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Developing a hybrid artificial intelligence model for outpatient visit forecasting in hospitals
Abstract
Accurate forecasting of outpatient visits aids in decision-making and planning for the future and is the foundation for greater and better utilization of resources and increased levels of outpatient care. It provides the ability to better manage the ways in which outpatient's needs and aspirations are planned and delivered. This study presents a hybrid artificial intelligence (AI) model to develop a Mamdani type fuzzy rule based system to forecast outpatient visits with high accuracy. The hybrid model uses genetic algorithm for evolving knowledge base of fuzzy system. Actually it extracts useful patterns of information with a descriptive rule induction approach based on Genetic Fuzzy Systems (GFS). This is the first study on using a GFS to constructing an expert system for outpatient visits forecasting problems. Evaluation of the proposed approach will be carried out by applying it for forecasting outpatient visits of the department of internal medicine in a hospital in Taiwan and four big hospitals in Iran. Results show that the proposed approach has high accuracy in comparison with other related studies in the literature, so it can be considered as a suitable tool for outpatient visits forecasting problems.
... After the introduction of different artificial intelligence algorithms, the new approach is to use a combination of these algorithms in order to obtain a better performance [21]. Therefore, the novelty of this paper is in that we use the particle swarm optimization algorithm to train the neural network weight parameters [22] which has been rarely seen in previous papers. ...
... Due to the complexity of the relationships between variables, using data pre-processing methods for reaching a more accurate model was taken into consideration. At the same time, clustering models were also used [20] [21]. An applications of clustering methods is to use them in developing modular models. ...
... presents the data value in the converted scale [21]. Clearly, by reversing this equation we can obtain the data value on the old scale. ...
Data mining is an interdisciplinary science which exploits different methods including statistics, pattern recognition, machine learning, and database to extract the knowledge hidden in huge datasets. In this paper, we sought to develop a model for paying pregnancy period wages compensation to the Social Security Organization (SSO) clients by using data mining techniques. The SSO is a public insurance organization, the main mission of which is to cover the stipendiary workers (mandatory) and self-employed people (optional). In order to develop the proposed model, 5931 samples were selected randomly from 11504 clients. Then the K-Means clustering algorithm was employed to divide data into cluster 1, consisting of 2732 samples, and cluster 2, consisting of 3199 samples. In each cluster, the data were divided into training and test sets with a ratio of 90 to 10. Then a multi-layer perceptron neural network was trained separately for each cluster. This paper utilized the MLP network model. The tanh transfer function was used as the activation function in the hidden activation layer. Numerous tests were conducted to develop the best neural network structure with the lowest error rate. It consisted of two hidden layers. There were 5 neurons in the first layer and 4 neurons in the second. Therefore, the neural network structure was in the 5-4-1 format. Finally, the best model was selected by using the error evaluation method. The MAPE and R2 criteria were employed to evaluate the proposed model. Regarding the test data, the result was 0.96 for cluster 1 and 0.95 for cluster 2. The proposed method produced a lower error rate than the other existing models.
... Outpatient department (OD) is the window of the hospital external service from the hospital actual operation, and is experiencing increasing stress from year to year because of the increasing patient volumes, the growing complexity of health conditions and so on. The ability to predict outpatient visits is crucial for resource planning and allocation as well as efficient appointment scheduling in OD aimed to avoiding overcrowding and providing high quality patient care service [1]. Moreover, as the key and major element to implement the hospital management, daily outpatient visits are directly related to the workload of medical examinations and hospitalization services. ...
... Being a general class of linear model, the ARIMA model can perfectly capture linear patterns in a time series with minimum computational efforts, so most studies adopt them to describe the relationship between the variables or use them as the benchmark to test the effectiveness of combined models with mixed results [9]. Sun et al. [11] and Kadri et al. [1] developed ARIMA models for forecasting daily attendances at ED of hospitals to prove time-series analysis to be a useful, readily available tool for predicting ED workload. Li et al. adopted ARIMA model to forecast monthly outpatient visits in a general hospital in China [9]. ...
... Li et al. adopted ARIMA model to forecast monthly outpatient visits in a general hospital in China [9]. However, relationship between target variable and factors in many time series is nonlinear and complex, many recent studies have focused on the use of machine learning techniques as alternatives to the traditional time series methods, such as Fuzzy logic, Artificial Neural Network(ANN), Genetic Algorithms, Support Vector Machine(SVM), Taylor expansion and non-parametric smoothing etc. [1,[12][13][14]. Cheng et al. [15] and Garg et al. [12] proposed new fuzzy time series methods to forecast the number of outpatient visits. ...
Background
Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors’ scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration.
Methods
We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead.
Results
The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better.
Conclusions
Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step.
... Forecasting Out-Patient Department (OPD) visits in healthcare centres have increasingly induced large interest in both theoretical and applied perspectives [1]. Accurately forecasting the number of patient visits in hospitals is key in the administration of human capital as well as essential equipment and materials resources of the hospital [2]. The OPD is the gateway to almost all of the services the hospital renders to the general public and can come under increasing pressure annually due to increasing patient volume [3]. ...
... To this end, accurately forecasting the OPD visits of the University of Cape Coast Hospital becomes very critical and beneficial. The ability to predict outpatient visits is crucial for resource planning and allotment as well as efficient scheduling of appointments in the OPD, which is aimed at avoiding overcrowding and providing high-quality patient healthcare service [2]. ...
Background: Accurate and reliable forecasting of outpatient department visits enhances decision-making and planning for future healthcare demands and is the foundation for greater and better utilization of healthcare resources and increased levels of outpatient care and satisfaction. Though the literature has proposed several candidate models for predicting outpatient visits in some hospitals in Ghana, the model regulating outpatient visits at the University of Cape Coast Hospital (UCC) is unknown. There is therefore a need to determine the best model applicable to the specific case of UCC Hospital. Aim: This study sought to determine and model the dynamics of outpatient visits in UCC Hospital and to project outpatient healthcare demands at the facility for the period July 2021 to July 2024. Methods: This paper employed a monthly periodicity of 114-time series data sourced from District Health Information Management Systems Two (DHIMS 2) on outpatient department visits at UCC Hospital from January 2012 to June 2021. The autoregressive integrated moving average (ARIMA) models which are a form of the classical Box-Jenkins approach of Time Series Analysis were used to analyse the data. Analysis was performed in EViews 12. Results: The study results showed twenty-five non-seasonal tentative models for the hospital and ARIMA (4, 1, 4) was selected as the best fit model with a fourth-order autoregressive and moving average terms each and one order of nonseasonal differencing. Residual analysis of the fitted model indicates that the model is adequate for forecasting. The findings revealed an overall rising trend in the incidence of outpatient department visits to the hospital over the study period with an average of 6000 visits per month. This is expected to increase to over 7000 visits per month over the next three-year period of July 2021 to June 2024 according to projections. Conclusion: The forecast of outpatient visits in this study serves as early signals to the management of the University hospital and is intended to enhance human and material resource planning and allocation for better-quality healthcare delivery.
... Therefore, scholars have paid more attention to the establishment of outpatient demand forecasting models. Existing research has focused on outpatient flow [4,5] and other related departments like internal medicine [6,7], cancer [8], anxiety disorder [3], diarrhea [9], and so on; however, the prediction of outpatient blood sampling room visits was ignored. In addition, the blood sampling room visits have the characteristics of the day-of-the-week effect and seasonality, which are due to the registration system as well as human behavior. ...
... The seasonal ARIMA model is used to model a series with significant seasonal effects (periodic effects), which is transformed into a stable one through trend difference and seasonal difference, along with the fitting of the ARMA model [48]. The structure of the ARIMA ðp, d, qÞ model is as follows [7]: ...
This paper is aimed at establishing a combined prediction model to predict the demand for medical care in terms of daily visits in an outpatient blood sampling room, which provides a basis for rational arrangement of human resources and planning. On the basis of analyzing the comprehensive characteristics of the randomness, periodicity, trend, and day-of-the-week effects of the daily number of blood collections in the hospital, we firstly established an autoregressive integrated moving average model (ARIMA) model to capture the periodicity, volatility, and trend, and secondly, we constructed a simple exponential smoothing (SES) model considering the day-of-the-week effect. Finally, a combined prediction model of the residual correction is established based on the prediction results of the two models. The models are applied to data from 60 weeks of daily visits in the outpatient blood sampling room of a large hospital in Chengdu, for forecasting the daily number of blood collections about 1 week ahead. The result shows that the MAPE of the combined model is the smallest overall, of which the improvement during the weekend is obvious, indicating that the prediction error of extreme value is significantly reduced. The ARIMA model can extract the seasonal and nonseasonal components of the time series, and the SES model can capture the overall trend and the influence of regular changes in the time series, while the combined prediction model, taking into account the comprehensive characteristics of the time series data, has better fitting prediction accuracy than a single model. The new model can well realize the short-to-medium-term prediction of the daily number of blood collections one week in advance.
... In recent years, the research on the prediction of hospital operation indicators is gradually increasing, but is still less overall, and most of the articles predict a certain indicator. In 2012, Chen Yuan [1] used the Holt-Winters' additive model to predict the discharged numbers of a hospital from 2007 to 2009, and Hadavandi et al. [2] proposed a hybrid model based on Mamdani-type fuzzy rules to predict the outpatient numbers. In 2013, Ma Chunliu et al [3] used the SARIMA model to fit and predict the inpatient numbers of a third-class hospital from 2000 to 2011. ...
... CSAE2018, October 2018, Hohhot, China Tao Zheng et al. 2 This paper studies on the monthly data of outpatient, inpatient, emergency, discharged and surgical cases of a thirdclass hospital from 2004 to 2017 and constructs a Holt-Winters model based on particle swarm optimization (PSO). According to the data characteristics of hospital operation indicators, the method is optimized to solve the parameter setting and optimization problem of traditional Holt-Winters model. ...
The prediction1 of hospital operation indicators is of great significance and can provide an important basis for hospital operation and management, so as to assist managers to make decisions such as resource allocation and task planning. In order to solve this problem, a novel Holt-Winters model based on particle swarm optimization (PSO) is proposed, aiming at the accurate prediction of hospital operating indicators. In the process of model construction, according to the characteristics of time series data of hospital operation indicators, a time decay mean square error function is constructed as an optimization function of particle swarm optimization algorithm, which enables particle swarm optimization algorithm to better fit recent historical data and grasp the characteristics of recent time series, so as to improve the prediction accuracy. An example is given to analyze the hospital operation index data of a third-class hospital from 2014 to 2017. By initializing the parameters of the model and optimizing the parameters, the improved PSO-Holt-Winters model of TDMSE-1 is established, which can accurately predict the outpatient, inpatient, emergency, discharged and surgical cases.
