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5G and Beyond [[electronic resource] /] / edited by Bharat Bhushan, Sudhir Kumar Sharma, Raghvendra Kumar, Ishaani Priyadarshini
| 5G and Beyond [[electronic resource] /] / edited by Bharat Bhushan, Sudhir Kumar Sharma, Raghvendra Kumar, Ishaani Priyadarshini |
| Autore | Bhushan Bharat |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (310 pages) |
| Disciplina | 621.382 |
| Altri autori (Persone) |
SharmaSudhir Kumar
KumarRaghvendra PriyadarshiniIshaani |
| Collana | Springer Tracts in Electrical and Electronics Engineering |
| Soggetto topico |
Telecommunication
Electronic circuits Computer engineering Computer networks Communications Engineering, Networks Electronic Circuits and Systems Computer Engineering and Networks Computer Communication Networks |
| ISBN | 981-9936-68-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 5G network architecture, management technologies, and services -- Network architectures and protocols for efficient exploitation of spectrum resources in 5G -- Smart radio resource allocation mechanisms for 5G -- Energy management in 5G networks -- Physical layer developments in 5G -- Smart cross-layer access node allocation mechanisms in 5G networks. |
| Record Nr. | UNINA-9910737296403321 |
Bhushan Bharat
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Big Data Processing Using Spark in Cloud [[electronic resource] /] / edited by Mamta Mittal, Valentina E. Balas, Lalit Mohan Goyal, Raghvendra Kumar
| Big Data Processing Using Spark in Cloud [[electronic resource] /] / edited by Mamta Mittal, Valentina E. Balas, Lalit Mohan Goyal, Raghvendra Kumar |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XIII, 264 p. 89 illus., 62 illus. in color.) |
| Disciplina | 005.7 |
| Collana | Studies in Big Data |
| Soggetto topico |
Big data
Computer security Big Data Systems and Data Security Big Data/Analytics |
| ISBN | 981-13-0550-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Concepts of Big Data and Apache Spark -- Big Data Analysis in Cloud and Machine Learning -- Security Issues and Challenges related to Big Data -- Big Data Security Solutions in Cloud -- Data Science and Analytics -- Big Data Technologies -- Data Analysis with Casandra and Spark -- Spin up the Spark Cluster -- Learn Scala -- IO for Spark -- Processing with Spark -- Spark Data Frames and Spark SQL -- Machine Learning and Advanced Analytics -- Parallel Programming with Spark -- Distributed Graph Processing with Spark -- Real Time Processing with Spark -- Spark in Real World -- Case Studies. . |
| Record Nr. | UNINA-9910739483403321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data Analytics for Smart Grids Applications--A Key to Smart City Development
| Data Analytics for Smart Grids Applications--A Key to Smart City Development |
| Autore | Kumar Sharma Devendra |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2024 |
| Descrizione fisica | 1 online resource (466 pages) |
| Altri autori (Persone) |
SharmaRohit
JeonGwanggil KumarRaghvendra |
| Collana | Intelligent Systems Reference Library |
| ISBN | 3-031-46092-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- About This Book -- Key Features -- Contents -- About the Editors -- 1 Data Analytics for Smart Grids and Applications-Present and Future Directions -- 1.1 Introduction -- 1.2 Literature Review -- 1.3 Smart Grid Infrastructure -- 1.4 Data Analytics in Smart Grids -- 1.4.1 Data Pre Processing Techniques in Smart Grids -- 1.4.2 Case Study of Data Analytics in Smart Grids -- 1.5 Artificial Intelligence in Smart Grids -- 1.5.1 Event Detection Using Data Analytics and Cloud Computing for Intelligent IoT System -- 1.6 Conclusion -- References -- 2 