An adaptive intelligent diagnostic system to predict early stage of parkinson's disease using two-stage dimension reduction with genetically optimized lightgbm algorithm

Dhar, Joy

doi:10.1007/s00521-021-06612-4

Cited by 18 publications

(2 citation statements)

References 45 publications

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“…Autoencoder is an unsupervised neural network with a symmetric structure, which can effectively learn the internal features of data [ 43 ] to obtain concise expressions of data; it is often used for data dimensionality reduction [ 44 ]. The standard autoencoder has a three-layer architecture [ 45 ], as shown in Figure 3 .…”

Section: Proposed Methodsmentioning

confidence: 99%

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

Song,

Zhao,

Zhang

et al. 2024

Sensors

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The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

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Section: Proposed Methodsmentioning

confidence: 99%

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

Song,

Zhao,

Zhang

et al. 2024

Sensors

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“…Joy Dhar proposes a hybrid method combining two-stage mutual information and autoencoder-based dimensionality reduction method with genetically optimized Light-GBM (MI-AE-GOLGBM) algorithm for Parkinson's disease prediction problem to improve system performance and predict the best outcome [23]. The proposed MI-AE-GOLGBM method includes four methods: mutual information, autoencoder, genetic algorithm, and LightGBM algorithm.…”

Section: Related Workmentioning

confidence: 99%

A RS-BOLGBM Algorithm Based on LightGBM for Supercomputing Center Job Running Status Prediction

Cao,

Tang,

Shi

et al. 2024

Preprint

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In recent years, more and more large-scale high-performance applications have been submitted to the Supercomputing Center for processing. These jobs often require intensive computations, and any running failure will lead to a lot of wasted costs. Therefore, the effective prediction of job running status will help to improve the resource management efficiency of the Supercomputing Center. In this paper, we first propose a combinatorial feature selection method combining ReliefF and SelectKBest, which can effectively obtain job running state features. Then, an improved LightGBM algorithm based on Bayesian hyperparameter optimization is proposed. This approach can further enhance the prediction accuracy and tuning efficiency. Thirdly, we achieve a RS-BOLGBM algorithm based on LightGBM for the Supercomputing Center job running status prediction on Spark. The experimental results show that our proposed RS-BOLGBM algorithm has good prediction accuracy on the common SWF (standard workloads format) dataset of the Supercomputing Center. Our proposed RS-BOLGBM also has good running efficiency on the Spark cluster.

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A new ensemble classification approach based on Rotation Forest and LightGBM

Sun

et al. 2023

Neural Comput & Applic

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An adaptive intelligent diagnostic system to predict early stage of parkinson's disease using two-stage dimension reduction with genetically optimized lightgbm algorithm

Cited by 18 publications

References 45 publications

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

A RS-BOLGBM Algorithm Based on LightGBM for Supercomputing Center Job Running Status Prediction

A new ensemble classification approach based on Rotation Forest and LightGBM

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