top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Advances in data science and analytics : concepts and paradigms / / edited by M. Niranjanamurthy, Hemant Kumar Gianey, and Amir H. Gandomi
Advances in data science and analytics : concepts and paradigms / / edited by M. Niranjanamurthy, Hemant Kumar Gianey, and Amir H. Gandomi
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2023]
Descrizione fisica 1 online resource (353 pages)
Disciplina 006.312
Soggetto topico Big data
Data mining
ISBN 1-119-79282-7
1-119-79281-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910677971203321
Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data Engineering and Data Science : Concepts and Applications
Data Engineering and Data Science : Concepts and Applications
Autore Kumar Kukatlapalli Pradeep
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica 1 online resource (467 pages)
Altri autori (Persone) UnalAynur
PillaiVinay Jha
MurthyHari
NiranjanamurthyM
ISBN 1-119-84199-2
1-119-84198-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Quality Assurance in Data Science: Need, Challenges and Focus -- 1.1 Introduction -- 1.1.1 Quality Assurance and Testing -- 1.1.2 Data Science and Quality Assurance -- 1.1.3 Background -- 1.2 Testing and Quality Assurance -- 1.2.1 Key Terminologies Associated With Testing -- 1.3 Product Quality and Test Efforts -- 1.3.1 Testing Metrics -- 1.3.2 How to Improve the Business Value to Products Using Test Automation -- 1.3.3 Data Analysis and Management in Test Automation -- 1.3.4 Data Models in Data Science -- 1.4 Data Masking in Data Model and Associated Risks -- 1.5 Prediction in Data Science -- Case Study -- 1.6 Role of Metrics in Evaluation -- 1.7 Quantity of Data in Quality Assurance -- 1.8 Identifying the Right Data Sources -- 1.8.1 Need to Gather Up-to-Date Data -- 1.8.2 Synthesising Existing Advanced Technologies for Continuous Business Improvements -- 1.9 Conclusion -- References -- Chapter 2 Design and Implementation of Social Media Mining - Knowledge Discovery Methods for Effective Digital Marketing Strategies -- 2.1 Introduction -- 2.1.1 Objectives of the Study -- 2.2 Literature Review -- 2.3 Novel Framework for Social Media Data Mining and Knowledge Discovery -- 2.4 Classification for Comparison Analysis -- 2.5 Clustering Methodology to Provide Digital Marketing Strategies -- 2.5.1 Status (Text Form) -- 2.5.2 Images (Photos) -- 2.5.3 Video Post -- 2.5.4 Link Post -- 2.6 Experimental Results -- 2.7 Conclusion -- References -- Chapter 3 A Study on Big Data Engineering Using Cloud Data Warehouse -- 3.1 Introduction -- 3.2 Comparison Study of Different Cloud Data Warehouses -- 3.2.1 Amazon Redshift -- 3.2.2 High-Level Architecture of Amazon Redshift -- 3.2.3 Features of Amazon Redshift Cloud Data Warehouse -- 3.2.4 Pricing of Amazon Redshift Cloud Data Warehouse.
3.3 Snowflake Cloud Data Warehouse -- 3.3.1 High-Level Architecture of Snowflake Cloud Data Warehouse -- 3.3.2 Features of Snowflake Cloud Data Warehouse -- 3.3.3 Snowflake Cloud Data Warehouse Pricing -- 3.4 Google BigQuery Cloud Data Warehouse -- 3.4.1 High-Level Architecture of Google BigQuery Cloud Data Warehouse -- 3.4.2 Features of Google BigQuery Cloud Data Warehouse -- 3.4.3 Google BigQuery Cloud Data Warehouse Pricing -- 3.5 Microsoft Azure Synapse Cloud Data Warehouse -- 3.5.1 Microsoft Azure Synapse Cloud Data Warehouse Architecture -- 3.5.2 Features of Microsoft Azure Synapse Cloud Data Warehouse -- 3.5.3 Pricing of Microsoft Azure Synapse Cloud Data Warehouse -- 3.6 Informatica Intelligent Cloud Services (IICS) -- 3.6.1 Informatica Intelligent Cloud Services Architecture -- 3.6.2 Salient Features of Informatica Intelligent Cloud Services -- 3.6.3 Informatica Intelligent Cloud Services Pricing Model -- 3.7 Conclusion -- Acknowledgements -- References -- Chapter 4 Data Mining with Cluster Analysis Through Partitioning Approach of Huge Transaction Data -- 4.1 Introduction -- 4.2 Methodology Used in Proposed Cluster Analysis System -- 4.2.1 Design of Algorithms -- 4.3 Literature Survey on Existing Systems -- 4.3.1 Experimental Results -- 4.4 Conclusion -- References -- Chapter 5 Application of Data Science in Macromodeling of Nonlinear Dynamical Systems -- 5.1 Introduction -- 5.2 Nonlinear Autonomous Dynamical System -- 5.3 Nonlinear System - MOR -- 5.3.1 Proper Orthogonal Decomposition -- 5.4 Data Science Life Cycle -- 5.4.1 Problem Identification -- 5.4.2 Identifying Available Data Sources and Data Collection -- 5.4.3 Data Processing -- 5.4.4 Data Exploration -- 5.4.5 Feature Extraction -- 5.4.6 Modeling -- 5.4.7 Model Performance Evaluation -- 5.5 Artificial Neural Network in Modeling -- 5.5.1 Machine Learning.
5.5.2 Biological Neuron Model -- 5.5.3 Artificial Neural Networks -- 5.5.4 Network Topologies -- 5.5.4.1 NARX Neural Network -- 5.5.5 ANN Modeling Using Mathematical Models -- 5.6 Neuron Spiking Model Using FitzHugh-Nagumo (F-N) System -- 5.6.1 Linearization of F-N System -- 5.6.2 Reduced Order Model of Linear System -- 5.6.3 Finite Difference Discretization of F-N System -- 5.6.4 MOR of F-N System Using POD-Galerkin Method -- 5.7 Ring Oscillator Model -- 5.7.1 Model Order Reduction of Ring Oscillator Circuit -- 5.7.2 Ring Oscillator Circuit Approximation Using Linear System MOR -- 5.7.3 POD-ANN Macromodel of Ring Oscillator Circuit -- 5.8 Nonlinear VLSI Interconnect Model Using Telegraph Equation -- 5.8.1 Macromodeling of VLSI Interconnect -- 5.8.2 Discretisation of Interconnect Model -- 5.8.3 Linearization of VLSI Interconnect Model -- 5.8.4 Reduced Order Linear Model of VLSI Interconnect -- 5.9 Macromodel Using Machine Learning -- 5.9.1 Activation Function -- 5.9.2 Bayesian Regularization -- 5.9.3 Optimization -- 5.10 MOR of Dynamical Systems Using POD-ANN -- 5.10.1 Accuracy and Performance Index -- 5.11 Numerical Results -- 5.11.1 F-N System -- 5.11.2 Ring Oscillator Model -- 5.11.3 Reduced Order POD Approximation of Ring Oscillator -- 5.11.3.1 Study of POD-ANN Approximation of Ring Oscillator for Variation in Amplitude of Input Signal and for Different Input Signals -- 5.11.3.2 POD-ANN Approximation of Ring Oscillator for Variation in Frequency -- 5.11.4 POD-ANN Approximation of VLSI Interconnect -- 5.12 Conclusion -- References -- Chapter 6 Comparative Analysis of Various Ensemble Approaches for Web Page Classification -- 6.1 Introduction -- 6.2 Literature Survey -- 6.3 Material and Methods -- 6.4 Ensemble Classifiers -- 6.4.1 Bagging -- 6.4.1.1 Bagging Meta Estimator -- 6.4.1.2 Random Forest -- 6.4.2 Boosting -- 6.4.2.1 AdaBoost.
