Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

Dutta, Ashit Kumar; Zakari, Nazik M. A.; Albagory, Yasser; Sait, Abdul Rahaman Wahab

doi:10.32604/csse.2023.026461

Cited by 6 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Long Short-Term Memory (LSTM) Model is a type of recurrent neural network designed to process sequential data such as time series or sentences [12]. It addresses the issues of vanishing and exploding gradients while effectively capturing long-term dependencies.…”

Section: Lstm Modelmentioning

confidence: 99%

“…After calculating the output of the hidden layer using the input data, an ELM model computes the weights of the output layer by solving a system of linear equations. The calculation of the output layer weights is given by Equation (12). The output layer weights, W 2 , are calculated using the output values H of the hidden layer and the actual output values Y.…”

Section: Elm Modelmentioning

confidence: 99%

See 1 more Smart Citation

A Study on the Performance Evaluation of the Convolutional Neural Network–Transformer Hybrid Model for Positional Analysis

Lee

2023

Applied Sciences

View full text Add to dashboard Cite

In this study, we identified the different causes of odor problems and their associated discomfort. We also recognized the significance of public health and environmental concerns. To address odor issues, it is vital to conduct precise analysis and comprehend the root causes. We suggested a hybrid model of a Convolutional Neural Network (CNN) and Transformer called the CNN–Transformer to tackle this challenge and assessed its effectiveness. We utilized a dataset containing 120,000 samples of odor to compare the performance of CNN+LSTM, CNN, LSTM, and ELM models. The experimental results show that the CNN+LSTM hybrid model has an accuracy of 89.00%, precision of 89.41%, recall of 91.04%, F1-score of 90.22%, and RMSE of 0.28, with a large prediction error. The CNN+Transformer hybrid model had an accuracy of 96.21%, precision and recall of 94.53% and 94.16%, F1-score of 94.35%, and RMSE of 0.27, showing a low prediction error. The CNN model had an accuracy of 87.19%, precision and recall of 89.41% and 91.04%, F1-score of 90.22%, and RMSE of 0.23, showing a low prediction error. The LSTM model had an accuracy of 95.00%, precision and recall of 92.55% and 94.17%, F1-score of 92.33%, and RMSE of 0.03, indicating a very low prediction error. The ELM model performed poorly with an accuracy of 85.50%, precision and recall of 85.26% and 85.19%, respectively, and F1-score and RMSE of 85.19% and 0.31, respectively. This study confirms the suitability of the CNN–Transformer hybrid model for odor analysis and highlights its excellent predictive performance. The employment of this model is expected to be advantageous in addressing odor problems and mitigating associated public health and environmental concerns.

show abstract

Section: Lstm Modelmentioning

confidence: 99%

Section: Elm Modelmentioning

confidence: 99%

A Study on the Performance Evaluation of the Convolutional Neural Network–Transformer Hybrid Model for Positional Analysis

Lee

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…This method uses the designed mathematical model to directly simulate and analyze without accumulating the previous operating conditions of the system. The fault tree analysis method takes the maximum fault of the diagnosis object as the main objective of analysis, finds out all factors that cause the fault, and expands level by level according to the event level [12].…”

Section: Ship Lock Electromechanical Remote Fault Diagnosis Modementioning

confidence: 99%

Ship Lock Electromagnetically Remote Fault Diagnosis Mode Based on Machine Learning

2022

View full text Add to dashboard Cite

The traditional electromechanical fault diagnosis mode of the ship lock adopts the wired connection control intelligent control system, which cannot be remotely and wirelessly controlled through the control field and more strict control mode. The design of ship lock electromechanical remote fault diagnosis mode based on machine learning is proposed. The concept of machine learning and the characteristics of neural network are summarized. The fault diagnosis mode and fault diagnosis technology of electrical communication system are proposed. The experimental comparison of fault diagnosis accuracy of CNN SVM GA-SVM model shows that the deep learning bearing fault diagnosis model based on CNN network model has better performance.

show abstract

A novel deep dual self-attention and Bi-LSTM fusion framework for Parkinson’s disease prediction using freezing of gait: a biometric application

Habib,

Mughal,

Khan

et al. 2024

Multimed Tools Appl

View full text Add to dashboard Cite

Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

Cited by 6 publications

References 21 publications

A Study on the Performance Evaluation of the Convolutional Neural Network–Transformer Hybrid Model for Positional Analysis

A Study on the Performance Evaluation of the Convolutional Neural Network–Transformer Hybrid Model for Positional Analysis

Ship Lock Electromagnetically Remote Fault Diagnosis Mode Based on Machine Learning

A novel deep dual self-attention and Bi-LSTM fusion framework for Parkinson’s disease prediction using freezing of gait: a biometric application

Contact Info

Product

Resources

About