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Projections for the number of students in the Polytechnic Institute of Guarda: Application of a mathematical model

+ 1 ) + W ( t + 1 ) . $$X\left( t+1 \right)=Y\left( t+1 \right)+W\left( t+1 \right).$$ In order to make simulations of the model, we need an algorithm to implement in a computer program. An appropriated algorithm is to compute the student population of a year t +1 in terms of the population of the year t . For this we consider the following matrices, T = d i a g ( t 1 , t 2 , ⋯ , t s ) ,   D = d i a g ( d 1 , d 2 , ⋯ , s s ) , $$T=diag\left( {{t}_{1}},{{t}_{2}},\cdots ,{{t}_{s}} \right),\ D=diag\left( {{d}_{1}},{{d}_{2}},\cdots ,{{s

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Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services

References Abdallah A., Maarof M.A., Zainal A. (2016) Fraud detection system: A survey, Journal of Network and Computer Applications, 68, 90-113. Balakrishnan P., Kumar S., Han P. (2011) Dual objective segmentation to improve targetability: An evolutionary algorithm approach, Decision Sciences, 42(4), 831-857. Bermúdez L., Pérez J.M., Ayuso M., Gómez E., Vázquez F.J. (2008) A Bayesian dichotomous model with asymmetric link for fraud in insurance, Insurance: Mathematics and Economics, 42(2), 779-786. Bodon F., (2010) Adatbányászati

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Performance analysis of the partial use of a local optimization operator on the genetic algorithm for the Travelling Salesman Problem

References Applegate, D., Bixby, R., Chvátal, V., Cook, W. (2001), "TSP cuts which do not conform to the template paradigm", in Jünger, M., Naddef, D., (Eds), Computational Combinatorial Optimization, Springer, London, pp. 261-303. Bosman, P., Thierens, D. (1999), "On the modelling of evolutionary algorithms", in Postma, E., Gyssens, M. (Eds), Proceedings of the 11th Belgium-Netherlands Conference on Artificial Intelligence, BNAIC, Vaeshartelt, pp. 67-74. Djordjevic, M., Brodnik, A

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Use of Constraints in the Hierarchical Aggregation Procedure Intramax

theory to practice”, Environment and Planning A, Vol. 11, No. 5, pp. 565–574. 11. Coombes, M. G., Green, A. E., Openshaw, S. (1986), “An efficient algorithm to generate official statistical reporting areas: the case of the 1984 travel-to-work-areas revision in Britain”, Journal of the Operational Research Society, Vol. 37, No. 10, pp. 943–953. 12. Cörvers, F., Hensen, M., Bongaerts, D. (2009), “Delimitation and coherence of functional and administrative regions”, Regional Studies, Vol. 43, No. 1, p. 19–31. 13. Daras, K. (2005), “An information

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The Proportions and Rates of Economic Activities as a Factor of Gross Value Added Maximization in Transition Economy

References Akbari, R., and Ziarati, K., 2011. A multi level evolutionary algorithm for optimizing numerical functions. International Journal of Industrial Engineering Computations, 2, 419-430. doi: Atencia, M., Joya, G., and Sandoval, F., 2005. Hopfield Neural Networks for Parametric Identification of Dynamical Systems. Neural Processing Letters, 21(2), 143-152. doi: Balakrishnan, S., Kannan, P. S., Aravindan, C., and

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Big Data in Health Care: Applications and Challenges

1 Introduction Big Data, the generic term for data sets of structured and unstructured data that are extremely large and complex so that the traditional software, algorithm, and data repositories are inadequate to collect, process, analyze, and store them ( Asante-Korang & Jacobs, 2016; Kyoungyoung Jee & Gang Hoon Kim, 2013 ; Khoury & Ioannidis, 2014 ; Tan, Gao, & Koch, 2015), has become an intensively studied area in recent years. With the development of the Internet, the mobile Internet, the Internet of things, social media, biology, finance, and digital

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Identifier Services: Modeling and Implementing Distributed Data Management in Cyberinfrastructure

in a dataset’s lifecycle. As long-term custodians of data, existing repository systems fell short of performing this function. Most have poor or no mechanisms to continuously assess identity beyond validating integrity via checksums completed at ingest ( Factor et al., 2009 ). Furthermore, checksum algorithms are of limited use for large data, because they cannot tell what has changed. Finally, repositories have limited mechanisms to link to external related datasets, and current initiatives are more focused on linking data to related publications ( Hoogerwerf et

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