6.4.2.2 Gradient Tree Boosting -- 6.4.2.3 XGBoost -- 6.4.3 Stacking -- 6.5 Results -- 6.5.1 Bagging Meta Estimator -- 6.5.2 Random Forest -- 6.5.3 AdaBoost -- 6.5.4 Gradient Tree Boosting -- 6.5.5 XGBoost -- 6.5.6 Stacking -- 6.5.7 Comparison with Single Classifiers -- 6.6 Conclusion -- Acknowledgement -- References -- Chapter 7 Feature Engineering and Selection Approach Over Malicious Image -- 7.1 Introduction -- 7.2 Feature Engineering Techniques -- 7.2.1 Methodologies in Feature Engineering -- 7.2.2 Strides in Feature Engineering -- 7.2.3 Feature Extraction -- 7.2.4 Feature Selection -- 7.2.5 Feature Engineering in Image Processing -- 7.2.6 Importance of Feature Engineering in Image Processing -- 7.3 Malicious Feature Engineering -- 7.4 Image Processing Technique -- 7.4.1 Steps Involved in Image Processing Technique -- 7.4.2 Image Processing Task -- 7.4.2.1 Image Enhancement -- 7.4.2.2 Image Restoration -- 7.4.2.3 Coloring Image Processing -- 7.4.2.4 Wavelets Processing and Multiple Solutions -- 7.4.2.5 Image Compression -- 7.4.2.6 Character Recognition -- 7.4.2.7 Characteristics of Image Processing -- 7.5 Image Processing Techniques for Analysis on Malicious Images -- 7.6 Conclusion -- References -- Blog -- Chapter 8 Cubic-Regression and Likelihood Based Boosting GAM to Model Drug Sensitivity for Glioblastoma -- 8.1 Introduction -- 8.1.1 Glioblastoma -- 8.2 Literature Survey -- 8.3 Materials and Methods -- 8.3.1 Methodology -- 8.3.1.1 Generalized Additive Models (GAMs) -- 8.3.1.2 Model-Based Boosting - Boosted GAM -- 8.3.2 Datasets Description -- 8.4 Evaluations, Results and Discussions -- 8.4.1 Akaike Information Criterion (AIC) -- 8.4.2 Adjusted R-Squared -- 8.4.3 Discussion -- Conclusion -- References -- Chapter 9 Unobtrusive Engagement Detection through Semantic Pose Estimation and Lightweight ResNet for an Online Class Environment.
9.1 Introduction -- 9.2 Related Work -- 9.2.1 Analysis for a Classroom Environment -- 9.2.2 Pose Estimation -- 9.2.3 Face Alignment and Landmark Estimation -- 9.2.4 Deep Networks for Emotional Analysis -- 9.3 Proposed Methodology -- 9.3.1 Data Description -- 9.3.2 Facial Detection and Recognition -- 9.3.2.1 Face Detection -- 9.3.2.2 Facial Landmark Detection -- 9.3.3 Emotion Quantification -- 9.3.4 Pose Estimation -- 9.3.4.1 Facial Pose Estimation -- 9.4 Experimentation -- 9.5 Results and Discussions -- Conclusion -- References -- Chapter 10 Building Rule Base for Decision Making - A Fuzzy-Rough Approach -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Discretization of the Dataset Using Fuzzy Set Theory -- 10.4 Description of the Dataset -- 10.5 Process Involved in Proposed Work -- 10.6 Experiment -- 10.7 Evaluation Result -- 10.8 Discussion -- Conclusion -- References -- Chapter 11 An Effective Machine Learning Approach to Model Healthcare Data -- 11.1 Introduction -- 11.2 Types of Data in Healthcare -- 11.3 Big Data in Healthcare -- 11.4 Different V's of Big Data -- 11.5 About COPD -- 11.6 Methodology Implemented -- Conclusion -- References -- Chapter 12 Recommendation Engine for Retail Domain Using Machine Learning Techniques -- 12.1 Introduction -- 12.2 Proposed System -- 12.2.1 Classification of Suppliers -- 12.2.2 Recommendation for Buyer -- 12.2.3 Forecasting Using ARIMA Model -- 12.3 Results -- 12.3.1 ARIMA Forecasting -- 12.4 Conclusion -- References -- Chapter 13 Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Methodology -- 13.3.1 Description of the Data -- 13.3.2 Image Preprocessing -- 13.3.3 Image Segmentation -- 13.3.4 Image Processing -- 13.3.5 Zero Component Analysis (ZCA) Whitening -- 13.3.6 Local Binary Pattern (LBP Feature).
13.3.7 LESH Vector.
Record Nr. UNINA-9910877333003321
Kumar Kukatlapalli Pradeep  
Newark : , : John Wiley & Sons, Incorporated, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data wrangling : concepts, applications and tools / / edited by M. Niranjanamurthy [and three others]
Data wrangling : concepts, applications and tools / / edited by M. Niranjanamurthy [and three others]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc., , [2023]
Descrizione fisica 1 online resource (357 pages)
Disciplina 006.312
Soggetto topico Data mining
ISBN 1-119-87986-8
1-119-87985-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910830956803321
Hoboken, NJ : , : John Wiley & Sons, Inc., , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Intelligent data analysis for COVID-19 pandemic / / M. Niranjanamurthy, Siddhartha Bhattacharyya, Neeraj Kumar, editors
Intelligent data analysis for COVID-19 pandemic / / M. Niranjanamurthy, Siddhartha Bhattacharyya, Neeraj Kumar, editors
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (377 pages)
Disciplina 362.1962414
Collana Algorithms for Intelligent Systems
Soggetto topico COVID-19 (Disease) - Economic aspects
COVID-19 (Disease) - Health aspects
ISBN 981-16-1574-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Editors and Contributors -- Machine Learning-Based Ensemble Approach for Predicting the Mortality Risk of COVID-19 Patients: A Case Study -- 1 Introduction -- 2 Literature Review -- 3 Dataset and Methodology Used -- 3.1 Dataset Description and Preparation -- 3.2 Data Preprocessing -- 3.3 Classification and Ensembling Approaches -- 4 Ensembling Approaches -- 4.1 Boosting -- 4.2 Bagging -- 5 Experiments and Results -- 5.1 Feature Selection of Patient Attributes -- 5.2 Performance of Individual Classifiers -- 6 Conclusion -- References -- Role of Internet of Health Things (IoHTs) and Innovative Internet of 5G Medical Robotic Things (IIo-5GMRTs) in COVID-19 Global Health Risk Management and Logistics Planning -- 1 Introduction -- 1.1 Background of the Study -- 1.2 Aims and Objective of the Study -- 2 Literature Review -- 3 Research Design and Implementation -- 3.1 Research Analysis -- 3.2 Research Discussion -- 4 Future Research Focus -- 5 Recommendation -- 6 Conclusion -- References -- Battling COVID-19 with Process Model of Integrated Digital Technology: An Analysis of Qualitative Data -- 1 Introduction -- 2 Research Design and Structure -- 3 Digital Technology to Combat COVID-19 -- 3.1 Mobile Applications and COVID-19 -- 3.2 Artificial Intelligence, Internet of Things (IoT), Big Data Analytics, and COVID 19 -- 3.3 Social Media and COVID -- 4 Summarization of Digital Technology Applications and COVID-19 -- 5 Process Model of Integrated Digital Technology -- 6 Conclusion -- References -- High-Fidelity Intelligence Ventilator to Help Infect with COVID-19 Based on Artificial Intelligence -- 1 Introduction -- 2 Operating and Revision Modes -- 3 Design and Condition of the Instrument -- 4 Materials and Technology -- 4.1 Typical Parts Needed -- 4.2 Arduino Nano Compatible V3.0 ATmega328.
