Pubbl/distr/stampa	Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica	1 online resource (158 pages)
Disciplina	060
Collana	Lecture Notes in Computer Science Ser.
Soggetto topico	Artificial intelligence - Medical applications
ISBN	3-031-16852-6
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Record Nr.	UNISA-996490359503316

Pubbl/distr/stampa	Cambridge, U.K., : Royal Society of Chemistry, c2002
Descrizione fisica	1 online resource (204 p.)
Disciplina	615.19
Altri autori (Persone)	FlowerDarren R
Collana	Special publication
Soggetto topico	Drugs - Design Drugs - Design - Mathematical models Artificial intelligence - Medical applications Drugs - Research - Methodology
Soggetto genere / forma	Electronic books.
ISBN	1-84755-070-3 1-60119-027-1
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	CONTENTS; Molecular Informatics: Sharpening Drug Design's Cutting Edge; High-Throughput X-Ray Crystallography for Drug Discovery; Trawling the Genome for G Protein-coupled Receptors: the Importance of Integrating Bioinformatic Approaches; Virtual Screening of Virtual Libraries - an Efficient Strategy for LeadGeneration; Virtual Techniques for Lead Optimisation; The Impact of Physical Organic Chemistry on the Control of Drug-likeProperties; Mutagenesis and Modelling Highlight the Critical Nature of theTM2-loop-TM3 Region of Biogenic Amine GPCRS; Computational Vaccine Design; Subject Index
Record Nr.	UNINA-9910450162003321

Pubbl/distr/stampa	Cambridge : , : Royal Society of Chemistry, , 2002
Descrizione fisica	1 online resource (x, 192 pages) : illustrations
Disciplina	615.19
Altri autori (Persone)	FlowerDarren R
Collana	Special publication
Soggetto topico	Drugs - Design Drugs - Design - Mathematical models Artificial intelligence - Medical applications Drugs - Research - Methodology
ISBN	1-84755-070-3 1-60119-027-1
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	CONTENTS; Molecular Informatics: Sharpening Drug Design's Cutting Edge; High-Throughput X-Ray Crystallography for Drug Discovery; Trawling the Genome for G Protein-coupled Receptors: the Importance of Integrating Bioinformatic Approaches; Virtual Screening of Virtual Libraries - an Efficient Strategy for LeadGeneration; Virtual Techniques for Lead Optimisation; The Impact of Physical Organic Chemistry on the Control of Drug-likeProperties; Mutagenesis and Modelling Highlight the Critical Nature of theTM2-loop-TM3 Region of Biogenic Amine GPCRS; Computational Vaccine Design; Subject Index
Record Nr.	UNINA-9910783441703321

Autore	Mangla Monika
Pubbl/distr/stampa	Newark : , : John Wiley & Sons, Incorporated, , 2021
Descrizione fisica	1 online resource (416 pages)
Disciplina	610.285
Altri autori (Persone)	SharmaNonita GargPoonam WadhwaVaishali KThirunavukkarasu KhanShahnawaz
Collana	Machine Learning in Biomedical Science and Healthcare Informatics
Soggetto topico	Artificial intelligence - Medical applications Medical innovations Medicine - Data processing
Soggetto non controllato	Medicine Medical
ISBN	1-119-79233-9 1-119-79234-7 1-119-79232-0
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Front Matter -- Basics of Smart Healthcare. An Overview of IoT in Health Sectors / PS Sheeba -- IoT-Based Solutions for Smart Healthcare / Pankaj Jain, Sonia F Panesar, Bableen Flora Talwar, Mahesh Kumar Sah -- QLattice Environment and Feyn QGraph Models-A New Perspective Toward Deep Learning / Vinayak Bharadi -- Sensitive Healthcare Data: Privacy and Security Issues and Proposed Solutions / Abhishek Vyas, Satheesh Abimannan, Ren-Hung Hwang -- Employment of Machine Learning in Disease Detection. Diabetes Prediction Model Based on Machine Learning / Ayush Kumar Gupta, Sourabh Yadav, Priyanka Bhartiya, Divesh Gupta -- Lung Cancer Detection Using 3D CNN Based on Deep Learning / Siddhant Panda, Vasudha Chhetri, Vikas Kumar Jaiswal, Sourabh Yadav -- Pneumonia Detection Using CNN and ANN Based on Deep Learning Approach / Priyanka Bhartiya, Sourabh Yadav, Ayush Gupta, Divesh Gupta -- Personality Prediction and Handwriting Recognition Using Machine Learning / Vishal Patil, Harsh Mathur -- Risk Mitigation in Children With Autism Spectrum Disorder Using Brain Source Localization / Joy Karan Singh, Deepti Kakkar, Tanu Wadhera -- Predicting Chronic Kidney Disease Using Machine Learning / Monika Gupta, Parul Gupta -- Advanced Applications of Machine Learning in Healthcare. Behavioral Modeling Using Deep Neural Network Framework for ASD Diagnosis and Prognosis / Tanu Wadhera, Deepti Kakkar, Rajneesh Rani -- Random Forest Application of Twitter Data Sentiment Analysis in Online Social Network Prediction / Arnav Munshi, M Arvindhan, K Thirunavukkarasu -- Remedy to COVID-19: Social Distancing Analyzer / Sourabh Yadav -- IoT-Enabled Vehicle Assistance System of Highway Resourcing for Smart Healthcare and Sustainability / Shubham Joshi, Radha Krishna Rambola -- Aids of Machine Learning for Additively Manufactured Bone Scaffold / Nimisha Rahul Shirbhate, Sanjay Bokade.