... The key to a successful implementation of SOMnn is to find suitable centers for the Gaussian functions (Kurt et al. 2008). The SOMnn consists of M neurons arranged in a 2-D rectangular or hexagonal grid (Hadavandi et al. 2012). ...
... σ t is kernel width and decreasing with time. This process of weight-updating will be performed for a specified number of iterations (Hadavandi et al. 2012). SOM networks' ability to associate new data with similar previously learnt data can be applied to forecasting applications (Lopez et al. 2012). ...
This paper intends to enhance the learning performance of radial basis function neural network (RBFnn) using self-organizing map (SOM) neural network (SOMnn). In addition, the particle swarm optimization (PSO) and genetic algorithm (GA) based (PG) algorithm is employed to train RBFnn for function approximation. The proposed mix of SOMnn with PG (MSPG) algorithm combines the automatically clustering ability of SOMnn and the PG algorithm. The simulation results revealed that SOMnn, PSO and GA approaches can be combined ingeniously and redeveloped into a hybrid algorithm which aims for obtaining a more accurate learning performance among relevant algorithms. On the other hand, method evaluation results for four continuous test function experiments and the demand estimation case showed that the MSPG algorithm outperforms other algorithms and the Box–Jenkins models in accuracy. Additionally, the proposed MSPG algorithm is allowed to be embedded into business’ enterprise resource planning system in different industries to provide suppliers, resellers or retailers in the supply chain more accurate demand information for evaluation and so to lower the inventory cost. Next, it can be further applied to the intelligent manufacturing system to cope with real situation in the industry to meet the need of customization.
... Li et al. (2015) adopted the ARIMA to forecast monthly outpatient visits. Hadavandi et al. (2012) 152 ...
... The experimental findings clearly indicated that the hybrid model surpassed the traditional single prediction model in terms of prediction accuracy. E. Hadavandil et al. [37] proposed a hybrid ANN model based on the GA, which was employed to predict emergency department visits and obtain better prediction results. The model provided useful guidance for emergency department managers and medical staff. ...
Precise outpatient volume prediction holds significant importance in hospital management. While the Gated Recurrent Unit (GRU) is a frequently utilized deep learning technique for forecasting hospital outpatient volumes, creating a proficient GRU model necessitates the fine-tuning of pertinent GRU parametersThe adjustment of suchparameters relies heavily on an individual’s practical experience and prior knowledge. The recently proposed Cheetah optimizer is a novel intelligent algorithm with unique optimization capabilities. The Cheetah optimizer holds significant research potential; however, additional investigations are warranted, as it may be vulnerable to issues related to local optimization. In the present study, the selection of hyperparameters for the GRU model wasoptimized through the utilization of the Modified Cheetah Optimization (MCO) algorithm, and a combined MCO-GRU model was established. Using the Successive Variational Mode Decomposition (SVMD) method to decompose outpatient volume sample data, the parameters of the GRU model were optimized with the MCO method to construct a hybrid forecasting model. This yielded the smallest Root Mean Square Error (RMSE) for the proposed model, with a value of 0.0843. Additionally, the results indicate that in comparison to SVMD, Long Short-Term Memory (LSTM), GRU, Particle Swarm Optimization-GRU (PSO-GRU), and Cheetah Optimization-GRU (CO-GRU), the proposed model significantly enhanced the accuracy of outpatient volume forecasting.
... Its sufferers are usually contagious, and outbreaks of infectious diseases can lead to serious morbidity. Due to the increase in the number of patients and the growing complexity of their health conditions, the pressure on the outpatient department, which is the hospital's external service window, increases every year [2]. The hospital outpatient department is an important part of the hospital organization and has the function of diagnosing, treating, and protecting the health of patients. ...
Background
Reasonable and accurate forecasting of outpatient visits helps hospital managers optimize the allocation of medical resources, facilitates fine hospital management, and is of great significance in improving hospital efficiency and treatment capacity.
Methods
Based on conjunctivitis outpatient data from the First Affiliated Hospital of Xinjiang Medical University Ophthalmology from 2017/1/1 to 2019/12/31, this paper built and evaluated Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for outpatient visits prediction.
Results
In predicting the number of conjunctivitis visits over the next 31 days, the LSTM model had a root mean square error (RMSE) of 2.86 and a mean absolute error (MAE) of 2.39, the GRU model has an RMSE of 2.60 and an MAE of 1.99.
Conclusions
The GRU method can better predict trends in hospital outpatient flow over time, thus providing decision support for medical staff and outpatient management.
... For example, concerning applying AIS in the business area of production, operations and supplychain management, prior studies reported successful AI research attempts in that context. First, machine-learning techniques were employed in organisations for effective/efficient forecasting of demand or any variable of interest (Crone et al., 2006;Hadavandi et al., 2012;Abbasi and El Hanandeh, 2016;Zor et al., 2017). Second, expert-systems were used in automating process planning and developing a sequence for operations as well as AI-enhanced robots were deployed in smart manufacturing (Kumar, 2017;Hagemann et al., 2019;Kreutzer and Sirrenberg, 2020). ...
Abstract
Purpose – To investigate the relationship between artificial intelligence strategy (AIS), creativity-oriented HRM (CHRM), and knowledge-sharing quality (KSQ). At individual and organisational levels, this paper measures also the innovative work behaviour (IIWB) and effective performance (OEP) of international organisations conducting AI-powered business practices in Egypt.
Design/methodology/approach – The authors presented a multilevel-model, after reviewing the relevant literature, and tested it through employing mixed-methods approach. Data were collected from 168 questionnaires answered by AI-experts at IT departments of 20 international AI-powered organisations in Egypt in addition to 25 depth interviews, AI-based focus group and international forum.
Findings – Following PLS-SEM approach, results revealed that AIS affects positively and significantly KSQ and CHRM. CHRM affects positively and significantly KSQ and IIWB. KSQ affects positively and significantly OEP and IIWB. The significant positive direct AIS-OEP relationship was not supported yet the significant positive indirect relationship via KSQ was supported.
Originality/value – Empirically, it is the first research that assessed AIS-CHRM-KSQ relationship and its effect on IIWB and OEP of AI-powered businesses from 7 sectors of an emerging economy. Conceptually, the authors adopted an interdisciplinary approach while reflecting on the literature that studied AIS implementation in different business functions (production, operations and supply-chain management, human resources management, strategic management and marketing).
Practical implications – Strategic leaders and managers of different functional areas can benefit from the empirical findings of this study as well as from the examples of best AI-enhanced practices drawn from the literature.
Keywords – Artificial intelligence strategy, AI-powered business functions, Creativity-oriented HRM, Knowledge-sharing quality, Organisational effective performance, Innovative work behaviour, Operations management, Strategic management, Expert system, Machine learning, Forecasting.
Track – e-Business and e-Government.
Suggested Citation: Younis, R.A.A. and Adel, H.M. (2020), “Artificial intelligence strategy, creativity-oriented HRM and knowledge-sharing quality: Empirical analysis of individual and organisational performance of AI-powered businesses”, The Annual International Conference of The British Academy of Management (BAM) 2020: Innovating for a Sustainable Future, London, United Kingdom, 2-4 September. http://dx.doi.org/10.2139/ssrn.4127128
... However, the relationship between the target variables and factors in many time series data is nonlinear and complex, resulting in a poor prediction for nonlinear data. Artificial intelligence (AI) models such as the neural network and the genetic algorithm have been applied to predict OV (9)(10)(11)(12)(13)(14)(15). AI strategies based on data-driven and nonparametric models provide a higher prediction accuracy with some limitations including unknown or difficultto-describe underlying relationships among datasets. ...
Background: The forecasting of daily outpatient visits has significant practical implications in outpatient clinic operation management, not only contributing to guiding long-term resource planning and scheduling but also making tactical resolutions for short-term adjustments on special days such as holidays. We here in propose an effective genetic programming (GP)-based forecasting model to predict daily outpatient visits (OV) in a primary hospital.
Methods: In the GP-based model, the holiday-based distance outlier mining algorithm was used to determine the holiday effect. In addition, solar terms were applied as the smallest unit to more accurately determine the impact of a change in the climate on the outpatient volume. A segmental learning strategy also was used to predict the daily outpatient volume for the time series data.
Results: The GP-based prediction could more effectively extract depth information from a finite training sample size and achieve a better performance for predicting daily outpatient visits, with lower root mean square error (RMSE) and higher coefficient of determination (R2) values, than the seasonal autoregressive integrated moving average (SARIMA) model in the time range of holidays and the holiday effect.
Conclusion: GP-based model can achieve better prediction performance by overcoming the shortcomings of the SARIMA model. The results can be applied to support decision-making and planning of outpatient clinic resources, to help managers implement periodic scheduling of available resources on the basis of periodic features, and to perform proactive scheduling of additional resources.
... [76]. These indicators can help OCCs to implement countermeasures that can reduce or eliminate the burden of uncertainties [77,78]. Thus, there are several research opportunities associated with integrating OCP optimization models with AI, ML, and other trending data analytics research methods. ...
Around the world, cancer care services are facing many operational challenges. Operations management research can provide important solutions to these challenges, from screening and diagnosis to treatment. In recent years, the growth in the number of papers published on cancer care operations management (CCOM) indicates that development has been fast. Within this context, the objective of this research was to understand the evolution of CCOM through a comprehensive study and an up-to-date bibliometric analysis of the literature. To achieve this aim, the Web of Science Core Collection database was used as the source of bibliographic records. The data-mining and quantitative tools in the software Biblioshiny were used to analyze CCOM articles published from 2010 to 2021. First, a historical analysis described CCOM research, the sources, and the subfields. Second, an analysis of keywords highlighted the significant developments in this field. Third, an analysis of research themes identified three main directions for future research in CCOM, which has 11 evolutionary paths. Finally, this paper discussed the gaps in CCOM research and the areas that require further investigation and development.
... [76]. These indicators can help OCCs to implement countermeasures that can reduce or eliminate the burden of uncertainties [77,78]. Thus, there are several research opportunities associated with integrating OCP optimization models with AI, ML, and other trending data analytics research methods. ...