4.3 Electronic Motor Speed Controller -- 4.4 Wi-Fi Module -- 5 Results and Discussion -- 6 Conclusion -- References -- Boon of Artificial Intelligence in Diagnosis of COVID-19 -- 1 Introduction -- 2 Novel Coronavirus (SARS-CoV-2) -- 3 Evolution of Artificial Intelligence -- 3.1 Strong AI -- 3.2 Weak AI -- 4 Applications of Computational Techniques -- 4.1 Speed Up Diagnosis -- 4.2 Computerized Tracking -- 4.3 Tracking of Infected Individual -- 4.4 Prediction of Incidence Rate and Mortality Rate -- 4.5 Designing and Development of New Drugs and Vaccines -- 4.6 Lowering the Work Load -- 4.7 Prevention of Infectious Disease -- 5 Traditional Diagnostic Methodology -- 5.1 Lateral Flow Immunoassay (LFIA) -- 5.2 Chemiluminescent Immunoassay (CLIA) -- 5.3 Neutralization Assay -- 6 Machine Learning -- 6.1 Algorithms -- 6.2 Random Forest -- 7 Contact Tracing -- 8 Detection Through Smell -- 9 Conclusion -- References -- Artificial Intelligence and Big Data Solutions for COVID-19 -- 1 Introduction -- 2 The COVID-19 Pandemic -- 3 AI and Big Data Techniques for COVID-19 -- 4 AI and Big Data Applications for COVID-19 -- 4.1 Early Detecting and Finding COVID-19 Cases -- 4.2 Early Detecting and Finding COVID-19 Cases -- 4.3 Following Up Contacts -- 4.4 Projection of Cases and Moralities -- 4.5 Reducing the Workload on Healthcare Workers -- 4.6 Prevention of the Infections -- 5 A Proposed Model of AI and Big Data for COVID-19: Smartphone for Surveillance -- 6 Discussions -- 7 Future Insights -- 8 Conclusions -- References -- Emerging Trends in Higher Education During Pandemic Covid-19: An Impact Study from West Bengal -- 1 Introduction -- 2 Research Background Literature -- 3 Methodology -- 3.1 Research Gaps -- 3.2 Research Objectives -- 3.3 Sample Design -- 3.4 Research Approach -- 3.5 Research Tools Usage in Current Research -- 3.6 SXUK Case Study Process.
4 Research Findings and Discussion -- 4.1 Teaching-Learning Context During Covid-19 -- 4.2 Content Development Orientation -- 4.3 ICT Technology Strategies in HEIs -- 4.4 "Big Five" Strategies -- 4.5 Technology-CI Adapted Teaching-Learning Strategies -- 4.6 Higher Educational Institutes Wise -- 4.7 CI Awareness and Application in HEIs -- 4.8 Computational Intelligence-ICT Factors Confluence -- 4.9 CI-Based HEIs Cluster Membership -- 5 Case Organization: SXUK -- 5.1 Introduction -- 5.2 Historical Millstones of SXUK -- 5.3 SXUK Organogram -- 5.4 AI-CI Interface Strategies for SXUK -- 6 Conclusion -- References -- COVID-19: Virology, Epidemiology, Diagnostics and Predictive Modeling -- 1 Introduction -- 2 Virology of SARS-CoV-2 -- 3 Diagnostics and Current Line of Treatment of Coronavirus Disease-2019 (COVID-19) -- 4 Comparison of Population Distribution of India, USA and Spain -- 5 Mathematical Modeling -- 6 Concluding Remarks -- References -- Improved Estimation in Logistic Regression Through Quadratic Bootstrap Approach: An Application in Indian Agricultural E-learning System During COVID-19 Pandemic -- 1 Introduction -- 2 Logistic Regression Model -- 2.1 Preliminaries -- 2.2 Identification of the Most Influential Variable -- 2.3 Estimation in Logistic Regression Model -- 2.4 Goodness of Fit -- 2.5 Predictive Ability -- 2.6 Comparison Measures -- 3 Empirical Results -- 3.1 Data and Implementation -- 3.2 Comparative Assessment Between MLE and Quadratic Bootstrap Estimation -- 3.3 Outcomes of the Simulation Study -- 4 Conclusion -- References -- COVID-19 and Stock Markets: Deaths and Strict Policies -- 1 Introduction -- 2 COVID-19 and Its Macroeconomic Effects -- 3 COVID-19 and Stock Markets -- 4 Data and Econometric Model -- 4.1 Diagnostic Statistics and Correlation Analysis -- 4.2 Analysis Results -- 5 Conclusion -- References.
Artificial Intelligence Techniques in Medical Imaging for Detection of Coronavirus (COVID-19/SARS-COV-2): A Brief Survey -- 1 Introduction -- 2 Literature Survey -- 3 Artificial Intelligence -- 4 Machine Learning -- 5 Neural Networks -- 5.1 Deep Learning -- 5.2 Transfer Learning -- 5.3 Convolutional Neural Networks -- 6 CNN Algorithms and Methods Used in the Survey -- 6.1 Inception V3 -- 6.2 ResNet-50 -- 6.3 Inception-ResNet-v2 -- 6.4 VGG-19 -- 6.5 MobileNet -- 7 Materials and Methods -- 7.1 Dataset -- 7.2 Performance Analysis Parameters -- 8 Results and Discussions -- 9 Conclusion and Future Challenges -- References -- A Travelling Disinfection-Man Problem (TDP) for COVID-19: A Nonlinear Binary Constrained Gaining-Sharing Knowledge-Based Optimization Algorithm -- 1 Coronavirus (COVID-19): An Overview -- 2 Coronavirus Decontamination Planning Process -- 3 Coronavirus Travelling Disinfection-Man Problem (TDP) -- 4 The Travelling Salesman Problem (TSP) and Its Variations -- 5 Mathematical Model Formulation for the Travelling Disinfection-Man Problem -- 6 Real Application Case Study Application: Ain Shams University, Cairo -- 7 Artificial Intelligence Techniques in Optimization -- 8 Proposed Methodology -- 8.1 Overview of Gaining-Sharing Knowledge-Based Optimization Algorithm (GSK) -- 8.2 Discrete Binary Gaining-Sharing Knowledge-Based Optimization Algorithm (DBGSK) -- 9 Experimental Results -- 10 Conclusions -- 11 Points for Future Researches -- References -- COVID-19 Lock Down Impact on Mental Health: A Cross-Sectional Online Survey from Kerala, India -- 1 Introduction -- 1.1 Motivation for Doing the Research -- 2 Review of Literature -- 3 Methods -- 4 Results and Discussions -- 4.1 Types of Activities -- 4.2 Mental Health Issues and Eating Behaviour -- 4.3 Awareness Among People -- 5 Conclusion -- References.
