Mean Average Percentage Error Research Articles

Application of CANFIS for modelling and predicting multiple output performances for different materials in µEDM

Micro Electrical Discharge Machining (µEDM) is one of the most demanding manufacturing processes available today. The selection of EDM parameters remains a challenge since it is frequently based on machinist intuition and heuristic approaches. Artificial intelligence algorithms have been used to model and predict the µEDM machining process in recent years. However, artificial intelligence has not been established for predicting µEDM performances based on material properties. Therefore, this paper has proposed a model that considers the material properties, such as thermal conductivity, melting point, and electrical resistivity. Since µEDM is a non-linear and stochastic process, Coactive Neuro-Fuzzy Inference Systems (CANFIS) was proposed to model and predict the multiple µEDM performances on various materials. The material properties, feed rate, capacitance, and gap voltage are input parameters in a three-level design based on a full factorial experiment. The CANFIS model can accurately predict the material removal rate (MRR), total discharge pulse, overcut, and taperness in a single model. The mean average percentage error (MAPE) of various outputs (predicted by the model) for test dataset such as MRR, total discharge pulse, overcut, and taper angle were found to be 4.5% (95.4% accuracy), 6.8% (93.2% accuracy), 15.4% (84.6% accuracy) and 15.2% (84.8% accuracy) respectively.

The transferability and validity of a population-level simulation model for the economic evaluation of interventions in diabetes: the MICADO model

AimsValid health economic models are essential to inform the adoption and reimbursement of therapies for diabetes mellitus. Often existing health economic models are applied in other countries and settings than those where they were developed. This practice requires assessing the transferability of a model developed from one setting to another. We evaluate the transferability of the MICADO model, developed for the Dutch 2007 setting, in two different settings using a range of adjustment steps. MICADO predicts micro- and macrovascular events at the population level.MethodsMICADO simulation results were compared to observed events in an Italian 2000–2015 cohort (Casale Monferrato Survey [CMS]) and in a Dutch 2008–2019 (Hoorn Diabetes Care Center [DCS]) cohort after adjusting the demographic characteristics. Additional adjustments were performed to: (1) risk factors prevalence at baseline, (2) prevalence of complications, and (3) all-cause mortality risks by age and sex. Model validity was assessed by mean average percentage error (MAPE) of cumulative incidences over 10 years of follow-up, where lower values mean better accuracy.ResultsFor mortality, MAPE was lower for CMS compared to DCS (0.38 vs. 0.70 following demographic adjustment) and adjustment step 3 improved it to 0.20 in CMS, whereas step 2 showed best results in DCS (0.65). MAPE for heart failure and stroke in DCS were 0.11 and 0.22, respectively, while for CMS was 0.42 and 0.41.ConclusionsThe transferability of the MICADO model varied by event and per cohort. Additional adjustments improved prediction of events for MICADO. To ensure a valid model in a new setting it is imperative to assess the impact of adjustments in terms of model accuracy, even when this involves the same country, but a new time period.

Artificial neural networks application on friction factor and heat transfer coefficients prediction in tubes with inner helical-finning

A study presents new model of the artificial neural network to predict friction factor and heat transfer coefficients for the turbulent flow in tubes with inner helical-finning. A fin geometry differs in its form, shape and fabrication method. The generalized equations, correlating thermal performance in such tube, available in earlier works by other authors primarily apply to a single type of inner helical-finning. In present work, we compile experimental results of other authors to an extended database that has been used further for artificial neural network training procedure. The presented model of artificial neural network applies to all types of inner helical tube finning. The mean average percent error values of 11.8% for friction factor and 16.3% for Nusselt number values for the ANN model over the whole database have been achieved. The performance validation of the obtained model was based on a comparison of predicted data with the independent experimental results obtained by authors, yielding excellent accuracy. • Extended database of Nusselt number and friction factor in helicallyfinned tubes. • Novel generalization model of artificial neural network. • Systematic analysis and validation of obtained artificial neural network model.

Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine

Crude oil market analysis has become one of the emerging financial markets and the volatility effect of the market is paramount and has been considered as an issue of utmost importance. This study examines the dynamics of this volatile market of crude oil by employing a hybrid approach based on an extreme learning machine (ELM) as a regressor and the improved grey wolf optimizer (IGWO) for prophesying the crude oil rate for West Texas Intermediate (WTI) and Brent crude oil datasets. The datasets are augmented using technical indicators (TIs) and statistical measures (SMs) to obtain better insight into the forecasting ability of this proposed model. The differential evolution (DE) strategy has been used for evolution and the survival of the fittest (SOF) principle has been used for elimination while implementing the GWO to achieve better convergence rate and accuracy. Whereas, the algorithmic simplicity, use of less parameters, and easy implementation of DE efficiently decide the evolutionary patterns of wolves in GWO and the SOF principle updates the wolf pack based on the fitness value of each wolf, thereby ensuring the algorithm does not fall into local optimum. Furthermore, the comparison and analysis of the proposed model with other models, such as ELM–DE, ELM–Particle Swarm Optimization (ELM–PSO), and ELM–GWO shows that the predictability evidence obtained substantially achieves better performance for ELM–IGWO with respect to faster error convergence rate and mean square error (MSE) during training and testing phases. The sensitivity study of the proposed ELM–IGWO provides better results in terms of the performance measures, such as Theil’s U, mean absolute error (MAE), average relative variance (ARV), mean average percentage error (MAPE), and minimal computational time.

Revealing Microclimate around Buildings with Long-Term Monitoring through the Neural Network Algorithms

The profile of urban microclimates is important in many engineering fields, such as occupant’s thermal comfort and health, and other building engineering. To predict the profile of urban microclimate, this study applies the artificial neural network and long short-term memory network predictive models, and an urban microclimate dataset was obtained with a long-term monitoring from year 2017 to 2019 with 5-min resolution including temperature, relative humidity, and solar radiation. Two predictive models were applied, and the first (Model 1) is to apply the predictive techniques to predict the urban microclimate in the real-time sequence, and then extract the characteristics of urban microclimate, while the second (Model 2) is to directly extract the characteristics of the microclimate, and then predict the characteristics of the microclimate. Backpropagation artificial neural network (BP-ANN) and long-short term memory (LSTM) techniques were applied in both models. The results show Model 1 with as the time-series prediction can reach the best (99.92%) of correlation coefficient and 98% of the mean average percentage error (MAPE), for temperature, while 99.66% and 98.18% for relative humidity, respectively, while accuracies in Model 2 decreased to 79% and 88.6% of MAPE for temperature and relative humidity, respectively. The prediction of solar radiation using ANN and LSTM are 51.1% and 57.8% of the correlation coefficient, respectively.

Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model

Load forecasting (LF) is an essential factor in power system management. LF helps the utility maximize the utilization of power-generating plants and schedule them both reliably and economically. In this paper, a novel and hybrid forecasting method is proposed, combining a long short-term memory network (LSTM) and neural prophet (NP) through an artificial neural network. The paper aims to predict electric load for different time horizons with improved accuracy as well as consistency. The proposed model uses historical load data, weather data, and statistical features obtained from the historical data. Multiple case studies have been conducted with two different real-time data sets on three different types of load forecasting. The hybrid model is later compared with a few established methods of load forecasting found in the literature with different performance metrics: mean average percentage error (MAPE), root mean square error (RMSE), sum of square error (SSE), and regression coefficient (R). Moreover, a guideline with various attributes is provided for different types of load forecasting considering the applications of the proposed model. The results and comparisons from our test cases showed that the proposed hybrid model improved the forecasting accuracy for three different types of load forecasting over other forecasting techniques.

Short-term load forecasting of the distribution system using cuckoo search algorithm

For solving the different optimization problems, the cuckoo search is one of the best nature's inspired algorithms. It is an effective technique compare to other optimization methods. For this manuscript, we are using a back propagation neural network for the Xintai power plant consist of short-term electrical load forecasting. The limitation of back propagation is overcome by the cuckoo search algorithm. The function is used for cuckoo search is Gamma probability distribution and its result is compared with other possible cuckoo search methods. The mean average percentage error of Gamma cuckoo search is 0.123%, cuckoo search with Pareto based is 0.127% and Levy based cuckoo search is 0.407%. Other results of the cuckoo search are also found by a linear decreasing switching parameter with a mean average error is 0.344% and 0.389% of mean average error with the use of an exponentially increasing switching parameter. This improved cuckoo search algorithm brings good results in the predicted load which is very important for the Xintai power plant using short-term load forecasting.

