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Cognizance of schizophrenia : a profound insight into the psyche / / Indranath Chatterjee, editor
| Cognizance of schizophrenia : a profound insight into the psyche / / Indranath Chatterjee, editor |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (313 pages) |
| Disciplina | 616.8980072 |
| Soggetto topico |
Schizophrenia - Psychological aspects
Schizophrenia |
| ISBN |
9789811970221
9789811970214 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Chapter 1: Understanding Schizophrenia: Introductory Aspect of the Mental Disorder from Various Perspectives -- 1.1 Introduction -- 1.2 Symptoms of Schizophrenia -- 1.2.1 Positive Symptoms -- 1.2.2 Negative Symptoms -- 1.2.3 Mixed and Cognitive Symptoms -- 1.3 Causes and Diagnosis of Schizophrenia -- 1.4 Perceptions on Schizophrenia -- 1.4.1 Negative Perceptions of Schizophrenia -- 1.4.2 Perceptions of Schizophrenia in Later Life -- 1.4.3 Psychodynamic Perspectives of Schizophrenia -- 1.4.4 Cognitive Perspective of Schizophrenia -- 1.4.5 Humanistic Perspectives of Schizophrenia -- 1.4.6 Sociocultural Perspective of Schizophrenia -- 1.4.7 Cross-Cultural Perspectives and Influences of Culture on Schizophrenia -- 1.4.8 Cognitive-Behavioral Perspective Schizophrenia -- 1.5 Recent Advancement in Schizophrenia -- 1.6 Conclusions -- References -- Chapter 2: Genetic Mutations and Alternative Splicing in Schizophrenia -- 2.1 What Is Neurogenetics? Related Disease and Disorder -- 2.1.1 Neurogenetic Diseases -- 2.1.2 Neurogenetic Disorders -- 2.2 Schizophrenia and Genetics -- 2.2.1 Schizophrenia -- 2.2.2 Genetics -- 2.3 Genetic Mutations and How They Are Responsible to Cause Schizophrenia -- 2.4 RNA Splicing and Miss-Splicing (Introduction) -- 2.5 Genes Involved in RNA Miss-Splicing to Increase Schizophrenia Risk -- 2.6 How RNA Miss-Splicing (Alternative Splicing) Related to Schizophrenia? -- 2.7 Treatment -- 2.7.1 Medication -- 2.7.2 Psychological -- 2.8 Future Goal and Discussion -- References -- Chapter 3: Understanding the Chemical Interactions in the Brain of Schizophrenia Patients -- 3.1 Introduction -- 3.2 Brain: Its Parts -- 3.2.1 Hindbrain -- 3.2.2 Midbrain -- 3.2.3 Forebrain -- 3.3 Schizophrenia: Its Neurochemistry -- 3.3.1 Dopamine and Schizophrenia -- 3.3.2 Serotonin and Schizophrenia.
3.3.3 Glutamate and Schizophrenia -- 3.3.4 GABA and Schizophrenia -- 3.3.5 Other Neurotransmitters and Schizophrenia -- 3.4 Neuroanatomical Changes During Schizophrenia -- 3.5 Conclusion -- References -- Chapter 4: A Diagnostic Perspective of Schizophrenia: From Past to Present -- 4.1 Introduction -- 4.2 Clinical Manifestations -- 4.2.1 Positive -- 4.2.2 Negative -- 4.2.3 Cognitive -- 4.3 Rationale -- 4.4 Diagnosis -- 4.5 Magnetic Resonance Spectroscopy (MRS) for Schizophrenia -- 4.6 Molecular Pathology of Schizophrenia -- 4.7 Neuroimaging in Schizophrenia: Advancement in Technique -- 4.8 Computed Tomography (CT Scan)Analysis -- 4.8.1 Magnetic Resonance Imaging -- 4.9 Psychometric Analysis -- 4.9.1 Cognitive Analysis -- 4.9.2 Clinical Observations and Analysis -- 4.10 Psychological -- 4.10.1 Stressors Presence -- 4.10.2 Presence of External Stress -- 4.10.3 Presence of Internal Stress -- 4.10.4 Stressors Appraisal -- 4.10.5 External Stress Appraisal -- 4.10.6 Internal Stress Appraisal -- 4.10.7 Impairment of Cognitive Processes and Cognitive Biases -- 4.10.8 Reasons and Problem-Solving of Biases -- 4.10.9 Related to Memory and Memory Retrieval Deficiencies -- 4.11 Questionnaire -- 4.11.1 Positive and Negative Symptoms Questionnaire -- 4.12 Conclusion and Scope of Future Work -- References -- Chapter 5: Is It Schizophrenia or Not? Different Biological Characterization -- 5.1 Introduction -- 5.2 Schizophrenia -- 5.3 Alzheimer´s Disease -- 5.4 Parkinson Disease (PD) -- 5.5 Chronic Depression (CD) -- 5.6 Bi-Polar Disorder -- 5.7 Conclusion -- References -- Chapter 6: Neurobiological Aspects of Schizophrenia and Relationship Between Neurological Disorders: Depression, Anxiety, and ... -- 6.1 Introduction -- 6.2 Neurobiology of a Brain and How Brain Works -- 6.2.1 Brain -- 6.2.2 What Is Neurobiology? -- 6.2.3 Nervous System. 