國立虎尾科技大學 |

Intelligent technologies and Parkinson's disease = prediction and diagnosis /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Intelligent technologies and Parkinson's disease/ Abhishek Kumar, Sachin Ahuja, Anupam Baliyan, Sreenatha Annawati, Abhineet Anand, editors.
Reminder of title:	prediction and diagnosis /
other author:	Anavatii, Sreenatha,
Published:	Hershey, Pennsylvania :IGI Global, : 2024.,
Description:	1 online resource (390 p.)
Subject:	Parkinson's disease. -
Online resource:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-1115-8
ISBN:	9798369311165

Intelligent technologies and Parkinson's disease = prediction and diagnosis /
Intelligent technologies and Parkinson's diseaseprediction and diagnosis /[electronic resource] :Abhishek Kumar, Sachin Ahuja, Anupam Baliyan, Sreenatha Annawati, Abhineet Anand, editors. - Hershey, Pennsylvania :IGI Global,2024. - 1 online resource (390 p.)

Includes bibliographical references and index.

Chapter 1. The power of data: leveraging machine learning for Parkinson's disease diagnosis -- Chapter 2. A study to find affordable AI techniques for early Parkinson's disease detection -- Chapter 3. The fusion of fog computing andintelligent technologies for Parkinson's disease care -- Chapter 4. Unmasking the movements: advancing Parkinson's disease management using wearable sensor-based technologies -- Chapter 5. Decision support framework for Parkinson's diseaseusing novel handwriting markers -- Chapter 6. Parkinson's disease diagnosis using voice features and effective machine learning methods -- Chapter 7. A review of the literature on automated Parkinson's disease diagnosis methods using machinelearning -- Chapter 8. Decoding Parkinson's disease: a deep learning approach to handwriting diagnosis -- Chapter 9. A functional gradient boost approach for identifying Parkinson's disease -- Chapter 10. Evolutionary wavelet neural network ensembles for breast cancer and Parkinson's disease prediction -- Chapter 11. Genetic determinants of Parkinson's disease: SNCA and lRRK2 in focus -- Chapter 12. IoT-based accelerometer sensors for early detection and continuous monitoring of Parkinson's disease symptoms -- Chapter 13. Early detection of Parkinson's disease using deep learning: a convolutional bi-directional GRU approach -- Chapter 14. Enhancing Parkinson's disease diagnosis through mayfly-optimized CNN BiGRU classification: a performance evaluation -- Chapter 15. Optimizing predictive models for Parkinson's disease diagnosis -- Chapter 16. Selection of gait parameters for differential diagnostics of patients with De Novo Parkinson's disease -- Chapter 17. Identifying Parkinson's patients by a functional gradient boosting approach -- Chapter 18. Evaluation of machine learning techniques for classification of early Parkinson's disease -- Chapter 19. Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease analysis.

"When it comes to Parkinson's disease, one of the most important issues revolves around early detection and accurate diagnosis. The intricacies of this neurodegenerative disorder often elude timely identification, leaving patients and healthcare providers grappling with its progressive symptoms. Ethical concerns surrounding the use of machine learning to aid in diagnosis further complicate this challenge. This issue is particularly significant for research scholars, PhD fellows, post-doc fellows, and medical and biomedical scholars seeking to unravel the mysteries of Parkinson's disease and develop more effective treatments.Intelligent Technologies and Parkinson's Disease: Prediction and Diagnosis serves as a beacon of hope in the quest to revolutionize Parkinson's disease diagnosis and treatment. It unveils the remarkable potential of artificial intelligence (AI) and machine learning (ML) in remodeling the way we approach this debilitating condition. With a comprehensive exploration of AI's capacity to analyze speech patterns, brain imaging data, and gait patterns, this book offers a powerful solution to the challenges of early detection and accurate diagnosis."--

ISBN: 9798369311165Subjects--Topical Terms:

