Patient visit forecasting in an emergency department using a deep neural network approach

Kybernetes ◽  
2019 ◽  
Vol 49 (9) ◽  
pp. 2335-2348 ◽  
Author(s):  
Milad Yousefi ◽  
Moslem Yousefi ◽  
Masood Fathi ◽  
Flavio S. Fogliatto
Keyword(s):  
Neural Network ◽  
Emergency Department ◽  
Linear Regression ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Neural Network Approach ◽  
Content Type

Purpose This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days. Design/methodology/approach In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor. Findings Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression. Research limitations/implications The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications. Originality/value To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.

scholarly journals Multi-Regional Online Car-Hailing Order Quantity Forecasting Based on the Convolutional Neural Network

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 193 ◽  
Author(s):  
Zihao Huang ◽  
Gang Huang ◽  
Zhijun Chen ◽  
Chaozhong Wu ◽  
Xiaofeng Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Travel Demand ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Image Feature ◽  
Support Vector ◽  
Data Set ◽  
Demand Distribution ◽  
Demand Forecasting Model

With the development of online cars, the demand for travel prediction is increasing in order to reduce the information asymmetry between passengers and drivers of online car-hailing. This paper proposes a travel demand forecasting model named OC-CNN based on the convolutional neural network to forecast the travel demand. In order to make full use of the spatial characteristics of the travel demand distribution, this paper meshes the prediction area and creates a travel demand data set of the graphical structure to preserve its spatial properties. Taking advantage of the convolutional neural network in image feature extraction, the historical demand data of the first twenty-five minutes of the entire region are used as a model input to predict the travel demand for the next five minutes. In order to verify the performance of the proposed method, one-month data from online car-hailing of the Chengdu Fourth Ring Road are used. The results show that the model successfully extracts the spatiotemporal features of the data, and the prediction accuracies of the proposed method are superior to those of the representative methods, including the Bayesian Ridge Model, Linear Regression, Support Vector Regression, and Long Short-Term Memory networks.

scholarly journals Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Abdullah-Al Nahid ◽  
Mohamad Ali Mehrabi ◽  
Yinan Kong
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Image Classification ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Statistical Information ◽  
Support Vector ◽  
Biomedical Image ◽  
Histopathological Images ◽  
Photography Analysis

Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F-Measure value is achieved on both the 40x and 100x datasets.

Demand forecasting at retail stage for selected vegetables: a performance analysis

2019 ◽  
Vol 14 (4) ◽  
pp. 1042-1063 ◽  
Author(s):  
Rahul Priyadarshi ◽  
Akash Panigrahi ◽  
Srikanta Routroy ◽  
Girish Kant Garg
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Forecast Error ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Forecasting Model ◽  
Content Type ◽  
Forecasting Models

Purpose The purpose of this study is to select the appropriate forecasting model at the retail stage for selected vegetables on the basis of performance analysis. Design/methodology/approach Various forecasting models such as the Box–Jenkins-based auto-regressive integrated moving average model and machine learning-based algorithms such as long short-term memory (LSTM) networks, support vector regression (SVR), random forest regression, gradient boosting regression (GBR) and extreme GBR (XGBoost/XGBR) were proposed and applied (i.e. modeling, training, testing and predicting) at the retail stage for selected vegetables to forecast demand. The performance analysis (i.e. forecasting error analysis) was carried out to select the appropriate forecasting model at the retail stage for selected vegetables. Findings From the obtained results for a case environment, it was observed that the machine learning algorithms, namely LSTM and SVR, produced the better results in comparison with other different demand forecasting models. Research limitations/implications The results obtained from the case environment cannot be generalized. However, it may be used for forecasting of different agriculture produces at the retail stage, capturing their demand environment. Practical implications The implementation of LSTM and SVR for the case situation at the retail stage will reduce the forecast error, daily retail inventory and fresh produce wastage and will increase the daily revenue. Originality/value The demand forecasting model selection for agriculture produce at the retail stage on the basis of performance analysis is a unique study where both traditional and non-traditional models were analyzed and compared.

Survey of Machine Learning Algorithms to Detect Malware in Consumer Internet of Things Devices

2021 ◽  
Vol 30 (04) ◽  
pp. 2150020
Author(s):  
Luke Holbrook ◽  
Miltiadis Alamaniotis
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Internet Of Things ◽  
Deep Neural Network ◽  
Learning Algorithms ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Iot Devices

With the increase of cyber-attacks on millions of Internet of Things (IoT) devices, the poor network security measures on those devices are the main source of the problem. This article aims to study a number of these machine learning algorithms available for their effectiveness in detecting malware in consumer internet of things devices. In particular, the Support Vector Machines (SVM), Random Forest, and Deep Neural Network (DNN) algorithms are utilized for a benchmark with a set of test data and compared as tools in safeguarding the deployment for IoT security. Test results on a set of 4 IoT devices exhibited that all three tested algorithms presented here detect the network anomalies with high accuracy. However, the deep neural network provides the highest coefficient of determination R2, and hence, it is identified as the most precise among the tested algorithms concerning the security of IoT devices based on the data sets we have undertaken.

scholarly journals Result and Performance Analysis of Rainfall Prediction System Based on Deep Neural Network

