Generative Adversarial Networks Time Series Models to Forecast Medicine Daily Sales in Hospital

Husein, Amir Mahmud; Arsyal, Muhammad; Sinaga, Sutrisno; Syahputa, Hendra

doi:10.33395/sinkron.v3i2.10044

Cited by 14 publications

(8 citation statements)

References 29 publications

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“…Amir Mahmud Husein [12] inscribed the research dimensions of GANs for the assorted domains related to the stock market, business analytics, financial predictions, and many others. The work is having effective predictions and outcomes with the implementation approaches towards the usage patterns of drugs and the predictive mining scenarios.…”

Section: Review Of Existing Workmentioning

confidence: 99%

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

Kumar

Harshavardhan²,

Bhukya

et al. 2020

Materials Today: Proceedings

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The domain of medical diagnosis and predictive analytics is one of the key domains of research with enormous dimensions whereby the diseases of different types can be predicted. Nowadays, there is a huge panic of impact and rapid mutation of the COVID-19 virus impression. The world is getting affected by this virus to a huge extent and there is no vaccine developed so far. India is also having more than 10,000 patients with than 300 deceased. The global human community is having around 20 lacs of Coronavirus patients. The Generative Adversarial Network (GAN) is the contemporary high-performance approach in which the use of advanced neural networks is done for the cavernous analytics of the images and multimedia data. In this research work, the analytics of key points from medical images of the COVID-19 dataset is to be presented using which the diagnosis and predictions can be done for the patients. The GANs are used for the generation, transformation as well as presentation of the dataset and key points using advanced deep learning models which can analyze the patterns in the medical images including X-Ray, CT Scan, and many others. Using such approaches with the integration of GANs, the overall predictive analytics can be made high performance aware as compared to the classical neural networks with multiple layers. In this research manuscript, the inscription of work is projected on the benchmark datasets with the advanced scripting so that the predictive mining and knowledge discovery can be done effectively with more accuracy.

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Section: Review Of Existing Workmentioning

confidence: 99%

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

Kumar

Harshavardhan²,

Bhukya

et al. 2020

Materials Today: Proceedings

View full text Add to dashboard Cite

show abstract

“…The real sample X and virtual sample G(z) produced by the generative model can be used as the input to the discriminative model. The two models learn alternately and continuously optimize through the min-max game to improve the performance of the network (Husein et al 2019). Finally, the trained generator can generate highquality new sample data, but the discriminator cannot distinguish it from the real data.…”

Section: Algorithm Framework Of Ganmentioning

confidence: 99%

Short-term prediction of wind power based on BiLSTM–CNN–WGAN-GP

Huang

Wei

et al. 2022

Soft Comput

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A short-term wind power prediction model based on BiLSTM–CNN–WGAN-GP (LCWGAN-GP) is proposed in this paper, aiming at the problems of instability and low prediction accuracy of short-term wind power prediction. Firstly, the original wind energy data are decomposed into subsequences of natural mode functions with different frequencies by using the variational mode decomposition (VMD) algorithm. The VMD algorithm relies on a decision support system for the decomposition of the data into natural mode functions. Once the decomposition is performed, the nonlinear and dynamic behavior are extracted from each natural mode function. Next, the BiLSTM network is chosen as the generation model of the generative adversarial network (WGAN-GP) to obtain the data distribution characteristics of wind power’s output. The convolutional neural network (CNN) is chosen as the discrimination model, and the semi-supervised regression layer is utilized to design the discrimination model to predict wind power. The minimum–maximum game is formed by the BiLSTM and CNN network models to improve the quality of sample generation and further improve the prediction accuracy. Finally, the actual data of a wind farm in Jiuquan City, Gansu Province, China is taken as an example to prove that the proposed method has superior performance compared with other prediction algorithms.

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“…In addition to the works mentioned above (Qingkui and Junhu, 2009;Lee and Palaniappan, 2014;Rohman and Rachmad, 2016) use ANN for hospital assets inventory/demand forecasting. Husein et al (2019) use the generative adversarial network time series model for predicting hospital drug sales of the next one-week to reduce drug excess and shortage. The mentioned authors prove that neural networks (feed-forward (ANN) or recurrent (RNN) ones) provide better accuracy.…”

Section: Related Workmentioning

confidence: 99%

A preliminary study for selecting the appropriate AI-based forecasting model for hospital assets demand under disasters

Jebbor

Chiheb

Afia

2021

JHLSCM

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PurposeHospitals recently search for more accurate forecasting systems, given the unpredictable demand and the increasing occurrence of disruptive incidents (mass casualty incidents, pandemics and natural disasters). Besides, the incorporation of automatic inventory and replenishment systems – that hospitals are undertaking – requires developed and accurate forecasting systems. Researchers propose different artificial intelligence (AI)-based forecasting models to predict hospital assets consumption (AC) for everyday activity case and prove that AI-based models generally outperform many forecasting models in this framework. The purpose of this paper is to identify the appropriate AI-based forecasting model(s) for predicting hospital AC under disruptive incidents to improve hospitals' response to disasters/pandemics situations.Design/methodology/approachThe authors select the appropriate AI-based forecasting models according to the deduced criteria from hospitals' framework analysis under disruptive incidents. Artificial neural network (ANN), recurrent neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS) and learning-FIS (FIS with learning algorithms) are generally compliant with the criteria among many AI-based forecasting methods. Therefore, the authors evaluate their accuracy to predict a university hospital AC under a burn mass casualty incident.FindingsThe ANFIS model is the most compliant with the extracted criteria (autonomous learning capability, fast response, real-time control and interpretability) and provides the best accuracy (the average accuracy is 98.46%) comparing to the other models.Originality/valueThis work contributes to developing accurate forecasting systems for hospitals under disruptive incidents to improve their response to disasters/pandemics situations.

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Generative Adversarial Networks Time Series Models to Forecast Medicine Daily Sales in Hospital

Cited by 14 publications

References 29 publications

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

Short-term prediction of wind power based on BiLSTM–CNN–WGAN-GP

A preliminary study for selecting the appropriate AI-based forecasting model for hospital assets demand under disasters

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