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ACM SIGBIO newsletter
| ACM SIGBIO newsletter |
| Pubbl/distr/stampa | [New York, N.Y.], : AMC Press, -2001 |
| Disciplina | 610.285 |
| Soggetto topico |
Medicine - Data processing
Biomedical engineering - Data processing Electronic Data Processing Medicine Génie biomédical - Informatique Médecine - Informatique Informatique |
| Soggetto genere / forma |
Periodical
Periodicals. |
| ISSN | 1557-9506 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | SIGBIO newsletter |
| Record Nr. | UNISA-996201007703316 |
| [New York, N.Y.], : AMC Press, -2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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ACM transactions on computing for healthcare
| ACM transactions on computing for healthcare |
| Pubbl/distr/stampa | New York, NY : , : Association for Computing Machinery, , [2020]- |
| Descrizione fisica | 1 online resource |
| Disciplina | 610.285 |
| Soggetto topico |
Medicine - Data processing
Medicine - Research - Data processing Médecine - Informatique Médecine - Recherche - Informatique |
| Soggetto genere / forma | Periodicals. |
| ISSN | 2637-8051 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Transactions on computing for healthcare
ACM health Association for Computing Machinery transactions on computing for healthcare |
| Record Nr. | UNINA-9910412143803321 |
| New York, NY : , : Association for Computing Machinery, , [2020]- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Ambient intelligence in health care : proceedings of ICAIHC 2022 / / Tripti Swarnkar [and four others] editors
| Ambient intelligence in health care : proceedings of ICAIHC 2022 / / Tripti Swarnkar [and four others] editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (490 pages) |
| Disciplina | 610.285 |
| Collana | Smart innovation, systems, and technologies |
| Soggetto topico | Medicine - Data processing |
| ISBN | 981-19-6068-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- ICAIHC Committee -- Preface -- Acknowledgements -- Contents -- About the Editors -- 1 An Adaptive Fuzzy-Based Clustering Model for Healthcare Wireless Sensor Networks -- 1.1 Introduction -- 1.1.1 Resource Management in WSN -- 1.2 Related Work -- 1.3 Proposed Model -- 1.3.1 Fuzzy Sets and Neuro-fuzzy Logic Theory -- 1.4 Simulation and Results -- 1.5 Conclusion and Future Work -- References -- 2 Artificial Pancreas (AP) Based on the JAYA Optimized PI Controller (JAYA-PIC) -- 2.1 Introduction -- 2.2 Problem Statements Formulations -- 2.2.1 Clinical Information -- 2.2.2 Simulation Model -- 2.2.3 MID -- 2.2.4 Patient Output -- 2.3 Control Algorithms -- 2.3.1 Development of JAYA-PIC -- 2.4 Results -- 2.4.1 JAYA-PIC Action in Patient -- 2.4.2 Analysis of Control Actions -- 2.5 Conclusions -- References -- 3 Security Issues and Solutions for Reliable WBAN-Based e-Healthcare Systems: A Systematic Review -- 3.1 Introduction -- 3.2 Methodology -- 3.3 Secured WBAN Requirements -- 3.3.1 Security Paradigms for WBAN -- 3.3.2 Security Needs in WBAN -- 3.4 Security Solutions -- 3.5 Case Studies on WBAN Prototypes -- 3.6 Conclusion -- References -- 4 Brain Tumour Detection by Multilevel Thresholding Using Opposition Equilibrium Optimizer -- 4.1 Introduction -- 4.2 Multilevel Thresholding Based on Entropy -- 4.2.1 Multilevel Thresholding: A Mathematical Approach -- 4.2.2 Entropy Functions -- 4.3 Opposition Equilibrium Optimizer (OEO) -- 4.4 Proposed Brain Tumour Detection Method Using OEO-Based Multilevel Thresholding Approach -- 4.5 Results and Discussion -- 4.6 Conclusion -- References -- 5 RescuePlus -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Existing Solution -- 5.4 Proposed Solution -- 5.5 Result -- 5.6 Future Scope -- 5.7 Conclusion -- References -- 6 Skeletal Bone Age Determination Using Deep Learning -- 6.1 Introduction -- 6.2 Methodology.