Analysis, Modelling and Prediction of COVID-19 Outbreaks Using Machine Learning Algorithms -- 1 Introduction -- 2 COVID-19 Around the Global -- 3 Machine Learning and Its Types -- 3.1 Supervised Learning -- 4 Implementation -- 4.1 Evaluation Metrics -- 5 Time Series Data set -- 5.1 Analysis, Modelling and Prediction of COVID-19 -- 5.2 Confirmed Cases and Death Cases as on 20 July 2020-World -- 5.3 Confirmed Cases and Death Cases as on 20th July 2020-India -- 5.4 Model of Machine Learning Algorithm -- 5.5 Predicting the Outgrowth in the Next 3 Months-India -- 6 Conclusion -- References -- Tracking and Analysis of Corona Disease Using Intelligent Data Analysis -- 1 Introduction -- 2 AI Versus COVID-19 -- 2.1 Prediction and Data Sharing -- 2.2 R& -- D Sector -- 2.3 Deception -- 2.4 Monitoring -- 2.5 Data Overload -- 2.6 Arrangement of Automated Vehicles -- 2.7 Variances Between the AI Techniques [1] -- 3 Using AI to Detect, Respond, and Recover from COVID-19 -- 3.1 Computer-Based Intelligence for COVID-19 Medical Response -- 4 AI for COVID-19 Social Control -- 4.1 Man-Made Reasoning in the Battle Against COVID-19 -- 4.2 Information Access -- 4.3 Security Ensuring Applications -- 5 How Artificial Intelligence Applications can Contain Coronavirus COVID-19 -- 5.1 Man-Made Reasoning in the Battle Against COVID-19 -- 5.2 Information Access -- 5.3 Security Ensuring Applications -- 6 Conclusion -- References -- Index.
Record Nr. UNINA-9910488705703321
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Medical imaging / / H. S. Sanjay and M. Niranjanamurthy
Medical imaging / / H. S. Sanjay and M. Niranjanamurthy
Autore Sanjay H. S.
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Descrizione fisica 1 online resource (260 pages)
Disciplina 616.0754
Soggetto topico Diagnostic imaging
ISBN 1-119-78559-6
1-119-78558-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgements -- Chapter 1 Introduction to Medical Imaging -- 1.1 Medical Imaging - An Insight -- 1.2 Types of Diagnostic Imaging Modalities -- 1.2.1 Radiography -- 1.2.2 Tomography -- 1.2.3 Ultrasound -- 1.2.4 Nuclear Medicine -- 1.2.5 Magnetic Resonance Imaging -- 1.2.6 Functional Magnetic Resonance Imaging (fMRI) -- 1.2.7 Functional Near Infrared Imaging -- 1.2.8 Elastography -- 1.2.9 Photoacoustic Imaging -- 1.2.10 Magnetic Particle Imaging -- 1.3 3D Rendering -- 1.4 Diagnostic Images -- 1.5 Medical Imaging in Pharmaceutical Applications -- Glossary-Appendix -- Chapter 2 Fundamentals of X-Rays -- 2.1 Electromagnetic Radiations -- 2.2 Wave Nature -- 2.2.1 Particle Nature -- 2.2.2 Intensity of an X-Ray Beam -- 2.2.3 Roentgen (R) -- 2.2.4 Radiation Absorbed Dose (rad) -- 2.2.5 X-Ray Interactions -- 2.2.6 Interaction Between X-Ray and Matter -- 2.2.7 Coherent Scattering -- 2.2.8 Compton Effect -- 2.3 Photoelectric Effect -- 2.3.1 Pair Production -- 2.3.2 Photodisintegration -- 2.4 Interaction Between X-Ray and Tissues -- 2.5 Factors Affecting Attenuation Coefficients -- 2.6 Attenuation Due to Coherent Scattering (βcoh) -- 2.7 Attenuation Due to Compton Scattering (βcom) and Photoelectric Effect (βpho) -- 2.8 Generation and Detection of X-Rays -- 2.8.1 Generation of X-Rays -- 2.8.2 White Radiation -- 2.8.3 Characteristic Radiation -- 2.9 X-Ray Generators -- 2.9.1 Line Focus Principle -- 2.9.2 X-Ray Tube Ratings -- 2.9.3 Target Material -- 2.9.4 Tube Voltage -- 2.9.5 Tube Current -- 2.9.6 Filament Current -- 2.10 Filters -- 2.10.1 Beam Restrictors -- 2.10.2 Aperture Diaphragms -- 2.10.3 Cones and Cylinders -- 2.10.4 Collimators -- 2.10.5 Grids -- 2.11 X-Ray Visualization -- 2.11.1 Intensifying Screens -- 2.11.2 Image Intensifiers -- 2.12 Detection of X-Rays -- 2.12.1 X-Ray Film.
2.12.2 Optical Density -- 2.12.3 Characteristic Curve -- 2.12.4 Film Gamma -- 2.12.5 Speed -- 2.12.6 Film Latitude -- 2.12.7 Double-Emulsion Film -- 2.13 Radiation Detectors -- 2.13.1 Scintillation Detector -- 2.13.2 Ionization Chamber -- 2.14 X-Ray Diagnostic Approaches -- 2.14.1 Conventional X-Ray Radiography -- 2.14.2 Penumbra -- 2.14.3 Field Size -- 2.14.4 Film Magnification -- 2.15 Fluoroscopy -- 2.16 Angiography -- 2.17 Mammography -- 2.18 Xeroradiography -- 2.19 Image Subtraction -- 2.19.1 Digital Subtraction Angiography (DSA) -- 2.19.2 Dual Energy Subtraction -- 2.19.3 K-Edge Subtraction -- 2.20 Conventional Tomography -- 2.20.1 X-Ray Image Attributes -- 2.20.2 Spatial Resolution -- 2.21 Point Spread Function (PSF) -- 2.21.1 Line Spread Function (LSF) -- 2.21.2 Edge Spread Function (ESF) -- 2.21.3 System Transfer Function (STF) -- 2.22 Image Noise -- 2.23 Image Contrast -- 2.24 Receiver Operating Curve (ROC) -- 2.25 Biological Effects of X-Ray Radiations -- 2.25.1 Determinants of Biological Effects -- Glossary-Appendix -- Chapter 3 X-Ray Computed Tomography -- 3.1 Introduction to X-Ray Computed Tomography -- 3.2 CT Number -- 3.3 X-Ray Detectors in CT Machines -- 3.3.1 Energy Integrating Detectors -- 3.3.2 Photon Counting Detectors -- 3.4 CT Imaging -- 3.4.1 Radon Transform -- 3.4.2 Sampling -- 3.4.3 2D Image Reconstruction -- 3.4.4 Direct Fourier Transform -- 3.4.5 Filtered Back Projection (FBP)/Convolution Back Projection (CBP) -- 3.4.6 Fan Beam Projections -- 3.5 Computer Tomography-Based Diagnostics -- 3.5.1 Single Slice Computed Tomography -- 3.5.2 Multislice Computed Tomography -- 3.5.3 Cardiac CT -- 3.5.4 Dual Energy Computer Tomography -- 3.6 Image Quality -- 3.6.1 Resolution -- 3.6.2 Noise -- 3.6.3 Contrast -- 3.6.4 Image Artifacts -- 3.7 CT Machine - The Hardware Aspects -- 3.8 Generations of CT Machines.