6.2.3.1 Gray Matter and White Matter -- 6.2.4 Brain and Functions -- 6.2.4.1 Cerebrum -- 6.2.4.2 Brain Stem -- 6.2.4.3 Cerebellum -- 6.3 What Is Schizophrenia and How It Changes the Structural Integrity of the Brain? -- 6.3.1 Schizophrenia -- 6.3.1.1 Symptoms -- 6.3.1.2 Reasons for Schizophrenia -- 6.3.2 Changes the Structure Integrity of the Brain -- 6.4 How Schizophrenia Brain Acts as Disorder Neurobiologically -- 6.4.1 Neurobiology of Schizophrenia Brain -- 6.5 What Depression and Anxiety Affect Neurobiologically -- 6.5.1 Depression and Anxiety -- 6.5.1.1 Depression -- 6.5.1.2 Anxiety Disorder -- 6.6 How Epilepsy and Schizophrenia Connected Depression and Anxiety Disorder -- 6.6.1 Epilepsy -- 6.6.2 Anxiety Disorder and Depression and Epilepsy -- References -- Chapter 7: Clinical Treatment Available for Schizophrenia -- 7.1 Introduction -- 7.2 Assessment and Evaluation -- 7.3 Decision Regarding Treatment Setting -- 7.4 Phases of Treatment -- 7.4.1 Acute Phase of Treatment -- 7.4.2 Continuation Phase of Treatment -- 7.4.3 Maintenance Phase of Treatment -- 7.5 Options Available for Treatment -- 7.5.1 Pharmacological Management -- 7.5.1.1 Chemical Classification of Antipsychotic -- 7.5.1.2 Generation and Mechanism-Wise Classification of Antipsychotic -- 7.5.1.3 First-Generation Antipsychotics (FGAs) Mechanism of Action -- 7.5.1.4 Second-Generation Antipsychotics (SGAs) Mechanism of Action -- 7.5.1.5 Nobel Antipsychotic -- 7.5.1.6 Receptor Interactions of Antipsychotics (Stahl 2021) -- 7.5.1.6.1 Effect on Other Receptors of Antipsychotics -- 7.5.1.6.2 Effect on Other Systems of Antipsychotics -- 7.5.1.6.3 Other Side Effects -- 7.5.1.6.4 Interactions -- 7.5.1.7 Side Effects of Antipsychotics at a Glance -- 7.5.1.7.1 Tardive Dyskinesia (TD) -- 7.5.1.7.2 Neuroleptic Malignant Syndrome (NMS) -- 7.5.1.8 Management of NMS. 7.5.1.9 How to Select Antipsychotics -- 7.5.1.10 Selection of Route of Administration -- 7.5.1.11 Long-Acting/Depot Injections Preparations -- 7.5.1.12 Adjunctive Medications Along with Antipsychotics -- 7.5.2 Electroconvulsive Therapy (ECT) -- 7.5.2.1 Mechanism of Action of ECT -- 7.5.2.2 Indications for ECT -- 7.5.2.3 Number of Sessions for ECT -- 7.5.2.4 Contraindications of ECT -- 7.5.2.5 Relative Contraindications of ECT -- 7.5.3 Psychological Management (Kreyenbuhl et al. 2010) -- 7.5.3.1 Psychoeducation for Patients and Caregivers -- 7.5.3.2 Family Intervention (Grover et al. 2017) -- 7.5.3.2.1 Components of Family Intervention -- 7.5.3.2.2 Advantages of Family Intervention -- 7.5.3.3 Cognitive Behavioural Therapy -- 7.5.3.3.1 Components of CBT -- 7.5.3.3.2 Steps of CBT -- 7.5.3.4 Social Skill Training -- 7.5.3.4.1 Components of Social Skill Training -- 7.5.3.4.2 Seven Blocks of Social Skill Training -- 7.5.3.4.3 Commonly Used Methods -- 7.5.3.5 Assertive Community Treatment -- 7.5.3.5.1 Elements of Assertive Community Treatment -- 7.5.3.5.2 Outcome of Assertive Community Treatment -- 7.5.3.6 Token Economy -- 7.5.3.6.1 Advantages of Token Economy -- 7.5.3.7 Vocational Rehabilitation -- 7.5.3.7.1 Advantages of Vocational Rehabilitation -- 7.6 Conclusion -- References -- Chapter 