Altri titoli varianti	Emerging Technologies for Healthcare
Record Nr.	UNINA-9910830212003321

Pubbl/distr/stampa	Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021
Descrizione fisica	1 online resource (352 pages)
Disciplina	610.285
Soggetto topico	Artificial intelligence - Medical applications Medical technology
Soggetto genere / forma	Electronic books.
ISBN	1-119-76906-X 1-119-76908-6 1-119-76907-8
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: MACHINE LEARNING FOR HANDLINGCOVID-19 -- 1 COVID-19 and Machine Learning Approaches to Deal With the Pandemic -- 1.1 Introduction -- 1.1.1 COVID-19 and its Various Transmission Stages Depending Upon the Severity of the Problem -- 1.2 COVID-19 Diagnosis in Patients Using Machine Learning -- 1.2.1 Machine Learning to Identify the People who are at More Risk of COVID-19 -- 1.2.2 Machine Learning to Speed Up Drug Development -- 1.2.3 Machine Learning for Re-Use of Existing Drugs in Treating COVID-19 -- 1.3 AI and Machine Learning as a Support System for Robotic System and Drones -- 1.3.1 AI-Based Location Tracking of COVID-19 Patients -- 1.3.2 Increased Number of Screenings Using AI Approach -- 1.3.3 Artificial Intelligence in Management of Resources During COVID-19 -- 1.3.4 Influence of AI on Manufacturing Industry During COVID-19 -- 1.3.5 Artificial Intelligence and Mental Health in COVID-19 -- 1.3.6 Can AI Replace the Human Brain Intelligence in COVID-19 Crisis? -- 1.3.7 Advantages and Disadvantages of AI in Post COVID Era -- 1.4 Conclusion -- References -- 2 Healthcare System 4.0 Perspectives on COVID-19 Pandemic -- 2.1 Introduction -- 2.2 Key Techniques of HCS 4.0 for COVID-19 -- 2.2.1 Artificial Intelligence (AI) -- 2.2.2 The Internet of Things (IoT) -- 2.2.3 Big Data -- 2.2.4 Virtual Reality (VR) -- 2.2.5 Holography -- 2.2.6 Cloud Computing -- 2.2.7 Autonomous Robots -- 2.2.8 3D Scanning -- 2.2.9 3D Printing Technology -- 2.2.10 Biosensors -- 2.3 Real World Applications of HCS 4.0 for COVID-19 -- 2.4 Opportunities and Limitations -- 2.5 Future Perspectives -- 2.6 Conclusion -- References -- 3 Analysis and Prediction on COVID-19 Using Machine Learning Techniques -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Types of Machine Learning. 3.4 Machine Learning Algorithms -- 3.4.1 Linear Regression -- 3.4.2 Logistic Regression -- 3.4.3 K-NN or K Nearest Neighbor -- 3.4.4 Decision Tree -- 3.4.5 Random Forest -- 3.5 Analysis and Prediction of COVID-19 Data -- 3.5.1 Methodology Adopted -- 3.6 Analysis Using Machine Learning Models -- 3.6.1 Splitting of Data into Training and Testing Data Set -- 3.6.2 Training of Machine Learning Models -- 3.6.3 Calculating the Score -- 3.7 Conclusion & -- Future Scope -- References -- 4 Rapid Forecasting of Pandemic Outbreak Using Machine Learning -- 4.1 Introduction -- 4.2 Effect of COVID-19 on Different Sections of Society -- 4.2.1 Effect of COVID-19 on Mental Health of Elder People -- 4.2.2 Effect of COVID-19 on our Environment -- 4.2.3 Effect of COVID-19 on International Allies and Healthcare -- 4.2.4 Therapeutic Approaches Adopted by Different Countries to Combat COVID-19 -- 4.2.5 Effect of COVID-19 on Labor Migrants -- 4.2.6 Impact of COVID-19 on our Economy -- 4.3 Definition and Types of