3.9 Biological Effects and Safety-Based Aspects -- Glossary-Appendix -- Chapter 4 Ultrasound Imaging -- 4.0 Ultrasound -- 4.1 Basics of Acoustic Waves -- 4.2 Propagation of Waves in Homogeneous Media -- 4.3 Linear Wave Equation -- 4.4 Loudness and Intensity -- 4.5 Interference -- 4.6 Attenuation -- 4.7 Nonlinearity -- 4.8 Propagation of Waves in Non-Homogeneous Media -- 4.9 Reflection and Refraction -- 4.10 Scattering -- 4.11 Doppler Effect in the Propagation of the Acoustic Wave -- 4.12 Generation and Detection of Ultrasound -- 4.13 Ultrasonic Transducer -- 4.14 Mechanical Matching -- 4.15 Electrical Matching -- 4.16 Ultrasound Imaging -- 4.16.1 Gray Scale Imaging -- 4.16.1.1 Data Acquisition -- 4.16.1.2 Amplitude Mode (A-Mode) -- 4.16.1.3 Brightness Mode (B-Mode) -- 4.16.1.4 Motion Mode (M-Mode) -- 4.17 Image Reconstruction -- 4.18 Schlieren System -- 4.19 Doppler Imaging Approaches -- 4.19.1 Continuous Wave Doppler System -- 4.19.2 Pulse Wave Doppler System -- 4.19.3 Color Doppler Flow Imaging -- 4.20 Tissue Characterization -- 4.20.1 Velocity -- 4.20.2 Absorption -- 4.20.3 Scattering -- 4.21 Ultrasound Image Characteristics -- 4.21.1 Spatial Resolution -- 4.21.2 Image Contrast -- 4.21.3 Ultrasonic Texture -- 4.22 Biological Effects of Ultrasound -- 4.22.1 Acoustic Aspects at High Intensity Levels -- 4.22.2 Cavitation -- 4.22.3 Transient Cavitation -- 4.22.4 Stable Cavitation -- 4.22.5 Wave Distortion -- 4.22.6 Bioeffects (Thermal and Non-Thermal Effects) -- Glossary-Appendix -- Chapter 5 Radionuclide Imaging -- 5.1 Radionuclide Imaging - A Brief History -- 5.2 An Insight Into Radioactivity -- 5.2.1 Nuclear Particles -- 5.2.2 Radioactive Decay -- 5.2.3 Specific Activity -- 5.2.4 Interactions Between Nuclear Particles and Matter -- 5.2.4.1 Alpha Particles -- 5.2.4.2 Beta Particles -- 5.2.4.3 Gamma Particles -- 5.2.5 Properties of Radionuclides.
5.2.5.1 Physical Properties -- 5.2.5.2 Biological Properties -- 5.3 Generation of Nuclear Emission -- 5.3.1 Nuclear Sources -- 5.3.2 99mTc Radionuclide Generator -- 5.3.3 Detection of Nuclear Emissions -- 5.3.3.1 Ion Collection Detectors -- 5.3.3.2 Scintillation Fetectors -- 5.3.3.3 Solid State Detectors -- 5.3.3.4 Collimator -- 5.4 Radionuclide Detection -- 5.4.1 Rectilinear Scanning Machines -- 5.4.2 Scintillation Camera (Gamma Camera) -- 5.4.2.1 Collimator -- 5.4.2.2 Scintillation Crystal -- 5.4.2.3 Photomultiplier Tube -- 5.4.3 Longitudinal Section Tomography (LST) -- 5.4.4 Single Photon Emission Computer Tomography (SPECT) -- 5.4.5 Positron Emission Tomography (PET) -- 5.5 Diagnostic Approaches Using Radiation Detector Probes -- 5.5.1 Thyroid Function Assessment -- 5.5.2 Renal Function Test -- 5.5.3 Blood Volume Assessment -- 5.6 Radionuclide Image Characteristics -- 5.6.1 Spatial Resolution -- 5.6.2 Image Contrast -- 5.6.3 Image Noise -- 5.7 Biological Effects of Radionuclides -- Glossary-Appendix -- Chapter 6 Magnetic Resonance Imaging -- 6.1 Basics of Nuclear Magnetic Resonance -- 6.2 Larmor Frequency -- 6.3 Relaxation -- 6.3.1 T1 (Longitudinal Relaxation) -- 6.3.2 T2 (Transverse Relaxation) -- 6.4 Image Contrast -- 6.5 Repetition Time (TR) and T1 Weighting -- 6.6 Echo Time (TE) and T2 Weighting -- 6.7 Saturation at Short Repetition Times -- 6.8 Flip Angle/Tip Angle -- 6.9 Presaturation -- 6.10 Magnetization Transfer -- 6.11 Slice Selection -- 6.12 Spatial Encoding -- 6.13 Phase Encoding -- 6.14 Frequency Encoding -- 6.15 K-Space -- 6.16 Image Noise -- 6.17 The MR Scanning Machine -- 6.17.1 The Magnet -- 6.17.2 Permanent Magnet -- 6.17.3 Resistive Magnets -- 6.17.4 Superconducting Magnets -- 6.17.5 Quenching -- 6.17.6 Shimming -- 6.17.7 Shielding -- 6.17.8 The Gradient System -- 6.17.9 The Radiofrequency System -- 6.17.10 The Computer System.
6.18 Pulse Sequences -- 6.18.1 Spin Echo Sequence -- 6.18.1.1 Black Blood Effect -- 6.18.2 Inversion Recovery Sequence -- 6.18.3 Short TI Inversion Recovery (STIR) Sequences -- 6.18.4 Fluid Attenuated Recovery (FLAIR) Sequences -- 6.18.5 Gradient Echo Sequence -- 6.19 Parallel Imaging -- 6.20 MR Artifacts -- 6.21 Motion Artifacts -- 6.22 Flow Artifacts -- 6.23 Phase Wrapping -- 6.24 Chemical Shift -- 6.25 Magnetic Susceptibility -- 6.26 Truncation Artifact -- 6.27 Magic Angle -- 6.28 Eddy Currents -- 6.29 Partial Volume Artifact -- 6.30 Inhomogeneous Fat Suppression -- 6.31 Zipper Artifacts -- 6.32 Crisscross Artifact -- 6.33 Bioeffects and Safety -- Glossary-Appendix -- About the Authors -- Index -- EULA.
Record Nr. UNINA-9910830561103321
Sanjay H. S.  
Hoboken, NJ : , : John Wiley & Sons, Inc. and Scrivener Publishing LLC, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Wireless Communication Security
Wireless Communication Security
Autore Khari Manju
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica 1 online resource (290 pages)
Altri autori (Persone) BhartiManisha
NiranjanamurthyM
ISBN 1-119-77746-1
1-119-77745-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 M2M in 5G Cellular Networks: Challenges, Proposed Solutions, and Future Directions -- 1.1 Introduction -- 1.2 Literature Survey -- 1.3 Survey Challenges and Proposed Solutions of M2M -- 1.3.1 PARCH Overload Problem -- 1.3.2 Inefficient Radio Resource Utilization and Allocation -- 1.3.3 M2M Random Access Challenges -- 1.3.4 Clustering Techniques -- 1.3.5 QoS Provisioning for M2M Communications -- 1.3.6 Less Cost and Low Power Device Requirements -- 1.3.7 Security and Privacy -- 1.4 Conclusion -- References -- Chapter 2 MAC Layer Protocol for Wireless Security -- 2.1 Introduction -- 2.2 MAC Layer -- 2.2.1 Centralized Control -- 2.2.2 Deterministic Access -- 2.2.3 Non-Deterministic Access -- 2.3 Functions of the MAC Layer -- 2.4 MAC Layer Protocol -- 2.4.1 Random Access Protocol -- 2.4.2 Controlled Access Protocols -- 2.4.3 Channelization -- 2.5 MAC Address -- 2.6 Conclusion and Future Scope -- References -- Chapter 3 Enhanced Image Security Through Hybrid Approach: Protect Your Copyright Over Digital Images -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Design Issues -- 3.3.1 Robustness Against Various Attack Conditions -- 3.3.2 Distortion and Visual Quality -- 3.3.3 Working Domain -- 3.3.4 Human Visual System (HVS) -- 3.3.5 The Trade-Off between Robustness and Imperceptibility -- 3.3.6 Computational Cost -- 3.4 A Secure Grayscale Image Watermarking Based on DWT-SVD -- 3.5 Experimental Results -- 3.6 Conclusion -- References -- Chapter 4 Quantum Computing -- 4.1 Introduction -- 4.2 A Brief History of Quantum Computing -- 4.3 Postulate of Quantum Mechanics -- 4.4 Polarization and Entanglement -- 4.5 Applications and Advancements -- 4.5.1 Cryptography, Teleportation and Communication Networks -- 4.5.2 Quantum Computing and Memories.