8: Insights into the Neuro-Pharmacological Treatment of Schizophrenia: Past, Present, and Future -- 8.1 Introduction -- 8.1.1 Definition of Schizophrenia -- 8.2 Development of Schizophrenia -- 8.3 Cause of Schizophrenia -- 8.4 Onset of Schizophrenia and Related Symptoms -- 8.5 Treatments for Schizophrenia -- 8.6 Neuropharmacological Treatments for Schizophrenia -- 8.6.1 Historical Evolution -- 8.6.2 Antipsychotic Drugs (Neuroleptics) -- 8.7 Mechanism of Action of Neuroleptics (Central Nervous System) -- 8.7.1 Drawbacks of Traditional Neuroleptics. 8.8 Cutting-Edge Alternatives -- 8.9 Conclusion -- References -- Chapter 9: Managing Schizophrenia: A Challenge for Physicians -- 9.1 Introduction -- 9.2 Pathomechanisms of Schizophrenia -- 9.2.1 Dopaminergic Hypothesis -- 9.2.2 Glutamatergic Hypothesis -- 9.2.3 Other Aminergic Receptors -- 9.2.4 GABAergic Hypothesis -- 9.2.5 Other Hypotheses -- 9.3 Clinical Management of Schizophrenia -- 9.3.1 Pharmacotherapy -- 9.3.1.1 First-Generation Antipsychotics (FGAs) -- Mechanism of Action -- Adverse Effects Profile -- Chlorpromazine Equivalent Dose -- 9.3.1.2 Second-Generation Antipsychotics (SGAs) -- Mechanism of Action -- Clozapine: The Most Atypical Antipsychotic -- 9.3.1.3 Treatment-Resistant Schizophrenia -- Risperidone -- Adverse Effects Profile -- 9.3.1.4 Third-Generation Antipsychotics -- Mechanism of Action -- Adverse Effects Profile -- 9.3.2 Non-pharmacotherapy -- 9.3.2.1 Psychotherapy -- Cognitive Behavioral Therapy -- 9.3.2.2 Electroconvulsive Therapy -- 9.3.2.3 Transcranial Magnetic Stimulation Therapy -- 9.4 Augmentation Strategies -- 9.5 Phase-Wise Management of Schizophrenia -- 9.5.1 Management of First-Episode Psychosis -- 9.5.1.1 Psychotherapy -- 9.5.1.2 Pharmacotherapy -- 9.5.2 Management of Acute Phase -- 9.6 Maintenance Phase of Schizophrenia -- 9.7 Dosing Regimens for Antipsychotics -- 9.8 Antipsychotics in Pregnancy -- 9.9 Antipsychotic Withdrawal -- 9.9.1 Neuroleptic Malignant Syndrome (NMS) -- 9.10 Future Research Recommendations -- References -- Chapter 10: Pharmacotherapy and Emerging Treatment Strategies for Schizophrenia -- 10.1 Introduction -- 10.2 First-Generation Antipsychotics (FGAs) -- 10.2.1 Examples of the First-Generation Antipsychotics -- 10.3 Second-Generation Antipsychotics -- 10.3.1 Clozapine -- 10.3.2 Olanzapine -- 10.3.3 Risperidone -- 10.3.4 Paliperidone -- 10.3.5 Ziprasidone -- 10.3.6 Quetiapine. 10.3.7 Aripiprazole. |
| Record Nr. | UNINA-9910639885703321 |
| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Explainable AI : foundations, methodologies and applications / / Mayuri Mehta, Vasile Palade, Indranath Chatterjee, editors
| Explainable AI : foundations, methodologies and applications / / Mayuri Mehta, Vasile Palade, Indranath Chatterjee, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (273 pages) |
| Disciplina | 006.301 |
| Collana | Intelligent systems reference library |
| Soggetto topico | Artificial intelligence - Philosophy |
| ISBN | 3-031-12807-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Contributors -- Abbreviations -- 1 Black Box Models for eXplainable Artificial Intelligence -- 1.1 Introduction to Machine Learning -- 1.1.1 Motivation -- 1.1.2 Scope of the Paper -- 1.2 Importance of Cyber Security in eXplainable Artificial Intelligence -- 1.2.1 Importance of Trustworthiness -- 1.3 Deep Learning (DL) Methods Contribute to XAI -- 1.4 Intrusion Detection System -- 1.4.1 Classification of Intrusion Detection System -- 1.5 Applications of Cyber Security and XAI -- 1.6 Comparison of XAI Using Black Box Methods -- 1.7 Conclusion -- References -- 2 Fundamental Fallacies in Definitions of Explainable