Biomedical data analysis and processing using explainable (XAI) and responsive artificial intelligence (RAI) / / Aditya Khamparia [and three others]

• Pubblicazione: Singapore : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (148 pages)
• Disciplina: 610.28
• Soggetto topico: Biomedical engineering - Data processing; Artificial intelligence - Data processing
• Persona (resp. second.): KhampariaAditya <1988->
• Nota di contenuto: Intro -- Preface -- Contents -- Editors and Contributors -- 1 Optimal Boosting Label Weighting Extreme Learning Machine for Mental Disorder Prediction and Classification -- 1.1 Introduction -- 1.2 The Proposed Model -- 1.2.1 Data Pre-processing -- 1.2.2 Process Involved in BWELM Model -- 1.2.3 Parameter Tuning Using CSPSO Algorithm -- 1.3 Experimental Validation -- 1.4 Conclusion -- References -- 2 Modeling of Explainable Artificial Intelligence with Correlation-Based Feature Selection Approach for Biomedical Data Analysis -- 2.1 Introduction -- 2.2 The Proposed Model -- 2.2.1 Stage 1: Pre-processing -- 2.2.2 Stage 2: Correlation-Based Feature Selection -- 2.2.3 Stage 3: FKNN-Based Classification -- 2.2.4 Stage 4: BWO-Based Classification -- 2.3 Experimental Validation -- 2.4 Conclusion -- References -- 3 Explainable Machine Learning Model for Diagnosis of Parkinson Disorder -- 3.1 Introduction -- 3.2 Literature Survey -- 3.3 Experimental Work -- 3.4 Discussion -- 3.5 Conclusion -- References -- 4 Explainable Artificial Intelligence with Metaheuristic Feature Selection Technique for Biomedical Data Classification -- 4.1 Introduction -- 4.2 Literature Review -- 4.3 The Proposed Model -- 4.3.1 Data Preprocessing -- 4.3.2 Algorithmic Design of CSMO Based Feature Selection -- 4.3.3 Process Involved in Optimal DNN-Based Classification -- 4.4 Experimental Validation -- 4.5 Conclusion -- References -- 5 Explainable AI in Neural Networks Using Shapley Values -- 5.1 Introduction -- 5.2 Literature Review -- 5.2.1 Explanation by Simplification -- 5.2.2 Explanation by Feature Attribution -- 5.3 Shapley Values and Game Theory -- 5.3.1 Using Shapley Values to Attributing Relevance -- 5.3.2 Shapley Value to SHAP -- 5.4 Explainer Architecture -- 5.4.1 Model Explainer -- 5.4.2 Visualization -- 5.4.3 Model Refinement -- 5.4.4 Reporting and Presentation. ; 5.5 Discussion -- 5.5.1 Comparison with Other Explainable Methods -- 5.5.2 Axiomatic Comparison -- 5.6 Conclusion and Future Work -- References -- 6 Design of Multimodal Fusion-Based Deep Learning Approach for COVID-19 Diagnosis Using Chest X-Ray Images -- 6.1 Introduction -- 6.2 Literature Survey -- 6.3 The Proposed MMFBDL Model -- 6.3.1 Feature Extraction Process -- 6.3.2 Image Classification Using MLP -- 6.4 Experimental Validation -- 6.5 Conclusion -- References -- 7 ECG Classification and Analysis for Heart Disease Prediction Using XAI-Driven Machine Learning Algorithms -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Dataset Methods and Classification -- 7.3.1 Tools and Techniques -- 7.3.2 ECG Results Implementation for Normal and Abnormal -- 7.3.3 Results for Individual Disease by Cross-Validation Score -- 7.3.4 Cross-Validation Score for ANN -- 7.4 Description of the ML Models -- 7.4.1 Logistic Regression -- 7.4.2 Naive Bayes -- 7.4.3 Decision Trees -- 7.4.4 Support Vector Machine (SVM) -- 7.4.5 Lime -- 7.4.6 DeepLIFT -- 7.4.7 Skater -- 7.4.8 Shapley -- 7.5 Results Analysis -- 7.6 Conclusion and Future Scope -- References -- 8 Rethinking the Transfer Learning Architecture for Respiratory Diseases and COVID-19 Diagnosis -- 8.1 Introduction -- 8.2 Literature Reviews -- 8.3 Description of Dataset -- 8.4 Methodology -- 8.4.1 VGG-16 -- 8.4.2 XceptionNet Model -- 8.5 Result Analysis -- 8.6 Conclusion -- References -- 9 Arithmetic Optimization Algorithm with Explainable Artificial Intelligence Technique for Biomedical Signal Analysis -- 9.1 Introduction -- 9.2 Related Works -- 9.3 The Proposed Model -- 9.3.1 Variation Mode Decomposition (VMD) Approach -- 9.3.2 Feature Extraction Using Bi-LSTM Model -- 9.3.3 ECG Recognition Using Optimal SVM Model -- 9.4 Experimental Validation -- 9.5 Conclusion -- References.
• Titolo autorizzato: Biomedical data analysis and processing using explainable (XAI) and responsive artificial intelligence (RAI)
• ISBN: 981-19-1476-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910559392203321
• Lo trovi qui: Univ. Federico II
• Serie: Intelligent Systems Reference Library