4.5.3 Satellite Communication Based on Quantum Computing -- 4.5.4 Machine Learning & -- Artificial Intelligence -- 4.6 Optical Quantum Computing -- 4.7 Experimental Realisation of Quantum Computer -- 4.7.1 Hetero-Polymers -- 4.7.2 Ion Traps -- 4.7.3 Quantum Electrodynamics Cavity -- 4.7.4 Quantum Dots -- 4.8 Challenges of Quantum Computing -- 4.9 Conclusion and Future Scope -- References -- Chapter 5 Feature Engineering for Flow-Based IDS -- 5.1 Introduction -- 5.1.1 Intrusion Detection System -- 5.1.2 IDS Classification -- 5.2 IP Flows -- 5.2.1 The Architecture of Flow-Based IDS -- 5.2.2 Wireless IDS Designed Using Flow-Based Approach -- 5.2.3 Comparison of Flow- and Packet-Based IDS -- 5.3 Feature Engineering -- 5.3.1 Curse of Dimensionality -- 5.3.2 Feature Selection -- 5.3.3 Feature Categorization -- 5.4 Classification of Feature Selection Technique -- 5.4.1 The Wrapper, Filter, and Embedded Feature Selection -- 5.4.2 Correlation, Consistency, and PCA-Based Feature Selection -- 5.4.3 Similarity, Information Theoretical, Sparse Learning, and Statistical-Based Feature Selection -- 5.4.4 Univariate and Multivariate Feature Selection -- 5.5 Tools and Library for Feature Selection -- 5.6 Literature Review on Feature Selection in Flow-Based IDS -- 5.7 Challenges and Future Scope -- 5.8 Conclusions -- Acknowledgement -- References -- Chapter 6 Environmental Aware Thermal (EAT) Routing Protocol for Wireless Sensor Networks -- 6.1 Introduction -- 6.1.1 Single Path Routing Protocol -- 6.1.2 Multipath Routing Protocol -- 6.1.3 Environmental Influence on WSN -- 6.2 Motivation Behind the Work -- 6.3 Novelty of This Work -- 6.4 Related Works -- 6.5 Proposed Environmental Aware Thermal (EAT) Routing Protocol -- 6.5.1 Sensor Node Environmental Modeling and Analysis -- 6.5.2 Single Node Environmental Influence Modeling -- 6.5.3 Multiple Node Modeling.
6.5.4 Sensor Node Surrounding Temperature Field -- 6.5.5 Sensor Node Remaining Energy Calculation -- 6.5.6 Delay Modeling -- 6.6 Simulation Parameters -- 6.7 Results and Discussion -- 6.7.1 Temperature Influence on Network -- 6.7.2 Power Consumption -- 6.7.3 Lifetime Analysis -- 6.7.4 Delay Analysis -- 6.8 Conclusion -- References -- Chapter 7 A Comprehensive Study of Intrusion Detection and Prevention Systems -- 7.1 Introduction -- 7.1.1 Intrusion and Detection -- 7.1.2 Some Basic Definitions -- 7.1.3 Intrusion Detection and Prevention System -- 7.1.4 Need for IDPS: More Than Ever -- 7.1.5 Introduction to Alarms -- 7.1.6 Components of an IDPS -- 7.2 Configuring IDPS -- 7.2.1 Network Architecture of IDPS -- 7.2.2 A Glance at Common Types -- 7.2.2.1 Network-Based IDS -- 7.2.2.2 Host-Based IDS -- 7.2.3 Intrusion Detection Techniques -- 7.2.3.1 Conventional Techniques -- 7.2.3.2 Machine Learning-Based and Hybrid Techniques -- 7.2.4 Three Considerations -- 7.2.4.1 Location of Sensors -- 7.2.4.2 Security Capabilities -- 7.2.4.3 Management Capabilities -- 7.2.5 Administrators' Functions -- 7.2.5.1 Deployment -- 7.2.5.2 Testing -- 7.2.5.3 Security Consideration of IDPS -- 7.2.5.4 Regular Backups and Monitoring -- 7.2.6 Types of Events Detected -- 7.2.7 Role of State in Network Security -- 7.3 Literature Review -- 7.4 Conclusion -- References -- Chapter 8 Hardware Devices Integration With IoT -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Component Description -- 8.3.1 Arduino Board UNO -- 8.3.2 Raspberry Pi -- 8.4 Case Studies -- 8.4.1 Ultrasonic Sensor -- 8.4.2 Temperature and Humidity Sensor -- 8.4.3 Weather Monitoring System Using Raspberry Pi -- 8.5 Drawbacks of Arduino and Raspberry Pi -- 8.6 Challenges in IoT -- 8.6.1 Design Challenges -- 8.6.2 Security Challenges -- 8.6.3 Development Challenges -- 8.7 Conclusion -- 8.8 Annexures -- References.
Additional Resources -- Chapter 9 Depth Analysis On DoS & -- DDoS Attacks -- 9.1 Introduction -- 9.1.1 Objective and Motivation -- 9.1.2 Symptoms and Manifestations -- 9.2 Literature Survey -- 9.3 Timeline of DoS and DDoS Attacks -- 9.4 Evolution of Denial of Service (DoS) & -- Distributed Denial of Service (DDoS) -- 9.5 DDoS Attacks: A Taxonomic Classification -- 9.5.1 Classification Based on Degree of Automation -- 9.5.2 Classification Based on Exploited Vulnerability -- 9.5.3 Classification Based on Rate Dynamics of Attacks -- 9.5.4 Classification Based on Impact -- 9.6 Transmission Control Protocol -- 9.6.1 TCP Three-Way Handshake -- 9.7 User Datagram Protocol -- 9.7.1 UDP Header -- 9.8 Types of DDoS Attacks -- 9.8.1 TCP SYN Flooding Attack -- 9.8.2 UDP Flooding Attack -- 9.8.3 Smurf Attack -- 9.8.4 Ping of Death Attack -- 9.8.5 HTTP Flooding Attack -- 9.9 Impact of DoS/DDoS on Various Areas -- 9.9.1 DoS/DDoS Attacks on VoIP Networks Using SIP -- 9.9.2 DoS/DDoS Attacks on VANET -- 9.9.3 DoS/DDoS Attacks on Smart Grid System -- 9.9.4 DoS/DDoS Attacks in IoT-Based Devices -- 9.10 Countermeasures to DDoS Attack -- 9.10.1 Prevent Being Agent/Secondary Target -- 9.10.2 Detect and Neutralize Attacker -- 9.10.3 Potential Threats Detection/Prevention -- 9.10.4 DDoS Attacks and How to Avoid Them -- 9.10.5 Deflect Attack -- 9.10.6 Post-Attack Forensics -- 9.11 Conclusion -- 9.12 Future Scope -- References -- Chapter 10 SQL Injection Attack on Database System -- 10.1 Introduction -- 10.1.1 Types of Vulnerabilities -- 10.1.2 Types of SQL Injection Attack -- 10.1.3 Impact of SQL Injection Attack -- 10.2 Objective and Motivation -- 10.3 Process of SQL Injection Attack -- 10.4 Related Work -- 10.5 Literature Review -- 10.6 Implementation of the SQL Injection Attack -- 10.6.1 Access the Database Using the 1=1 SQL Injection Statement.
