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- Training neural networks by resilient backpropagation algorithm for tourism forecastingPublication . Fernandes, Paula Odete; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana GarridoThe main objective of this study is to presents a set of models for tourism destinations competitiveness, using the Artificial Neural Networks (ANN) methodology. The time series of two regions (North and Centre of Portugal) has used to predict the tourism demand. The prediction for two years ahead gives a mean absolute percentage error between 5 and 9 %. Therefore, the ANN model is adequate for modelling and prediction of the reference time series. This model is an important and useful framework for better planning and development of these two regions as they operate in highly competitive markets.
- Acoustic analysis of vocal dysphoniaPublication . Teixeira, João Paulo; Fernandes, Paula OdeteVoice acoustic analysis is becoming more and more usefúl in diagnosis of voice disorders or laryngological pathologies. The facility to record a voice sigiial is an advantage over other invasive techniques. This paper presents the statistical analyzes ofa set of voice parameters like jitter, shimmer and HNR over a 4 groups of subjects vvith dysphonia, fünctional dysphonia, hyperfünctional dysphonia, and psychogenic dysphonia and a control group. No statistical signifícance differences over pathologic groups were found but clear tendencies can be seen between pathologic and control group. The tendencies indicates this parameters as a good features to be used in an intelligent diagnosis system, moreover the jitter and shimmer parameters measured over different tones and vowels.
- Artificial neural networks versus Box Jenkins methodology in tourism demand analysisPublication . Fernandes, Paula Odete; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana GarridoSeveral empirical studies in the tourism area have been performed and published during the last decades. The researchers are unanimous upon considering that in the planning process, decisionmaking and control of the tourism sector, the forecast of the tourism demand assumes an important role. Nowadays, there is a great variety of methods for forecasting that have been developed and which can be applied in a set of situations presenting different characteristics and methodologies, going from simple approaches to more complex ones. In this context, the present study aims to explore and to evidence the usefulness of the Artificial Neural Networks methodology (ANN), in the analysis of the tourism demand, as an alternative to the Box-Jenkins methodology. ANN has been under attention in the area of business and economics since, in this field, it presents this methodology as a valid alternative to classical methods of forecasting allowing its application for problems in which the traditional ones would be difficult to use (Thawornwong & Enke, 2004). As referred by Hill et al. (1996) and Hansen et al. (1999), ANN shows 1 ability for improving time-series forecasts by mining additional information, diminishing their dimensionality, and reducing their complexity. In this way, for each methodology treatment, analysis and modeling of the tourism time-series: “Nights Spent in Hotel Accommodation per Month” registered between January 1987 and December 2006, was carried out since is one of the variables that better explains the effective tourism demand. The study was performed for the North and Center regions of Portugal. Considering the results, and according to the Criteria of MAPE for model evaluation in Lewis (1982), the ANN model presented an acceptable goodness of fit and good statistical properties and is, therefore, adequate for modelling and prediction of the reference time series, when compared to the results obtained by the methodology of Box-Jenkins.
- Modelação e previsão da procura turística em Moçambique: um estudo comparativo entre os modelos de redes neuronais artificiais e regressão linear múltiplaPublication . Constantino, Hortêncio; Fernandes, Paula Odete; Teixeira, João PauloO principal objetivo do presente trabalho assenta num estudo comparativo entre o modelo de Regressão Linear Múltipla e de Redes Neuronais Artificiais, para prever a procura turística em Moçambique. Utilizou-se para tal o número de dormidas mensais registadas nos estabelecimentos hoteleiros, para o período de Janeiro de 2004 a Dezembro de 2013. Para tal, foram selecionadas as variáveis explicativas: índice Harmonizado de Preços ao Consumidor, Produto Interno Bruto e Taxa de Câmbio para os principais mercados emissores: África Sul, Estados Unidos da América, Moçambique, Portugal e Reino Unido.
- Tourism time series forecast with artificial neural networksPublication . Teixeira, João Paulo; Fernandes, Paula OdeteThe modulation of tourism time series was used in this work for forecast purposes. The Tourism Revenue and Total Overnights registered in the hotels of the North region of Por- tugal were used for the experimented models. Several feed-forward Artificial Neural Networks (ANN) models using different input features and number of hidden nodes were experimented to forecast the Tourism time series. Empirical results indicate that the Dedicated ANN models perform better than models with several outputs. Generally the usage of previous 12 values of the same time series is very important to a good quality forecast. For the prediction of Tourism Revenue the Foreign Overnights and GDP of contributing countries are relevant. This time series was predicted with an error of 4.7% and a Pearson correlation of 0.98. The forecast of Total Overnights had an error of 6.0% and Pearson correlation of 0.98. Domestic Overnights are more predictable than Foreign Overnights.
- Tourism time series forecastPublication . Teixeira, João Paulo; Fernandes, Paula OdeteIn this chapter four combinations of input features and the feedforward, cascade forward and recurrent architectures are compared for the task of forecast tourism time series. The input features of the ANNs consist in the combination of the previous 12 months, the index time modeled by two nodes used to the year and month and one input with the daily hours of sunshine (insolation duration). The index time features associated to the previous twelve values of the time series proved its relevance in this forecast task. The insolation variable can improved results with some architectures, namely the cascade forward architecture. Finally, the experimented ANN models/architectures produced a mean absolute percentage error between 4 and 6%, proving the ability of the ANN models based to forecast this time series. Besides, the feedforward architecture behaved better considering validation and test sets, with 4.2% percentage error in test set.
- Pure-tone audiogram: measuring auditory sensitivity over the agePublication . Teixeira, João Paulo; Fernandes, Paula OdeteIn this study apure tone audiogram was developed under the Matlab® mathemalical software. Audiogram measurements were performed lo 35 subjects belonging to the female and male and aged between 10 and 88 years old. Some of the subjects with more advanced age had hearing problems over the course of age, however, none of Ihem was carrying any type of hearing aid.
- Artificial intelligence in the recrutment & selection: innovation and impacts for the human resources managementPublication . Jatoba, Mariana Namen; Gutierriz, Ives Emídio; Fernandes, Paula Odete; Teixeira, João Paulo; Moscon, DanielaThe objective of the article was to investigate the use of Artificial Intelligence (AI) in recruitment and selection (R&S) and impacts on Human Resource Management (HRM). The study used a survey of scientific papers and conferences materials indexed to the database of Web of Science and Scopus and published between the period 2000 and 2018. In addition, it was decided to make an opinion survey conducted with professionals and managers on the use of the tool as facilitator of the recruitment and selection process and impacts on HRM, contributing to the strategic positioning of the area within the organizations. Data was collected, through survey questionnaires applied between March and May 2019. A total of 150 questionnaires were collected. A quantitative descriptive analysis was performed to analyse the perception of the professionals about AI in HR, as well as the use of this technology in the automation of processes and contributions when used in R&S processes. Of the 150 respondents, 74% are female, 61% are over 36 years old, 67.3% works in the service sector, and about 49% perform functions in the area of Human Resources. It was also observed that around 63% of respondents' companies have never used AI and about 19% use or have already used AI in the area of customer service. The lack of solid research lines in the subject was verified and it was concluded that the practice is still very embryonic, although the view of the respondents, ones is positive about the benefits that the AI can bring to the recruitment and selection of candidates. It is hoped that the questions pointed out in this essay elicit new theoretical and empirical studies that show the interactions between AI and HR.
- O Impacto da variável páscoa na previsão da procura turísticaPublication . Fernandes, Paula Odete; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana Garrido
- New approachof the ANN methodology for forecasting time series: use of time indexPublication . Fernandes, Paula Odete; Teixeira, João Paulo