Worldwide, chemotherapy centers that provide outpatient services face significant challenges owing to increased demand and limited resources. Therefore, outpatient chemotherapy process (OCP) optimization has attracted the attention of operations management scholars. This review seeks to provide a comprehensive analysis of existing quantitative optimization-oriented research that addresses OCP problems and identifies departure points for future research.
Various scientific databases were searched to collect the maximum number of OCP optimization-oriented publications. Bibliometric data mining tools were used to provide descriptive analyses of the publications. The OCP optimization-oriented research framework was obtained through social network analysis of the formulation narratives of the models. Content analysis was performed to classify the literature based on several optimization-oriented perspectives. From 1500 publications, 45 studies were screened and included in the review. The current literature lacks a holistic solution to OCP challenges, as most publications are pure optimization studies that consider narrow scopes and idealized problems. This review proposes future research opportunities based on the gaps discovered, which may lead to more insightful results for real-life OCP problems.
... To test whether a significant difference in forecasting precision exists between LSTM and the baseline models, the relative error (RE) generated by each model was used to conduct a paired t-test (Hadavandi, Shavandi, Ghanbari, & Abbasian-Naghneh, 2012). RE can be denoted as (y t −ŷ t /y t ) × 100%, where y t and y t are the observed and forecasted values, respectively. ...
... Hybrid learning methods are characterized by combining concepts from the previously presented methods, e.g. when training neural networks by applied evolutionary algorithms to adapt network weights [63]. Such combinations have been proposed for a wide variety of areas like finance applications [64], oceanographic forecast [65], outpatient visits forecasting [66], compounding meta-atoms into metamolecules [67] and classification of tea specimens [68]. ...
Many artificial intelligence (AI) technologies developed over the past decades have reached market maturity and are now being commercially distributed in digital products and services. Therefore, national and international AI standards are currently being developed in order to achieve technical interoperability as well as reliability and transparency. To this end, we propose to classify AI applications in terms of the algorithmic methods used, the capabilities to be achieved and the level of criticality. The resulting three-dimensional classification scheme, termed the AI Methods, Capabilities and Criticality (AI-MC2) Grid, combines current recommendations of the EU Commission with an ethical dimension proposed by the Data Ethics Commission of the German Federal Government (Datenethikkommission der Bundesregierung: Gutachten. Berlin, 2019). As a whole, the AI-MC2 Grid allows not only to gain an overview of the implications of a given AI application as well as to compare efficiently different AI applications within a given market or implemented by different AI technologies. It is designed as a core tool to define and manage norms, standards and compliance of AI applications, but helps to manage AI solutions in general as well.
... At present, Artificial Intelligence has more complex techniques such as artificial neural networks, fuzzy logic, and genetic algorithms, which are common research topics because they can handle complicated problems in prediction and other areas, which are hard to solve by traditional methods. When one compares the capacity of AI techniques with conventional techniques such as ARIMA and Regression in the field of prediction and one notices that systems based on AI have more accurate results in these conventional approaches, which for this reason they have been used successfully in place of the prediction problems [19]. ...
... There are other applications of AI in the field of health care related with administrative aspects. For example, in the optimization of health inspections in restaurants using social media data to reduce food poisoning and derived hospital admissions [20], outpatient visit forecasting in hospitals [21] or optimization of bed rotation in hospitals [22]. The potential of AI in the field of public health care still has many frontiers to be explored, but we must be aware that medical data are among the most sensitive personal data and that with undesired processing they can produce serious harm to citizens [1]. ...
bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In the last
decade, there has been an explosion in the progress and applications of artificial intelligence (AI) in our society. For the first time, the applications of AI have left the laboratory to reach society in a broad, visible, and relevant way. This fact has raised numerous questions about the potential of AI in the future and its implications in our lives. The benefits of AI do not come alone; they also bring with them responsibilities that if not considered properly can become misuses, intentional or not.
... In the area of hospital pharmacies, the combination of AI and BDA has already been proved effective, notably in the design and development of new drugs (Wang et al., 2019). Other benefits of AI include very precise forecasts of ambulatory hospital visits (Hadavandi et al., 2012). Thus, thanks to this capacity to process information, hospitals can better plan these resources in terms of staff needed to care for patients (doctors, nurses, nursing assistants) and anticipate their need for such items as equipment and drugs for a given period. ...
Big data analytics and artificial intelligence (BDA-AI) technologies have attracted increasing interest in recent years from academics and practitioners. However, few empirical studies have investigated the benefits of BDA-AI in the supply chain integration process and its impact on environmental performance. To fill this gap, we extended the organizational information processing theory by integrating BDA-AI and positioning digital learning as a moderator of the green supply chain process. We developed a conceptual model to test a sample of data from 168 French hospitals using a partial least squares regression-based structural equation modeling method. The findings showed that the use of BDA-AI technologies has a significant effect on environmental process integration and green supply chain collaboration. The study also underlined that both environmental process integration and green supply chain collaboration have a significant impact on environmental performance. The results highlight the moderating role of green digital learning in the relationships between BDA-AI and green supply chain collaboration, a major finding that has not been highlighted in the extant literature. This article provides valuable insight for logistics/supply chain managers, helping them in mobilizing BDA-AI technologies for supporting green supply processes and enhancing environmental performance.
... These diseases put considerable pressure on hospital management. At the same time, the media have reported that strengthening the medical power of the respiratory departments and emergency departments in winter is needed (Hadavandi et al. 2012). Therefore, it is of great significance for hospitals to monitor, compare and study the changes in outpatient visits to respiratory departments for sound management. ...
Effective hospital outpatient forecasting is an important prerequisite for modern hospitals to implement intelligent management of medical resources. As outpatient visits flow may be complex and diverse volatility, we propose a hybrid Autoregressive Integrated Moving Average (ARIMA)-Long Short Term Memory (LSTM) model, which hybridizes the ARIMA model and LSTM model to obtain the linear tendency and nonlinear tendency correspondingly. Instead of the traditional methods that artificially assume the linear components and nonlinear components should be linearly added, we propose employing backpropagation neural networks (BP) to imitate the real relationship between them. The proposed hybrid model is applied to real data analysis and experimental analysis to justify its performance against single ARIMA model, single LSTM model and the hybrid ARIMA-LSTM model based on the traditional method. Compared with competitors, the proposed hybrid model produced the lowest RMSE, MAE and MAPE. It achieves more accurate and stable prediction. Therefore, the proposed model can be a promising alternative in outpatient visit predictive problems.
... For example, concerning applying AIS in the business area of production, operations and supplychain management, prior studies reported successful AI research attempts in that context. First, machine-learning techniques were employed in organisations for effective/efficient forecasting of demand or any variable of interest (Crone et al., 2006;Hadavandi et al., 2012;Abbasi and El Hanandeh, 2016;Zor et al., 2017). Second, expert-systems were used in automating process planning and developing a sequence for operations as well as AI-enhanced robots were deployed in smart manufacturing (Kumar, 2017;Hagemann et al., 2019;Kreutzer and Sirrenberg, 2020). ...
Purpose – To investigate the relationship between artificial intelligence strategy (AIS), creativity-oriented HRM (CHRM), and knowledge-sharing quality (KSQ). At individual and organisational levels, this paper measures also the innovative work behaviour (IIWB) and effective performance (OEP) of international organisations conducting AI-powered business practices in Egypt.
Design/methodology/approach – The authors presented a multilevel-model, after reviewing the relevant literature, and tested it through employing mixed-methods approach. Data were collected from 168 questionnaires answered by AI-experts at IT departments of 20 international AI-powered organisations in Egypt in addition to 25 depth interviews, AI-based focus group and international forum.
Findings – Following PLS-SEM approach, results revealed that AIS affects positively and significantly KSQ and CHRM. CHRM affects positively and significantly KSQ and IIWB. KSQ affects positively and significantly OEP and IIWB. The significant positive direct AIS-OEP relationship was not supported yet the significant positive indirect relationship via KSQ was supported.
Originality/value – Empirically, it is the first research that assessed AIS-CHRM-KSQ relationship and its effect on IIWB and OEP of AI-powered businesses from 7 sectors of an emerging economy. Conceptually, the authors adopted an interdisciplinary approach while reflecting on the literature that studied AIS implementation in different business functions (production, operations and supply-chain management, human resources management, strategic management and marketing).
Practical implications – Strategic leaders and managers of different functional areas can benefit from the empirical findings of this study as well as from the examples of best AI-enhanced practices drawn from the literature.
Keywords – Artificial intelligence strategy, AI-powered business functions, Creativity-oriented HRM, Knowledge-sharing quality, Organisational effective performance, Innovative work behaviour, Operations management, Strategic management, Expert system, Machine learning, Forecasting.
https://youtu.be/vIOFu7kwU3s
... Forecasting the number of patient visits to hospitals has aroused an increasingly large interest from both theoretic and application perspectives (Yu et al. 2017). This can be attributed to the fact that forecasting the number of patient visits to hospitals is paramount in allocating human and material resources of hospitals ( Hadavandi et al. 2012). The Outpatient Department (OPD) is the window of the hospital external service from the hospital actual operation, and can experience increasing stress from year to year due to increasing patient volumes ( Luo et al. 2017). ...
This paper employs monthly time series data on outpatient visits at Silobela District Hospital (SDH) from January 2012 to December 2019, to predict healthcare demand (outpatient visits) using Artificial Neural Networks (ANNs). Residual analysis of the ANN model indicates that the employed model is adequate. This paper is the first of its kind in Zimbabwe and its primary contribution is finding that there is need for more prudent resource planning and allocation at SDH as warned by high numbers of projected outpatient visits over the period January 2020 to December 2021. The study managed to come up with a 3-fold policy recommendation envisaged to improve healthcare management at SDH.
... To test whether a significant difference in forecasting precision exists between LSTM and the baseline models, the relative error (RE) generated by each model was used to conduct a paired t-test (Hadavandi, Shavandi, Ghanbari, & Abbasian-Naghneh, 2012). RE can be denoted as (y t −ŷ t /y t ) × 100%, where y t and y t are the observed and forecasted values, respectively. ...
This study introduces the concept of long short-term memory (LSTM) network to handle complex time series forecasting problems in the tourism industry. To validate the efficiency of the developed method, we used the daily tourist flow and consumer search data of Jiuzhaigou, a popular tourist spot in China, from 8 October 2013 to 7 August 2017 as the experimental dataset for empirical analysis. According to the 150-day forecasting results, LSTM shows the best statistical performance in the training and test sets compared with its counterparts.
