scholarly journals Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoyi Guo ◽  
Wei Zhou ◽  
Qun Lu ◽  
Aiyan Du ◽  
Yinghua Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Dry Weight ◽  
Hemodialysis Patients ◽  
Normal Weight ◽  
The Body ◽  
Functional Link ◽  
Patient Will ◽  
Better Than

Dry weight is the normal weight of hemodialysis patients after hemodialysis. If the amount of water in diabetes is too much (during hemodialysis), the patient will experience hypotension and shock symptoms. Therefore, the correct assessment of the patient’s dry weight is clinically important. These methods all rely on professional instruments and technicians, which are time-consuming and labor-intensive. To avoid this limitation, we hope to use machine learning methods on patients. This study collected demographic and anthropometric data of 476 hemodialysis patients, including age, gender, blood pressure (BP), body mass index (BMI), years of dialysis (YD), and heart rate (HR). We propose a Sparse Laplacian regularized Random Vector Functional Link (SLapRVFL) neural network model on the basis of predecessors. When we evaluate the prediction performance of the model, we fully compare SLapRVFL with the Body Composition Monitor (BCM) instrument and other models. The Root Mean Square Error (RMSE) of SLapRVFL is 1.3136, which is better than other methods. The SLapRVFL neural network model could be a viable alternative of dry weight assessment.

scholarly journals Electric Vehicle Relay Lifetime Prediction Model Using the Improving Fireworks Algorithm–Grey Neural Network Model

2020 ◽  
Vol 10 (6) ◽  
pp. 1940 ◽  
Author(s):  
Xuelian Pang ◽  
Zhuo Li ◽  
Ming-Lang Tseng ◽  
Kaihua Liu ◽  
Kimhua Tan ◽  
...  
Keyword(s):  
Neural Network ◽  
Electric Vehicle ◽  
Network Model ◽  
Neural Network Model ◽  
Standard Test ◽  
Running Speed ◽  
Test Function ◽  
Fireworks Algorithm ◽  
Convergence Performance ◽  
Better Than

The relay reliability has an impact on the reliability of the entire electric vehicle system. This paper contributes to propose the improving fireworks algorithm optimizing the grey neural network model to predict the relay lifetime. This paper shows how the mutation operation and mapping operation in the fireworks algorithm are used to improve the convergence ability and running speed; the convergence performance and running speed of improved fireworks algorithm are tested with standard test function and compared with fireworks algorithm; and the grey neural network model–improved fireworks algorithm is used to predict the relay life and compared with grey model, grey neural network, and grey neural network model–fireworks algorithm. The results show that the convergence accuracy of the improved fireworks algorithm is better than the fireworks algorithm. The running time of improved fireworks algorithm is the shortest; the improved fireworks algorithm–grey neural network model has the best prediction effect and the root mean square error value is 6.75% smaller than the fireworks algorithm–grey neural network model.

scholarly journals An Efficient Neural Network Model for the Identification of Stress using Electrocardiogram

2019 ◽  
Vol 8 (2S6) ◽  
pp. 502-507
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
The Body ◽  
Mental Wellbeing ◽  
Daily Routine ◽  
Back Propagation Algorithm ◽  
Suggested Technique

People are facing numerous pressures in their daily routine in the latest society. Stress has traditionally has been described as action from a calm state to an emotional state in order to preserve the integrity of organism. Stress observation is very important for mental wellbeing and early identification of stress related disorders. Stress is to learn the body response in stressful state, whenever the body reaction is activated that means the heart rate and blood pressure will raise and several hormones enter our bloodshed. These hormones and bodily changes may increases our performances to a particular extent. Everyone's response to stress is discreet, and not all stress is bad. Someone may discover a significant condition of pressure to be enjoyable, while others may find it stressful. However, individuals also have different stress symptoms. stress area can also recognize using frequency and excitation of a speech signal, Since the biomedical signals are consistently related to central nervous system, therefore physiological parameters are the best way to understand the human emotions. The present work is focused on stress identification from Electrocardiogram using ECG physiologic net database, then entire environment of ECG signal characteristics i.e. mean heart rate variability (HRV), standard deviation of all R-R interval (SDNN), square root mean of the sum of the square difference between R-R interval (RMSSD) and number of consecutive R-R interval variations greater than 50ms (NN50), these features are extracted using Pan-Tompkins algorithm, then it is trained and validated to machine learning using back-propagation algorithm in neural network model. With the help of these features (mean HRV, SDNN, RMSSD and NN50), the study can be analyzed whether a person is under stress or not. Thus how the suggested technique provides the subjective information which helps the doctor to find out whether the person is under stress or not.

scholarly journals An Optimized Hybrid Neural Network Model for Detecting Depression among Twitter Users