Comparison of different approaches in estimating the time-varying reproductive number for COVID-19

PurposeThe time-varying reproductive number (Rt) is an indicator of transmissibility that has utility in evaluating public health interventions and assessing transmission factors. However, the Rt may be biased by generation time misspecification, reporting delays, underestimation of cases, and day-to-day variations. We compared several methods of adjustments in developing an approach to estimating an unbiased Rt.Methods & MaterialsA meta-analysis of generations times was conducted to reduce misspecification. A probabilistic bias approach was compared to standardization by a test positivity of 5% in adjusting for underestimation. A Poisson deconvolution process using an incubation period of 5.2 days (95% CI: 4.9-5.5) and laboratory turnover times between 2-, 5- and 10-days was utilized to adjust for reporting delays. We compared smoothing (7- and 14-day moving averages), a generalized additive model (GAM), and a local regression (LOESS) model to adjust for day-to-day variation. The adjusted Rt was compared to a crude Rt by eyeballing, Mean Average Percentage Error (MAPE), and Mean Absolute Deviation (MAD). We estimated the Rt using Malaysian COVID-19 daily case data from 7 March 2020-20 June 2021 utilizing Cori et al.’s method.ResultsWe estimated a pooled serial interval of 4.95 days (95% CI: 4.62-5.29). The Rt estimated using case counts adjusted for underestimation using standardization by test positivity (MAPE: 0.31; 95% CI: 0.30-0.49, MAD: 0.5; 95%CI: 0.5-0.54) were more volatile, exhibited larger peaks and wider confidence intervals, especially in periods of lower incidence, compared to the probabilistic bias approach (MAPE: 0.07; 95% CI: 0.06-0.07, MAD: 0.26; 95%CI: 0.26-0.28). GAM (MAPE: 1.85, 95% CI: 1.63-2.08) and LOESS (MAPE: 0.29, 95% CI: 0.29-0.29) models had smoothed out almost all variations in the Rt. Longer lab turnover periods created smoother Rt with larger peaks and resulted in greater volatility in the estimates.ConclusionBiases in the estimation of the Rt may critically change its interpretation for public health interventions. It is important to adjust for these biases and understand the underlying limitations of these estimations; primarily when utilized within the context of pandemic control.

Nonlinear Model-Based Subway Station-Level Peak-Hour Ridership Estimation Approach in the Context of Peak Deviation

Existing techniques for estimation of subway station-level long-term peak-hour ridership (PHR) may produce underestimated PHR values that may result in stations being designed with insufficient capacity during the planning stage; this in turn may increase congestion on the platforms in actual operation. One of the reasons for this potential undesirable outcome is that peak deviation phenomena often arise between stations and lines in subway systems, which could create underestimated PHR values. The default assumption has always been that the peak hour of passenger flow of each station always overlaps with its attributed line. This paper presents a framework of a station-level long-term PHR estimation method calibrated using the peak deviation coefficient (PDC) and a nonlinear model (eXtreme Gradient Boosting). This approach can estimate the PDC values for PHR prediction, and can also quantify the relative importance of PDC associated factors, yielding an explanation of the main causes of peak deviation phenomena. Using a real-world, large-scale passenger flow dataset from Xi’an, China, the approach produces more stable and accurate predictive performance compared with conventional methods (i.e., absolute percentage error controlled within 20% versus 50%, and mean average percentage error reduced by 3.26%–8.35%). Meanwhile, it is found that the relative importance of the unimproved land use ratio ranks in the top four for all types of peak periods; this ratio is a key factor that may be used to mitigate ridership deviations between stations and line peaks. In addition, for subway networks, the influence of land use entropy increases from the morning peak hour to the evening peak hour and weakens across the route from origin to destination.

Comprehensive Output Estimation of Double Scattering Proton System With Analytical and Machine Learning Models.

