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Ubiquitous Computing and Computing Security of IoT [[electronic resource] /] / edited by N. Jeyanthi, Ajith Abraham, Hamid Mcheick
| Ubiquitous Computing and Computing Security of IoT [[electronic resource] /] / edited by N. Jeyanthi, Ajith Abraham, Hamid Mcheick |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (132 pages) |
| Disciplina | 005.8 |
| Collana | Studies in Big Data |
| Soggetto topico |
Computational intelligence
Data protection Big data Artificial intelligence Computational Intelligence Security Big Data Artificial Intelligence |
| ISBN | 3-030-01566-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Security Protocols for IoT -- Security in Ubiquitous Computing Environment: Vulnerabilities, Attacks and Defences -- Security of Big Data in Internet of Things -- Trust Management Approaches in Mobile Adhoc Networks -- IoT for Ubiquitous Learning Applications: Current Trends and Future. |
| Record Nr. | UNINA-9910737299303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Understanding COVID-19 : the role of computational intelligence / / edited by Janmenjoy Nayak, Bighnaraj Naik, and Ajith Abraham
| Understanding COVID-19 : the role of computational intelligence / / edited by Janmenjoy Nayak, Bighnaraj Naik, and Ajith Abraham |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (569 pages) |
| Disciplina | 614.592414 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
COVID-19 (Disease) - Data processing |
| ISBN | 3-030-74761-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Learning from Various Modalities of COVID-19 Data -- COVID-19 Pandemic: Theory, Concepts and Challenges -- 1 Introduction -- 2 Related Works -- 3 Classification and Modes of Transmission of COVID-19 -- 3.1 Classification of Coronavirus and COVID-19 -- 3.2 Modes of Transmission of COVID-19 -- 4 How COVID-19 Affects the Body -- 5 Symptoms of COVID-19 -- 6 Severity of COVID-19 -- 7 COVID-19 Statistics -- 8 Common Misconceptions and Misinformation -- 9 Preventive Measures -- 10 Curative Measures -- 11 Current and Future Challenges -- 11.1 Current Challenges -- 11.2 Future Challenges -- 12 Conclusion -- References -- Evolutionary Algorithm Based Summarization for Analyzing COVID-19 Medical Reports -- 1 Introduction -- 1.1 Intermediate Representation -- 1.2 Sentence Scoring -- 1.3 Summary Selection Strategy -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Preprocessing -- 3.2 Initial Population -- 3.3 Fitness Function -- 3.4 External Population -- 3.5 Selection -- 3.6 Crossover -- 3.7 Mutation -- 3.8 Elitism -- 4 Experimental Results -- 4.1 Experimental Setup -- 4.2 Performance Analysis Using ROUGE -- 4.3 Performance Comparison with Other Summarization Techniques -- 5 Conclusion and Future Direction -- References -- Chest CT in COVID-19 Pneumonia: Potentials and Limitations of Radiomics and Artificial Intelligence -- 1 Introduction -- 2 Literature Study and Methodology -- 2.1 Radiomics and Artificial Intelligence -- 2.2 The Workflow of Radiomics: A Short Practical and Easy Guide for Radiologists -- 2.3 From Radiomics to Artificial Intelligence: A Short Practical and Easy Guide for Radiologists -- 3 Radiological Findings in COVID-19 Pneumonia - A Clinical Approach -- 3.1 Chest X-ray -- 3.2 Chest CT -- 4 Clinical Applications of Radiomics -- 5 Clinical Applications of Artificial Intelligence -- 6 Conclusion -- References.
