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Artificial Intelligence : a Multidisciplinary Approach Towards Teaching and Learning
| Artificial Intelligence : a Multidisciplinary Approach Towards Teaching and Learning |
| Autore | Khan Tahmeena |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Sharjah : , : Bentham Science Publishers, , 2024 |
| Descrizione fisica | 1 online resource (0 pages) |
| Altri autori (Persone) |
SinghManisha
RazaSaman |
| Soggetto topico | EDUCATION / Teaching / Methods & Strategies |
| ISBN |
9789815305180
9815305182 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title -- Copyright -- End User License Agreement -- Contents -- Foreword I -- Foreword II -- Preface -- List of Contributors -- The Evolution of Artificial Intelligence from Philosophy to New Frontier -- Manisha Singh1,*, Arbind K. Jha2, Tahmeena Khan3 and Saman Raza4 -- INTRODUCTION -- THE HISTORY OF ARTIFICIAL INTELLIGENCE (AI) -- PHILOSOPHY AND AI: A PHILOSOPHICAL JOURNEY -- PHILOSOPHICAL CONSIDERATION OF AI -- Metaphysics and AI -- Epistemology and AI -- Axiology and AI -- Framework of AI -- HUMAN-MACHINE TEAMING FRAMEWORK -- FORMS OF AI -- Based on Capabilities -- Artificial Narrow Intelligence -- Artificial General Intelligence -- Artificial Super Intelligence -- Generative AI -- Based on Functionality Artificial Intelligence -- Reactive Machines -- Limited AI -- Theory of Mind AI -- Self-aware AI -- Some other forms of AI -- AI AND NEW FRONTIERS -- AI and Medical Science -- AI and Life Science -- AI and Mathematics -- AI and Architecture -- AI and Environmental Science -- AI in Education -- AI in Research -- ChatGPT/Perplexity/GoogleBard -- PDFgear -- Wordvice AI -- Consensus -- Trinka -- QuillBot AI -- Page.AI -- Zotero, EndNote Online, Mendeley, RefWorks, etc -- AI, HUMAN INTELLIGENCE AND HUMAN WISDOM -- CONCLUDING REMARKS -- REFERENCES -- Artificial Intelligence and Bioinformatics: A Powerful Synergy for Drug Design and Discovery -- Chanda Hemantha Manikumar Chakravarthi1, Viswajit Mulpuru1 and Nidhi Mishra2,* -- INTRODUCTION -- Overview of Machine Learning -- Supervised Learning -- Unsupervised Learning -- Reinforcement Learning -- Importance of Drug Design -- Challenges in Traditional Drug Discovery -- DATA ANALYSIS AND PREPROCESSING -- Utilizing Biological Databases -- Omics Data Integration -- Data Cleaning and Feature Extraction -- Data Cleaning and Pre-processing -- Feature Extraction Techniques.
Handling Imbalanced Datasets -- Oversampling and Undersampling -- Advanced Algorithms for Imbalanced Data -- Addressing Batch Effects -- Definition of Batch Effects -- Ensuring Consistency -- PREDICTIVE MODELLING -- Classification Algorithms -- Support Vector Machines (SVM) -- Random Forests -- Neural Networks -- Regression Analysis -- Quantitative Structure-Activity Relationship (QSAR) -- Predicting Molecular Properties -- VIRTUAL SCREENING -- Target Identification and Validation -- Omics Data Integration -- Disease Gene Prediction -- Expression Profiling and Differential Analysis -- Pharmacogenomics -- Text Mining and Literature Analysis -- Validation through High-Throughput Screening (HTS) -- Integration of Structural Biology Data -- Ligand-Based Virtual Screening Techniques -- Molecular Descriptors and Fingerprints -- Quantitative Structure-Activity Relationship (QSAR) -- Machine Learning Classifiers -- Pharmacophore Modeling -- Chemical Similarity Networks -- Ensemble Methods -- Structure-Based Virtual Screening -- Protein-Ligand Docking -- Scoring Functions -- Deep Learning in Binding Affinity Prediction -- Machine Learning Filters -- Consensus Scoring -- Machine Learning for Binding Site Prediction -- Fragment-Based Virtual Screening -- DE NOVO DRUG DESIGN -- Generative Models in Drug Design -- Generative AI in bioinformatics -- Generative