scholarly journals Comparisons of Prediction Models of Myofascial Pain Control after Dry Needling: A Prospective Study

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yuan-Ting Huang ◽  
Choo-Aun Neoh ◽  
Shun-Yuan Lin ◽  
Hon-Yi Shi
Keyword(s):  
Pain Control ◽  
Prediction Models ◽  
Myofascial Pain ◽  
Training Dataset ◽  
Percentage Error ◽  
Support Vector ◽  
Ann Model ◽  
Dry Needling ◽  
Patient Reported ◽  
Ann Models

Background. This study purposed to validate the use of artificial neural network (ANN) models for predicting myofascial pain control after dry needling and to compare the predictive capability of ANNs with that of support vector machine (SVM) and multiple linear regression (MLR).Methods. Totally 400 patients who have received dry needling treatments completed the Brief Pain Inventory (BPI) at baseline and at 1 year postoperatively.Results. Compared to the MLR and SVM models, the ANN model generally had smaller mean square error (MSE) and mean absolute percentage error (MAPE) values in the training dataset and testing dataset. Most ANN models had MAPE values ranging from 3.4% to 4.6% and most had high prediction accuracy. The global sensitivity analysis also showed that pretreatment BPI score was the best parameter for predicting pain after dry needling.Conclusion. Compared with the MLR and SVM models, the ANN model in this study was more accurate in predicting patient-reported BPI scores and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

scholarly journals Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

2019 ◽  
Vol 9 (21) ◽  
pp. 4554 ◽  
Author(s):  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Trung Nguyen-Thoi ◽  
Prashanth Ragam ◽  
Hossein Moayedi
Keyword(s):  
State Of The Art ◽  
Promising Result ◽  
Absolute Error ◽  
Original Data ◽  
Training Data ◽  
Open Pit ◽  
Training Dataset ◽  
Percentage Error ◽  
Ann Model ◽  
Ann Models

Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, called EANNs model (ensemble of ANN models); 210 fly-rock events were recorded to develop and test the ANN and EANNs models. Of thi sample, 80% of the whole dataset was assigned to develop the models, the remaining 20% was assigned to confirm the models developed. Accordingly, five ANN models were designed and developed using the training dataset (i.e., 80% of the whole original data) first; then, their predictions on the training dataset were ensembled to generate a new training dataset. Subsequently, another ANN model was developed based on the new set of training data (i.e., EANNs model). Its performance was evaluated through a variety of performance indices, such as MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-square error), R2 (correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R2 = 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure.

scholarly journals Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing

2019 ◽  
Vol 9 (21) ◽  
pp. 4594 ◽  
Author(s):  
Hossein Moayedi ◽  
Bahareh Kalantar ◽  
Anastasios Dounis ◽  
Dieu Tien Bui ◽  
Loke Kok Foong
Keyword(s):  
Bearing Capacity ◽  
Prediction Models ◽  
Soil Layer ◽  
Ultimate Bearing Capacity ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Fe Modelling ◽  
Ann Models ◽  
Circular Footing

In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the problem of bearing capacity of shallow circular footing systems. Many types of research have shown that ANNs are valuable techniques for estimating the bearing capacity of the soils. However, most ANN training models have some drawbacks. This study aimed to focus on the application of two well-known hybrid ICA–ANN and PSO–ANN models to the estimation of bearing capacity of the circular footing lied in layered soils. In order to provide the training and testing datasets for the predictive network models, extensive finite element (FE) modelling (a database includes 2810 training datasets and 703 testing datasets) are performed on 16 soil layer sets (weaker soil rested on stronger soil and vice versa). Note that all the independent variables of ICA and PSO algorithms are optimized utilizing a trial and error method. The input includes upper layer thickness/foundation width (h/B) ratio, footing width (B), top and bottom soil layer properties (e.g., six of the most critical soil characteristics), vertical settlement of circular footing (s), where the output was taken ultimate bearing capacity of the circular footing (Fult). Based on coefficient of determination (R2) and Root Mean Square Error (RMSE), amounts of (0.979, 0.076) and (0.984, 0.066) predicted for training dataset and amounts of (0.978, 0.075) and (0.983, 0.066) indicated in the case of the testing dataset of proposed PSO–ANN and ICA–ANN models of prediction network, respectively. It demonstrates a higher reliability of the presented PSO–ANN model for predicting ultimate bearing capacity of circular footing located on double sandy layer soils.

