidi E., Luigi Sedda, Chris Sumner, Elene Stefanakos, Irene Ruberto, and Matthew Roach. "Understanding Mosquito Surveillance Data for Analytic Efforts: A Case Study." Journal of Medical Entomology 58, no. 4 (2021): 1619–25. http://dx.doi.org/10.1093/jme/tjab018.

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Abstract Mosquito surveillance data can be used for predicting mosquito distribution and dynamics as they relate to human disease. Often these data are collected by independent agencies and aggregated to state and national level portals to characterize broad spatial and temporal dynamics. These larger repositories may also share the data for use in mosquito and/or disease prediction and forecasting models. Assumed, but not always confirmed, is consistency of data across agencies. Subtle differences in reporting may be important for development and the eventual interpretation of predictive mode

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