6.2.1 Data Set Selection -- 6.2.2 Data Pre-processing -- 6.3 Custom Model -- 6.3.1 Model Architecture -- 6.3.2 Approaches -- 6.4 Experiments -- 6.4.1 Loss Functions -- 6.4.2 Comparing with Pre-trained Models -- 6.5 Conclusion -- References -- 7 Chimp Optimization Algorithm-Based Feature Selection for Cardiac Image-Based Heart Disease Diagnosis -- 7.1 Introduction -- 7.2 Chimp Optimization Algorithm-Based Feature Selection for Heart Disease Diagnosis -- 7.2.1 Pre-processing -- 7.2.2 Feature Extraction -- 7.2.3 Feature Selection Using ChOA -- 7.2.4 Classification Using SVNN -- 7.3 Results and Discussion -- 7.4 Conclusion -- References -- 8 The Hospitality Industry's Impact on the COVID-19 Epidemic: A Case Study of Ukraine -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Research Methods -- 8.4 Results -- 8.4.1 State and Legal Measures -- 8.5 Conclusion -- References -- 9 Society in Front of a COVID-19 Pandemic: India Versus Ukraine -- 9.1 Introduction -- 9.1.1 Research Background -- 9.2 Research Methods and Materials -- 9.3 Results -- 9.3.1 The Speed of Virus Spreading -- 9.3.2 The Risk and Danger Rate Estimation -- 9.4 The Level of Trust in the National Healthcare System and the Satisfaction of Government's Response to COVID-19 Situation -- 9.5 The Emotions and General Psychological State of Respondents -- 9.6 Conclusions -- Notes -- References -- 10 Breast Cancer Detection Using Concatenated Deep Learning Model -- 10.1 Introduction -- 10.2 Proposed Methodology -- 10.2.1 Convolutional Neural Network -- 10.2.2 LSTM -- 10.3 Result Analysis -- 10.3.1 Dataset Description -- 10.4 Conclusion -- References -- 11 Recommendation of Pesticides Based on Automation Detection of Citrus Fruits and Leaves Diseases Using Deep Learning -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Proposed Work -- 11.3.1 Image Acquisition and Preprocessing. 11.3.2 Image Segmentation -- 11.3.3 Diseases Prediction -- 11.3.4 Experimental Results -- 11.3.5 Fertilizer Recommendation -- 11.4 Conclusion -- References -- 12 Comprehensive Information Retrieval Using Fine-Tuned Bert Model and Topic-Assisted Query Expansion -- 12.1 Introduction -- 12.2 Review of Literature -- 12.3 Comprehensive Information Retrieval -- 12.3.1 BERT Model -- 12.3.2 Topic Modelling Using Latent Dirichlet Allocation -- 12.3.3 The Proposed CIR Architecture -- 12.4 The Proposed Approach -- 12.5 Experimental Results -- 12.5.1 Dataset -- 12.5.2 Evaluation Metrics -- 12.5.3 Performance Evaluations -- 12.5.4 Performance Comparison -- 12.5.5 Discussions -- 12.6 Conclusion -- References -- 13 Diabetic Retinopathy Detection Using CNN Model -- 13.1 Introduction -- 13.2 Related Work -- 13.3 Dataset Used -- 13.3.1 Overview -- 13.3.2 Augmentation -- 13.4 Defining CNN Model -- 13.4.1 Summary of Model Architecture -- 13.4.2 Model Layers -- 13.4.3 Loss Function -- 13.5 Result -- 13.6 Conclusion -- References -- 14 Digi-CANE-An IoT Savior for Visually Sensitive -- 14.1 Introduction -- 14.2 Literature Survey -- 14.3 Objective -- 14.4 General Flowchart -- 14.5 The Sensory Architecture -- 14.5.1 Emergency Sensor (Gyroscope) -- 14.5.2 Cane Module -- 14.5.3 Ultrasonic Sensor -- 14.5.4 Buzzer -- 14.5.5 Arduino Uno -- 14.5.6 Working -- 14.6 Circuit Diagram -- 14.6.1 Hardware Details -- 14.6.2 Working of Ultrasonic Sensor (HC-SR04) -- 14.6.3 Software Requirement Arduino IDE -- 14.7 Conclusion and Future Scope -- References -- 15 Link Performance in Community Detection Using Social Network -- 15.1 Introduction -- 15.2 Literature Review -- 15.3 Methods -- 15.4 Metrics -- 15.5 Experimental Results -- 15.6 Discussion -- 15.7 Conclusion -- References -- 16 Analyze Ego-Centric Nodes in Social Network Using Machine Learning Technique -- 16.1 Introduction. 