... The calculations of these statistical indicators are shown in Equations (9) - (12). A significant test of the difference in the predictive accuracy between LSTM and its benchmark models was performed using the Paired t test [36]. ...
Accurate forecasting of the hotel accommodation demands is extremely critical to the sustainable development of tourism-related industries. In view of the ever-increasing tourism data, this paper constructs a deep learning framework to handle the prediction problem in the hotel accommodation demands. Taking China’s Hainan province as an empirical example, the internet search index is used from August 2008 to May 2019 to forecast the overnight passenger flows for hotels accommodation in Hainan Province, China. Forecasting results indicate that compared to benchmark models, the constructed forecasting method can effectively simulate dynamic characteristics of the overnight passenger flows for the hotel accommodation and significantly improve the forecasting performance of the model. Forecasting results can provide necessary references for decision-making in tourism-related industries, and this forecasting framework can also be extended to other similar complex time series forecasting problems.
... In Brest et al. (2006), it is studied that the convergence in dynamic Differential Evolution is not straightforward because of random changes in control parameters. The convergence measures for Differential Evolution are studied in Feoktistov (2006). One of the measures is discussed here which depicts the convergence of the algorithm. ...
In this paper, a novel hybrid model for forecasting low dimensional numerical data is proposed which is named as ClusFuDE. The proposed method uses an improved automatic clustering approach for clustering the historical numerical data. Further fuzzy logical relationships are used to forecast the approximate values which are then defuzzified to calculate the exact forecasted values of the data. The fuzzy logical relationships are useful in modelling the fuzzy relations and help in forecasting the fuzzy time series data in a very simplified manner. The forecasted sub-optimal candidate solutions are optimized using Differential Evolution. The Differential Evolution method uses a dynamic differential crossover rate (í µí° ¶í µí± í µí±) for the í µí±th solution, for identifying and discarding suboptimal candidate solutions in early stages of the iterative run. This makes the method more suitable for iterative modification of candidate solutions by using differential mutation and crossover, and suitable for global search. The proposed method is applied for forecasting, the year wise enrollments of the University of Alabama, Lahi (crop) production, monthly amount of outpatients visit in a hospital, inventory demand and population of India from years 1930-2000 and the results are consistent. We have compared our method with the recent forecasting methods available in literature and the proposed method outperforms all the existing methods in the literature. The accuracy of the proposed method is computed by calculating the Mean square error (MSE) and Mean Absolute percentage error (MAPE). The proposed method provides the lowest MSE and MAPE when compared to all other methods available in the literature.
... -Particle swarm optimization and support vector machine that can be used for diagnosis of arrhythmia cordis [69], for cancer feature selection and classification [70], for disease detection in medical images processing [71], for ECG beat classification [72], -Neural networks and the decision tree that can be used for eye diseases diagnosis [73], for migraine diagnosis [74], -Genetic algorithms and fuzzy logic that can be used for developing systems to forecast outpatient visits in hospitals with high accuracy [75], for the breast cancer diagnosis problem [76], for designing diagnostic system for identifying disease of Chikungunya [77], heart disease prediction [78], -Fuzzy and K-means that can be used for designing diagnostic systems for thyroid disease [79], for effective detection of Parkinsons disease [80], for medical image segmentation [81]. ...
Healthcare systems are facing various challenges such as high healthcare costs, aging population, increased number of patients with chronic illnesses, dissatisfied patients, lack of medical specialists. Use of big data analytics and technologies is one of the ways to overcome problems and improve the current health systems. The main idea of this paper is to give a review of big data concept, summarize big data applications, and identify challenges in medical and healthcare with an overview of the current situation in Bosnia and Herzegovina.
... Hadavandi et al. [15] proposed a hybrid artificial intelligence model for outpatient visits forecasting in hospitals that used genetic fuzzy systems to construct an expert system for outpatient visits forecasting problems. The author reported that the proposed method was able to handle complex and nonlinear time ...
The Maldives is an island nation and the islands are scattered over 26 atolls. The government of Maldives is trying to improve health services in the country and improve the accessibility of services throughout the country at the peripheral levels. The healthcare industry collects a large amount of healthcare information, which contains several patterns, such as outbreaks of diseases. However, this data frequently goes unexploited. Accurate forecasting using this past data could help healthcare managers in taking appropriate decisions especially in implementing preventing measures. Due to the geographical nature of Maldives, it is difficult to implement preventive measures in case of an outbreak. There is no single approach to be used for health forecasting; thus, various methods have been used to specific health conditions or healthcare resources. Healthcare comprises of both complex linear and nonlinear patterns, which can affect the forecasting accuracy if only linear models or neural networks are used. In this research, a hybrid of the ARIMA model and Neural Network has been proposed to forecast healthcare data. A dataset comprising of 10 diseases including unique cases reported for each disease, between the years 2012 and 2016 have been used in this research. It was found that the proposed model performed well on 7 out of the 10 diseases.
... Hadavandi et al. [15] proposed a hybrid artificial intelligence model for outpatient visits forecasting in hospitals that used genetic fuzzy systems to construct an expert system for outpatient visits forecasting problems. The author reported that the proposed method was able to handle complex and nonlinear time ...
The Maldives is an island nation and the islands are scattered over 26 atolls. The government of Maldives is trying to improve health services in the country and improve the accessibility of services throughout the country at the peripheral levels. The healthcare industry collects a large amount of healthcare information, which contains several patterns, such as outbreaks of diseases. However, this data frequently goes unexploited. Accurate forecasting using this past data could help healthcare managers in taking appropriate decisions especially in implementing preventing measures. Due to the geographical nature of Maldives, it is difficult to implement preventive measures in case of an outbreak. There is no single approach to be used for health forecasting; thus, various methods have been used to specific health conditions or healthcare resources. Healthcare comprises of both complex linear and nonlinear patterns, which can affect the forecasting accuracy if only linear models or neural networks are used. In this research, a hybrid of the ARIMA model and Neural Network has been proposed to forecast healthcare data. A dataset comprising of 10 diseases including unique cases reported for each disease, between the years 2012 and 2016 have been used in this research. It was found that the proposed model performed well on 7 out of the 10 diseases.
... The prediction of admissions is one piece of larger equation in the using hospital census, patient acuity, disease burden, allocation of resources and general management to improve hospital performance and improve patient outcomes. Much of research on hospital management focuses on the emergence of demand predicting [7][8][9][10], forecasting of outpatient visits [11,12], inpatients discharge [13], and patient volume [14]. However, little published research is available regarding predicting the number of new admission inpatients. ...
Background
Hospital crowding is a rising problem, effective predicting and detecting managment can helpful to reduce crowding. Our team has successfully proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in the schistosomiasis and hand, foot, and mouth disease forecasting study. In this paper, our aim is to explore the application of the hybrid ARIMA-NARNN model to track the trends of the new admission inpatients, which provides a methodological basis for reducing crowding. Methods
We used the single seasonal ARIMA (SARIMA), NARNN and the hybrid SARIMA-NARNN model to fit and forecast the monthly and daily number of new admission inpatients. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the forecasting performance among the three models. The modeling time range of monthly data included was from January 2010 to June 2016, July to October 2016 as the corresponding testing data set. The daily modeling data set was from January 4 to September 4, 2016, while the testing time range included was from September 5 to October 2, 2016. ResultsFor the monthly data, the modeling RMSE and the testing RMSE, MAE and MAPE of SARIMA-NARNN model were less than those obtained from the single SARIMA or NARNN model, but the MAE and MAPE of modeling performance of SARIMA-NARNN model did not improve. For the daily data, all RMSE, MAE and MAPE of NARNN model were the lowest both in modeling stage and testing stage. Conclusions
Hybrid model does not necessarily outperform its constituents’ performances. It is worth attempting to explore the reliable model to forecast the number of new admission inpatients from different data.
... In reality, the most of industrial problems are too complicated to be solved with a simple prediction model. Therefore, various investigations were conducted to combine different types of prediction models to solve a specific problem [21,22]. A combination of prediction models and generation of an ensemble of experts would assist to model every single aspect of a problem and cover all behaviors of a system which leads to a better overall prediction and a high performance [23]. ...
In this article, a new ensemble based multi-agent system called “EMAS” is introduced for prediction of
problems in data mining. The EMAS is constructed using a four-layer multi-agent system architecture to
generate a data mining process based on the coordination of intelligent agents. The EMAS performance
is based on data preprocessing and prediction. The first layer is dedicated to clean and normalize data.
The second layer is designed for data preprocessing by using intelligent variable ranking to select the
most effective agents (select the most important input variables to model an output variable). In the
third layer, a negative correlation learning (NCL) algorithm is used to train a neural network ensemble
(NNE). Fourth layer is dedicated to do three different subtasks including; knowledge discovery, prediction
and data presentation. The ability of the EMAS is evaluated by using a robust coal database (3238
records) for prediction of Free Swelling Index (FSI) as an important problem in coke making industry, and
comparing the outcomes with the results of other conventional modeling methods Coal particles have
complex structures and EMAS can explore complicated relationships between their structural parameters
and select the most important ones for FSI modeling. The results show that the EMAS outperforms
all presented modeling methods; therefore, it can be considered as a suitable tool for prediction of problems.
Moreover, the results indicated that the EMAS can be further employed as a reliable tool to select
important variables, predict complicated problems, model, control, and optimize fuel consumption in
iron making plants and other energy facilities.
... The high efficiency of hospital management depends to some degree on appropriate allocation of material resources and proper physician and nurse staffing because of the limit of those resources and hospital budget pressure. Forecasting the number of patient visits to hospitals can be helpful in allocating limited human and material resources of hospitals (Hadavandi et al., 2012). For instance, forecasting of short-term hospital census may result in improvement of inpatient bed allocation and decrease in the incidence of overstaffing and understaffing (Littig et al., 2007). ...
Forecasting the number of patient visits to hospitals has aroused an increasingly large interest from both theoretic and application perspectives. To enhance the accuracy of forecasting hospital visits, this paper proposes a hybrid approach by coupling wavelet decomposition (WD) and artificial neural network (ANN) under the framework of "decomposition and ensemble". In this model, the WD is first employed to decompose the original monthly data of the number of patient visits to hospitals into several components and one residual term. Then, the ANN as a powerful prediction tool is implemented to fit each decomposed component and generate individual prediction results. Finally, all individual prediction values are fused into the final prediction output by simple addition method. For illustration and verification, four sets of monthly series data of the number of patient visits to hospitals are used as the sample data, and the results show that the proposed model can obtain significantly more accurate forecasting results than all considered popular forecasting techniques.