Machine Learning -- 4.3.1 Machine Learning & -- Its Types -- 4.3.2 Applications of Machine Learning -- 4.4 Machine Learning Approaches for COVID-19 -- 4.4.1 Enabling Organizations to Regulate and Scale -- 4.4.2 Understanding About COVID-19 Infections -- 4.4.3 Gearing Up Study and Finding Treatments -- 4.4.4 Predicting Treatment and Healing Outcomes -- 4.4.5 Testing Patients and Diagnosing COVID-19 -- References -- 5 Rapid Forecasting of Pandemic Outbreak Using Machine Learning: The Case of COVID-19 -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Suggested Methodology -- 5.4 Models in Epidemiology -- 5.4.1 Bayesian Inference Models -- 5.5 Particle Filtering Algorithm -- 5.6 MCM Model Implementation -- 5.6.1 Reproduction Number -- 5.7 Diagnosis of COVID-19 -- 5.7.1 Predicting Outbreaks Through Social Media Analysis -- 5.8 Conclusion -- References. Part 2: EMERGING TECHNOLOGIES TO DEAL WITH COVID-19 -- 6 Emerging Technologies for Handling Pandemic Challenges -- 6.1 Introduction -- 6.2 Technological Strategies to Support Society During the Pandemic -- 6.2.1 Online Shopping and Robot Deliveries -- 6.2.2 Digital and Contactless Payments -- 6.2.3 Remote Work -- 6.2.4 Telehealth -- 6.2.5 Online Entertainment -- 6.2.6 Supply Chain 4.0 -- 6.2.7 3D Printing -- 6.2.8 Rapid Detection -- 6.2.9 QRT-PCR -- 6.2.10 Immunodiagnostic Test (Rapid Antibody Test) -- 6.2.11 Work From Home -- 6.2.12 Distance Learning -- 6.2.13 Surveillance -- 6.3 Feasible Prospective Technologies in Controlling the Pandemic -- 6.3.1 Robotics and Drones -- 6.3.2 5G and Information and Communications Technology (ICT) -- 6.3.3 Portable Applications -- 6.4 Coronavirus Pandemic: Emerging Technologies That Tackle Key Challenges -- 6.4.1 Remote Healthcare -- 6.4.2 Prevention Measures -- 6.4.3 Diagnostic Solutions -- 6.4.4 Hospital Care -- 6.4.5 Public Safety During Pandemic -- 6.4.6 Industry Adapting to the Lockdown -- 6.4.7 Cities Adapting to the Lockdown -- 6.4.8 Individuals Adapting to the Lockdown -- 6.5 The Golden Age of Drone Delivery -- 6.5.1 The Early Adopters are Winning -- 6.5.2 The Golden Age Will Require Collaboration and Drive -- 6.5.3 Standardization and Data Sharing Through the Smart City Network -- 6.5.4 The Procedure of AI and Non-AI-Based Applications -- 6.6 Technology Helps Pandemic Management -- 6.6.1 Tracking People With Facial Recognition and Big Data -- 6.6.2 Contactless Movement and Deliveries Through Autonomous Vehicles, Drones, and Robots -- 6.6.3 Technology Supported Temperature Monitoring -- 6.6.4 Remote Working Technologies to Support Social Distancing and Maintain Business Continuity -- 6.7 Conclusion -- References -- 7 Unfolding the Potential of Impactful Emerging Technologies Amid COVID-19. 7.1 Introduction -- 7.2 Review of Technologies Used During the Outbreak of Ebola and SARS -- 7.2.1 Technological Strategies and Tools Used at the Time of SARS -- 7.2.2 Technological Strategies and Tools Used at the Time of Ebola -- 7.3 Emerging Technological Solutions to Mitigate the COVID-19 Crisis -- 7.3.1 Artificial Intelligence -- 7.3.2 IoT & -- Robotics -- 7.3.3 Telemedicine -- 7.3.4 Innovative Healthcare -- 7.3.5 Nanotechnology -- 7.4 Conclusion -- References -- 8 Advances in Technology: Preparedness for Handling Pandemic Challenges -- 8.1 Introduction -- 8.2 Issues and Challenges Due to Pandemic -- 8.2.1 Health Effect -- 8.2.2 Economic Impact -- 8.2.3 Social Impact -- 8.3 Digital Technology and Pandemic -- 8.3.1 Digital Healthcare -- 8.3.2 Network and Connectivity -- 8.3.3 Development of Potential Treatment -- 8.3.4 Online Platform for Learning and Interaction -- 8.3.5 Contactless Payment -- 8.3.6 Entertainment -- 8.4 Application of Technology for Handling Pandemic -- 8.4.1 Technology for Preparedness and Response -- 8.4.2 Machine Learning for Pandemic Forecast -- 8.5 Challenges with Digital Healthcare -- 8.6 Conclusion -- References -- 9 Emerging Technologies for COVID-19 -- 9.1 Introduction -- 9.2 Related Work -- 9.3 Technologies to Combat COVID-19 -- 9.3.1 Blockchain -- 9.3.2 Unmanned Aerial Vehicle (UAV) -- 9.3.3 Mobile APK -- 9.3.4 Wearable Sensing -- 9.3.5 Internet of Healthcare Things -- 9.3.6 Artificial Intelligence -- 9.3.7 5G -- 9.3.8 Virtual Reality -- 9.4 Comparison of Various Technologies to Combat COVID-19 -- 9.5 Conclusion -- References -- 10 Emerging Techniques for Handling Pandemic Challenges -- 10.1 Introduction to Pandemic -- 10.1.1 How Pandemic Spreads? -- 10.1.2 Background History -- 10.1.3 Corona -- 10.2 Technique Used to Handle Pandemic Challenges -- 10.2.1 Smart Techniques in Cities. 10.2.2 Smart Technologies in Western Democracies -- 10.2.3 Technoor Human-Driven Approach -- 10.3 Working Process of Techniques -- 10.4 Data Analysis -- 10.5 Rapid Development Structure -- 10.6 Conclusion & -- Future Scope -- References -- Part 3: ALGORITHMIC TECHNIQUES FOR HANDLING PANDEMIC -- 11 A Hybrid Metaheuristic Algorithm for Intelligent Nurse Scheduling -- 11.1 Introduction -- 11.2 Methodology -- 11.2.1 Data Collection -- 11.2.2 Mathematical Model Development -- 11.2.3 Proposed Hybrid Adaptive PSO-GWO (APGWO) Algorithm -- 11.2.4 Discrete Version of APGWO -- 11.3 Computational Results -- 11.4 Conclusion -- References -- 12 Multi-Purpose Robotic Sensing Device for Healthcare Services -- 12.1 Introduction -- 12.2 Background and Objectives -- 12.3 The Functioning of Multi-Purpose Robot -- 12.4 Discussion and Conclusions -- References -- 13 Prevalence of Internet of Things in Pandemic -- 13.1 Introduction -- 13.2 What is IoT? -- 13.2.1 History of IoT -- 13.2.2 Background of IoT for COVID-19 Pandemic -- 13.2.3 Operations Involved in IoT for COVID-19 -- 13.2.4 How is IoT Helping in Overcoming the Difficult Phase of COVID-19? -- 13.3 Various Models Proposed for Managing a Pandemic Like COVID-19 Using IoT -- 13.3.1 Smart Disease Surveillance Based on Internet of Things -- 13.3.2 IoT PCR for Spread Disease Monitoring and Controlling -- 13.4 Global Technological Developments to Overcome Cases of COVID-19 -- 13.4.1 Noteworthy Applications of IoT for COVID-19 Pandemic -- 13.4.2 Key Benefits of Using IoT in COVID-19 -- 13.4.3 A Last Word About Industrial Maintenance and IoT -- 13.4.4 Issues Faced While Implementing IoT in COVID-19 Pandemic -- 13.5 Results & -- Discussions -- 13.6 Conclusion -- References -- 14 Mathematical Insight of COVID-19 Infection-A Modeling Approach -- 14.1 Introduction -- 14.1.1 A Brief on Coronaviruses. 14.2 Epidemiology and Etiology.
Record Nr.	UNINA-9910555012303321