16.2 Review of Literature -- 16.3 Materials and Method -- 16.4 Link Influence Strategies -- 16.5 Result and Discussion -- 16.6 Conclusion -- 16.7 Future Enhancement -- References -- 17 5G Revolution Transforming the Delivery in Healthcare -- 17.1 Introduction -- 17.2 Literature Review -- 17.3 Features of 5G Technology -- 17.4 Problems Posed by Current Healthcare Industry -- 17.4.1 Lack of Universal Access -- 17.4.2 Long-Term Chronic Care Burden -- 17.4.3 Aging Population -- 17.4.4 Resource Constraints and Healthcare Disparities -- 17.5 Areas in Healthcare that 5G Will Boost -- 17.5.1 Imaging -- 17.5.2 Diagnostics -- 17.5.3 Data Analytics and Treatment -- 17.6 New Realms that 5G Will Enable -- 17.7 Real-World Trials -- 17.8 Positive Impacts of 5G on Our Healthcare System -- 17.9 Recommendations -- 17.10 Conclusion and Future Works -- References -- 18 A Systematic Review of AI Privileges to Combat Widen Threat of Flavivirus -- 18.1 Significance of AI in Flavivirus Disease Eradication -- 18.1.1 AI in Healthcare -- 18.1.2 Flavivirus-A Detailed Synopsis -- 18.1.3 Flavivirus Characteristics -- 18.1.4 Flavivirus Lifecycle -- 18.1.5 Flavivirus Epidemiology -- 18.2 Detection and Estimation of Flavivirus Disease with Machine Learning Models -- 18.3 Conclusions and Future Direction -- References -- 19 Early Detection of Sepsis Using LSTM Neural Network with Electronic Health Record -- 19.1 First Section -- 19.2 Method -- 19.2.1 Sepsis Data Preprocessing -- 19.2.2 LSTM Model -- 19.3 Result and Discussion -- 19.4 Conclusion -- References -- 20 Detection of COVID-19 Infection from Clinical Findings Using Machine Learning Algorithm -- 20.1 Introduction -- 20.2 Related Works -- 20.3 Materials and Methods -- 20.3.1 Medical Dataset Used -- 20.3.2 Data Preprocessing -- 20.3.3 Feature Selection -- 20.3.4 Classification Algorithms. 20.4 Performance Evaluation and Results -- 20.4.1 Evaluation Measures -- 20.4.2 Results -- 20.5 Conclusion -- References -- 21 Development of Real-Time Cloud Based Smart Remote Healthcare Monitoring System -- 21.1 Introduction -- 21.2 System Framework -- 21.3 System Modeling -- 21.4 Results and Discussion -- 21.5 Conclusion -- References -- 22 Performance Analysis of Hyperparameters of Convolutional Neural Networks for COVID-19 X-ray Image Classification -- 22.1 Introduction -- 22.2 CNN and Its Optimizers -- 22.3 Experimental Results -- 22.4 Conclusion -- References -- 23 Sequence Rule Mining for Insulin Dose Prediction Using Temporal Dataset -- 23.1 Introduction -- 23.2 Literature Review -- 23.3 Definitions -- 23.3.1 Sequential Rule Mining -- 23.3.2 Support and Confidence -- 23.3.3 Sequence Rule Mining Algorithms -- 23.4 Methodology -- 23.5 Experiment Work -- 23.5.1 Dataset Used -- 23.5.2 Result and Discussion -- 23.6 Conclusion -- 23.7 Future Scope -- References -- 24 Ensemble Deep Learning Approach with Attention Mechanism for COVID-19 Detection and Prediction -- 24.1 Introduction -- 24.2 Related Work -- 24.3 Proposed Model -- 24.4 Data sets, Experiments, and Results -- 24.5 Conclusion and Future Scope -- References -- 25 Semantic Segmentation of Cardiac Structures from USG Images Using Few-Shot Prototype Learner Guided Deep Networks -- 25.1 Introduction -- 25.2 Proposed Work -- 25.2.1 Prototype Learner -- 25.2.2 Segmentor -- 25.3 Experiments -- 25.3.1 Dataset Description and Measures Used -- 25.3.2 Parameters -- 25.3.3 Results and Analysis -- 25.4 Conclusion -- References -- 26 Quantification of Homa Effect on Air Quality in NCR, India: Pollution and Pandemic Challenges in Cities and Healthcare Remedies -- 26.1 Introduction -- 26.1.1 Air Quality Global Challenge in Twenty-First Century -- 26.1.2 How Yajna and Vedic Rituals May Curb Pollution. 26.2 Literature Review. |
| Record Nr. | UNINA-9910632481303321 |
| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applications of software agent technology in the health care domain / Antonio Moreno, John L. Nealon, editors
| Applications of software agent technology in the health care domain / Antonio Moreno, John L. Nealon, editors |
| Pubbl/distr/stampa | Basel ; Boston : Birkhauser Verlag, 2003 |
| Descrizione fisica | 212 p. : ill. ; 24 cm |
| Disciplina | 620.28563 |
| Altri autori (Persone) |
Moreno, Antonio, Dr.author
Nealon, J. L. (John L.) |
| Collana | Whitestein series in software agent technologies |
| Soggetto topico |
Medicine - Data processing
Intelligent agents (Computer software) Medical informatics |
| ISBN | 376432662X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991000619169707536 |
| Basel ; Boston : Birkhauser Verlag, 2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
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Applied clinical informatics
| Applied clinical informatics |
| Pubbl/distr/stampa | [Hölderlinstr, Germany], : Schattauer, ©2009-©2017 |
| Soggetto topico |
Medical informatics
Medicine - Data processing Hospital care - Data processing Information storage and retrieval systems - Medicine Medical records - Data processing Medical Informatics Delivery of Health Care Information Storage and Retrieval Medical Records Systems, Computerized |