2020 ◽  
pp. 633-638
Author(s):  
Akshay Rajendra Naik ◽  
A. V. Deorankar ◽  
P. B. Ambhore
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Methods ◽  
Rainfall Prediction ◽  
Machine Learning Methods ◽  
Vector Machines ◽  
And Performance

Rainfall prediction is useful for all people for decision making in all fields, such as out door gamming, farming, traveling, and factory and for other activities. We studied various methods for rainfall prediction such as machine learning and neural networks. There is various machine learning algorithms are used in previous existing methods such as naïve byes, support vector machines, random forest, decision trees, and ensemble learning methods. We used deep neural network for rainfall prediction, and for optimization of deep neural network Adam optimizer is used for setting modal parameters, as a result our method gives better results as compare to other machine learning methods.

An Investigation Screen for Sleep Disorders Using AI/ML

2021 ◽  
pp. 161-183
Author(s):  
Shilpa Hiremath ◽  
Chandra Prabha R. ◽  
Sushil Kumar I.
Keyword(s):  
Neural Network ◽  
Sleep Disorders ◽  
Deep Neural Network ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Age Group ◽  
Eeg Signal ◽  
Factors Affecting ◽  
Detailed Review

In this chapter, the authors have discussed a detailed review on sleep, sleep disorders, and their diagnosis. This chapter provides an insight study of sleep, sleep illness characterized by The International Classification of Sleep Disorders (ICSD), factors affecting sleep, and types of sleep based on age group. Artificial intelligence and machine learning algorithms are also applied in recognizing sleep disorders based on EEG signal attributes. It also highlights the classification of insomnia using different classifiers such as support vector machine, decision tree, and deep neural network.

Comparison of machine learning algorithms for concentration detection and prediction of formaldehyde based on electronic nose

Sensor Review ◽  
2016 ◽  
Vol 36 (2) ◽  
pp. 207-216 ◽  
Author(s):  
Liyuan Xu ◽  
Jie He ◽  
Shihong Duan ◽  
Xibin Wu ◽  
Qin Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Electronic Nose ◽  
Bp Neural Network ◽  
Sensor Arrays ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Formaldehyde Concentration ◽  
Content Type ◽  
Depth Analysis

Purpose Sensor arrays and pattern recognition-based electronic nose (E-nose) is a typical detection and recognition instrument for indoor air quality (IAQ). The E-nose is able to monitor several pollutants in the air by mimicking the human olfactory system. Formaldehyde concentration prediction is one of the major functionalities of the E-nose, and three typical machine learning (ML) algorithms are most frequently used, including back propagation (BP) neural network, radial basis function (RBF) neural network and support vector regression (SVR). Design/methodology/approach This paper comparatively evaluates and analyzes those three ML algorithms under controllable environment, which is built on a marketable sensor arrays E-nose platform. Variable temperature (T), relative humidity (RH) and pollutant concentrations (C) conditions were measured during experiments to support the investigation. Findings Regression models have been built using the above-mentioned three typical algorithms, and in-depth analysis demonstrates that the model of the BP neural network results in a better prediction performance than others. Originality/value Finally, the empirical results prove that ML algorithms, combined with low-cost sensors, can make high-precision contaminant concentration detection indoor.

scholarly journals Deep neural network-based classification of cardiotocograms outperformed conventional algorithms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jun Ogasawara ◽  
Satoru Ikenoue ◽  
Hiroko Yamamoto ◽  
Motoshige Sato ◽  
Yoshifumi Kasuga ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Fetal Heart ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

AbstractCardiotocography records fetal heart rates and their temporal relationship to uterine contractions. To identify high risk fetuses, obstetricians inspect cardiotocograms (CTGs) by eye. Therefore, CTG traces are often interpreted differently among obstetricians, resulting in inappropriate interventions. However, few studies have focused on quantitative and nonbiased algorithms for CTG evaluation. In this study, we propose a newly constructed deep neural network model (CTG-net) to detect compromised fetal status. CTG-net consists of three convolutional layers that extract temporal patterns and interrelationships between fetal heart rate and uterine contraction signals. We aimed to classify the abnormal group (umbilical artery pH < 7.20 or Apgar score at 1 min < 7) and the normal group from CTG data. We evaluated the performance of the CTG-net with the F1 score and compared it with conventional algorithms, namely, support vector machine and k-means clustering, and another deep neural network model, long short-term memory. CTG-net showed the area under the receiver operating characteristic curve of 0.73 ± 0.04, which was significantly higher than that of long short-term memory. CTG-net, a quantitative and automated diagnostic aid system, enables early intervention for putatively abnormal fetuses, resulting in a reduction in the number of cases of hypoxic injury.

scholarly journals Water Quality Prediction Using Artificial Intelligence Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Theyazn H. H Aldhyani ◽  
Mohammed Al-Yaari ◽  
Hasan Alkahtani ◽  
Mashael Maashi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Water Quality ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Neural Network Models ◽  
Statistical Parameters ◽  
Water Quality Prediction

During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), K -nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient ( RNARNET = 96.17 % and RLSTM = 94.21 % ). This kind of promising research can contribute significantly to water management.

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