... The data had two features to be monitored, month of the year and the number of outpatient. Another similar study was carried out by Hadavandi et al. [39] were a hybrid model was built that combine genetic algorithm with fuzzy rule based learning to forecast outpatient visits. The data were collected from the department of internal medicine in a hospital in Taiwan and four big hospitals in Iran. ...
Business Intelligence and Analytics (BI&A) is the process of extracting and predicting business-critical insights from data. Traditional BI focused on data collection, extraction, and organization to enable efficient query processing for deriving insights from historical data. With the rise of big data and cloud computing, there are many challenges and opportunities for the BI. Especially with the growing number of data sources, traditional BI\&A are evolving to provide intelligence at different scales and perspectives - operational BI, situational BI, self-service BI. In this survey, we review the evolution of business intelligence systems in full scale from back-end architecture to and front-end applications. We focus on the changes in the back-end architecture that deals with the collection and organization of the data. We also review the changes in the front-end applications, where analytic services and visualization are the core components. Using a uses case from BI in Healthcare, which is one of the most complex enterprises, we show how BI\&A will play an important role beyond the traditional usage. The survey provides a holistic view of Business Intelligence and Analytics for anyone interested in getting a complete picture of the different pieces in the emerging next generation BI\&A solutions.
... To test for statistical significance, we implemented a paired t-test (two samples for mean) (Hadavandi, Shavandi, Ghanbari, & Abbasian-Naghneh, 2012) on the forecasting precision (or average relative error percentage) between BA-SVR and each its counterparts, using the following formula: ...
The precise prediction of tourism demand has long presented a challenge for both tourism professionals and academics. Tourist volume forecasting is a nonlinear problem, support vector regression (SVR) can approximate a nonlinear system with enough precision, but parameters tuning has always been an obstacle to developing SVR with good generalization potential. Furthermore, previous research mainly used historical observations of tourism demand as the inputs of SVR. This study introduces an approach that hybridizes SVR with the Bat algorithm (BA), namely BA-SVR, to forecast tourist volume by incorporating search engine data. In this model, BA is used to adjust the SVR parameters. To validate our proposed approach, tourist volume data for China’s Hainan province from August 2008 to October 2015 were used in conjunction with corresponding search engine data as numerical examples. The 12-month simulation forecasts indicate that the BA-SVR is an effective method that can outperform its traditional counterparts.
Purpose
Forecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch medical resources on time. Based on the background of standard hospital operation and Coronavirus disease (COVID-19) periods, this paper constructs a hybrid grey model to forecast the outpatient volume to provide foresight decision support for hospital decision-makers.
Design/methodology/approach
This paper proposes an improved hybrid grey model for two stages. In the non-COVID-19 stage, the Aquila Optimizer (AO) is selected to optimize the modeling parameters. Fourier correction is applied to revise the stochastic disturbance. In the COVID-19 stage, this model adds the COVID-19 impact factor to improve the grey model forecasting results based on the dummy variables. The cycle of the dummy variables modifies the COVID-19 factor.
Findings
This paper tests the hybrid grey model on a large Chinese hospital in Jiangsu. The fitting MAPE is 2.48%, and the RMSE is 16463.69 in the training group. The test MAPE is 1.91%, and the RMSE is 9354.93 in the test group. The results of both groups are better than those of the comparative models.
Originality/value
The two-stage hybrid grey model can solve traditional hospitals' seasonal outpatient volume forecasting and provide future policy formulation references for sudden large-scale epidemics.
This paper examines how big data analytics and AI improve hospital supply chain sustainability. Hospitals are recognizing the need for eco-friendly operations due to environmental issues and rising healthcare needs. It analyzes data from 68 UK hospitals using a conceptual model and partial least squares regression-based structural equation modeling. The research begins by examining hospital supply networks' environmental impact. Energy use, trash, and transportation emissions are major issues. It then explains how big data analytics and AI can transform these implications. This study prioritizes big data analytics for inventory management, demand forecasting, and procurement. Hospitals can reduce inventory, waste, and supply shortages using data-driven insights, saving money and the environment. AI also boosts hospital supply chain logistics and transportation efficiency, according to the study. Fuel consumption, carbon emissions, and delivery routes are optimized by AI. Predictive maintenance preserves medical equipment. In conclusion, hospital supply chains benefit greatly from big data analytics and AI. Hospitals can improve the healthcare business, reduce their environmental impact, and preserve resources for future generations. Healthcare leaders, politicians, and researchers seeking data-driven solutions for sustainable hospital supply chains gain valuable insights.
Integrating Artificial Intelligence (AI) technology in hospitals offers a unique opportunity to improve hospitals’ operating and financial performance. This study is among the first to investigate the determinants and subsequent performance outcomes associated with AI adoption. Using an extensive dataset encompassing 941 AI hospital-year observations and 941 non-AI hospital-year observations, we find that hospitals with a larger market share are great candidates to adopt AI. Furthermore, these hospitals can leverage AI technology to enhance various aspects of performance, including total outpatient revenue, total inpatient revenue, productivity, and occupancy. Importantly, we demonstrate that controlling for endogeneity is essential in assessing the performance outcomes of AI adoption. Our findings shed light on the determinants of AI adoption decisions in healthcare and underscore the manifold benefits AI technology brings to hospital operations and financial outcomes.
Web search queries become essential drivers to forecast air passenger demand for operational benefits. Scholars and marketing experts. Forecasting passenger demand is one of the most important marketing problems that experts frequently encounter, but there are very few studies in the literature using search queries. The main novelty of this study is to show that Destination Insight (DI) can be useful as an air passenger demand proxy in the UK. To prove this primary objective, this work uses several machine and deep learning multi layer perceptron (MLP) methods based on a big-data framework. The findings indicate that DI is a crucial predictor of the UK air passenger demand. Besides, various error metrics (e.g., RMSE, MAPE, MAD) were compared to find the best model in this study. Specifically, results indicate that MLP following feed forward neural networks works better for the UK air passenger market.
Demand forecasting is one of the important issues related to operations management in health sector. Forecasting patient volume in hospitals provides an important input regarding the correct planning of financial resources, human resources, and material resources. In this chapter, the authors first discuss forecasting patient volume in hospital services and then present a case study involving patient volume forecasting for a local hospital in Turkey. Different traditional statistical methods and machine learning methods are applied to both inpatient and outpatient demand from six polyclinics and a surgery room. Results show that damped trend exponential smoothing method outperforms other methods based on overall performance.
The achievement of the carbon peaking and carbon neutrality targets requires the adjustment of the energy structure, in which the dual-carbon progress of the power industry will directly affect the realization process of the goal. In such terms, an accurate demand forecast is imperative for the government and enterprises’ decision makers to develop an optimal strategy for electric energy planning work in advance. According to the data of the whole social electricity consumption in Jiangsu Province of China from 2015 to 2019, this paper uses the improved particle swarm optimization algorithm to calculate the fractional-order r of the FGM (1, 1) model and establishes a metabolic FGM (1, 1) model to predict the whole social electricity consumption in Jiangsu Province of China from 2020 to 2023. The results show that in the next few years the whole social electricity consumption in Jiangsu Province will show a growth trend, but the growth rate will slow down generally. It can be seen that the prediction accuracy of the metabolic FGM (1, 1) model is higher than that of the GM (1, 1) and FGM (1, 1) models. In addition, the paper analyzes the reasons for the changes in the whole society electricity consumption in Jiangsu Province of China and provides support for government decision making.
Increasing competition and adoption of revenue management practices in the hotel industry fuel the need for accurate forecasting to maximize profits and optimize operations. Considering the limitations of relevant research, this study focuses on the daily hotel demand with consideration of agglomeration effect, and proposes a novel deep learning-based model, namely, Deep Learning Model with Spatial and Temporal correlations. This model contributes to relevant research by introducing the agglomeration effect and integrating the attention mechanism and Bayesian optimization algorithm. Historical daily demand data of 210 hotels in Xiamen, China are used to verify the model performance. Results show that the proposed model is significantly better than the benchmarks. This study can help hotel managers improve revenue management through better matching potential demand to available capacity.
The volume of daily patient arrivals at Emergency Departments (EDs) is unpredictable and is a significant reason of ED crowding in hospitals worldwide. Timely forecast of patients arriving at ED can help the hospital management in early planning and avoiding of overcrowding. Many different ED patient arrivals forecasting models have previously been proposed by using time series analysis methods. Even though the time series methods such as Linear and Logistic Regression, Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Exponential Smoothing (ES), and Artificial Neural Network (ANN) have been explored extensively for the ED forecasting model development, the few significant limitations of these methods associated in the analysis of time series data make the models inadequate in many practical situations. Therefore, in this paper, ML-based Random Forest (RF) regressor, and DNN-based Long Short-term Memory (LSTM) and Convolutional Neural network (CNN) methods, which have not been explored to the same extent as the other time series techniques, are implemented by incorporating meteorological and calendar parameters for the development of forecasting models. The performances of developed three models in forecasting the ED patient arrivals are evaluated. Among the three models, CNN outperformed for short-term (3 days in advance) patient arrivals prediction with MAPE of 9.24% and LSTM performed better for moderate-term (7 days in advance) patient arrivals prediction with MAPE of 8.91% using weather forecast information. Whereas, LSTM model outperformed with MAPE of 8.04% compared to 9.53% by CNN and 10.10% by RF model for current day prediction of patient arrivals using 3 days past weather information. Thus, for short-term ED patient arrivals forecasting, DNN-based model performed better compared to RF regressor ML-based model.
Probability‐based models are developed using information from a variety of datasets to predict daily surgical volumes weeks in advance. The quest was motivated by the need to make real‐time adjustments to staff capacity and reallocation of the operating room block time based on predicted future demand. We test the notion that more data always leads to better predictions. Four probabilistic prediction models are presented, each parameterized based on real data and information from different sources. We hypothesize that the accuracy of the prediction improves by incorporating additional information. Models are tested for a surgical service at a large hospital using data of 20 months (January 19, 2015–August 31, 2016). We find that incorporating additional information may not improve prediction accuracy if that information is prone to data errors. However, deploying analytical data treatment to ameliorate these errors leads to better predictions. We also compare the predictive ability of the probability‐based models to neural network–based models and find that the neural network models do not outperform simpler models. Managers should critically review the accuracy of the data used in decision‐making. While a greater amount of inherently error‐free information is the best, analytics can enhance the utility of error‐prone data.