| Soggetto genere / forma |
Fulltext
Internet Resources. Periodicals. |
| Soggetto non controllato | Medical & Biomedical Informatics |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
ACI
Appl clin inf |
| Record Nr. | UNINA-9910138760003321 |
| [Hölderlinstr, Germany], : Schattauer, ©2009-©2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Applied medical informatics
| Applied medical informatics |
| Pubbl/distr/stampa | Romania, : SRIMA Pub. House |
| Soggetto topico |
Medical informatics
Medicine - Research - Data processing Medicine - Data processing |
| Soggetto genere / forma | Periodicals. |
| Soggetto non controllato | Medical & Biomedical Informatics |
| ISSN | 2067-7855 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910139916203321 |
| Romania, : SRIMA Pub. House | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied medical informatics
| Applied medical informatics |
| Pubbl/distr/stampa | Romania, : SRIMA Pub. House |
| Soggetto topico |
Medical informatics
Medicine - Research - Data processing Medicine - Data processing |
| Soggetto genere / forma | Periodicals. |
| Soggetto non controllato | Medical & Biomedical Informatics |
| ISSN | 2067-7855 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996321806303316 |
| Romania, : SRIMA Pub. House | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Automated experimentation
| Automated experimentation |
| Pubbl/distr/stampa | [London], : BioMed Central, 2009-[2012] |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Medical informatics
Medicine, Experimental - Data processing Medicine - Data processing Medical Informatics Medical Informatics Applications Decision Making, Computer-Assisted Information Systems - statistics & numerical data Biomedicine Automation |
| Soggetto genere / forma |
Periodical
Periodicals. |
| ISSN | 1759-4499 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910139783003321 |
| [London], : BioMed Central, 2009-[2012] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Automated experimentation
| Automated experimentation |
| Pubbl/distr/stampa | [London], : BioMed Central, 2009-[2012] |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Medical informatics
Medicine, Experimental - Data processing Medicine - Data processing Medical Informatics Medical Informatics Applications Decision Making, Computer-Assisted Information Systems - statistics & numerical data Biomedicine Automation |
| Soggetto genere / forma |
Periodical
Periodicals. |
| ISSN | 1759-4499 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996206283503316 |
| [London], : BioMed Central, 2009-[2012] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Les Big Data et L'éthique : le cas de la datasphère médicale / / Jérôme Béranger
| Les Big Data et L'éthique : le cas de la datasphère médicale / / Jérôme Béranger |
| Autore | Béranger Jérôme |
| Pubbl/distr/stampa | London : , : ISTE Editions Ltd., , 2016 |
| Descrizione fisica | 1 online resource (314 pages) |
| Disciplina | 610.285 |
| Collana | Collection systèmes d'information, web et société |
| Soggetto topico |
Medicine - Data processing
Data protection Medical informatics |
| ISBN | 1-78406-129-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | fre |
| Nota di contenuto | Cover -- Table des matières -- Remerciements -- Préface -- Introduction -- Chapitre 1. Vers une médecine connectée, mesurée et personnalisée centrée sur le traitement de la datasphère médicale -- Chapitre 2. Valorisation éthique de la datasphère médicale -- Chapitre 3. Gestion et gouvernance des données personnelles de santé -- Conclusion -- Annexe 1. Avantages des technologies Big Data selon les sources de données et les acteurs -- Annexe 2. Enjeux entourant une plateforme de gestion des données (DMP) -- Annexe 3. Critères d'évaluation sur la valorisation éthique des données de santé à caractère personnel -- Annexe 4. Principes des lignes directrices de l'OCDE régissant la sécurité des systèmes et réseaux d'information -- Glossaire -- Bibliographie -- Index. |
| Record Nr. | UNINA-9910798162203321 |
Béranger Jérôme
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| London : , : ISTE Editions Ltd., , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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