UID:
almahu_9949687751202882
Umfang:
1 online resource (370 pages)
Ausgabe:
First edition.
ISBN:
9780443240003
,
0443240000
,
9780443240010
,
0443240019
Anmerkung:
Front Cover -- A Biologist's Guide to Artificial Intelligence -- A Biologist's Guide to Artificial Intelligence: Building the foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences -- Copyright -- Contents -- Contributors -- 1 - Exploring artificial intelligence through a biologist's lens -- Introduction -- Machine learning algorithms-the foundations of AI -- Integrating AI with biological science -- Logistic regression -- Support vector machine -- Gradient boosting -- Clustering -- Genetic algorithm -- Fuzzy logic -- Neural network/multilayer perceptron -- Convolutional neural network -- Recurrent neural network -- Graph convolutional network -- Research challenges -- Conclusion -- References -- 2 - The synergy of AI and biology: A transformative partnership -- Introduction -- The transformative power of AI in biology -- Machine learning -- Deep learning -- Natural language processing -- Reinforcement learning -- Data integration and fusion -- Genetic algorithms -- The need for AI in biology -- Some applications -- Healthcare -- Precision medicine and personalized treatment -- Genomics and genetic research -- Image analysis and medical imaging -- Biological network analysis -- Ecology and conservation -- Synthetic biology and bio-engineering -- Robot-assisted surgery using artificial intelligence -- The promise of AI -- Limitations of using AI in biology -- Conclusion -- References -- 3 - Understanding life and evolution using AI -- Introduction -- AI algorithms and techniques -- Machine learning -- Deep learning -- Natural language processing -- Image analysis and computer vision -- Genetic algorithms -- Bayesian networks -- Data integration and pattern recognition -- 3D modeling and reconstruction -- Significance of AI in biology -- AI in genomics research -- AI and the Human Genome Project.
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AI in ecology -- AI in evolutionary biology -- Conclusion -- References -- 4 - Decoding life: Genetics, bioinformatics, and artificial intelligence -- Introduction -- Genetics: Bioinformatics and artificial intelligence interface -- Bioinformatics: a boon for new-age genetics research -- Genome analysis -- Nucleotide sequencing and analysis -- Gene prediction -- Genome annotation -- Transcriptome analysis -- Protein analysis -- Artificial intelligence in biological research -- AI and ML in plant breeding -- Using AI to study biochemical phenotype -- How does AI aid crop improvement efforts by changing the breeding paradigm? -- Machine learning for biochemical phenotypes -- Machine learning for genomic prediction -- Potential applications of AI and ML in classical and modern plant breeding -- Assessments of biotic and abiotic stress -- Artificial intelligence in crop genomics -- Prediction of functional genomic regions -- Application of AI in phenomics -- Application of ML in image processing -- Research challenges -- Conclusion -- References -- 5 - AI in healthcare: Pioneering innovations for a healthier tomorrow -- Introduction -- Technological advancement -- How is AI used in healthcare? -- Applications of artificial intelligence in healthcare -- Wearable sensors -- Radiology -- Medical image analysis -- Electronic health records and analytics -- Precision medicine -- Smart internet of medical things and diagnostic analysis -- Conclusion -- References -- 6 - Reimagining occupational health and safety in the era of AI -- Introduction -- NLP in occupational health and safety -- RBES in occupational health and safety -- ML for occupational health and safety -- DL for occupational health and safety -- Understanding the application of AI/ML in workplace safety through vision algorithms -- Workplace exposure assessment of toxic gases using AI techniques.
