Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data
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Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data
Srinivasu, Parvathaneni Naga; Marques, Goncalo; Bhoi, Akash Kumar; de Albuquerque, Victor Hugo Costa
Elsevier Science & Technology
01/2022
294
Mole
Inglês
9780323857512
15 a 20 dias
610
Descrição não disponível.
Section 1: Cognitive Technology for processing of Healthcare data 1. Cognitive technology in personalized Medicine/healthcare solutions 2. Cognitive technology for blend of personalized healthcare information with scientific data for better clinical risk analysis and healthcare innovation 3. Healthcare data encryption, data processing for the data acquired from smart sensors and approaches
Section 2: Artificial Intelligence Approaches for Healthcare Industry 4. Artificial Neural Networks based approaches for computer-aided disease diagnosis and treatment 5. AI and Deep Learning for processing the huge amount of patient centric data that assists in clinical decisions 6. Pattern Recognition and Computer vision approaches for handling healthcare data 7. Applications of Recurrent Neural Networks, Generative Neural Networks, Ensemble methods, Weakly Trained Approaches towards Data associated with healthcare solutions
Section 3: Evolutionary Algorithms for Healthcare Data Analysis 8. Optimization inspired by biological evolution for high dimensional data for forecasting of illness in advance like Cancer, Heart disease, Brain tumors 9. Swarm Intelligence and Evolutionary Algorithms in processing the Healthcare Data 10. Recent advancements in evolutionary algorithms for handling the information related to healthcare industry
Section4: Computational Intelligence and soft computing models in processing the data related to healthcare industry 11. Natural computing and Unsupervised Learning Methods in healthcare data-centric operations 12. Soft Computing and Machine Learning Techniques for healthcare data analytics 13. Probabilistic approaches for minimizing the healthcare diagnosis cost through data-centric operations 14. Computational Intelligence in Human-machine interface (HMI) e.g. ECG, EEG, EMG, PCG and predictive data analysis
Section 2: Artificial Intelligence Approaches for Healthcare Industry 4. Artificial Neural Networks based approaches for computer-aided disease diagnosis and treatment 5. AI and Deep Learning for processing the huge amount of patient centric data that assists in clinical decisions 6. Pattern Recognition and Computer vision approaches for handling healthcare data 7. Applications of Recurrent Neural Networks, Generative Neural Networks, Ensemble methods, Weakly Trained Approaches towards Data associated with healthcare solutions
Section 3: Evolutionary Algorithms for Healthcare Data Analysis 8. Optimization inspired by biological evolution for high dimensional data for forecasting of illness in advance like Cancer, Heart disease, Brain tumors 9. Swarm Intelligence and Evolutionary Algorithms in processing the Healthcare Data 10. Recent advancements in evolutionary algorithms for handling the information related to healthcare industry
Section4: Computational Intelligence and soft computing models in processing the data related to healthcare industry 11. Natural computing and Unsupervised Learning Methods in healthcare data-centric operations 12. Soft Computing and Machine Learning Techniques for healthcare data analytics 13. Probabilistic approaches for minimizing the healthcare diagnosis cost through data-centric operations 14. Computational Intelligence in Human-machine interface (HMI) e.g. ECG, EEG, EMG, PCG and predictive data analysis
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Breast cancer prediction; Activation functions; ADVI; AI; Artificial intelligence; Bayesian neural network (BNN; Clinical decisions; Cognitive computing; Cognitive IoT; Computer-aided diagnosis and treatment; Computer-aided diagnostic systems; Convolutional neural networks; Data science; Data-centric operations; Decision boundary; Decision; Deep learning; Diabetes; Ensemble of classifiers; Epilepsy; GRNN; Health care; Health monitoring; Healthcare diagnosis; Healthcare industry; ICH; Image classification; Internet of Things; Intraensemble method; K-means; Learning; Machine; Markov chain Monte Carlo (MCMC)Principal component analysis (PCA)Variational inference (VI)Medical image classification; Medical imaging; Mobile application; Natural computation; Neural networks; Numerical data augmentation; Optimized adaptive Kalman filter; Patient-centric data; PCA-Net; Polysomnography; Problem-solving methods; RBF; Recommendation system; Regression modeling; Risk analysis; Scoring procedure; Sleep parameters; Sleep stages; Small data approach; Supervised; SVR; Tree seed algorithm; Unsupervised deep learning; Unsupervised learning; Voice automation