scholarly journals Efficacy of dry needling in TMD treatment: clinical case report

2021 ◽  
Vol 10 (3) ◽  
pp. e16810313131
Author(s):  
Andressa Silva de Oliveira ◽  
Italo Hudson Tavares Maia ◽  
Tainah Oliveira Rifane ◽  
Francisbênia Alves Silvestre ◽  
Bárbara de Fátima Barboza de Freitas ◽  
...  
Keyword(s):  
Clinical Case ◽  
Myofascial Pain ◽  
Behavioral Therapy ◽  
Correct Diagnosis ◽  
Temporal Region ◽  
Dry Needling ◽  
Dental Clinic ◽  
Patient Reports ◽  
Patient Reported ◽  
The Face

The purpose of this article is to report a clinical case of a patient with TMD, evaluating the efficacy of dry needling as a treatment. The study was carried out in a dental clinic of higher education, where the evaluation of the patient was performed in order to conclude a correct diagnosis complementing with the use of dry needling. Patient, female, 38 years old, reported pain throughout the muscular region of the face, where it was more pronounced in the parotid-massometric region, with spreading to the temporal region. After performing the evaluation in addition to the percussion and palpation tests, the patient was diagnosed with spreading myofascial pain, in addition to headache attributed to TMD bilaterally. As a therapeutic approach, the dry needling technique was performed, in addition to the orientation of Cognitive Behavioral Therapy, since the patient reported having parafunctional habits. The techniques used produced an effective improvement to the patient, where the patient reports a relief of the muscular pains when performing the dry needling.

scholarly journals Dielectric technique combined with artificial neural network and support vector regression in moisture content prediction of olive

2020 ◽  
Vol 66 (No. 1) ◽  
pp. 1-7
Author(s):  
Mahdi Rashvand ◽  
Mahmoud Soltani Firouz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Support Vector Regression ◽  
Prediction Models ◽  
Electronic Device ◽  
Support Vector ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Olives are one of the most important agriculture crops in the world, which are harvested in different stages of growth for various uses. One of the ways to detect the adequate time to process the olives is to determine their moisture content. In this study, to determine the moisture content of olives, a dielectric technique was used in seven periods of harvesting and three different varieties of olive including Oily, Mary and Fishemi. The dielectric properties of the olive fruits were measured using an electronic device in the range of 0.1–30 MHz. Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods were applied to develop the prediction models by using the obtained data acquired by the system. The best results (R = 0.999 and MSE = 0.014) were obtained by the ANN model with a topology of 384–12–1 (384 features in the input vector, 12 neurons in the hidden layer and 1 output). The results obtained indicated the acceptable accuracy of the dielectric technique combined with the ANN model.

Real-Time Determination of Rheological Properties of Spud Drilling Fluids Using a Hybrid Artificial Intelligence Technique

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Khaled Abdelgawad ◽  
Salaheldin Elkatatny ◽  
Tamer Moussa ◽  
Mohamed Mahmoud ◽  
Shirish Patil
Keyword(s):  
Rheological Properties ◽  
Apparent Viscosity ◽  
Drilling Fluid ◽  
Differential Evolution Algorithm ◽  
Percentage Error ◽  
Drilling Fluids ◽  
Rheological Parameter ◽  
Ann Model ◽  
Hole Cleaning ◽  
Ann Models

The rheological properties of the drilling fluid play a key role in controlling the drilling operation. Knowledge of drilling fluid rheological properties is very crucial for drilling hydraulic calculations required for hole cleaning optimization. Measuring the rheological properties during drilling sometimes is a time-consuming process. Wrong estimation of these properties may lead to many problems, such as pipe sticking, loss of circulation, and/or well control issues. The aforementioned problems increase the non-productive time and the overall cost of the drilling operations. In this paper, the frequent drilling fluid measurements (mud density, Marsh funnel viscosity (MFV), and solid percent) are used to estimate the rheological properties of bentonite spud mud. Artificial neural network (ANN) technique was combined with the self-adaptive differential evolution algorithm (SaDe) to develop an optimum ANN model for each rheological property using 1029 data points. The SaDe helped to optimize the best combination of parameters for the ANN models. For the first time, based on the developed ANN models, empirical equations are extracted for each rheological parameter. The ANN models predicted the rheological properties from the mud density, MFV, and solid percent with high accuracy (average absolute percentage error (AAPE) less than 5% and correlation coefficient higher than 95%). The developed apparent viscosity model was compared with the available models in the literature using the unseen dataset. The SaDe-ANN model outperformed the other models which overestimated the apparent viscosity of the spud drilling fluid. The developed models will help drilling engineers to predict the rheological properties every 15–20 min. This will help to optimize hole cleaning and avoid pipe sticking and loss of circulation where bentonite spud mud is used. No additional equipment or special software is required for applying the new method.

scholarly journals Applying neural networks to predict HPC-I/O bandwidth over seismic data on lustre file system for ExSeisDat

2021 ◽  
Author(s):  
Abdul Jabbar Saeed Tipu ◽  
Padraig Ó Conbhuí ◽  
Enda Howley
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Large Scale ◽  
Prediction Models ◽  
File System ◽  
Predictive Modelling ◽  
Absolute Error ◽  
Percentage Error ◽  
Ann Models ◽  
Computationally Intensive

AbstractHPC or super-computing clusters are designed for executing computationally intensive operations that typically involve large scale I/O operations. This most commonly involves using a standard MPI library implemented in C/C++. The MPI-I/O performance in HPC clusters tends to vary significantly over a range of configuration parameters that are generally not taken into account by the algorithm. It is commonly left to individual practitioners to optimise I/O on a case by case basis at code level. This can often lead to a range of unforeseen outcomes. The ExSeisDat utility is built on top of the native MPI-I/O library comprising of Parallel I/O and Workflow Libraries to process seismic data encapsulated in SEG-Y file format. The SEG-Y File data structure is complex in nature, due to the alternative arrangement of trace header and trace data. Its size scales to petabytes and the chances of I/O performance degradation are further increased by ExSeisDat. This research paper presents a novel study of the changing I/O performance in terms of bandwidth, with the use of parallel plots against various MPI-I/O, Lustre (Parallel) File System and SEG-Y File parameters. Another novel aspect of this research is the predictive modelling of MPI-I/O behaviour over SEG-Y File benchmarks using Artificial Neural Networks (ANNs). The accuracy ranges from 62.5% to 96.5% over the set of trained ANN models. The computed Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) values further support the generalisation of the prediction models. This paper demonstrates that by using our ANNs prediction technique, the configurations can be tuned beforehand to avoid poor I/O performance.

A Novel SVM-Based Method for Seismic First-Arrival Detecting

2010 ◽  
Vol 29-32 ◽  
pp. 973-978 ◽  
Author(s):  
Ming Chen ◽  
Yong Li ◽  
Jun Xie
Keyword(s):  
Support Vector ◽  
The Novel ◽  
Ann Model ◽  
Seismic Record ◽  
Feature Vectors ◽  
Svm Model ◽  
First Arrival ◽  
Ann Models ◽  
Novel Method ◽  
Artificial Neural Network Ann

First arrivals detecting on seismic record is important at all times. A novel support vector machine (SVM)-based method for seismic first-arrival pickup is proposed in this research. Firstly, the multi-resolution wavelet decomposition is used to de-noise the seismic record. And then, feature vectors are extracted from the denoise data. Finally, both SVM and artificial neural network (ANN) models are employed to train and predict the feature vectors. Experimental results demonstrate that the SVM model gives better accuracy than the ANN model. It is promising that the novel method is very prospective.

MODELING AND PREDICTING THE GLASS TRANSITION TEMPERATURE OF VINYL POLYMERS BY USING HYBRID PSO-SVR METHOD

2013 ◽  
Vol 12 (03) ◽  
pp. 1350002 ◽  
Author(s):  
J. F. PEI ◽  
C. Z. CAI ◽  
Y. M. ZHU
Keyword(s):  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Independent Set ◽  
Percentage Error ◽  
Support Vector ◽  
Ann Model ◽  
Prediction Ability ◽  
Vinyl Polymers ◽  
Sar Model

Based on four physicochemical descriptors (the rigidness descriptor R OM resulted by hydrogen-bonding moieties group and/or rings, the chain mobility n, the molecular average polarizability α and the net charge of the most negative atom q-) derived from the polymers' monomers structure, the support vector regression (SVR) approach combined with particle swarm optimization (PSO), is proposed to construct a model for prediction of the glass transition temperature T g of three classes of vinyl polymers, including polystyrenes, polyacrylates and polymethacrylates. The mean absolute error (MAE = 13.68 K), mean absolute percentage error (MAPE = 4.22%) and correlation coefficient (R2 = 0.9252) calculated by SVR are superior to those (MAE = 16.74 K, MAPE = 5.30% and R2 = 0.9059) achieved by S-SAR model, and (MAE = 16.83 K, MAPE = 5.27% and R2 = 0.9057) achieved by ANN model for the identical training set (124 vinyl polymers), whereas the MAE = 15.09 K, MAPE = 4.82% and R2 = 0.9253 calculated by SVR are also better than those of MAE = 17.96 K, MAPE = 5.94% and R2 = 0.8952 achieved by S-SAR, and MAE = 16.603 K, MAPE = 5.4% and R2 = 0.9120 achieved by ANN for the same 68 test samples. Furthermore, the MAE, MAPE and R2 for an independent set (10 vinyl polymers) predicted by SVR also reached 14.132 K, 4.25% and 0.9475, respectively. The results strongly support that the comprehensive modeling and prediction ability of SVR model surpass those of S-SAR and ANN models by applying identical training, test and independent samples. It is demonstrated that the established SVR model is more suitable to be used for prediction of the T g values for unknown vinyl polymers possessing similar structure than S-SAR model or ANN model.

scholarly journals Prediction of Compressive Strengths with Partial Fine Aggregate of Plastic Using Artificial Neural Network and Revisions

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Cornelius Ngunjiri Ngandu
Keyword(s):  
Compressive Strength ◽  
Prediction Models ◽  
Plastic Waste ◽  
Partial Replacement ◽  
Percentage Error ◽  
Fine Aggregate ◽  
Ann Model ◽  
Waste Plastic ◽  
Green Concrete ◽  
Artificial Neural

In recent past years, plastic waste has been a environmental menace. Utilization of plastic waste as fine aggregate substitution could reduce the demand and negative impacts of sand mining while addressing waste plastic challenges.This study aims at evaluating compressive strengths prediction models for concrete with plastic—mainly recycled plastic—as partial replacement or addition of fine aggregates, by use of artificial neural networks (ANNs), developed in OCTAVE 5.2.0 and datasets from reviews. 44 datasets from 8 different sources were used, that included four input variables namely:- water: binder ratio; control compressive strength (MPa); % plastic replacement or additive by weight and plastic type; and the output variable was the compressive strength of concrete with partial plastic aggregates.Various models were run and the selected model, with 14 nodes in hidden layer and 320,000 iterations, indicated overall root mean square error (RMSE) , absolute factor of variance (R2), mean absolute error (MAE) and mean absolute percentage error (MAPE) values of 1.786 MPa, 0.997, 1.329 MPa and 4.44 %. Both experimental and predicted values showed a generally increasing % reduction of compressive strengths with increasing % plastic fine aggregate.The model showed reasonably low errors, reasonable accuracy and good generalization. ANN model could be used extensively in modeling of green concrete, with partial waste plastic fine aggregate. The study recommend ANNs models application as possible alternative for green concrete trial mix design. Sustainable techniques such as low-cost superplasticizers from recycled material and cost-effective technologies to adequately sizing and shaping plastic for fine aggregate application should be encouraged, so as to enhance strength of concrete with partial plastic aggregates.

scholarly journals Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak

2021 ◽  
Vol 7 ◽  
pp. e746
Author(s):  
Muhammad Naeem ◽  
Jian Yu ◽  
Muhammad Aamir ◽  
Sajjad Ahmad Khan ◽  
Olayinka Adeleye ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Comparative Analysis ◽  
Prediction Models ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Support Vector ◽  
Absolute Percentage Error ◽  
Statistical Measures ◽  
The Impact

Background Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread. Methods In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic. Results Statistical measures—Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE)—are used for model accuracy. The values of MAPE for the best-selected models for confirmed, recovered and deaths cases are 0.003, 0.006 and 0.115, respectively, which falls under the category of highly accurate forecasts. In addition, we computed 15 days ahead forecast for the daily deaths, recovered, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting the decision-making of evolving short-term policies.

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