2019 ◽  
Vol 8 (10) ◽  
pp. 2781-2795
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Research Work ◽  
Hybrid Neural Network ◽  
The Neural Network ◽  
Twitter Users ◽  
Trained Neural Network ◽  
User Friendly ◽  
Better Than

The proposed work is to extensively evaluate if a user is depressed or not using his Tweets on Twitter. With the omni presence of social media, this method should help in identifying the depression of users. We propose an Optimized Hybrid Neural Network model to evaluate the user tweets on Twitter to analyze if a user is depressed or not. Where Neural Network is trained using Tweets to predict the polarity of Tweets. The Neural Network is trained in such a way that at any point when presented with a Tweet the model outputs the polarity associated with the Tweet. Also, a user-friendly GUI is presented to the user that loads the trained neural network in no time and can be used to analyze the users’ state of depression. The aim of this research work is to provide an algorithm to evaluate users’ sentiment on Twitter in a way better than all other existing techniques

Improved Accuracy of an Ultrasonic Based Neuro-Navigation System Using Neural Networks

2005 ◽  
Author(s):  
Jennifer Akers ◽  
Sanjeevi Chitikeshi ◽  
Ajay Mahajan ◽  
Sumeer Lal
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Navigation Systems ◽  
The Neural Network ◽  
Neuronavigation System ◽  
Electronic Hardware ◽  
Improved Accuracy ◽  
2D And 3D ◽  
Better Than

This paper presents the design and development of an ultrasonic based neuronavigation system to be used for real time surgery. The system formulation, hardware and a neural network model is presented that improves the accuracy of the system considerably. 1D, 2D and 3D results from the neural network model are presented along with designs for the physical and electronic hardware. The 3D system presented in this paper eliminates the space intensive camera, has an accuracy better than 1.0 mm in the operating range of about 20–40 cm, makes the system independent of line-of-sight occlusion problems, and is expected to pave the way for accurate fusion models of the future that may account for brain shifts during surgery. The results show that the performance of the proposed system provides many advantages over existing neuro-navigation systems without compromising on the accuracy.

Research on Aviation Material with Unsafe Events Prediction Based on BP Neural Network with PCA Feature Extraction

2014 ◽  
Vol 540 ◽  
pp. 488-491 ◽  
Author(s):  
Xu Sheng Gan ◽  
Hua Ping Li ◽  
Jing Shun Duanmu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Aviation Material ◽  
Training Samples ◽  
Basic Features ◽  
Bp Neural Network Model ◽  
Better Than

In order to better predict the aviation material unsafe events, a BP neural network model based on PCA feature extraction is proposed. Firstly, the training samples of aviation material unsafe events are used to carry out the PCA feature extraction, and then using the extracted basic features, BP neural network model is established. The numerical example shows that, the hybrid model proposed is better than that of alone BP neural network model, and it is effective and feasible to establish the unsafe events model for aviation material.

Neural Network Modeling of Maximum Insertion Force of Bevel-Tip Surgical Needle

2018 ◽  
Author(s):  
Sai Teja Reddy Gidde ◽  
Tololupe Verissimo ◽  
Nuo Chen ◽  
Parsaoran Hutapea ◽  
Byoung-gook Loh
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Modeling ◽  
The Body ◽  
Neural Network Modeling ◽  
Tissue Deformation ◽  
Input Output ◽  
Insertion Force ◽  
Tissue Phantom

Recently there has been a growing interest to develop innovative surgical needles for percutaneous interventional procedures. Needles are commonly used to reach target locations inside of the body for various medical interventions. The effectiveness of these procedures depends on the accuracy with which the needle tips reach the targets, such as a biopsy procedure to assess cancerous cells and tumors. One of the major issues in needle steering is the force during insertion, also known as the insertion (penetration) force. The insertion force causes tissue damage as well as tissue deformation. It has been well studied that tissue deformation causes the needle to deviate from its target thus causing an ineffective procedure. Simulation of surgical procedures provides an effective method for a robot-assisted surgery for pre- and intra-operative planning. Accurate modeling of the mechanical behavior on the interface of surgical needles and organs, specifically the insertion force, has been well recognized as a major challenge. Overcoming such obstacle by development of robust numerical models will enable realistic force feedback to the user during surgical simulation. This study investigates feasibility of predicting the insertion force of bevel-tip needles based on experimental data using neural network modeling. Simulation of the proposed neural network model is performed using Kera’s Python Deep Learning Library with TensorFlow as a backend. The insertion forces of needles with different bevel-tip angles in gel tissue phantom are measured using a specially designed automated needle insertion test setup. Input-output datasets are generated where the inputs are defined as bevel-tip angles and gel tissue phantom stiffness, and the output is defined as the insertion force. A properly trained neural network then maps the input data to the output data and the input-output dataset is supplied to train a neural network. Its performance is then evaluated using different and unseen input-output dataset. This paper shows that the proposed neural network model accurately predicts the insertion force.

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