10.6.2 Access the Database Using the ""='''' SQL Injection Statement -- 10.6.3 Access and Upgrade the Database by Using Batch SQL Injection Statement -- 10.7 Detection of SQL Injection Attack -- 10.8 Prevention/Mitigation from SQL Injection Attack -- 10.9 Conclusion -- References -- Chapter 11 Machine Learning Techniques for Face Authentication System for Security Purposes -- 11.1 Introduction -- 11.2 Face Recognition System (FRS) in Security -- 11.3 Theory -- 11.3.1 Neural Networks -- 11.3.2 Convolutional Neural Network (CNN) -- 11.3.3 K-Nearest Neighbors (KNN) -- 11.3.4 Support Vector Machine (SVM) -- 11.3.5 Logistic Regression (LR) -- 11.3.6 Naive Bayes (NB) -- 11.3.7 Decision Tree (DT) -- 11.4 Experimental Methodology -- 11.4.1 Dataset -- 11.4.2 Convolutional Neural Network (CNN) -- 11.4.3 Other Machine Learning Techniques -- 11.5 Results -- 11.6 Conclusion -- References -- Chapter 12 Estimation of Computation Time for Software-Defined Networking-Based Data Traffic Offloading System in Heterogeneous Network -- 12.1 Introduction -- 12.1.1 Motivation -- 12.1.2 Objective -- 12.1.3 The Main Contributions of This Chapter -- 12.2 Analysis of SDN-TOS Mechanism -- 12.2.1 Key Components of SDN-TOS -- 12.2.2 LTE/Wi-Fi in a Heterogeneous Network (HetNet) -- 12.2.3 Centralized SDN Controller -- 12.2.4 Key Design Considerations of SDN-TOS -- 12.2.4.1 The System Architecture -- 12.2.4.2 Mininet Wi-Fi Emulated Networks -- 12.2.4.3 Software-Defined Networking Controller -- 12.3 Materials and Methods -- 12.3.1 Estimating Time Consumption for Mininet Wi-Fi Emulator -- 12.3.1.1 Total Time Consumption for Offloading the Data Traffic by Service Provider -- 12.3.1.2 Total Time Consumption of Mininet Wi-Fi Emulator (Time Consumption for Both LTE and Wi-Fi Network) -- 12.3.2 Estimating Time Consumption for SDN Controller.
12.3.2.1 Total Response Time for Sub-Controller.
Record Nr. UNINA-9910646197503321
Khari Manju  
Newark : , : John Wiley & Sons, Incorporated, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Wireless communication security : mobile and network security protocols / / edited by Manju Khari, Manisha Bharti, M. Niranjanamurthy
Wireless communication security : mobile and network security protocols / / edited by Manju Khari, Manisha Bharti, M. Niranjanamurthy
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023]
Descrizione fisica 1 online resource (290 pages)
Disciplina 002
Soggetto topico Wireless communication systems - Security measures
ISBN 1-119-77746-1
1-119-77745-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 M2M in 5G Cellular Networks: Challenges, Proposed Solutions, and Future Directions -- 1.1 Introduction -- 1.2 Literature Survey -- 1.3 Survey Challenges and Proposed Solutions of M2M -- 1.3.1 PARCH Overload Problem -- 1.3.2 Inefficient Radio Resource Utilization and Allocation -- 1.3.3 M2M Random Access Challenges -- 1.3.4 Clustering Techniques -- 1.3.5 QoS Provisioning for M2M Communications -- 1.3.6 Less Cost and Low Power Device Requirements -- 1.3.7 Security and Privacy -- 1.4 Conclusion -- References -- Chapter 2 MAC Layer Protocol for Wireless Security -- 2.1 Introduction -- 2.2 MAC Layer -- 2.2.1 Centralized Control -- 2.2.2 Deterministic Access -- 2.2.3 Non-Deterministic Access -- 2.3 Functions of the MAC Layer -- 2.4 MAC Layer Protocol -- 2.4.1 Random Access Protocol -- 2.4.2 Controlled Access Protocols -- 2.4.3 Channelization -- 2.5 MAC Address -- 2.6 Conclusion and Future Scope -- References -- Chapter 3 Enhanced Image Security Through Hybrid Approach: Protect Your Copyright Over Digital Images -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Design Issues -- 3.3.1 Robustness Against Various Attack Conditions -- 3.3.2 Distortion and Visual Quality -- 3.3.3 Working Domain -- 3.3.4 Human Visual System (HVS) -- 3.3.5 The Trade-Off between Robustness and Imperceptibility -- 3.3.6 Computational Cost -- 3.4 A Secure Grayscale Image Watermarking Based on DWT-SVD -- 3.5 Experimental Results -- 3.6 Conclusion -- References -- Chapter 4 Quantum Computing -- 4.1 Introduction -- 4.2 A Brief History of Quantum Computing -- 4.3 Postulate of Quantum Mechanics -- 4.4 Polarization and Entanglement -- 4.5 Applications and Advancements -- 4.5.1 Cryptography, Teleportation and Communication Networks -- 4.5.2 Quantum Computing and Memories.
4.5.3 Satellite Communication Based on Quantum Computing -- 4.5.4 Machine Learning & -- Artificial Intelligence -- 4.6 Optical Quantum Computing -- 4.7 Experimental Realisation of Quantum Computer -- 4.7.1 Hetero-Polymers -- 4.7.2 Ion Traps -- 4.7.3 Quantum Electrodynamics Cavity -- 4.7.4 Quantum Dots -- 4.8 Challenges of Quantum Computing -- 4.9 Conclusion and Future Scope -- References -- Chapter 5 Feature Engineering for Flow-Based IDS -- 5.1 Introduction -- 5.1.1 Intrusion Detection System -- 5.1.2 IDS Classification -- 5.2 IP Flows -- 5.2.1 The Architecture of Flow-Based IDS -- 5.2.2 Wireless IDS Designed Using Flow-Based Approach -- 5.2.3 Comparison of Flow- and Packet-Based IDS -- 5.3 Feature Engineering -- 5.3.1 Curse of Dimensionality -- 5.3.2 Feature Selection -- 5.3.3 Feature Categorization -- 5.4 Classification of Feature Selection Technique -- 5.4.1 The Wrapper, Filter, and Embedded Feature Selection -- 5.4.2 Correlation, Consistency, and PCA-Based Feature Selection -- 5.4.3 Similarity, Information Theoretical, Sparse Learning, and Statistical-Based Feature Selection -- 5.4.4 Univariate and Multivariate Feature Selection -- 5.5 Tools and Library for Feature Selection -- 5.6 Literature Review on Feature Selection in Flow-Based IDS -- 5.7 Challenges and Future Scope -- 5.8 Conclusions -- Acknowledgement -- References -- Chapter 6 Environmental Aware Thermal (EAT) Routing Protocol for Wireless Sensor Networks -- 6.1 Introduction -- 6.1.1 Single Path Routing Protocol -- 6.1.2 Multipath Routing Protocol -- 6.1.3 Environmental Influence on WSN -- 6.2 Motivation Behind the Work -- 6.3 Novelty of This Work -- 6.4 Related Works -- 6.5 Proposed Environmental Aware Thermal (EAT) Routing Protocol -- 6.5.1 Sensor Node Environmental Modeling and Analysis -- 6.5.2 Single Node Environmental Influence Modeling -- 6.5.3 Multiple Node Modeling.