582860
Parkinson's disease.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: RC382 / .I58 2024e

Dewey Class. No.: 616.8/33075

National Library of Medicine Call No.: WL 359 / .I58 2024e

Intelligent technologies and Parkinson's disease = prediction and diagnosis /
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245 0 0 $a Intelligent technologies and Parkinson's disease $h [electronic resource] : $b prediction and diagnosis / $c Abhishek Kumar, Sachin Ahuja, Anupam Baliyan, Sreenatha Annawati, Abhineet Anand, editors.
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300 $a 1 online resource (390 p.)
504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. The power of data: leveraging machine learning for Parkinson's disease diagnosis -- Chapter 2. A study to find affordable AI techniques for early Parkinson's disease detection -- Chapter 3. The fusion of fog computing andintelligent technologies for Parkinson's disease care -- Chapter 4. Unmasking the movements: advancing Parkinson's disease management using wearable sensor-based technologies -- Chapter 5. Decision support framework for Parkinson's diseaseusing novel handwriting markers -- Chapter 6. Parkinson's disease diagnosis using voice features and effective machine learning methods -- Chapter 7. A review of the literature on automated Parkinson's disease diagnosis methods using machinelearning -- Chapter 8. Decoding Parkinson's disease: a deep learning approach to handwriting diagnosis -- Chapter 9. A functional gradient boost approach for identifying Parkinson's disease -- Chapter 10. Evolutionary wavelet neural network ensembles for breast cancer and Parkinson's disease prediction -- Chapter 11. Genetic determinants of Parkinson's disease: SNCA and lRRK2 in focus -- Chapter 12. IoT-based accelerometer sensors for early detection and continuous monitoring of Parkinson's disease symptoms -- Chapter 13. Early detection of Parkinson's disease using deep learning: a convolutional bi-directional GRU approach -- Chapter 14. Enhancing Parkinson's disease diagnosis through mayfly-optimized CNN BiGRU classification: a performance evaluation -- Chapter 15. Optimizing predictive models for Parkinson's disease diagnosis -- Chapter 16. Selection of gait parameters for differential diagnostics of patients with De Novo Parkinson's disease -- Chapter 17. Identifying Parkinson's patients by a functional gradient boosting approach -- Chapter 18. Evaluation of machine learning techniques for classification of early Parkinson's disease -- Chapter 19. Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease analysis.
520 3 $a "When it comes to Parkinson's disease, one of the most important issues revolves around early detection and accurate diagnosis. The intricacies of this neurodegenerative disorder often elude timely identification, leaving patients and healthcare providers grappling with its progressive symptoms. Ethical concerns surrounding the use of machine learning to aid in diagnosis further complicate this challenge. This issue is particularly significant for research scholars, PhD fellows, post-doc fellows, and medical and biomedical scholars seeking to unravel the mysteries of Parkinson's disease and develop more effective treatments.Intelligent Technologies and Parkinson's Disease: Prediction and Diagnosis serves as a beacon of hope in the quest to revolutionize Parkinson's disease diagnosis and treatment. It unveils the remarkable potential of artificial intelligence (AI) and machine learning (ML) in remodeling the way we approach this debilitating condition. With a comprehensive exploration of AI's capacity to analyze speech patterns, brain imaging data, and gait patterns, this book offers a powerful solution to the challenges of early detection and accurate diagnosis."-- $c Provided by publisher.
650 0 $a Parkinson's disease. $3 582860
650 0 $a Artificial intelligence. $3 559380
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650 2 $a Artificial Intelligence. $3 646849
650 2 $a Biotechnology $x methods. $3 590491
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700 1 $a Anavatii, Sreenatha, $d 1961- $3 1458802
700 1 $a Baliyan, Anupam, $d 1976- $3 1340754
700 1 $a Ahuja, Sachin, $d 1979- $3 1458803
700 1 $a Kumar, Abhishek, $d 1989- $3 1458804
700 1 $a Anand, Abhineet, $d 1975- $3 1458805
710 2 $a IGI Global. $3 805187
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-1115-8