A Novel Diagnosis System Using Regularized Encoder-Decoder Based Generative Probabilistic Network for Parkinson's Disease

Sarker, Yeahia; Mondal, Md. Nazrul İslam; Fahim, Shahriar Rahman; Shahriar, Saquib; Sarker, Subrata; Das, Sajal K.

doi:10.1109/tensymp50017.2020.9230792

Cited by 10 publications

(5 citation statements)

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“…However, CNN and transfer learning techniques were not limited to imaging data; they also learn complex features from voices and signal data [ 29 ]. Numerous studies used the biomedical voice ( n = 21) [ 4 , 6 , 22 , 23 , 29 , 33 , 44 , 48 , 50 , 52 , 53 , 55 , 60 , 61 , 73 , 74 , 84 , 93 , 100 , 104 , 105 ] and biometric signal ( n = 14) [ 26 , 31 , 34 , 36 , 45 , 46 , 57 , 62 , 64 , 65 , 68 , 89 , 96 , 98 ]; a few of the included studies used EEG and EMG signals ( n = 5) [ 32 , 39 , 51 , 83 , 85 ].…”

Section: Resultsmentioning

confidence: 99%

“…We found that most of the biomedical voice measurements dataset was obtained from the University of California (UCI) Irvine Machine Learning repository; in [ 53 , 84 ] and [ 23 ], the same dataset is used; however, 19 achieved outstanding result using the sequential model in a deep neural network for detection PD based on voice measurement. In [ 33 , 44 ], and [ 4 ], the same voice measurement datasets with 756 instances and 754 attributes were used to identify PD, and the autoencoder neural network in [ 33 ] achieved better results than other studies.…”

Section: Discussionmentioning

confidence: 99%

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The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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“…Clove bud, also known as Eugenia caryophyllata ( Syzygium aromaticum (L.) (Family Myrtaceae) is a dried flower bud. This species is widely cultivated in Indonesia, Sri Lanka, Madagascar, and Tanzania 9 10 Clove bud essential oil is known for its biological activities, which are due to its richness in many phytochemicals, such as sesquiterpenes, monoterpenes, and phenolic compounds. 11,12 Eugenol represents between 45 and 90% of the oil of Eugenia caryophyllata (L.) (Merr.…”

Section: Protection Of Eugenolmentioning

confidence: 99%

Oxidation of Eugenol Derivatives with KMnO4 and CrO3

Abdou,

Dakir,

Maaghloud

et al. 2024

Synthesis

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This study aims to delineate the synthesis of eugenol derivatives, starting with hydroxyl group protection and then the subsequent oxidation stages. Initially, eugenol underwent conversion into acetyleugenol and benzyleugenol during the protection phase. Subsequently, a kinetic oxidation of acetyleugenol with KMnO4 via GC-MS analysis resulted in the identification of four compounds. The kinetic investigation indicated the primary formation of diolacetyleugenol, succeeded by aldehyde eugenol, which further gets converted into its respective carboxylic acid. Additionally, acetyleugenol and benzyleugenol underwent oxidation with CrO3, yielding the corresponding carboxylic acids.

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“…Meanwhile, the RMSE takes the square root of the mean squared error to obtain the mean root value. These metrics' formulations are defined in (11) and (12).…”

Section: Training Strategies and Evaluation Metricsmentioning

confidence: 99%

Enhancing Multi-Output Time Series Forecasting with Encoder-Decoder Networks

Hartomo,

Lopo,

Purnomo

2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: Multi-output Time series forecasting is a complex problem that requires handling interdependencies and interactions between variables. Traditional statistical approaches and machine learning techniques often struggle to predict such scenarios accurately. Advanced techniques and model reconstruction are necessary to improve forecasting accuracy in complex scenarios. Objective: This study proposed an Encoder-Decoder network to address multi-output time series forecasting challenges by simultaneously predicting each output. This objective is to investigate the capabilities of the Encoder-Decoder architecture in handling multi-output time series forecasting tasks. Methods: This proposed model utilizes a 1-Dimensional Convolution Neural Network with Bidirectional Long Short-Term Memory, specifically in the encoder part. The encoder extracts time series features, incorporating a residual connection to produce a context representation used by the decoder. The decoder employs multiple unidirectional LSTM modules and Linear transformation layers to generate the outputs each time step. Each module is responsible for specific output and shares information and context along the outputs and steps. Results: The result demonstrates that the proposed model achieves lower error rates, as measured by MSE, RMSE, and MAE loss metrics, for all outputs and forecasting horizons. Notably, the 6-hour horizon achieves the highest accuracy across all outputs. Furthermore, the proposed model exhibits robustness in single-output forecast and transfer learning, showing adaptability to different tasks and datasets. Conclusion: The experiment findings highlight the successful multi-output forecasting capabilities of the proposed model in time series data, with consistently low error rates (MSE, RMSE, MAE). Surprisingly, the model also performs well in single-output forecasts, demonstrating its versatility. Therefore, the proposed model effectively various time series forecasting tasks, showing promise for practical applications. Keywords: Bidirectional Long Short-Term Memory, Convolutional Neural Network, Encoder-Decoder Networks, Multi-output forecasting, Multi-step forecasting, Time-series forecasting

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A Novel Diagnosis System Using Regularized Encoder-Decoder Based Generative Probabilistic Network for Parkinson's Disease

Cited by 10 publications

References 10 publications

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

Oxidation of Eugenol Derivatives with KMnO4 and CrO3

Enhancing Multi-Output Time Series Forecasting with Encoder-Decoder Networks

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