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Workplace exposure assessment of hazardous chemicals using AI techniques -- Generative AI models -- AI for diagnostic and prevention of occupational lung diseases -- NLP utility for workplace health education and awareness -- References -- 7 - From data to insights: Leveraging machine learning for diabetes management -- Introduction -- Overview of diabetes and its management challenges -- Type 1 (juvenile diabetes) -- Type 2 (diabetes mellitus) -- Gestational diabetes -- Role of machine learning in diabetes management -- Understanding data collection and preprocessing of diabetes-related data -- Collection and preprocessing of diabetes-related data -- Data collection -- Data preprocessing -- Standard datasets for diabetes research -- Machine learning models for diabetes risk prediction -- Logistic regression model -- Decision tree model -- Random forest -- Support vector machine -- K-means clustering -- Neural networks -- Deep learning models -- Predictive modeling for blood glucose monitoring -- Ensemble models for blood glucose monitoring -- Continuous glucose monitoring and machine learning -- Ethical considerations in machine learning for diabetes -- Privacy and security in diabetes data handling -- Addressing bias and fairness in machine learning models -- Explainability and interpretability of ML-based diabetes solutions -- Conclusion -- References -- 8 - Smiles 2.0: The AI dentistry frontier -- Introduction -- Applications of AI in dentistry -- Operative dentistry -- Endodontics -- Endodontic diagnosis -- Treatment planning -- Regenerative endodontics -- Periodontics -- Orthodontics -- Oral and maxillofacial pathology -- Prosthodontics -- Oral and maxillofacial surgery -- Public health dentistry -- Forensic odontology -- Patient management -- Ethical considerations -- Future scope -- Conclusion -- References.
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9 - Applications and impact of artificial intelligence in veterinary sciences -- Introduction -- Big data in veterinary sciences -- AI in diagnoses -- Imaging -- AI for disease prediction and surveillance -- Veterinary precision medicine -- Robots in veterinary sciences -- AI and the future of veterinary medicine -- Robots as pets -- Conclusion -- Abbreviations -- References -- 10 - Advancing precision agriculture through artificial intelligence: Exploring the future of cultivation -- Introduction -- Understanding precision agriculture -- Need for AI in precision agriculture -- Application of AI in precision agriculture -- Data collection and analysis -- Crop monitoring and management -- Weed and pest control -- Irrigation and soil management -- Autonomous farming -- Benefits of precision agriculture using AI -- Increased productivity and yield -- Resource efficiency -- Cost reduction and economic viability -- Environmental sustainability -- Challenges and considerations -- Data quality and integration -- Infrastructure and connectivity -- Technical expertise and training -- Ethical and regulatory considerations -- Conclusion -- Abbreviations -- References -- 11 - Artificial intelligence in animal farms for management and breeding -- Introduction -- AI and big data in livestock farms -- Identification of animals -- Animal monitoring -- Disease detection and prevention -- Precision nutrition and feed management -- Automation for precision farming -- Genetic improvement and breeding -- Decision support systems -- Improving animal production using AI -- Conclusion -- References -- 12 - Food manufacturing, processing, storage, and marketing using artificial intelligence -- Introduction -- Food manufacturing -- Application of AI in food manufacturing -- Benefits of AI in food manufacturing -- Implementation of AI in food manufacturing -- Food processing.
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Application of AI in food processing -- AI potential in food processing -- Food storage using AI -- The role of AI in food storage practices -- Application of AI for food storage -- Food marketing -- The use of AI in food marketing strategies -- Implementation of AI in marketing -- Challenges of AI in food industry -- Future directions of AI in food industry -- Ethical considerations, data privacy concerns, and potential biases -- Recommendation for future research -- References -- 13 - Use of AI in conservation and for understanding climate change -- Introduction -- Ecological modeling -- History -- Ecological modeling for river water quality -- Lake modeling -- Forest modeling -- Integrated models -- Section summary -- Biodiversity monitoring and conservation -- Climate change -- A brief history of the origin of AI usage in climate monitoring and change -- Present usage of AI in climate change research -- Harnessing AI for energy efficiency -- Harnessing AI for environment monitoring, planning, and resource management -- Deforestation prediction and monitoring -- Ecosystem restoration planning -- Water resource management -- Land use planning -- AI in renewable energy -- Harnessing AI to counter-forest fires, flooding, and desertification -- Flooding -- Wildfires -- Desertification -- Future of AI in climate change and challenges -- Section summary -- Use of AI in smart farming through the Internet of Things -- Introduction to AI in smart farming through the Internet of Things -- Using IoT for precision agriculture -- Crop management -- Crop productivity -- Weed detection -- Precision fertilizers -- Pest detection and management -- The main application domains of IoT in agriculture -- Open issues and challenges in smart farming -- Section summary -- Conclusion -- Author contributions -- References -- 14 - Artificial intelligence in marine biology.
,
Introduction.
Sprache:
Englisch
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