Use of Deep Learning Based Frameworks on Pixel Scaled Images of Chest CT Scans for Detection of COVID-19 -- 1 Introduction -- 2 Literature Study -- 3 Proposed Methodology -- 3.1 CNN Architecture -- 3.2 Dataset -- 3.3 Preprocessing and Data Augmentation -- 3.4 Hyperparameters -- 3.5 Architectures Used for Training -- 4 Result Analysis and Discussion -- 4.1 Results on Original Dataset -- 4.2 Results on Augmented Dataset -- 5 Discussion and Conclusion -- References -- SARS-CoV-2 Infection and Antibody-Dependent Enhancement -- 1 Introduction -- 2 Methodology -- 3 Discussion with Literature Findings -- 3.1 Viral Characteristics and Important Viral Genome Structures for Cellular Entry -- 3.2 Neutralizing Antibody (NAb) Response in SARS CoV-2 Infection -- 3.3 The Probability of SARS CoV-2 Reinfection -- 3.4 The Risks and Mechanisms of ADE -- 3.5 Evidence of ADE in Viral Infections -- 3.6 Risk of ADE for Therapeutic Interventions: Plasma Therapies, Monoclonal Antibodies, and Vaccines -- 4 Conclusion -- References -- Graph-Based Clustering Algorithm for Social Community Transmission Control of COVID-19 During Lockdown -- 1 Introduction -- 2 Related Work -- 3 Goals and Objectives -- 4 Requirements and Assumptions -- 5 Methodology Overview -- 6 Data Acquisition -- 6.1 Infection Data -- 6.2 Location Data -- 7 Pre-processing -- 7.1 Co-ordinate Mapping -- 7.2 Data Cleaning -- 7.3 Restructuring Data -- 8 Graph Construction -- 8.1 Vertices -- 8.2 Edges -- 8.3 Visualization -- 9 Impact Measures and Danger Level -- 9.1 Nearest Without Self -- 9.2 Nearest with Self -- 9.3 Inverse-Square Law -- 10 Predicting Potential Hotspots -- 11 Experimental Results -- 12 Conclusion -- 13 Future Work -- References -- COVID-19 Recommendation System of Chest X-Ray Images Using CNN Deep Learning Technique with Optimizers and Activation Functions -- 1 Introduction -- 2 Background. 2.1 Convolutional Neural Network (CNN) -- 2.2 Optimizer Functions -- 2.3 Activation Functions -- 3 Proposed Model -- 4 Result Analysis -- 5 Conclusion -- References -- Application of Deep Learning Techniques for COVID-19 Management -- 1 Introduction -- 2 Deep Learning Concepts -- 2.1 Neurons and Neural Network -- 2.2 Layers -- 2.3 Weights and Bias -- 2.4 Activation Functions -- 2.5 Cost Function -- 2.6 Gradient Descent -- 2.7 Learning Rate -- 2.8 Batches and Epochs -- 2.9 Forward and Backward-Propagation -- 2.10 Dropout -- 2.11 Filters and Pooling -- 2.12 Padding and Data Augmentation -- 3 Deep Learning Techniques -- 3.1 Shallow Neural Networks -- 3.2 Deep Neural Networks -- 3.3 Radial Basis Function Network -- 3.4 Restricted Boltzmann Machines -- 3.5 Recurrent Neural Network and LSTM -- 3.6 Convolutional Neural Networks -- 3.7 Auto-Encoders -- 3.8 Reinforcement Learning and Deep Reinforcement Learning -- 3.9 Generative Adversarial Networks -- 4 Literature Review -- 4.1 Influenza -- 4.2 Alzheimer's -- 4.3 Pneumonia -- 4.4 Flu Detection Using Social Media -- 4.5 Other Diseases -- 4.6 Research Direction -- 5 Methodology -- 6 Findings -- 6.1 Growth Curve Fitting and Trends Forecasting -- 6.2 Study of Virus Characteristics and Drug Development -- 6.3 Patient Diagnosis -- 6.4 Clinical Analysis of Patients Using X-Rays and Scans -- 6.5 Monitoring Public Sentiment, Social Distancing, and Mask Usage -- 7 Discussion and Conclusion -- 7.1 Research Framework Analysis -- 7.2 Findings Discussion -- 7.3 Limitations of Deep Learning Techniques -- 7.4 Limitations of the Study -- 7.5 Future Direction -- 7.6 Conclusion -- References -- Prediction and Risk Analysis of COVID-19 Susceptibility -- An Exploratory Study of Disaster Risk Management Mobile Applications in Pandemic Periods -- 1 Introduction -- 2 Literature Survey -- 3 Mobile Applications. 