In this study, linear regression and neural network-based hybrid models are developed for modelling the daily ED visits. Month and week of the year, day of the week, and period of the day, are used as input variables of the linear regression model. Generated forecasts and the residuals are further processed through a multilayer perceptron model to improve the performance of forecasting. To obtain forecasts for daily number of patient visits, aggregation is used where the obtained periodical forecasts are summed up. By comparing the performances of models in generating periodical and daily forecasts, this chapter not only shows that hybrid model improves the forecasting performance significantly, but also aggregation fits well in practice.
Amaç – Kıt kaynakların etkili ve verimli kullanılması için uygun yöntemler kullanılarak doğru
tahmin yapılması gerekir. Sağlıkta kaynakların yokluğunun telafisi yoktur. Araştırmada Gazi
Üniversitesi yoğun bakım ünitesinde cerrahi gazlı bez tüketiminde kantitatif tahmin yöntemleri
kullanılarak gelecek dönemler için ne kadar tüketileceğinin tahmini yapılmıştır.
Yöntem – Araştırmada talep tahmin yöntemlerinden hareketli ortalama, üstel düzeltme, HoltWinters tahmin yöntemleri kullanılmıştır. Tahmin sonuçlarının doğrululuğunun ölçümünde
ortalama mutlak hata yüzdesi (Mean Absolute Percent Eror-MAPE) ve ortalama mutlak hata (Mean
Absolute Deviation-MAD) kullanılmıştır.
Bulgular – Yöntemler arası karşılaştırma sonucu hareketli ortalama yönteminde hata oranı en az
bulunmuştur.
Tartışma – Hastane giderlerinde tıbbi malzemelerin maliyetleri fazla olduğu için doğru zamanda,
doğru miktarda malzeme tahmini yapılarak hastane karlılığı artırılıp, israf önlenebilir.
Purpose – For effective and efficient use of scarce resources, by using appropriate methods is
required accurate estimation. There is no compensation for the lack of resources in health. In the
study, by using quantitative estimation methods in surgical gauze consumption in the intensive care
unit of Gazi University were estimated how much it will be consumed for future periods.
Design/methodology/approach – In this study, moving average, exponential smoothing, Winters
Holt- and linear regression forecasting methods that are demand estimate have been used. Mean
Absolute Percent Error(MAPE) and Mean Absolute Deviation (MAD) have been used for the
measurement of the accuracy of forecast results.
Findings – As a result of the comparison between methods, the error rate was found to be the least
in the moving average method.
Discussion – Since the cost of medical equipment is high in hospital expenses, the profitability of
hospital can be increased and waste can be prevented by estimating the right amount of material at
the right time.
Background:
The uncertainty and ambiguity of not knowing how many patients will be discharged impact patient throughput in hospitals, causing concerns for responding to demand for admissions. Understanding the potential number of patients to be discharged can support caregivers, ability to concentrate on the range of interactions that patients require to ensure early discharge. Accurate forecasting of patients expected to be discharged by noon is beneficial in accommodating patients who need services and in achieving sustainable patient satisfaction.
Method:
Models to predict patient discharge before noon (DBN) were formulated using Holt's double exponential smoothing and Box-Jenkins' methods with the aim of achieving minimal errors in each model. The models are applied to 24 months of weekly patient discharge historic data in a medical observation unit and a short-stay clinical unit of a health care hospital system located on the East Coast of United States.
Results:
DBN prediction outcomes were more accurate when applying Box-Jenkins' method than Holt's method. Analysis revealed that the model of ARIMA(3,1,2) is most suitable for forecasting. Upon the outcomes of forecast error metrics, the study identifies the mean absolute percent error for the ARIMA model is 14%.
Conclusion:
Box-Jenkins forecasting performance is superior in predicting DBN with the least forecast error. Predicted values are significant to decision-making interventions aimed at taking new patients, improving quality patient care, and meeting patient throughput performance goals.
Background:
Patient safety culture (PSC) as a main component of the organizational culture plays a key role in providing safe, effective and economic cares and services in healthcare organizations. PSC provides a way to assist hospitals in order to improve patient safety and prevent medical errors.
Objective:
The present study aimed to measure PSC and healthcare professionals' attitude towards voluntary reporting of adverse events in two hospitals in Iran and to develop a hybrid intelligent approach for modeling PSC grades.
Methods:
The Hospital Survey on Patient Safety Culture (HSOPSC) questionnaire and a two-part questionnaire were used for examining the PSC and healthcare professionals' attitude towards voluntary reporting of adverse events, respectively. Principal component analysis (PCA) was applied to extract of the main components in the HSOPSC questionnaire and to construct 12 dimensions of safety culture. The overall grade of patient safety culture was modeled using adaptive neuro-fuzzy inference systems (ANFIS) as a classification problem.
Results:
Almost half of the participants have experienced a medical error and adverse events. The PSC grade was acceptable from the point of view of 55.5% and 50% of participants in hospital No.1 and hospital No.2, respectively. The overall accuracy of ANFIS in modeling overall grades of patient safety culture in both study hospitals was 0.84. Of those individuals gave an acceptable grade on patient safety culture in both study hospitals, more than 50% believed that all medical errors and near misses should be reported.
Conclusions:
The ANFIS algorithm was proposed for modeling and predicting of PSC for healthcare organizations. The results confirm the capability of the proposed model to predict patient safety grades in healthcare settings.
z Bu araştırmanın amacı Süleyman Demirel Üniversitesi Hastanesinin serum seti tüketiminin kantitatif tahmin yöntemleri ile analiz edilmesi ve en uygun tahmin modelinin belirlenerek gelecek dönemlere ait serum seti tüketimi tahmininin yapılmasıdır. Araştırmada talep tahmin yöntemlerinden hareketli ortalama, üstel düzeltme, Holt-Winters ve doğrusal regresyon tahmin yöntemleri kullanılmıştır. Tahmin sonuçlarının doğrululuğunun ölçümünde ortalama mutlak hata yüzdesi (Mean Absolute Percent Eror-MAPE) ve ortalama mutlak hata (Mean Absolute Deviation-MAD) kullanılmıştır. Ayrıca araştırmada serum seti tüketiminde en iyi tahmini veren yöntemi tespit edebilmek için yöntemler arası karşılaştırmalar yapılmıştır. Araştırma sonucuna göre en düşük MAPE değeri 5 ve en düşük MAD değeri 971 olarak Holt-Winter'ın toplamsal tahmin yönteminde gerçekleşmiştir. Bu yönteme göre yapılan analizde hastanenin gelecek 12 aylık dönemde serum seti tüketiminin 335.556 adet olabileceği tahmin edilmiştir. Hastane yöneticileri ilaç ve tıbbi malzeme ihtiyaç tahmini yaparken kalitatif tahmin yöntemleri yanında mutlaka kantitatif tahmin yöntemlerinden de yararlanmalıdır. Böylece tıbbı malzeme ve ilaç ihtiyaçları daha doğru tahmin edilerek hastanenin verimliliği, kârlılığı, finansal performansı ve sürdürülebilirliği üzerinde olumlu etki sağlayabilecektir. Abstract The aim of this study is to analyse consumption of serum set by quantitative estimating method at Suleyman Demirel University Hospital and to estimate the consumption of serum set by determining the most appropriate forecasting model. In this study, moving average, exponential smoothing, Winters Holt-and linear regression forecasting methods that are demand estimate have been used. Mean Absolute Percent Error(MAPE) and Mean Absolute Deviation (MAD) have been used for the measurement of the accuracy of forecast results. Also, in the study comparisons have been made among methods in order to determine the method that estimates best at serum set consumption. According to research results, the lowest MAPE is value of 5 and the lowest MAD is value of 971 as occurred in Holt-Winter's additive method.According to this method and the analysis, it has been estimated that the next 12 months serum set consumption could be of 335.556 units at the hospital. When hospital managers estimate the need of medicines and medical supplies, they must certainly benefit from the quantitative forecasting methods besides qualitative forecasting methods. Thus, by more accurately estimating medical materials and medication needs, it is going to be positive impact on the hospital's efficiency, profitability, financial performance and sustainability.
Marketing-oriented firms are especially concerned with modeling con- sumer behavior to improve their information and aid their decision processes on markets. For this purpose, marketing experts use complex models and apply statistical methodologies to infer conclusions from data. Recently, the application of machine learning has been detected as a promising approach to complement these classical techniques of analysis. In this paper, we follow this idea and propose a system, addressed as Fuzzy-CSar, to extract fuzzy association rules from certain consumption problem analyzed. But, as a dif- ferentiating sign of identity from other methods, Fuzzy-CSar does not assume any aprioristic causality (so model) within the variables forming the consumer database. Instead, the system is responsible for extracting the strongest asso- ciations among variables, and so, the structure of the problem. Fuzzy-CSar is applied to a real-world marketing problem and the results are compared with those obtained by a multi-objective genetic fuzzy system expressly designed for this marketing problem. The results show the advantages of evolving fuzzy association rules and the competitiveness of Fuzzy-CSar in general.
Fuzzy Rule-Based Systems Evolutionary Computation Introduction to Genetic Fuzzy Systems Genetic Tuning Processes Learning with Genetic Algorithms Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach Genetic Fuzzy Rule-Based Systems Based on the lterative Rule Learning Approach Other Genetic Fuzzy Rule-Based System Other Kinds of Evolutionary Fuzzy Systems Applications.
This paper shows part of a larger interdisciplinary research focused on developing artificial intelligence-based analytical tools to aid the marketing managers' decisions on consumer markets. In particular, here it is presented and tested a knowledge discovery methodology based on genetic-fuzzy systems - a Soft Computing (SC) method that jointly makes use of fuzzy logic and genetic algorithms - to be applied in marketing modelling. Its characteristics are very coherent with the requirements that marketing managers currently demand to market analytical methods. Specifically, it has been paid attention to illustrate, in detail, how this proposed (Knowledge Discovery in Databases) KDD method performs with an empirical application to a Web-based trust consumer model.
Accurate forecasts of tourist arrivals and study of the tourist arrival patterns are essential for the tourism-related industries to formulate efficient and effective strategies on maintaining and boosting tourism industry in a country. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. This study presents a hybrid artificial intelligence (AI) model to develop a Mamdani-type fuzzy rule-based system to forecast tourist arrivals with high accuracy. The hybrid model uses genetic algorithm for learning rule base and tuning data base of fuzzy system. Actually it extracts useful information patterns with a descriptive rule induction approach based on Genetic Fuzzy Systems (GFS). This is the first study on using a GFS with the ability of learning rule base and tuning data base of fuzzy system for tourist arrival forecasting problem. Evaluation of the proposed approach will be carried out by applying it to a case study of tourist arrivals to Taiwan and results will be compared with other studies which have used the same data set. Results show that the proposed approach has high accuracy, so it can be considered as a suitable tool for tourism arrival forecasting problems.
Within the field of linguistic fuzzy modeling with fuzzy rule-based sys-tems, the automatic derivation of the knowledge base from numerical data is an important task. In this contribution, we propose a new ap-proach to automatically learn the whole knowledge base, combining two different strategies for rules derivation and fuzzy partitions defi-nition, working cooperatively in or-der to obtain accurate and inter-pretable models.
Cluster analysis, the determination of natural subgroups in a data set, is an important statistical methodology that is used in many contexts. A major problem with hierarchical clustering methods used today is the tendency for classification errors to occur when the empirical data departs from the ideal conditions of compact isolated clusters. Many empirical data sets have structural imperfections that confound the identification of clusters. We use a Self Organizing Map (SOM) neural network clustering methodology and demonstrate that it is superior to the hierarchical clustering methods. The performance of the neural network and seven hierarchical clustering methods is tested on 252 data sets with various levels of imperfections that include data dispersion, outliers, irrelevant variables, and nonuniform cluster densities. The superior accuracy and robustness of the neural network can improve the effectiveness of decisions and research based on clustering messy empirical data.
Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model.
Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.
This paper presents an overview of the GFSs, showing the use of the GAs in the construction of the fuzzy logic controllers knowledge bases comprising the known knowledge about the controlled system. To achieve that, this paper is divided into 4 sections the first being this introduction. The section 2 introduces the fuzzy systems with a special attention to FLCs, while section 3 presents the GFSs. Some final remarks are made in section 4. cbook 2/9/1997 17:36---PAGE PROOFS for John Wiley & Sons Ltd (jwbook.sty v3.0, 12-1-1995) A GENERAL STUDY ON GENETIC FUZZY SYSTEMS 3
The self-organized map, an architecture suggested for artificial
neural networks, is explained by presenting simulation experiments and
practical applications. The self-organizing map has the property of
effectively creating spatially organized internal representations of
various features of input signals and their abstractions. One result of
this is that the self-organization process can discover semantic
relationships in sentences. Brain maps, semantic maps, and early work on
competitive learning are reviewed. The self-organizing map algorithm (an
algorithm which order responses spatially) is reviewed, focusing on best
matching cell selection and adaptation of the weight vectors.
Suggestions for applying the self-organizing map algorithm,
demonstrations of the ordering process, and an example of hierarchical
clustering of data are presented. Fine tuning the map by learning vector
quantization is addressed. The use of self-organized maps in practical
speech recognition and a simulation experiment on semantic mapping are
discussed
This book focuses on various techniques of computational intelligence, both single ones and those which form hybrid methods. Those techniques are today commonly applied issues of artificial intelligence, e.g. to process speech and natural language, build expert systems and robots. The first part of the book presents methods of knowledge representation using different techniques, namely the rough sets, type-1 fuzzy sets and type-2 fuzzy sets. Next various neural network architectures are presented and their learning algorithms are derived. Moreover, the family of evolutionary algorithms is discussed, in particular the classical genetic algorithm, evolutionary strategies and genetic programming, including connections between these techniques and neural networks and fuzzy systems. In the last part of the book, various methods of data partitioning and algorithms of automatic data clustering are given and new neuro-fuzzy architectures are studied and compared. This well-organized modern approach to methods and techniques of intelligent calculations includes examples and exercises in each chapter and a preface by Jacek Zurada, president of IEEE Computational Intelligence Society (2004-05).
Fuzzy clustering is now a mature and vibrant area of research with highly innovative advanced applications. Encapsulating this through presenting a careful selection of research contributions, this book addresses timely and relevant concepts and methods, whilst identifying major challenges and recent developments in the area. Split into five clear sections, Fundamentals, Visualization, Algorithms and Computational Aspects, Real-Time and Dynamic Clustering, and Applications and Case Studies, the book covers a wealth of novel, original and fully updated material, and in particular offers: a focus on the algorithmic and computational augmentations of fuzzy clustering and its effectiveness in handling high dimensional problems, distributed problem solving and uncertainty management. presentations of the important and relevant phases of cluster design, including the role of information granules, fuzzy sets in the realization of human-centricity facet of data analysis, as well as system modelling demonstrations of how the results facilitate further detailed development of models, and enhance interpretation aspects a carefully organized illustrative series of applications and case studies in which fuzzy clustering plays a pivotal role This book will be of key interest to engineers associated with fuzzy control, bioinformatics, data mining, image processing, and pattern recognition, while computer engineers, students and researchers, in most engineering disciplines, will find this an invaluable resource and research tool.
Cluster analysis is a collective term covering a wide variety of techniques for delineating natural groups or clusters in data sets. This book integrates the necessary elements of data analysis, cluster analysis, and computer implementation to cover the complete sequence of steps from raw data to the finished analysis. The author develops a conceptual and philosophical basis for using cluster analysis as a tool of discovery and applies it systematically throughout the book. He provides a comprehensive discussion of variables, scales, and measures of association that establishes a sound basis for constructing an operational definition of similarity tailored to the needs of any particular operational definition of similarity tailored to the needs of any particular problem, and devotes special attention to the problems of analyzing data sets containing mixtures of nominal, ordinal, and interval variables. (Author)
From the Publisher:Foundations of Neuro-Fuzzy Systems reflects the current trend in intelligent systems research towards the integration of neural networks and fuzzy technology. The authors demonstrate how a combination of both techniques enhances the performance of control, decision-making and data analysis systems. Smarter and more applicable structures result from marrying the learning capability of the neural network with the transparency and interpretability of the rule-based fuzzy system. Foundations of Neuro-Fuzzy Systems highlights the advantages of integration making it a valuable resource for graduate students and researchers in control engineering, computer science and applied mathematics. The authors' informed analysis of practical neuro-fuzzy applications will be an asset to industrial practitioners using fuzzy technology and neural networks for control systems, data analysis and optimization tasks.
Nowadays because of the complicated nature of making decision in stock market and making real-time strategy for buying and selling stock via portfolio selection and maintenance, many research papers has involved stock price prediction issue. Low accuracy resulted by models may increase trade cost such as commission cost in more sequenced buy and sell signals because of insignificant alarms and otherwise bad diagnosis in price trend do not satisfy trader’s expectation and may involved him/her in irrecoverable cost. Therefore, in this paper, Neuro-Fuzzy Inference System adopted on a Takagi–Sugeno–Kang (TSK) type Fuzzy Rule Based System is developed for stock price prediction. The TSK fuzzy model applies the technical index as the input variables and the consequent part is a linear combination of the input variables. Fuzzy C-Mean clustering implemented for identifying number of rules. Initial membership function of the premise part approximately defined as Gaussian function. TSK parameters tuned by Adaptive Nero-Fuzzy Inference System (ANFIS). Proposed model is tested on the Tehran Stock Exchange Indexes (TEPIX). This index with high accuracy near by 97.8% has successfully forecasted with several experimental tests from different sectors.
The synthesis of genetics-based machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multi-variate non-linear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to genetics-based machine learning of fuzzy controllers, called a Pittsburgh Fuzzy Classifier System # 1 (P-FCS1) is proposed. P-FCS1 is based on the Pittsburgh model of learning classifier systems and employs variable length rule-sets and simultaneously evolves fuzzy set membership functions and relations. A new crossover operator which respects the functional linkage between fuzzy rules with overlapping input fuzzy set membership functions is introduced. Experimental results using P-FCS 1 are reported and compared with other published results. Application of P-FCS1 to a distributed control problem (dynamic routing in computer networks) is also described and experimental results are presented.
In financial time series forecasting, the problem that we often encounter is how to increase the prediction accuracy as possible using the financial data with noise. In this study, we discuss the use of supervised neural networks as a meta-learning technique to design a financial time series forecasting system to solve this problem. In this system, some data sampling techniques are first used to generate different training subsets from the original datasets. In terms of these different training subsets, different neural networks with different initial conditions or training algorithms are then trained to formulate different prediction models, i.e., base models. Subsequently, to improve the efficiency of predictions of metamodeling, the principal component analysis (PCA) technique is used as a pruning tool to generate an optimal set of base models. Finally, a neural-network-based nonlinear metamodel can be produced by learning from the selected base models, so as to improve the prediction accuracy. For illustration and verification purposes, the proposed metamodel is conducted on four typical financial time series. Empirical results obtained reveal that the proposed neural-network-based nonlinear metamodeling technique is a very promising approach to financial time series forecasting.
This paper presents an adaptive network based fuzzy inference system (ANFIS)–auto regression (AR)–analysis of variance (ANOVA) algorithm to improve oil consumption estimation and policy making. ANFIS algorithm is developed by different data preprocessing methods and the efficiency of ANFIS is examined against auto regression (AR) in Canada, United Kingdom and South Korea. For this purpose, mean absolute percentage error (MAPE) is used to show the efficiency of ANFIS. The algorithm for calculating ANFIS performance is based on its closed and open simulation abilities. Moreover, it is concluded that ANFIS provides better results than AR in Canada, United Kingdom and South Korea. This is unlike previous expectations that auto regression always provides better estimation for oil consumption estimation. In addition, ANOVA is used to identify policy making strategies with respect to oil consumption. This is the first study that introduces an integrated ANFIS–AR–ANOVA algorithm with preprocessing and post processing modules for improvement of oil consumption estimation in industrialized countries.
Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems.
Hourly energy prices in a competitive electricity market are volatile. Forecast of energy price is key information to help producers and purchasers involved in electricity market to prepare their corresponding bidding strategies so as to maximize their profits. It is difficult to forecast all the hourly prices with only one model for different behaviors of different hourly prices. Neither will it get excellent results with 24 different models to forecast the 24 hourly prices respectively, for there are always not sufficient data to train the models, especially the peak price in summer. This paper proposes a novel technique to forecast day-ahead electricity prices based on Self-Organizing Map neural network (SOM) and Support Vector Machine (SVM) models. SOM is used to cluster the data automatically according to their similarity to resolve the problem of insufficient training data. SVM models for regression are built on the categories clustered by SOM separately. Parameters of the SVM models are chosen by Particle Swarm Optimization (PSO) algorithm automatically to avoid the arbitrary parameters decision of the tester, improving the forecasting accuracy. The comparison suggests that SOM–SVM–PSO has considerable value in forecasting day-ahead price in Pennsylvania–New Jersey–Maryland (PJM) market, especially for summer peak prices.
In order to obtain a better control of market trend and profit for the company, timely identification of sales is very important for businesses. Upward and downward trends in sales signify new market trends and understanding of sales trends is important for marketing as well as for customer retention. This research develops a hybrid model by integrating K-mean cluster and fuzzy neural network (KFNN) to forecast the future sales of a printed circuit board factory. Based on the K-mean clustering technique, the historical data can be classified into different clusters. The accuracy of the forecasted model can be further improved by referring the new data to be forecasted from a more focused region, i.e., a smaller region after clustering. Numerical data of various affecting factors and actual demand of the past 5 years of the printed circuit board (PCB) factory are collected and input into the hybrid model for future monthly sales forecasted. The experimental results derived from the proposed model show the effectiveness of the hybrid model when compared with other approaches.
Accurate tourist demand forecasting systems are essential in tourism planning, particularly in tourism-based countries. Artificial neural networks are attracting attention to forecast tourism demands due to their general non-linear mapping capabilities. Unlike most conventional neural network models, which are based on the empirical risk minimization principle, support vector regression (SVR) applies the structural risk minimization principle to minimize an upper bound of the generalization error, rather than minimizing the training error. This investigation presents a SVR model with chaotic genetic algorithm (CGA), namely SVRCGA, to forecast the tourism demands. With the increase of the complexity and the larger problem scale of tourism demands, genetic algorithms (GAs) are often faced with the problems of premature convergence, slowly reaching the global optimal solution or trapping into a local optimum. The proposed CGA based on the chaos optimization algorithm and GAs, which employs internal randomness of chaos iterations, is used to overcome premature local optimum in determining three parameters of a SVR model. Empirical results that involve tourism demands data from existed paper reveal the proposed SVRCGA model outperforms other approaches in the literature.
In this paper, a new forecast strategy is proposed for day-ahead prediction of electricity prices, which are so valuable for both producers and consumers in the new competitive electric power markets. However, electricity price has a nonlinear, volatile and time dependent behavior owning many outliers. Our forecast strategy is composed of a preprocessor and a Hybrid Neuro-Evolutionary System (HNES). Preprocessor selects the input features of the HNES according to MRMR (Maximum Relevance Minimum Redundancy) principal. The HNES is composed of three Neural Networks (NN) and Evolutionary Algorithms (EA) in a cascaded structure with a new data flow among its building blocks. The effectiveness of the whole proposed method is demonstrated by means of real data of the PJM and Spanish electricity markets. Also, the proposed price forecast strategy is compared with some of the most recent techniques in the area.
This paper presents a new method to forecast university enrollments based on fuzzy time series. The data of historical enrollments of the University of Alabama shown in Song and Chissom (1993a, 1994) are adopted to illustrate the forecasting process of the proposed method. The robustness of the proposed method is also tested. The proposed method not only can make good forecasts of the university enrollments, but also can make robust forecasts when the historical data are not accurate. The proposed method is more efficient than the one presented in Song and Chissom (1993a) due to the fact that the proposed method uses simplified arithmetic operations rather than the complicated max-min composition operations presented in Song and Chissom (1993a).
In its introduction this paper discusses why marketing professionals do not make satisfactory use of the marketing models posed by academics in their studies. The main body of this research is characterised by the proposal of a brand new and complete methodology for knowledge discovery in databases (KDD), to be applied in marketing causal modelling and with utilities to be used as a marketing management decision support tool. Such methodology is based on Genetic Fuzzy Systems, a specific hybridization of artificial intelligence methods, highly suited to the research problem we face. The use of KDD methodologies based on intelligent systems like this can be considered as an avant-garde evolution, exponent nowadays of the so-called knowledge-based Marketing Management Support Systems; we name them as Marketing Intelligent Systems. The most important questions to the KDD process–i.e. pre-processing; machine learning and post-processing–are discussed in depth and solved. After its theoretical presentation, we empirically experiment with it, using a consumer behaviour model of reference. In this part of the paper, we try to offer an overall perspective of how it works. The valuation of its performance and utility is very positive.
In this paper, a Takagi–Sugeno–Kang (TSK) type Fuzzy Rule Based System is developed for stock price prediction. The TSK fuzzy model applies the technical index as the input variables and the consequent part is a linear combination of the input variables. The fuzzy rule based model is tested on the Taiwan Electronic Shares from the Taiwan Stock Exchange (TSE). Through the intensive experimental tests, the model has successfully forecasted the price variation for stocks from different sectors with accuracy close to 97.6% in TSE index and 98.08% in MediaTek. The results are very encouraging and can be implemented in a real-time trading system for stock price prediction during the trading period.
This research develops a hybrid model by integrating Self Organization Map (SOM) neural network, Genetic Algorithms (GA) and Fuzzy Rule Base (FRB) to forecast the future sales of a printed circuit board factory. This hybrid model encompasses two novel concepts: (1) clustering an FRB into different clusters, thus the interaction between fuzzy rules is reduced and a more accurate prediction model can be established, and (2) evolving an FRB by optimizing the number of fuzzy terms of the input and output variables, thus the prediction accuracy of the FRB is further improved. Numerical data of various affecting factors and actual demand of the past 5 years of the printed circuit board (PCB) factory are collected and inputted into the hybrid model for future monthly sales forecasting. Experimental results show the effectiveness of the hybrid model when comparing it with other approaches. However, the theoretical development of the validity of clustering an FRB into sub clusters remains to be proven.
Time series models have been applied to forecast stock index movements and make reasonably accurate predictions. There are, however, two major drawbacks of conventional time series models: (1) most conventional time series models use only one variable to forecast; and (2) the rules that are mined from artificial neural networks (ANNs) are not easily understandable. To solve these problems and enhance the forecasting performance of fuzzy time series models, this paper proposes a hybrid adaptive network-based fuzzy inference system (ANFIS) model that is based on AR and volatility to forecast stock price problems of the Taiwan stock exchange capitalization weighted stock index (TAIEX). To evaluate forecasting performance, the proposed model is compared with Chen's model and Yu's model. Our results indicate that the proposed model is superior to other methods with regard to root mean squared error (RMSE).
Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) models are one of the most important time series models used in financial market forecasting over the past three decades. Recent research activities in time series forecasting indicate that two basic limitations detract from their popularity for financial time series forecasting: (a) ARIMA models assume that future values of a time series have a linear relationship with current and past values as well as with white noise, so approximations by ARIMA models may not be adequate for complex nonlinear problems; and (b) ARIMA models require a large amount of historical data in order to produce accurate results. Both theoretical and empirical findings have suggested that integration of different models can be an effective method of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, ARIMA models are integrated with Artificial Neural Networks (ANNs) and Fuzzy logic in order to overcome the linear and data limitations of ARIMA models, thus obtaining more accurate results. Empirical results of financial markets forecasting indicate that the hybrid models exhibit effectively improved forecasting accuracy so that the model proposed can be used as an alternative to financial market forecasting tools.
This study proposes weighted models to tackle two issues in fuzzy time series forecasting, namely, recurrence and weighting. It is argued that recurrent fuzzy relationships, which were simply ignored in previous studies, should be considered in forecasting. It is also recommended that different weights be assigned to various fuzzy relationships. In previous studies, these fuzzy relationships were treated as if they were equally important, which might not have properly reflected the importance of each individual fuzzy relationship in forecasting. The weighted models are compared with the local regression models in which weight functions also play an important role. Both models are different by nature, but certain theoretical backgrounds in local regression models are adopted. By using the Taiwan stock index as the forecasting target, the empirical results show that the weighted model outperforms one of the conventional fuzzy time series models.
An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.
The key to successful stock market forecasting is achieving best results with minimum required input data. Given stock market model uncertainty, soft computing techniques are viable candidates to capture stock market nonlinear relations returning significant forecasting results with not necessarily prior knowledge of input data statistical distributions. This paper surveys more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets. Classifications are made in terms of input data, forecasting methodology, performance evaluation and performance measures used. Through the surveyed papers, it is shown that soft computing techniques are widely accepted to studying and evaluating stock market behavior.
Forecasting the number of outpatient visits can help the expert of healthcare administration to make a strategic decision. If the number of outpatient visits could be forecast accurately, it would provide the administrators of healthcare with a basis to manage hospitals effectively, to make up a schedule for human resources and finances reasonably, and distribute hospital material resources suitably. This paper proposes a new fuzzy time series method, which is based on weighted-transitional matrix, also proposes two new forecasting methods: the Expectation Method and the Grade-Selection Method. From the verification and results, the proposed methods exhibit a relatively lower error rate in comparison to the listing methods, and could be more stable in facing the ever-changing future trends. The characteristics of the proposed methods could overcome the drawback of the insufficient handling of information to construct a forecasting rule in previous researches.
This paper tracks access, utilization, and costs of mental health care for a private employer over nine years during which mental health benefits were carved out of the medical plan and managed care was introduced. Prior to the carve-out, mental health costs increased by around 30 percent annually; in the first year after the change, costs dropped by more than 40 percent; in the six follow-up years, costs continued to decline slowly. This cost reduction was not attributable to decreased initial access, as the number of persons using any mental health care increased following the change. Instead, the cost reduction was the result of (1) fewer outpatient sessions per user, (2) reduced probability of an inpatient admission, (3) reduced length-of-stay for an inpatient episode, and (4) substantially lower costs per unit of service.
Two univariate time-series analysis methods have been used to model and forecast the monthly patient volume at the family and community medicine primary health care clinic of King Faisal University, Al-Khobar, Saudi Arabia. Models were based on nine years of data and forecasts made for 2 years. The optimum ARIMA model selected is an autoregressive model of the fourth order operating on the data after differencing twice at the nonseasonal level and once at the seasonal level. It gives mean and maximum absolute percentage errors of 1.86 and 4.23%, respectively, over the forecasting interval. A much simpler method based on extrapolating the growth curve of the annual means of the patient volume using a polynomial fit gives the better figures of 0.55 and 1.17%, respectively. This is due to the fairly regular nature of the data and the lack of strong random components that require ARIMA processes for modeling.