ObjectivesThe beam output of a double scattering proton system varies for each combination of beam option, range, and modulation and therefore is difficult to be accurately modeled by the treatment planning system (TPS). This study aims to design an empirical method using the analytical and machine learning (ML) models to estimate proton output in a double scattering proton system.Materials and MethodsThree analytical models using polynomial, linear, and logarithm–polynomial equations were generated on a training dataset consisting of 1,544 clinical measurements to estimate proton output for each option. Meanwhile, three ML models using Gaussian process regression (GPR) with exponential kernel, squared exponential kernel, and rational quadratic kernel were also created for all options combined. The accuracy of each model was validated against 241 additional clinical measurements as the testing dataset. Two most robust models were selected, and the minimum number of samples needed for either model to achieve sufficient accuracy ( ± 3%) was determined by evaluating the mean average percentage error (MAPE) with increasing sample number. The differences between the estimated outputs using the two models were also compared for 1,000 proton beams with a randomly generated range, and modulation for each option.ResultsThe polynomial model and the ML GPR model with exponential kernel yielded the most accurate estimations with less than 3% deviation from the measured outputs. At least 20 samples of each option were needed to build the polynomial model with less than 1% MAPE, whereas at least a total of 400 samples were needed for all beam options to build the ML GPR model with exponential kernel to achieve comparable accuracy. The two independent models agreed with less than 2% deviation using the testing dataset.ConclusionThe polynomial model and the ML GPR model with exponential kernel were built for proton output estimation with less than 3% deviations from the measurements. They can be used as an independent output prediction tool for a double scattering proton beam and a secondary output check tool for a cross check between themselves.

Forecasting Sales of Hex Nut Using Trend Linier Line (TLL) Methode and Monte Carlo Simulation in PT. KMS East Java

This study presenting the result of forecasting sales of Hex Nuts between the Trend Linear Line (TLL) method and Monte Carlo Simulation. To determine the appropriate method, the Mean Average Percentage Error (MAPE) is used to evaluate theerror rate. We find that the Monte Carlo simulation outperforms the TTL method, where the MAPE value of the Monte Carlo simulation is 7,61%. Based on the result, the Monte Carlo simulation is the appropriate method to forecast the sales rate of Hex Nuts in the PT. KMS.
 

A modeling approach to account for unstable stratification, flow acceleration, and variable thermophysical properties for supercritical carbon dioxide

A first of its kind reduced-order predictive heat transfer model is developed to account for the effects of unstable stratification, flow acceleration, and variable thermophysical properties for supercritical carbon dioxide. These phenomena govern thermal transport in the proximity of the pseudo-critical point when the applied heating is limited to the bottom wall of the flow channel. The reduced order model assumes two-dimensional thermal transport and involves the iterative solution of the turbulent Prandtl number. The predictions of this model were compared against experimental data. Out of a total of 16 test data sets, each comprising over 200 individual data points, the model was able to predict 14 data sets with a mean average percent error (MAPE) of less than 20%. Additionally, a heat transfer design correlation is proposed which can predict the experimental data with a MAPE of less than 22%. The modeling approaches outlined in this work provide an alternative to using CFD to model coupled and counteracting phenomena that governs thermal transport for supercritical fluids in asymmetrically bottom heated ducts.

SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting

Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections one to four weeks in advance. It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases.

Parametric estimation of supplier's plant construction costs

Cost engineers of buying enterprises perform detailed product cost calculations of externally manufactured components. The aim of these calculations is to determine what a product should cost and to support purchasing functions in fact-based negotiations. While product cost engineers have deep knowledge in the calculation of direct cost, they need support in the calculation of supplier´s indirect cost categories. The calculation of industrial rent, which is expressed in annual cost per m² of occupied plant building floor space can be improved by providing accurate construction cost estimates. Construction costs are strongly impacting the calculation of supplier´s annual building depreciation, which is a crucial cost driver for the determination of the industrial rent. Academic literature is actually not providing an accurate and suitable cost model for product cost engineers, which is estimating construction cost per m² depending on different industrial building categories and alternative supplier plant locations. The paper aims to close this gap by applying linear regression analysis on a set of European construction cost data considering two industrial building categories: “warehouses/basic factory units” and “high-tech factories”. By regressing construction cost against construction labor rates within different supplier plant locations it was possible to form suitable and accurate parametric regression functions with R² values between 0.74 and 0.88. Next to high R² values acceptable mean average percentage errors between 7.45% and 11.77% could be realized by comparing estimated with observed construction cost. The estimation of industrial construction costs based on the paper´s results can be used to improve the calculation of industrial rent, which is one cost element, that has to be covered within product cost engineer´s Should Cost Calculations.

