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Biomedical data analysis and process...

Khamparia, Aditya.

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Biomedical data analysis and processing using explainable (XAI) and responsive artificial intelligence (RAI)

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Biomedical data analysis and processing using explainable (XAI) and responsive artificial intelligence (RAI)/ edited by Aditya Khamparia ... [et al.].
其他作者:	Khamparia, Aditya.
出版者:	Singapore :Springer Singapore : : 2022.,
面頁冊數:	xv, 137 p. :ill. (some col.), digital ;24 cm.
內容註:	Optimal Boosting Label Weighting Extreme Learning Machine for Mental Disorder Prediction and Classification -- Modeling of Explainable Artificial Intelligence with Correlation based Feature Selection Approach for Biomedical Data Analysis -- Explainable machine learning model for diagnosis of Parkinson disorder -- Explainable Artificial Intelligence with Metaheuristic Feature Selection Technique for Biomedical Data Classification -- Explainable AI in Neural Networks using Shapley Values -- Design of Multimodal Fusion based Deep Learning Approach for COVID-19 Diagnosis using Chest X-Ray Images.
Contained By:	Springer Nature eBook
標題:	Biomedical engineering - Data processing. -
電子資源:	https://doi.org/10.1007/978-981-19-1476-8
ISBN:	9789811914768

Biomedical data analysis and processing using explainable (XAI) and responsive artificial intelligence (RAI)
Biomedical data analysis and processing using explainable (XAI) and responsive artificial intelligence (RAI)[electronic resource] /edited by Aditya Khamparia ... [et al.]. - Singapore :Springer Singapore :2022. - xv, 137 p. :ill. (some col.), digital ;24 cm. - Intelligent systems reference library,v. 2221868-4408 ;. - Intelligent systems reference library ;v. 222..

Optimal Boosting Label Weighting Extreme Learning Machine for Mental Disorder Prediction and Classification -- Modeling of Explainable Artificial Intelligence with Correlation based Feature Selection Approach for Biomedical Data Analysis -- Explainable machine learning model for diagnosis of Parkinson disorder -- Explainable Artificial Intelligence with Metaheuristic Feature Selection Technique for Biomedical Data Classification -- Explainable AI in Neural Networks using Shapley Values -- Design of Multimodal Fusion based Deep Learning Approach for COVID-19 Diagnosis using Chest X-Ray Images.

The book discusses Explainable (XAI) and Responsive Artificial Intelligence (RAI) for biomedical and healthcare applications. It will discuss the advantages in dealing with big and complex data by using explainable AI concepts in the field of biomedical sciences. The book explains both positive as well as negative findings obtained by explainable AI techniques. It features real time experiences by physicians and medical staff for applied deep learning based solutions. The book will be extremely useful for researchers and practitioners in advancing their studies.

ISBN: 9789811914768

Standard No.: 10.1007/978-981-19-1476-8doiSubjects--Topical Terms:

1067462
Biomedical engineering
--Data processing.

LC Class. No.: R857.D34 / B56 2022

Dewey Class. No.: 610.28563

Biomedical data analysis and processing using explainable (XAI) and responsive artificial intelligence (RAI)
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505 0 $a Optimal Boosting Label Weighting Extreme Learning Machine for Mental Disorder Prediction and Classification -- Modeling of Explainable Artificial Intelligence with Correlation based Feature Selection Approach for Biomedical Data Analysis -- Explainable machine learning model for diagnosis of Parkinson disorder -- Explainable Artificial Intelligence with Metaheuristic Feature Selection Technique for Biomedical Data Classification -- Explainable AI in Neural Networks using Shapley Values -- Design of Multimodal Fusion based Deep Learning Approach for COVID-19 Diagnosis using Chest X-Ray Images.
520 $a The book discusses Explainable (XAI) and Responsive Artificial Intelligence (RAI) for biomedical and healthcare applications. It will discuss the advantages in dealing with big and complex data by using explainable AI concepts in the field of biomedical sciences. The book explains both positive as well as negative findings obtained by explainable AI techniques. It features real time experiences by physicians and medical staff for applied deep learning based solutions. The book will be extremely useful for researchers and practitioners in advancing their studies.
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