6.5.4 Sensor Node Surrounding Temperature Field -- 6.5.5 Sensor Node Remaining Energy Calculation -- 6.5.6 Delay Modeling -- 6.6 Simulation Parameters -- 6.7 Results and Discussion -- 6.7.1 Temperature Influence on Network -- 6.7.2 Power Consumption -- 6.7.3 Lifetime Analysis -- 6.7.4 Delay Analysis -- 6.8 Conclusion -- References -- Chapter 7 A Comprehensive Study of Intrusion Detection and Prevention Systems -- 7.1 Introduction -- 7.1.1 Intrusion and Detection -- 7.1.2 Some Basic Definitions -- 7.1.3 Intrusion Detection and Prevention System -- 7.1.4 Need for IDPS: More Than Ever -- 7.1.5 Introduction to Alarms -- 7.1.6 Components of an IDPS -- 7.2 Configuring IDPS -- 7.2.1 Network Architecture of IDPS -- 7.2.2 A Glance at Common Types -- 7.2.2.1 Network-Based IDS -- 7.2.2.2 Host-Based IDS -- 7.2.3 Intrusion Detection Techniques -- 7.2.3.1 Conventional Techniques -- 7.2.3.2 Machine Learning-Based and Hybrid Techniques -- 7.2.4 Three Considerations -- 7.2.4.1 Location of Sensors -- 7.2.4.2 Security Capabilities -- 7.2.4.3 Management Capabilities -- 7.2.5 Administrators' Functions -- 7.2.5.1 Deployment -- 7.2.5.2 Testing -- 7.2.5.3 Security Consideration of IDPS -- 7.2.5.4 Regular Backups and Monitoring -- 7.2.6 Types of Events Detected -- 7.2.7 Role of State in Network Security -- 7.3 Literature Review -- 7.4 Conclusion -- References -- Chapter 8 Hardware Devices Integration With IoT -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Component Description -- 8.3.1 Arduino Board UNO -- 8.3.2 Raspberry Pi -- 8.4 Case Studies -- 8.4.1 Ultrasonic Sensor -- 8.4.2 Temperature and Humidity Sensor -- 8.4.3 Weather Monitoring System Using Raspberry Pi -- 8.5 Drawbacks of Arduino and Raspberry Pi -- 8.6 Challenges in IoT -- 8.6.1 Design Challenges -- 8.6.2 Security Challenges -- 8.6.3 Development Challenges -- 8.7 Conclusion -- 8.8 Annexures -- References.
Additional Resources -- Chapter 9 Depth Analysis On DoS & -- DDoS Attacks -- 9.1 Introduction -- 9.1.1 Objective and Motivation -- 9.1.2 Symptoms and Manifestations -- 9.2 Literature Survey -- 9.3 Timeline of DoS and DDoS Attacks -- 9.4 Evolution of Denial of Service (DoS) & -- Distributed Denial of Service (DDoS) -- 9.5 DDoS Attacks: A Taxonomic Classification -- 9.5.1 Classification Based on Degree of Automation -- 9.5.2 Classification Based on Exploited Vulnerability -- 9.5.3 Classification Based on Rate Dynamics of Attacks -- 9.5.4 Classification Based on Impact -- 9.6 Transmission Control Protocol -- 9.6.1 TCP Three-Way Handshake -- 9.7 User Datagram Protocol -- 9.7.1 UDP Header -- 9.8 Types of DDoS Attacks -- 9.8.1 TCP SYN Flooding Attack -- 9.8.2 UDP Flooding Attack -- 9.8.3 Smurf Attack -- 9.8.4 Ping of Death Attack -- 9.8.5 HTTP Flooding Attack -- 9.9 Impact of DoS/DDoS on Various Areas -- 9.9.1 DoS/DDoS Attacks on VoIP Networks Using SIP -- 9.9.2 DoS/DDoS Attacks on VANET -- 9.9.3 DoS/DDoS Attacks on Smart Grid System -- 9.9.4 DoS/DDoS Attacks in IoT-Based Devices -- 9.10 Countermeasures to DDoS Attack -- 9.10.1 Prevent Being Agent/Secondary Target -- 9.10.2 Detect and Neutralize Attacker -- 9.10.3 Potential Threats Detection/Prevention -- 9.10.4 DDoS Attacks and How to Avoid Them -- 9.10.5 Deflect Attack -- 9.10.6 Post-Attack Forensics -- 9.11 Conclusion -- 9.12 Future Scope -- References -- Chapter 10 SQL Injection Attack on Database System -- 10.1 Introduction -- 10.1.1 Types of Vulnerabilities -- 10.1.2 Types of SQL Injection Attack -- 10.1.3 Impact of SQL Injection Attack -- 10.2 Objective and Motivation -- 10.3 Process of SQL Injection Attack -- 10.4 Related Work -- 10.5 Literature Review -- 10.6 Implementation of the SQL Injection Attack -- 10.6.1 Access the Database Using the 1=1 SQL Injection Statement.
10.6.2 Access the Database Using the ""='''' SQL Injection Statement -- 10.6.3 Access and Upgrade the Database by Using Batch SQL Injection Statement -- 10.7 Detection of SQL Injection Attack -- 10.8 Prevention/Mitigation from SQL Injection Attack -- 10.9 Conclusion -- References -- Chapter 11 Machine Learning Techniques for Face Authentication System for Security Purposes -- 11.1 Introduction -- 11.2 Face Recognition System (FRS) in Security -- 11.3 Theory -- 11.3.1 Neural Networks -- 11.3.2 Convolutional Neural Network (CNN) -- 11.3.3 K-Nearest Neighbors (KNN) -- 11.3.4 Support Vector Machine (SVM) -- 11.3.5 Logistic Regression (LR) -- 11.3.6 Naive Bayes (NB) -- 11.3.7 Decision Tree (DT) -- 11.4 Experimental Methodology -- 11.4.1 Dataset -- 11.4.2 Convolutional Neural Network (CNN) -- 11.4.3 Other Machine Learning Techniques -- 11.5 Results -- 11.6 Conclusion -- References -- Chapter 12 Estimation of Computation Time for Software-Defined Networking-Based Data Traffic Offloading System in Heterogeneous Network -- 12.1 Introduction -- 12.1.1 Motivation -- 12.1.2 Objective -- 12.1.3 The Main Contributions of This Chapter -- 12.2 Analysis of SDN-TOS Mechanism -- 12.2.1 Key Components of SDN-TOS -- 12.2.2 LTE/Wi-Fi in a Heterogeneous Network (HetNet) -- 12.2.3 Centralized SDN Controller -- 12.2.4 Key Design Considerations of SDN-TOS -- 12.2.4.1 The System Architecture -- 12.2.4.2 Mininet Wi-Fi Emulated Networks -- 12.2.4.3 Software-Defined Networking Controller -- 12.3 Materials and Methods -- 12.3.1 Estimating Time Consumption for Mininet Wi-Fi Emulator -- 12.3.1.1 Total Time Consumption for Offloading the Data Traffic by Service Provider -- 12.3.1.2 Total Time Consumption of Mininet Wi-Fi Emulator (Time Consumption for Both LTE and Wi-Fi Network) -- 12.3.2 Estimating Time Consumption for SDN Controller.
12.3.2.1 Total Response Time for Sub-Controller.
Record Nr. UNINA-9910676533903321
Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui