scholarly journals Deep Learning Techniques Applied to Predict and Measure Finger Movement in Patients with Multiple Sclerosis

2021 ◽  
Vol 11 (7) ◽  
pp. 3137
Author(s):  
Dmitry Viatkin ◽  
Begonya Garcia-Zapirain ◽  
Amaia Méndez Méndez Zorrilla
Keyword(s):  
Neural Network ◽  
Multiple Sclerosis ◽  
Neural Networks ◽  
Mean Square Error ◽  
Ring Finger ◽  
Absolute Error ◽  
Input Image ◽  
Finger Joint ◽  
Mean Square ◽  
Joint Angles

This research focuses on the development of a system for measuring finger joint angles based on camera image and is intended for work within the field of medicine to track the movement and limits of hand mobility in multiple sclerosis. Measuring changes in hand mobility allows the progress of the disease and its treatment process to be monitored. A static RGB camera without depth vision was used in the system developed, with the system receiving only the image from the camera and no other input data. The research focuses on the analysis of each image in the video stream independently of other images from that stream, and 12 measured hand parameters were chosen as follows: 3 joint angles for the index finger, 3 joint angles for the middle finger, 3 joint angles for the ring finger, and 3 joint angles for the pinky finger. Convolutional neural networks were used to analyze the information received from the camera, and the research considers neural networks based on different architectures and their combinations as follows: VGG16, MobileNet, MobileNetV2, InceptionV3, DenseNet, ResNet, and convolutional pose machine. The final neural network used for image analysis was a modernized neural network based on MobileNetV2, which obtained the best mean absolute error value of 4.757 degrees. Additionally, the mean square error was 67.279 and the root mean square error was 8.202 degrees. This neural network analyzed a single image from the camera without using other sensors. For its part, the input image had a resolution of 512 by 512 pixels, and was processed by the neural network in 7–15 milliseconds by GPU Nvidia 2080ti. The resulting neural network developed can measure finger joint angle values for a hand with non-standard parameters and positions.

Neural networks for modelling the energy consumption of metro trains

2019 ◽  
Vol 234 (7) ◽  
pp. 722-733 ◽  
Author(s):  
Pablo Martínez Fernández ◽  
Pablo Salvador Zuriaga ◽  
Ignacio Villalba Sanchís ◽  
Ricardo Insa Franco
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Consumption ◽  
Mean Square Error ◽  
Mean Square ◽  
The Neural Network ◽  
Train Speed ◽  
Input Variables ◽  
Trained Neural Network ◽  
Traction Power

This paper presents the application of machine learning systems based on neural networks to model the energy consumption of electric metro trains, as a first step in a research project that aims to optimise the energy consumed for traction in the Metro Network of Valencia (Spain). An experimental dataset was gathered and used for training. Four input variables (train speed and acceleration, track slope and curvature) and one output variable (traction power) were considered. The fully trained neural network shows good agreement with the target data, with relative mean square error around 21%. Additional tests with independent datasets also give good results (relative mean square error = 16%). The neural network has been applied to five simple case studies to assess its performance – and has proven to correctly model basic consumption trends (e.g. the influence of the slope) – and to properly reproduce acceleration, holding and braking, although it tends to slightly underestimate the energy regenerated during braking. Overall, the neural network provides a consistent estimation of traction power and the global energy consumption of metro trains, and thus may be used as a modelling tool during further stages of research.

APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF POSITIVE DYNAMICS FOR TREATMENT OF PATIENTS WITH TUBERCULOSIS

2020 ◽  
Author(s):  
Marina Ermolickaya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Square Error ◽  
Network Model ◽  
Neural Network Model ◽  
Test Sample ◽  
Mean Square ◽  
Tuberculosis Patients ◽  
The Mean

Using the RStudio program, a neural network model has been developed that predicts positive dynamics in the treatment of tuberculosis patients in a tuberculosis dispensary hospital. The accuracy of the presented model on the test sample is 99.4%, the mean square error (MSE) is 0.013.

Estimations of fission barrier heights for Ra, Ac, Rf and Db nuclei by neural networks

2014 ◽  
Vol 23 (10) ◽  
pp. 1450064 ◽  
Author(s):  
Serkan Akkoyun ◽  
Tuncay Bayram
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mean Square Error ◽  
Fission Barrier ◽  
Accurate Information ◽  
Mean Square ◽  
Fission Process ◽  
Barrier Heights ◽  
R Process ◽  
Artificial Neural Network Ann

Accurate information about the fission barrier is important for studying of the fission process. Fission barrier is needed for discovering the island of stability in superheavy region and searching of the superheavy elements. Furthermore, the astrophysical r-process is closely related to the fission barrier of the neutron-rich nuclei. In this study, by using artificial neural network (ANN) method, we have estimated the fission barrier heights of the Rf , Db , Ra and Ac nuclei covering 230 isotopes. For inner barrier calculation, we have used Rf and Db nuclei and the barrier heights have been determined between nearly 1 MeV and 7 MeV. The related mean square error value has been obtained as 0.108 MeV. For outer barrier calculation, we have used Ra and Ac nuclei and the heights have been determined between nearly 8 MeV and 28 MeV. The related mean square error has been obtained as 0.407. The results of this study indicate that ANN is capable for the estimations of inner and outer fission barrier heights.

scholarly journals Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines

2019 ◽  
pp. 1-13 ◽  
Author(s):  
Jasleen Kaur ◽  
Khushdeep Dharni
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Mean Square Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Mean Square ◽  
Data Mining Techniques ◽  
Vector Machines

Uniqueness in economies and stock markets has given rise to an interesting domain of exploring data mining techniques across global indices. Previously, very few studies have attempted to compare the performance of data mining techniques in diverse markets. The current study adds to the understanding regarding the variations in performance of data mining techniques across the global stock indices. We compared the performance of Neural Networks and Support Vector Machines using accuracy measures Mean Absolute Error (MAE) and R­­­­oot Mean Square Error (RMSE) across seven major stock markets. For prediction purpose, technical analysis has been employed on selected indicators based on daily values of indices spanning a period of 12 years. We created 196 data sets spanning different time periods for model building such as 1 year, 2 years, 3 years, 4 years, 6 years and 12 years for selected seven stock indices. Based on prediction models built using Neural Networks and Support Vector Machines, the findings of the study indicate there is a significant difference, both for MAE and RMSE, across the selected global indices. Also, Mean Absolute Error and Root Mean Square Error of models built using NN were greater than Mean Absolute Error and Root Mean Square Error of models built using SVM.

Prediction of Significant Wave Height Using Neural Network in the Java Sea (North of Surabaya)

2017 ◽  
Vol 862 ◽  
pp. 72-77
Author(s):  
Wimala L. Dhanistha ◽  
R.A. Atmoko ◽  
P. Juniarko ◽  
Ridho Akbar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Wave Height ◽  
Mean Square ◽  
The North ◽  
Java Sea ◽  
The City

Indonesia is an archipelago, Surabaya is the second crowded city in Indonesia. So the shipping lane and the city is comparable. Neural network is models inspired by biological neural networks and used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Neural network is used to predict the wave height in Java Sea (The North of Surabaya). The Root Mean Square Error average for the next one hour is 0.03 and the Root Mean Square Error average for the next six hours is 0.09. That’s mean the longest the prediction, the biggest Root Mean Square error.

Modeling Drying Properties of Pistachio Nuts, Squash and Cantaloupe Seeds under Fixed and Fluidized Bed Using Data-Driven Models and Artificial Neural Networks

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Mohammad Kaveh ◽  
Reza Amiri Chayjan ◽  
Behrooz Khezri
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mean Square Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Moisture Diffusivity ◽  
Effective Moisture Diffusivity ◽  
Mean Square ◽  
Feed Forward ◽  
Effective Moisture

AbstractThis paper presents the application of feed forward and cascade forward neural networks to model the non-linear behavior of pistachio nut, squash and cantaloupe seeds during drying process. The performance of the feed forward and cascade forward ANNs was compared with those of nonlinear and linear regression models using statistical indices, namely mean square error ($MSE$), mean absolute error ($MAE$), standard deviation of mean absolute error (SDMAE) and the correlation coefficient (${R^2}$). The best neural network feed forward back-propagation topology for the prediction of effective moisture diffusivity and energy consumption were 3-3-4-2 with the training algorithm of Levenberg-Marquardt (LM). This structure is capable to predict effective moisture diffusivity and specific energy consumption with${R^2}$= 0.9677 and 0.9716, respectively and mean-square error ($MSE$) of 0.00014. Also the highest${R^2}$values to predict the drying rate and moisture ratio were 0.9872 and 0.9944 respectively.

scholarly journals USING ARTIFICIAL NEURAL NETWORKS TO PREDICT POSITIVE DYNAMICS IN THE TREATMENT OF TUBERCULOSIS PATIENT

2020 ◽  
pp. 139-147
Author(s):  
M.Z. Ermolitskaya ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Square Error ◽  
Neural Network Model ◽  
Test Sample ◽  
Tuberculosis Patient ◽  
Mean Square ◽  
Tuberculosis Patients ◽  
The Mean

R Studio program was used to develop a neural network model that predicts positive dynamics in the treatment of tuberculosis patients in the hospitals. The accuracy of the presented model in the test sample is 99.4%, and the mean square error is 0.013.

scholarly journals Future Optimization Algorithm to Estimate Attenuation in 532 nm Laser Beam of UWOC-Channel: Improved Neural Network Model

2021 ◽  
Vol 8 (3) ◽  
pp. 453-460
Author(s):  
Salah A. Adnan ◽  
Ahmed Alchalaby ◽  
Hassan Ahmed Hassan
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Attenuation Coefficient ◽  
Search Algorithm ◽  
Water Channel ◽  
Absolute Error ◽  
Optical Signal ◽  
Superior Performance ◽  
Mean Square ◽  
Optical Wireless

Underwater Optical Wireless Communication (UWOC) becomes an emerging underwater communication technology, with high-data rates over relatively medium transmission ranges. When optical wireless signal transmitted in ocean water channel, it will suffer from drastic scattering and absorption due to water molecules, dissolved particles, air bubbles, and turbulence. Absorption and scattering of the transmitted wireless optical signal in underwater channel led to attenuation in optical signal power. Optical signal attenuation over underwater channel is an aggregate of` different parameters effects that changed frequently, then practical measuring of this attenuation is complicated, difficult, expensive, and time-consuming process. In this work, improved neural network optimized with future search algorithm (FANN) was proposed, as an efficacious solution to obtain an accurate, relabel values of attenuation coefficient in different water types and conditions. The proposed FANN model provides a good much results to the practical measured values. The performance of the proposed FANN model was evaluated using mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE) error indices. The errors in attenuation coefficient values obtained by the proposed FANN model had been calculated and its values are very acceptable which are lie lower than 10-4. The performance of the proposed FANN model shows excellent results which indicate the superior performance of the proposed FANN model.

scholarly journals Gold price prediction using an evolutionary pi-sigma neural network

2018 ◽  
Vol 7 (4.5) ◽  
pp. 742
Author(s):  
Rajashree Dash ◽  
Anuradha Routray ◽  
Rasmita Rautray ◽  
Rasmita Dash
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Gold Price ◽  
Mean Square ◽  
Price Prediction ◽  
Future Value ◽  
Predictor Model ◽  
Result Analysis

Gold Price Prediction has always been a fascinating area of study for researchers and decision makers who wish to determine its future value efficiently and accurately. In this study a predictor model is designed using a Pi-Sigma Neural Network (PSNN) for prediction of future gold price. Two evolutionary estimation paradigms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE) are suggested during training step of the Pi-Sigma Neural Network to optimize the tunable weights of the network. The model is evaluated based on certain performance evaluation criteria such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) over two dataset such as Gold/INR and Gold/AED accumulated within the same period of time. The result analysis illustrates the better prediction  

scholarly journals Artificial Neural Network (FFBP-ANN) Based Grey’s Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

2020 ◽  
Author(s):  
Srinidhi . ◽  
Deepak Patel ◽  
Vasantha Kumara S A
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Mean Square Error ◽  
Supercritical Co2 ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Mean Square ◽  
Relational Analysis ◽  
Supercritical Carbon

The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey’s relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed.

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