AI: Explainable to Whom and Why? -- 2.1 Introduction -- 2.1.1 A Short History of Explainable AI -- 2.1.2 Diversity of Motives for Creating Explainable AI -- 2.1.3 Internal Inconsistency of Motives for Creating XAI -- 2.1.4 The Contradiction Between the Motives for Creating Explainable AI -- 2.1.5 Paradigm Shift of Explainable Artificial Intelligence -- 2.2 Proposed AI Model -- 2.2.1 The Best Way to Optimize the Interaction Between Human and AI -- 2.2.2 Forecasts Are not Necessarily Useful Information -- 2.2.3 Criteria for Evaluating Explanations -- 2.2.4 Explainable to Whom and Why? -- 2.3 Proposed Architecture -- 2.3.1 Fitness Function for Explainable AI -- 2.3.2 Deep Neural Network is Great for Explainable AI -- 2.3.3 The More Multitasking the Better -- 2.3.4 How to Collect Multitasking Datasets -- 2.3.5 Proposed Neural Network Architecture -- 2.4 Conclusions -- References -- 3 An Overview of Explainable AI Methods, Forms and Frameworks -- 3.1 Introduction -- 3.2 XAI Methods and Their Classifications -- 3.2.1 Based on the Scope of Explainability -- 3.2.2 Based on Implementation -- 3.2.3 Based on Applicability -- 3.2.4 Based on Explanation Level -- 3.3 Forms of Explanation -- 3.3.1 Analytical Explanation.
3.3.2 Visual Explanation -- 3.3.3 Rule-Based Explanation -- 3.3.4 Textual Explanation -- 3.4 Frameworks for Model Interpretability and Explanation -- 3.4.1 Explain like I'm 5 -- 3.4.2 Skater -- 3.4.3 Local Interpretable Model-Agnostic Explanations -- 3.4.4 Shapley Additive Explanations -- 3.4.5 Anchors -- 3.4.6 Deep Learning Important Features -- 3.5 Conclusion and Future Directions -- References -- 4 Methods and Metrics for Explaining Artificial Intelligence Models: A Review -- 4.1 Introduction -- 4.1.1 Bringing Explainability to AI Decision-Need for Explainable AI -- 4.2 Taxonomy of Explaining AI Decisions -- 4.3 Methods of Explainable Artificial Intelligence -- 4.3.1 Techniques of Explainable AI -- 4.3.2 Stages of AI Explainability -- 4.3.3 Types of Post-model Explaination Methods -- 4.4 Metrics for Explainable Artificial Intelligence -- 4.4.1 Evaluation Metrics for Explaining AI Decisions -- 4.5 Use-Case: Explaining Deep Learning Models Using Grad-CAM -- 4.6 Challenges and Future Directions -- 4.7 Conclusion -- References -- 5 Evaluation Measures and Applications for Explainable AI -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Basics Related to XAI -- 5.3.1 Understanding -- 5.3.2 Explicability -- 5.3.3 Explainability -- 5.3.4 Transparency -- 5.3.5 Explaining -- 5.3.6 Interpretability -- 5.3.7 Correctability -- 5.3.8 Interactivity -- 5.3.9 Comprehensibility -- 5.4 What is Explainable AI? -- 5.4.1 Fairness -- 5.4.2 Causality -- 5.4.3 Safety -- 5.4.4 Bias -- 5.4.5 Transparency -- 5.5 Need for Transparency and Trust in AI -- 5.6 The Black Box Deep Learning Models -- 5.7 Classification of XAI Methods -- 5.7.1 Global Methods Versus Local Methods -- 5.7.2 Surrogate Methods Versus Visualization Methods -- 5.7.3 Model Specific Versus Model Agnostic -- 5.7.4 Pre-Model Versus In-Model Versus Post-Model -- 5.8 XAI's Evaluation Methods. 5.8.1 Mental Model -- 5.8.2 Explanation Usefulness and Satisfaction -- 5.8.3 User Trust and Reliance -- 5.8.4 Human-AI Task Performance -- 5.8.5 Computational Measures -- 5.9 XAI's Explanation Methods -- 5.9.1 Lime -- 5.9.2 Sp-Lime -- 5.9.3 DeepLIFT -- 5.9.4 Layer-Wise Relevance Propagation -- 5.9.5 Characteristic Value Evaluation -- 5.9.6 Reasoning from Examples -- 5.9.7 Latent Space Traversal -- 5.10 Explainable AI Stakeholders -- 5.10.1 Developers -- 5.10.2 Theorists -- 5.10.3 Ethicists -- 5.10.4 Users -- 5.11 Applications -- 5.11.1 XAI for Training and Tutoring -- 5.11.2 XAI for 6G -- 5.11.3 XAI for Network Intrusion Detection -- 5.11.4 XAI Planning as a Service -- 5.11.5 XAI for Prediction of Non-Communicable Diseases -- 5.11.6 XAI for Scanning Patients for COVID-19 Signs -- 5.12 Possible Research Ideology and Discussions -- 5.13 Conclusion -- References -- 6 Explainable AI and Its Applications in Healthcare -- 6.1 Introduction -- 6.2 The Multidisciplinary Nature of Explainable AI in Healthcare -- 6.2.1 Technological Outlook -- 6.2.2 Legal Outlook -- 6.2.3 Medical Outlook -- 6.2.4 Ethical Outlook -- 6.2.5 Patient Outlook -- 6.3 Different XAI Techniques Used in Healthcare -- 6.3.1 Methods to Explain Deep Learning Models -- 6.3.2 Explainability by Using White-Box Models -- 6.3.3 Explainability Methods to Increase Fairness in Machine Learning Models -- 6.3.4 Explainability Methods to Analyze Sensitivity of a Model -- 6.4 Application of XAI in Healthcare -- 6.4.1 Medical Diagnostics -- 6.4.2 Medical Imaging -- 6.4.3 Surgery -- 6.4.4 Detection of COVID-19 -- 6.5 Conclusion -- References -- 7 Explainable AI Driven Applications for Patient Care and Treatment -- 7.1 General -- 7.2 Benefits of Technology and AI in Healthcare Sector -- 7.3 Most Common AI-Based Healthcare Applications -- 7.4 Issues/Concerns of Using AI in Health Care. 7.5 Why Explainable AI? -- 7.6 History of XAI -- 7.7 Explainable AI's Benefits in Healthcare -- 7.8 XAI Has Proposed Applications for Patient Treatment and Care -- 7.9 Future Prospects of XAI in Medical Care -- 7.10 Case Study on Explainable AI -- 7.11 Framework for Explainable AI -- 7.12 Conclusion -- References -- 8 Explainable Machine Learning for Autonomous Vehicle Positioning Using SHAP -- 8.1 Introduction -- 8.1.1 Global Navigation Satellite System (GNSS) and Autonomous Vehicles -- 8.1.2 Navigation Using Inertial Measurement Sensors -- 8.1.3 Inertial Positioning Using Wheel Encoder Sensors -- 8.1.4 Motivation for Explainability in AV Positioning -- 8.2 eXplainable Artificial Intelligence (XAI): Background and Current Challenges -- 8.2.1 Why XAI in Autonomous Driving? -- 8.2.2 What is XAI? -- 8.2.3 Types of XAI -- 8.3 XAI in Autonomous Vehicle and Localisation -- 8.4 Methodology -- 8.4.1 Dataset: IO-VNBD (Inertial and Odometry Vehicle Navigation Benchmark Dataset) -- 8.4.2 Mathematical Formulation of the Learning Problem -- 8.4.3 WhONet's Learning Scheme -- 8.4.4 Performance Evaluation Metrics -- 8.4.5 Training of the WhONet Models -- 8.4.6 WhONet's Evaluation -- 8.4.7 SHapley Additive exPlanations (SHAP) Method -- 8.5 Results and Discussions -- 8.6 Conclusions -- References -- 9 A Smart System for the Assessment of Genuineness or Trustworthiness of the Tip-Off Using Audio Signals: An Explainable AI Approach -- 9.1 Introduction -- 9.2 Background -- 9.3 Proposed Methodology -- 9.3.1 Dataset Used -- 9.3.2 Pre-processing -- 9.3.3 Feature Extracted -- 9.3.4 Feature Selected -- 9.3.5 Machine Learning in SER -- 9.3.6 Performance Index -- 9.4 Results and Discussion -- 9.5 Conclusion -- References -- 10 Face Mask Detection Based Entry Control Using XAI and IoT -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Methodology. 10.3.1 Web Application Execution -- 10.3.2 Implementation -- 10.3.3 Activation Functions -- 10.3.4 Raspberry Pi Webserver -- 10.4 Results -- 10.4.1 Dataset -- 10.4.2 Model Summary -- 10.4.3 Model Evaluation -- 10.5 Conclusion -- References -- 11 Human-AI Interfaces are a Central Component of Trustworthy AI -- 11.1 Introduction -- 11.2 Regulatory Requirements for Trustworthy AI -- 11.3 Explicability-An Ethical Principle for Trustworthy AI -- 11.4 User-Centered Approach to Trustworthy AI -- 11.4.1 Stakeholder Analysis and Personas for AI -- 11.4.2 User-Testing for AI -- 11.5 An Example Use Case: Computational Pathology -- 11.5.1 AI in Computational Pathology -- 11.5.2 Stakeholder Analysis for Computational Pathology -- 11.5.3 Human-AI Interface in Computational Pathology -- 11.6 Conclusion -- 11.7 List of Abbreviations -- References. |
| Record Nr. | UNINA-9910627258703321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning applications : from computer vision to robotics / / edited by Indranath Chatterjee, Sheetal Zalte
| Machine learning applications : from computer vision to robotics / / edited by Indranath Chatterjee, Sheetal Zalte |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024] |
| Descrizione fisica | 1 online resource (240 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Deep learning (Machine learning) |
| ISBN |
1-394-17335-0
1-394-17333-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Statistical similarity in machine learning -- Development of machine learning-based methodologies for adaptive intelligent e-learning systems and time series analysis techniques -- Time-series forecasting for stock market using convolutional neural networks -- Comparative study for applicability of color histograms for CBIR used for crop leaf disease detection -- Stock Index Forecasting Using RNN-Long Short Term Memory -- Study and analysis of machine learning models for detection of phishing URLs -- Real-world applications of blockchain technology in internet of things -- Advanced persistent threat: Korean cyber security knack model impost and applicability -- Integration of Blockchain Technology and Internet of Things: Challenges and its Solutions -- Machine learning techniques for swot analysis of online education system -- Crop yield and soil moisture prediction using machine learning algorithms. -- Multi-rate signal processing in WSN for channel capacity and energy efficiency using machine learning. -- Introduction to mechanical design of AI-based robotic system. |
| Record Nr. | UNINA-9910829959703321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Nutrition in Brain Aging and Dementia / / edited by Nasrollah Moradikor, Indranath Chatterjee, Wael Mohamed
| Nutrition in Brain Aging and Dementia / / edited by Nasrollah Moradikor, Indranath Chatterjee, Wael Mohamed |
| Autore | Moradikor Nasrollah |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (324 pages) |
| Disciplina | 570 |
| Altri autori (Persone) |
ChatterjeeIndranath
MohamedWael |
| Collana | Nutritional Neurosciences |
| Soggetto topico |
Biology
Neurosciences Neural networks (Neurobiology) Nervous system - Diseases Nutrition Biological Sciences Neuroscience Systems Neuroscience Neurological Disorders |
| ISBN |
9789819741175
9789819741168 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Pathogenesis of Dementia -- 2. Genetic and Non-Genetic Risk Factors for Dementia -- 3. Symptoms and Diagnosis of Dementia -- 4. Biomarkers in Dementia Research -- 5. Neurocognitive Aspects of Dementia -- 6. Role of Nutrition in Maintaining Brain Health -- 7. Micronutrients for dementia prevention -- 8. Exploring functional foods in prevention of dementia -- 9. Alterations in trace elements and dementia -- 10. Carotenoids in Alzheimer's Disease and Dementia -- 11. Probiotic agents for Alzheimer and dementia -- 12. Traditional Herbal Medicine for Dementia Therapy -- 13. Non-pharmacological approaches for dementia management -- 14. Dietary Recommendations for Managing Dementia. |
| Record Nr. | UNINA-9910882900503321 |
Moradikor Nasrollah
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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