AI in Drug Design -- Generative AI revolutionizes Drug Discovery Processes -- Variational Autoencoders (VAEs) -- Generative Adversarial Networks (GANs) -- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks -- Transformer-Based Models -- Graph Generative Models -- Conditional Generative Models -- Transfer Learning in Generative Models -- Reinforcement Learning for Molecule Generation -- Objective Function Definition -- Policy Networks -- Action Space Representation. Monte Carlo Tree Search (MCTS) -- Actor-Critic Models -- Exploration Strategies -- Transfer Learning and Pre-training -- DRUG REPURPOSING -- Identifying New Indications for Existing Drugs -- Biological Data Integration -- Drug Similarity and Similarity Networks -- Disease Similarity and Phenotype Matching -- Text Mining and Literature Analysis -- Predictive Modeling for Drug-Disease Associations -- Network Propagation Algorithms -- Electronic Health Records (EHR) Analysis -- Multi-Omics Data Integration -- Utilizing Machine Learning for Drug Repositioning -- Data Integration and Representation -- Feature Extraction and Engineering -- Predictive Modelling for Drug-Disease Associations -- Network-Based Approaches -- Deep Learning Models -- Text Mining and Literature Analysis -- Clinical Data Mining -- Ensemble Learning -- PHARMACOPHORE MODELLING -- Molecular Interaction Understanding -- Drug Design and Optimization -- Virtual Screening -- Lead Identification and Optimization -- Polypharmacology Analysis -- Structure-Activity Relationship (SAR) Analysis -- Fragment-Based Drug Design -- Target Druggability Assessment -- Pharmacokinetic and Toxicity Prediction -- Adverse Effects Mitigation -- Feature Selection and Descriptor Generation -- Training Data Generation -- Enhanced Pharmacophore Screening -- Predictive Pharmacophore Modeling -- Polypharmacology Prediction -- Druggability Assessment -- Hybrid Approaches -- Pharmacophore Optimization -- Data-Driven Drug Design -- PERSONALIZED MEDICINE -- Tailoring Treatments Based on Individual Genetic Profiles -- Importance and Benefits -- Application of Machine Learning -- Examples of Personalized Medicine Applications -- Ethical and Regulatory Considerations -- Future Directions -- Machine Learning in Patient Stratification -- Key Components of Patient Stratification -- Importance and Benefits. Applications of Machine Learning -- Examples of Patient Stratification -- Challenges and Considerations -- Future Directions -- CHALLENGES AND FUTURE DIRECTIONS -- Data Quality and Availability -- Data Quality Issues -- Data Standardization and Integration -- Limited Accessibility -- Small Sample Sizes -- Biological Variability -- Ethical Considerations -- Future Directions -- Advancements in Personalized Medicine -- Ethical and Regulatory Considerations -- Patient Privacy and Informed Consent -- Data Ownership and Sharing -- Bias and Fairness in Models -- Regulatory Compliance -- Inclusivity in Research -- Transparency in AI Decision-Making -- Future Directions -- Emerging Technologies and Trends in Drug Design -- Artificial Intelligence (AI) and Machine Learning -- Quantum Computing -- Structural Biology Advancements -- Immunotherapy and Personalized Medicine -- CRISPR and Gene Editing -- Nanotechnology in Drug Delivery -- Data Integration and Systems Biology -- 3D Printing in Drug Manufacturing -- Blockchain for Data Security -- CONCLUDING REMARKS -- Artificial Intelligence (AI) and Machine Learning -- Quantum Computing -- Immunoinformatics -- CRISPR-Cas9 and Gene Editing -- 3D Bioprinting -- Nanotechnology -- RNA Therapeutics -- Pharmacogenomics -- Virtual Reality (VR) and Augmented Reality (AR) -- Blockchain in Drug Development -- Metabolomics and Systems Biology -- Synthetic Biology -- Potential Impact on the Pharmaceutical Industry -- Acceleration of Drug