Automatic segmentation of the great arteries for computational hemodynamic assessment

BackgroundComputational fluid dynamics (CFD) is increasingly used for the assessment of blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific anatomy, typically obtained from segmented 3D cardiovascular magnetic resonance (CMR) images. However, segmentation is time-consuming and requires expert input. This study aims to develop and validate a machine learning (ML) method for segmentation of the aorta and pulmonary arteries for CFD studies.Methods90 CHD patients were retrospectively selected for this study. 3D CMR images were manually segmented to obtain ground-truth (GT) background, aorta and pulmonary artery labels. These were used to train and optimize a U-Net model, using a 70-10-10 train-validation-test split. Segmentation performance was primarily evaluated using Dice score. CFD simulations were set up from GT and ML segmentations using a semi-automatic meshing and simulation pipeline. Mean pressure and velocity fields across 99 planes along the vessel centrelines were extracted, and a mean average percentage error (MAPE) was calculated for each vessel pair (ML vs GT). A second observer (SO) segmented the test dataset for assessment of inter-observer variability. Friedman tests were used to compare ML vs GT, SO vs GT and ML vs SO metrics, and pressure/velocity field errors.ResultsThe network’s Dice score (ML vs GT) was 0.945 (interquartile range: 0.929–0.955) for the aorta and 0.885 (0.851–0.899) for the pulmonary arteries. Differences with the inter-observer Dice score (SO vs GT) and ML vs SO Dice scores were not statistically significant for either aorta or pulmonary arteries (p = 0.741, p = 0.061). The ML vs GT MAPEs for pressure and velocity in the aorta were 10.1% (8.5–15.7%) and 4.1% (3.1–6.9%), respectively, and for the pulmonary arteries 14.6% (11.5–23.2%) and 6.3% (4.3–7.9%), respectively. Inter-observer (SO vs GT) and ML vs SO pressure and velocity MAPEs were of a similar magnitude to ML vs GT (p > 0.2).ConclusionsML can successfully segment the great vessels for CFD, with errors similar to inter-observer variability. This fast, automatic method reduces the time and effort needed for CFD analysis, making it more attractive for routine clinical use.

Short-Term Load Forecasting Based on Improved TCN and DenseNet

With the grid-connected application of renewable energy sources such as wind and photovoltaic power, the nonlinearity and fluctuation of load data makes load forecasting more difficult than ever before. In order to extract the implicit relationship between multiple features and power load to construct a long-term sequence dependency, this paper proposes a short-term load forecasting based on improved temporal convolutional network (TCN) and densely connected convolutional network (DenseNet). Firstly, multiple features are reconstructed by using a fixed-length sliding window, and then the high-dimensional features reflecting the complex and non-stationary characteristics of power load are extracted by the DenseNet to construct a feature matrix. Secondly, we innovatively improve the TCN and introduce a parallel pooling into the traditional TCN to mine the features of time sequences. Finally, the self-attention mechanism (SAM) is used to further enhance the weight of key features to eliminate the influences of interference signals. Experiments were performed on Southern China and ISO-NE (New England) public datasets to verify the effectiveness and generalization of the proposed model. Compared with the traditional TCN, the mean average percentage error (MAPE) of the improved TCN on the two datasets decreases by 23.38% and 8.14%, respectively. Furthermore, when compared to the TCN-SAM hybrid model, the MAPE of proposed model is significantly reduced by 42.41% and 26.89%, respectively.

Energy Theft Identification Using Adaboost Ensembler in the Smart Grids

One of the major concerns for the utilities in the Smart Grid (SG) is electricity theft. With the implementation of smart meters, the frequency of energy usage and data collection from smart homes has increased, which makes it possible for advanced data analysis that was not previously possible. For this purpose, we have taken historical data of energy thieves and normal users. To avoid imbalance observation, biased estimates, we applied the interpolation method. Furthermore, the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing. By proposing an improved version of Zeiler and Fergus Net (ZFNet) as a feature extraction approach, we had able to reduce the model's time complexity. To minimize the overfitting issues, increase the training accuracy and reduce the training loss, we have proposed an enhanced method by merging Adaptive Boosting (AdaBoost) classifier with Coronavirus Herd Immunity Optimizer (CHIO) and Forensic based Investigation Optimizer (FBIO). In terms of low computational complexity, minimized over-fitting problems on a large quantity of data, reduced training time and training loss and increased training accuracy, our model outperforms the benchmark scheme. Our proposed algorithms Ada-CHIO and Ada-FBIO, have the low Mean Average Percentage Error (MAPE) value of error, i.e., 6.8% and 9.5%, respectively. Furthermore, due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93% and 90%. Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms, which also depicts the superiority of our proposed techniques

Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

In the Smart Grid (SG) residential environment, consumers change their power consumption routine according to the price and incentives announced by the utility, which causes the prices to deviate from the initial pattern. Thereby, electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability. Due to the massive amount of data, big data analytics for forecasting becomes a hot topic in the SG domain. In this paper, the changing and non-linearity of consumer consumption pattern complex data is taken as input. To minimize the computational cost and complexity of the data, the average of the feature engineering approaches includes: Recursive Feature Eliminator (RFE), Extreme Gradient Boosting (XGboost), Random Forest (RF), and are upgraded to extract the most relevant and significant features. To this end, we have proposed the DensetNet-121 network and Support Vector Machine (SVM) ensemble with Aquila Optimizer (AO) to ensure adaptability and handle the complexity of data in the classification. Further, the AO method helps to tune the parameters of DensNet (121 layers) and SVM, which achieves less training loss, computational time, minimized overfitting problems and more training/test accuracy. Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes. Our proposed method has achieved a minimal value of the Mean Average Percentage Error (MAPE) rate i.e., 8% by DenseNet-AO and 6% by SVM-AO and the maximum accurateness rate of 92% and 95%, respectively.

Predicting the future: introducing business analytics to endoscopy units.

currently, most endoscopy software only provides limited statistics of past procedures, while none allows patterns to be extrapolated. To overcome this need, the authors applied business analytic models to predict future demand and the need for endoscopists in a tertiary hospital Endoscopy Unit. a query to the endoscopy database was performed to retrieve demand from 2015 to 2021. The graphical inspection allowed inferring of trends and seasonality, perceiving the impact of the COVID-19 pandemic, and selecting the best forecasting models. Considering COVID-19's impact in the second quarter of 2020, data for esophagogastroduodenoscopy (EGD) and colonoscopy was estimated using linear regression of historical data. The actual demand in the first two quarters of 2022 was used to validate the models. during the study period, 53,886 procedures were requested. The best forecasting models were: a) simple seasonal exponential smoothing for EGD, colonoscopy and percutaneous endoscopic gastrostomy (PEG); b) double exponential smoothing for capsule endoscopy and deep enteroscopy; and c) simple exponential smoothing for endoscopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasound (EUS). The mean average percentage error ranged from 6.1 % (EGD) to 33.5 % (deep enteroscopy). Overall, 8,788 procedures were predicted for 2022. The actual demand in the first two quarters of 2022 was within the predicted range. Considering the usual time allocation for each technique, 3.2 full-time equivalent endoscopists (40 hours-dedication to endoscopy) will be required to perform all procedures in 2022. the incorporation of business analytics into the endoscopy software and clinical practice may enhance resource allocation, improving patient-focused decision-making and healthcare quality.

Back propagation mathematical model for stock price prediction

Abstract Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task filled with challenges. However, in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock prices using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function neural network, general regression neural network, support vector machine regression (SVMR) and least squares support vector machine regression. We apply the five models to make price predictions for three individual stocks, namely, Bank of China, Vanke A and Guizhou Maotai. Adopting mean square error and average absolute percentage error as criteria, we find that BP neural network consistently and robustly outperforms the other four models. Then some theoretical and practical implications have been discussed.