An expert system for financial performance assessment of health care structures based on fuzzy sets and KPIs

Muriana, Cinzia; Piazza, Tommaso; Vizzini, Giovanni

doi:10.1016/j.knosys.2016.01.026

Cited by 19 publications

(15 citation statements)

References 23 publications

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“…ARIMA model [25], can reflect the structure and characteristics of time series more essentially and then achieve the optimal prediction of minimum variance, which is different from the time series method that depends on different constraints [26,27]. This model has been widely used in public transport [28], health care [29] and other aspects of evaluation [30].…”

Section: Development Of Four Methodsmentioning

confidence: 99%

Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

Wang

Zhan

2019

Sustainability

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Africa has abundant energy resources, but African energy research level is relatively low. In response to this gap, this paper takes Middle Africa as an example to systematically predict energy demand to give support. In this paper, we utilize four models, metabolic grey model (MGM), modified exponential curve method (MECM), autoregressive integrated moving average (ARIMA) and BP neural network model (BP), to predict the energy consumption of Middle Africa in the next 14 years. Comparing four completely different types of predictive models can fully depict the characteristics of the predictive data and give an all-round analysis of the predicted results. These proposed models are applied to simulate Middle Africa’s energy consumption between 1994 and 2016 to test their accuracy. Among them, the mean absolute percent error (MAPE) of MGM, MECM, ARIMA and BP are 2.41%, 4.80%, 1.91%, and 0.88%. The results show that MGM, MECM, ARIMA, and BP presented in this paper can produce reliable forecasting results. Therefore, the four models are used to forecast energy demand in the next 14 years (2017–2030). Forecasts show that energy demand of Middle Africa will continue to grow at a rate of about 5.37%.

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Section: Development Of Four Methodsmentioning

confidence: 99%

Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

Wang

Zhan

2019

Sustainability

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“…They are computer programs that use problem-solving knowledge and simulations at a level comparable to human experts [19,37]. The main components of an expert system are knowledge base, inference engine, fuzzifier, defuzzifier, and user interface [14,38], as shown in Figure 2. The inference engine is responsible for bringing decisions based on its understanding and interpretation of the expert's knowledge.…”

Section: Expert Systemsmentioning

confidence: 99%

A Fuzzy Logic Expert System for Pricing Digital Services: The Case of Price Adjustment for a Mobile Service Provider

Dairo¹,

Szűcs²

2020

IJFIS

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As connected smart devices and terminals continue to grow along with digital content, data traffic of mobile service providers is also growing, and the price war in mobile markets is driving traffic growth without a commensurable increase in revenue. As a result, network capital expenditure (CAPEX) investment, quality of experience, and customer experience are under enormous pressure. In a competitive mobile market, strategic pricing may play an essential role in managing this pressure only if appropriate tools are available for the service providers. In this paper, a fuzzy knowledge-based expert pricing system was developed with a focus on solving network traffic, price war, and business revenue challenges in a competitive mobile market. Its core lay in its ability to recommend digital-and data services-related price points within a competitive and price war mobile environment. The proposed pricing system was experimentally evaluated through a pilot conducted on a few segments of a mobile service provider's customer base in an emerging market and later scaled up to a broader base. Upon implementation, data services revenue increased, and overall gross margin increased with a reduction in data traffic, resulting in better throughput and network quality and, consequently, better customer experience with improved net promoter score.

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“…In addition to the field of energy research, this approach is also applicable to the prediction of non-stationary behavior in the field of agrifood and health. The adaptive MA model has been assessed and forecasted in vineyard management and health care structures [22,23]. Based on the above three models, scholars have successively proposed the following complex linear model.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models

Yang

2018

Sustainability

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Construction of new coal-fired power plants in China has posed a huge challenge to energy sustainability. Forecasting the installed capacity more accurately can serve to develop better energy sustainability strategy. A comparison between linear and non-linear forecasting models can more comprehensively describe the characteristics of the prediction data and provide multi-angle analysis of the prediction results. In this paper, we develop four time-series forecasting techniques-metabolism grey model (MGM), autoregressive integrated moving average (ARIMA), grey model (GM)-ARIAM, and nonlinear metabolism grey model (NMGM)-for better forecasting of coal-fired power installed capacity. The average relative errors between the simulation and actual data of the MGM, GM-ARIMA, ARIMA, and NMGM model are 3.37%, 2.13%, 3.71% and 2.36% respectively, which indicate those four models can produce highly accurate results. The forecasting results show the average annual growth rate of China's coal-fired power installed capacity in the next ten years (2017-2016) will be 5.26% a year, which is slower than the average annual growth rate (8.20% a year) for 2007-2016. However, the average annual new added installed capacity for 2017-2026 will be 74 gigawatts, which is higher than the average annual added installed capacity (56 gigawatts) for 2007-2016.

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An expert system for financial performance assessment of health care structures based on fuzzy sets and KPIs

Cited by 19 publications

References 23 publications

Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

A Fuzzy Logic Expert System for Pricing Digital Services: The Case of Price Adjustment for a Mobile Service Provider

Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models

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