Discovery -- Revolutionizing Vaccine Development -- Precision Medicine and Personalized Therapies -- Efficient Drug Testing and Development -- Targeted Drug Delivery and Formulation -- Innovations in RNA Therapeutics -- Optimizing Drug Responses -- Immersive Research Environments -- Ensuring Data Integrity and Compliance -- Comprehensive Understanding of Drug Impact. Biosynthesis and Customized Biological Systems -- REFERENCES -- Artificial Intelligence Assisted Teaching and Learning and Research of Environmental Sciences -- Tahmeena Khan1,*, Priya Mishra2, Kulsum Hashmi2, Saman Raza2, Manisha Singh3, Seema Joshi2 and Abdul Rahman Khan1 -- INTRODUCTION -- Generative AI in Education -- AI In Teaching, Learning and Academic Achievement -- AI-Based Tools and Methodologies in Environmental/Geoscience Teaching -- Different AI Techniques Used in Environment and Geosciences-Based Research -- Hazard Identification -- Risk Assessment -- Risk Evaluation -- Decision Making -- Earthquakes -- Volcano -- Landslide -- Rainfall -- Cyclones -- Meteorological Drought -- Wildfire -- Dust storm -- Anthropogenic Air Pollutants -- AI in Biosphere -- Chat GP and Environmental Science -- CHALLENGES IN AI IN ENVIRONMENTAL SCIENCE BASED RESEARCH -- Choosing a Suitable Model -- Training Optimization -- Data Preparation -- Ethical Issues -- CONCLUDING REMARKS -- REFERENCES -- Integrating AI Approaches in Teaching-Learning Associated with the Mitigation of Air Pollution: A Comprehensive Analysis -- Rahila Rahman Khan1,*, Ahmad Faiz Minai2 and Rushda Sharf1 -- INTRODUCTION -- OVERVIEW OF THE CURRENT STATE OF AIR POLLUTION AND ITS IMPACT -- APPLICATIONS OF AI IN ENVIRONMENTAL CHALLENGES -- Environmental Monitoring -- Climate Modeling -- Biodiversity Conservation -- Renewable Energy -- POTENTIAL OF AI IN ADDRESSING AIR POLLUTION -- Data Analysis and Prediction -- Source Identification -- Early Warning Systems -- Policy Formulation -- PROBLEMS WITH CONVENTIONAL AIR QUALITY MONITORING TECHNIQUES -- Restricted Coverage -- Temporal Limitations -- High Installation and Maintenance Costs -- Data Timeliness -- AI-BASED AIR QUALITY MONITORING -- Remote Sensing and Satellite Technology -- Integration of Satellite Data. AI Algorithms for Data Analysis and Interpretation. |
| Record Nr. | UNINA-9911069651903321 |
Khan Tahmeena
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| Sharjah : , : Bentham Science Publishers, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Educating Children Outdoors : Lessons in Nature-Based Learning
| Educating Children Outdoors : Lessons in Nature-Based Learning |
| Autore | Butler Amy |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Ithaca : , : Cornell University Press, , 2024 |
| Descrizione fisica | 1 online resource (262 pages) |
| Disciplina | 371.3/84 |
| Altri autori (Persone) | CharlesCheryl |
| Soggetto topico |
Nature study
Outdoor education Place-based education EDUCATION / Teaching / Methods & Strategies |
| ISBN |
9781501771910
1501771914 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Contents -- Foreword -- Introduction: Teach Who You Are -- Chapter 1Nature-Based Routines for Outdoor Learning -- Chapter 2 Awareness and Safety as a Daily Practice -- Unit 1. Welcome to the Outdoors, Welcome to Nature -- Unit 2. Build It and They Will Come: The Power of Sticks -- Unit 3. Teaching with Fire: The Heart of an Outdoor Classroom -- Unit 4. Winter Weather, Animals, and Us: Learning Outdoors with Resilience and Wonder -- Unit 5. What Does Spring Bring? -- Afterword -- References -- Contributors |
| Record Nr. | UNINA-9911026175903321 |
|
Butler Amy
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| Ithaca : , : Cornell University Press, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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