3.1 Medical Store APPs -- 3.2 Collection of Relief Materials Food -- 3.3 Online Vocational Training Courses and Support to Unorganized Sector Workers -- 3.4 Hospital Admissions and Cluster Geo-Fencing -- 3.5 Volunteers Registration and Assigning Work -- 3.6 Home Treatment and Alarm System for Routine -- 3.7 Food Supply to Isolated Patients -- 3.8 Essential Service Transport Pass and Personal Pass Service Request -- 4 COVID-19 Pandemic Contact Tracker Applications -- 4.1 TraceTogether App, Country: Singapore -- 4.2 Smittestopp App, Country: Norway -- 4.3 StopKorona App, Country: North Macedonia -- 4.4 HaMagen App, Country: Israel -- 4.5 CoronaApp, Country: Colombia -- 4.6 Gerak Malaysia App, Country: Malaysia -- 4.7 MySejahtera App, Country: Malaysia -- 4.8 The Corona DataSpende, Country: Germany -- 4.9 NHS COVID-19 Tracker, Country: UK -- 4.10 eRouska (eFacemask), Country: Czech Republic -- 4.11 Aarogya Setu Coronavirus Tracker App, Country: India -- 4.12 Unmaze App, Country: India - Kerala -- 4.13 Sahyog App by Survey of India, Country: India -- 4.14 CG Covid-19 ePass, Country: India - Chhattisgarh -- 4.15 Quarantine Watch App, Country: India - Karnataka -- 4.16 SMC App, Country: India - Gujarat -- 4.17 T-COVID 19 App, Country: India -- 4.18 Usage Statistics -- 5 Integrated Mobile Platform Application Framework -- 6 Challenges in Implementation of Mobile Apps -- 6.1 Network Connectivity -- 6.2 People Awareness -- 6.3 Security and Privacy Issues -- 7 Conclusion -- References -- Potential of Deep Learning Algorithms in Mitigating the Spread of COVID-19 -- 1 Introduction -- 2 Deep Learning -- 2.1 DL Algorithms -- 3 Methodology -- 3.1 Description of the Dataset and the Pre-processing Methods -- 3.2 Convolutional Neural Networks (CNN) -- 3.3 Long-Short Time Memory (LSTM) Algorithm -- 3.4 Performance Evaluation Metrics -- 4 Experimental Set up and Results. 5 Discussions -- 6 Deep Learning Challenges -- 7 Conclusion -- References -- Predicting Antiviral Drugs for COVID-19 Treatment Using Artificial Intelligence Based Approach -- 1 Introduction -- 1.1 Literature Review -- 1.2 Objective and Proposed Outcome -- 2 Methodology -- 2.1 Virus Amplicon Sequencing Assembly Pipeline -- 2.2 Feature Extraction -- 2.3 Advanced Matched Molecular Pair (AMMP) Analysis -- 2.4 Generative Adversarial Convolutional Neural Networks -- 3 Result Analysis and Performance Evaluation -- 3.1 Performance Evaluation -- 3.2 Generative Adversarial Convolution Neural Network -- 3.3 Comparative Study -- 4 Discussion -- References -- Geographic Spread and Control of 2019-nCoV in the Absence of Vaccine -- 1 Introduction -- 2 The Model -- 3 The Basic Reproduction Number mathcalR0 -- 4 Re-parametrisation -- 5 Method of Solution -- 5.1 Phase Plane Analysis -- 5.2 Analysis of Analytic Solution -- 6 Experimental Set up -- 7 Result Analysis and Discussion -- 8 Conclusion -- References -- Machine Learning Based Anxiety Prediction of General Public from Tweets During COVID-19 -- 1 Introduction -- 2 Related Work -- 3 Methodology and Dataset -- 3.1 Dataset -- 3.2 Sentiment Analysis Procedure -- 4 Discussion on Results -- 4.1 Sentiment Polarity -- 4.2 Subjectivity -- 4.3 Comparison of Models: -- 5 Conclusion -- References -- A Role of Emerging Technologies in the Design of Novel Framework for COVID-19 Data Analysis and Decision Support System -- 1 Introduction -- 2 Diagnosis Procedures and Safety Measures -- 2.1 Diagnosis Procedures and Symptoms of COVID-19 -- 2.2 Self-preventive Measures -- 2.3 Preventive Measures Opted by Countries and Their Impacts -- 3 Related Work -- 3.1 Role of AI and Robotics -- 3.2 Drones -- 3.3 Machine Learning -- 3.4 Natural Language Processing -- 3.5 Digital Learning and Internet Technologies. 3.6 Role of Cloud Computing in COVID-19 Pandemic. |
| Record Nr. | UNINA-9910522998903321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