Section 1: Cognitive Technology for processing of Healthcare data 1. Cognitive technology in personalized Medicine/healthcare solutions 2. Cognitive technology for blend of personalized healthcare information with scientific data for better clinical risk analysis and healthcare innovation 3. Healthcare data encryption, data processing for the data acquired from smart sensors and approaches
Section 2: Artificial Intelligence Approaches for Healthcare Industry 4. Artificial Neural Networks based approaches for computer-aided disease diagnosis and treatment 5. AI and Deep Learning for processing the huge amount of patient centric data that assists in clinical decisions 6. Pattern Recognition and Computer vision approaches for handling healthcare data 7. Applications of Recurrent Neural Networks, Generative Neural Networks, Ensemble methods, Weakly Trained Approaches towards Data associated with healthcare solutions
Section 3: Evolutionary Algorithms for Healthcare Data Analysis 8. Optimization inspired by biological evolution for high dimensional data for forecasting of illness in advance like Cancer, Heart disease, Brain tumors 9. Swarm Intelligence and Evolutionary Algorithms in processing the Healthcare Data 10. Recent advancements in evolutionary algorithms for handling the information related to healthcare industry
Section4: Computational Intelligence and soft computing models in processing the data related to healthcare industry 11. Natural computing and Unsupervised Learning Methods in healthcare data-centric operations 12. Soft Computing and Machine Learning Techniques for healthcare data analytics 13. Probabilistic approaches for minimizing the healthcare diagnosis cost through data-centric operations 14. Computational Intelligence in Human-machine interface (HMI) e.g. ECG, EEG, EMG, PCG and predictive data analysis
Section 2: Artificial Intelligence Approaches for Healthcare Industry 4. Artificial Neural Networks based approaches for computer-aided disease diagnosis and treatment 5. AI and Deep Learning for processing the huge amount of patient centric data that assists in clinical decisions 6. Pattern Recognition and Computer vision approaches for handling healthcare data 7. Applications of Recurrent Neural Networks, Generative Neural Networks, Ensemble methods, Weakly Trained Approaches towards Data associated with healthcare solutions
Section 3: Evolutionary Algorithms for Healthcare Data Analysis 8. Optimization inspired by biological evolution for high dimensional data for forecasting of illness in advance like Cancer, Heart disease, Brain tumors 9. Swarm Intelligence and Evolutionary Algorithms in processing the Healthcare Data 10. Recent advancements in evolutionary algorithms for handling the information related to healthcare industry
Section4: Computational Intelligence and soft computing models in processing the data related to healthcare industry 11. Natural computing and Unsupervised Learning Methods in healthcare data-centric operations 12. Soft Computing and Machine Learning Techniques for healthcare data analytics 13. Probabilistic approaches for minimizing the healthcare diagnosis cost through data-centric operations 14. Computational Intelligence in Human-machine interface (HMI) e.g. ECG, EEG, EMG, PCG and predictive data analysis
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Breast cancer prediction; Activation functions; ADVI; AI; Artificial intelligence; Bayesian neural network (BNN; Clinical decisions; Cognitive computing; Cognitive IoT; Computer-aided diagnosis and treatment; Computer-aided diagnostic systems; Convolutional neural networks; Data science; Data-centric operations; Decision boundary; Decision; Deep learning; Diabetes; Ensemble of classifiers; Epilepsy; GRNN; Health care; Health monitoring; Healthcare diagnosis; Healthcare industry; ICH; Image classification; Internet of Things; Intraensemble method; K-means; Learning; Machine; Markov chain Monte Carlo (MCMC)Principal component analysis (PCA)Variational inference (VI)Medical image classification; Medical imaging; Mobile application; Natural computation; Neural networks; Numerical data augmentation; Optimized adaptive Kalman filter; Patient-centric data; PCA-Net; Polysomnography; Problem-solving methods; RBF; Recommendation system; Regression modeling; Risk analysis; Scoring procedure; Sleep parameters; Sleep stages; Small data approach; Supervised; SVR; Tree seed algorithm; Unsupervised deep learning; Unsupervised learning; Voice automation