Design, Optimization and Performance Analysis of Microgrids Using Multi-agent Q-Learning -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Proposed Model -- 2.4 Experiments -- 2.5 Conclusion -- References -- 3 Big Data Analytics for Smart Grid: A Review on State-of-Art Techniques and Future Directions -- 3.1 Introduction -- 3.2 State-of-Art Techniques for Big Data Analytics in Smart Grids -- 3.3 Challenges in Big Data Analytics for Smart Grids -- 3.4 Big Data Analytics for Smart Grids -- 3.5 Applications of Big Data Analytics in Smart Grids -- 3.6 Challenges and Future Directions for Big Data Analytics in Smart Grids -- 3.7 Case Studies of Big Data Analytics in Smart Grids -- 3.7.1 Case Study 1: Duke Energy's Grid Modernization Program -- 3.7.2 Case Study 2: National Grid's Smart Grid Program -- 3.7.3 Case Study 3: ENEL's Smart Grid Program -- 3.8 Future Directions for Big Data Analytics in Smart Grids -- 3.9 Real-Time Big Data Analytics for Smart Grids -- 3.10 Conclusion -- References -- 4 Smart Grid Management for Smart City Infrastructure Using Wearable Sensors -- 4.1 Introduction -- 4.1.1 Smart Grid Versus Traditional Electricity Grids -- 4.1.2 Why Do We Need Smart Grids? -- 4.1.3 Smart Grid Features -- 4.1.4 Smart Grid Technologies -- 4.1.5 Smart Grid Approaches.
4.1.6 Smart Meters and Home EMS -- 4.1.7 Smart Appliances -- 4.1.8 Home Power Generation -- 4.1.9 Machine Learning for Data Analytics in Smart Grids and Energy Management -- 4.1.10 Security for Industrial Control Systems in Smart Grids -- 4.1.11 Power Flow Modelling and Optimization in Smart Grids -- 4.1.12 Grid Stability and Security in Smart Grids -- 4.1.13 Integration of Renewable Energy Sources in Smart Grid Management -- 4.1.14 Demand Response Strategies for Efficient Smart Grid Management -- 4.1.15 Cybersecurity Measures for Smart Grid Management -- 4.1.16 Energy Storage Systems and Their Role in Smart Grid Management -- 4.1.17 Data Analytics and Artificial Intelligence in Smart Grid Management -- 4.1.18 Smart Grid Communication Protocols and Infrastructure -- 4.1.19 Advantages of Smart Grids -- 4.1.20 Disadvantages of Smart Grids -- 4.2 Conclusion -- References -- 5 Studies on Conventional and Advanced Machine Learning Algorithm Towards Framing of Robust Data Analytics for the Smart Grid Application -- 5.1 Introduction -- 5.2 Review of Different Smart Grid Based Approaches -- 5.3 Smart Grid Model -- 5.3.1 Smart Grids as Coordinators for Data Flow and Energy Flow -- 5.3.2 Big Data -- 5.4 Features of Big Data to Be Integrated into the Smart Grid -- 5.5 Contribution of the Smart Grid as Data Source -- 5.6 Smart Grid in Supply of Data Gathering -- 5.6.1 Data Transmission Methodology -- 5.6.2 Data Analysis Methodology -- 5.6.3 Data Extraction from Smart Grid -- 5.6.4 Grid for Production of Renewable Source of Energy -- 5.6.5 Big Data in Smart Grid -- 5.6.6 Machine Learning Approach to the Data Grid -- 5.6.7 Application of IOT to the Smart Grid Technology -- 5.7 IOT Based Solutions Towards Grid Problems -- 5.7.1 Stability of IOT Based Connection -- 5.7.2 Cost Effectiveness in Implementation -- 5.7.3 Security to the Information. 5.8 Application of Data Grid in Mobile Sink Based Wireless Sensor Network -- 5.8.1 Assumptions of Network Characteristics -- 5.9 Virtual Grid Architecture -- 5.9.1 Different Structures of Virtual Grids -- 5.9.2 Virtual Grid Construction Cost -- 5.9.3 Reading of the Smart Meter Data and Its Analysis by the Smart Grid with Future Prediction -- 5.9.4 Prediction Analysis of Smart Meter Data -- 5.10 Future Research Direction -- 5.11 Conclusion -- References -- 6 Prediction and Classification for Smart Grid Applications -- 6.1 Introduction -- 6.2 Smart Grid -- 6.3 Predictive and Classification Models in Smart Grid Applications -- 6.4 Predictive Modeling -- 6.5 Classification Modeling -- 6.6 Smart Grid Management -- 6.7 Intelligent Data Collection Devices -- 6.8 Data Science Pertaining to Smart Grid Analytics -- 6.9 Machine Learning for Data Analytics -- 6.10 Data Security for Smart Grid Applications -- 6.11 Conclusion -- References -- 7 A Review on Smart Metering Using Artificial Intelligence and Machine Learning Techniques: Challenges and Solutions -- 7.1 Introduction -- 7.1.1 Trends of the Smart Metering Systems -- 7.1.2 Challenges of Smart Meters -- 7.1.3 Key Elements of Smart Meter -- 7.1.4 IoT in Smart Metering -- 7.1.5 Integration of IoT with AI and Machine Learning for Smart Meter -- 7.1.6 Artificial Intelligence Techniques -- 7.2 Conclusion -- References -- 8 Machine Learning Applications for the Smart Grid Infrastructure -- 8.1 Introduction -- 8.2 IoT in Distribution System -- 8.3 Techniques Using Machine Learning -- 8.4 Conclusion -- References -- 9 A Privacy Mitigating Framework for the Smart Grid Internet of Things Data -- 9.1 Introduction -- 9.1.1 Overview of the Smart Grid and Its Significance in Modern Energy Systems -- 9.1.2 Introduction to the IoT and Its Integration with the Smart Grid -- 9.1.3 Importance of Privacy in Smart Grid IoT Data. 9.2 Privacy Challenges in Smart Grid IoT Data -- 9.3 Privacy Mitigation Techniques -- 9.4 Privacy Mitigation Framework for Smart Grid -- 9.4.1 Privacy Monitoring Engine Description -- 9.5 Results -- 9.6 Conclusion -- References -- 10 Protecting Future of Energy: Data Security and Privacy for Smart Grid Applications Using MATLAB -- 10.1 Introduction -- 10.1.1 Data Security and Privacy Threats -- 10.1.2 Data Security and Privacy Solutions -- 10.1.3 MATLAB Solution -- 10.1.4 Key Features and Capabilities -- 10.2 MATLAB Tools and Inbuilt Functions for Data Security in Applications of Smart Grid -- 10.3 MATLAB Functions for Data Security and Privacy in Smart Grid Applications Include -- 10.4 MATLAB Techniques for Data Security and Privacy in Smart Grid Applications -- 10.5 Matlab Algorithm for Privacy-Preserving Data Mining for Smart Grid Applications -- 10.6 Threats to Data Security and Privacy in Smart Grid Applications -- 10.6.1 Preventive Measures -- 10.7 Case Studies and Practical Implementations of Data Security and Privacy in Smart Grid Applications -- 10.7.1 Case Study 1: Securing Smart Meters Using Blockchain -- 10.7.2 Case Study 2: Machine Learning-Based Anomaly Detection in Power Grids -- 10.7.3 Case Study 3: Privacy-Preserving Data Aggregation in Smart Grids -- 10.7.4 Case Study 4: Secure Data Sharing in Smart Grids Using Homomorphic Encryption -- 10.7.5 Case Study 5: Anomaly Detection in Smart Grids Using Machine Learning (ML) with Matlab -- 10.8 Conclusion -- References -- 11 Revolutionizing Smart Grids with Big Data Analytics: A Case Study on Integrating Renewable Energy and Predicting Faults -- 11.1 Introduction -- 11.2 Current Trends in Smart Grid Based Big Data Analytics -- 11.2.1 There is a Notable Surge in Speculation in Smart Grid Projects and, Consequently, Smart Grid Analytics [9-11]. 11.2.2 Smart Grid Analytics Effectively Handle Real-Time Data Despite the Increased Speed and Diverse Requirements -- 11.2.3 Digital Technologies and Cloud Computing Will Continue to Improve, Facilitating Enhanced Data Computation Capabilities -- 11.2.4 Smart Grid and Its Benefits for Renewable Energy -- 11.3 Challenges of Smart Grid Analytics -- 11.3.1 Benefits of Analytics in Smart Grid -- 11.3.2 Trends in the Utility Industry -- 11.4 Technologies for Smart Grid Analytics and Its Importance -- 11.4.1 Business Intelligence (BI) and Data Analysis -- 11.4.2 Other Framework Technologies-Databases Such as Apache Hadoop, MapReduce, and SQL -- 11.4.3 The Significance of Big Data in Smart Grid Analytics -- 11.5 Gaining Perceptions Through a Smart Grid and Big Data: A Case Study -- 11.5.1 Case Studies in Focus -- 11.5.2 Smart Grid Based Data Analytics Use-Cases in Europe -- 11.6 Future and Scope of Big Data Analytics in Smart Grids -- 11.6.1 Customer Acceptance and Engagement -- 11.6.2 Regulatory Policies -- 11.6.3 Innovative Structures -- 11.7 Conclusion -- References -- 12 Fake User Account Detection in Online Social Media Networks Using Machine Learning and Neural Network Techniques -- 12.1 Introduction -- 12.1.1 Statistics of Social Media Usage -- 12.1.2 Why Are Fake Profiles Created? -- 12.2 Literature Review -- 12.3 Proposed System for Detecting Fake Accounts on Twitter Using AI -- 12.3.1 Artificial Neural Network (ANN) -- 12.3.2 Support Vector Machine (SVM) -- 12.3.3 Random Forest (RF) -- 12.4 Findings and Discussions -- 12.5 Conclusion -- References -- 13 Data Analytics for Smart Grids Applications to Improve Performance, Optimize Energy Consumption, and Gain Insights -- 13.1 Introduction -- 13.2 Leveraging Smart Grids for Predictive Energy Analytics -- 13.3 Big Data Analytics for Grid Resiliency and Security. 13.4 Machine Learning Techniques for Smart Grid Optimization. |
| Record Nr. | UNINA-9910767529203321 |
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Kumar Sharma Devendra
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| Cham : , : Springer International Publishing AG, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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A Handbook of Internet of Things in Biomedical and Cyber Physical System [[electronic resource] /] / edited by Valentina E. Balas, Vijender Kumar Solanki, Raghvendra Kumar, Md. Atiqur Rahman Ahad
| A Handbook of Internet of Things in Biomedical and Cyber Physical System [[electronic resource] /] / edited by Valentina E. Balas, Vijender Kumar Solanki, Raghvendra Kumar, Md. Atiqur Rahman Ahad |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (331 pages) |
| Disciplina | 004.678 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Computer engineering
Internet of things Embedded computer systems Computational intelligence Biomedical engineering Cyber-physical systems, IoT Computational Intelligence Biomedical Engineering/Biotechnology Biomedical Engineering and Bioengineering |
| ISBN | 3-030-23983-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910366605503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Internet of things and analytics for agriculture . Volume 3 / / Prasant Kumar Pattnaik, Raghvendra Kumar, Souvik Pal
| Internet of things and analytics for agriculture . Volume 3 / / Prasant Kumar Pattnaik, Raghvendra Kumar, Souvik Pal |
| Autore | Pattnaik Prasant Kumar |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (385 pages) |
| Disciplina | 338.10285 |
| Collana | Studies in Big Data |
| Soggetto topico |
Artificial intelligence - Agricultural applications
Internet of things |
| ISBN |
981-16-6210-X
981-16-6209-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743249303321 |
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Pattnaik Prasant Kumar
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| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Internet of Things and Analytics for Agriculture, Volume 2 [[electronic resource] /] / edited by Prasant Kumar Pattnaik, Raghvendra Kumar, Souvik Pal
| Internet of Things and Analytics for Agriculture, Volume 2 [[electronic resource] /] / edited by Prasant Kumar Pattnaik, Raghvendra Kumar, Souvik Pal |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xii, 288 pages) |
| Disciplina | 338.10285 |
| Collana | Studies in Big Data |
| Soggetto topico |
Robotics
Automation Big data Engineering—Data processing Robotics and Automation Big Data Data Engineering Big Data/Analytics |
| ISBN | 981-15-0663-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preamble -- Preface -- Acknowledgments -- About the Authors -- IoT-Agro Paradigm -- IoT: Foundations and Applications -- Smart Monitoring for Irrigation and Water Level Retention Functional Framework for IoT-based Agricultural System -- Intelligent Agro-Food Chain System -- Case Studies and Applications in IoT based Agriculture System -- Glossary -- Index. |
| Record Nr. | UNINA-9910739463103321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Internet of Things and Analytics for Agriculture, Volume 3
| Internet of Things and Analytics for Agriculture, Volume 3 |
| Autore | Pattnaik Prasant Kumar |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2021 |
| Descrizione fisica | 1 online resource (385 pages) |
| Altri autori (Persone) |
KumarRaghvendra
PalSouvik |
| Collana | Studies in Big Data Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9789811662102
9789811662096 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910508462603321 |
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Pattnaik Prasant Kumar
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| Singapore : , : Springer Singapore Pte. Limited, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Internet of Things and Big Data Analytics for Smart Generation [[electronic resource] /] / edited by Valentina E. Balas, Vijender Kumar Solanki, Raghvendra Kumar, Manju Khari
| Internet of Things and Big Data Analytics for Smart Generation [[electronic resource] /] / edited by Valentina E. Balas, Vijender Kumar Solanki, Raghvendra Kumar, Manju Khari |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (309 pages) |
| Disciplina | 006.3 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Computational intelligence
Artificial intelligence Computational Intelligence Artificial Intelligence |
| ISBN | 3-030-04203-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483559903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Multimedia technologies in the internet of things environment / / edited by Raghvendra Kumar, Rohit Sharma, Prasant Kumar Pattnaik
| Multimedia technologies in the internet of things environment / / edited by Raghvendra Kumar, Rohit Sharma, Prasant Kumar Pattnaik |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (XVIII, 208 p. 88 illus., 57 illus. in color.) |
| Disciplina | 929.605 |
| Collana | Studies in Big Data |
| Soggetto topico |
Multimedia systems
Big data Computational intelligence |
| ISBN | 981-15-7965-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Smart Control and Monitoring of Irrigation System using Internet of Things -- Chapter 2. Blockchain-based Cyber Threat Mitigation Systems for Smart Vehicles and Industrial Automation -- Chapter 3. IT Convergence related Security Challenges for Internet of Things and Big Data -- Chapter 4. Applicability of Industrial IoT in Diversified Sectors: Evolution, Applications and Challenges -- Chapter 5. Recent emergine for inteliigent learning and analytics in Big data -- Chapter 6. Real Time Health System (RTHS) centered Internet of Things (IoT) in healthcare industry: benefits, use cases and advancements in 2020 -- Chapter 7. Building intelligent Integrated Development Environment for IoT in the context of Statistical modeling for Software Source code -- Chapter 8. Visualization of COVID-19 Pandemic: an Analysis through Machine Intelligent Technique towards Big Data Paradigm -- Chapter 9.Multimedia Security and Privacy on Real Time Behavioral Monitoring in Machine Learning IoT Application using Big Data Analytics Chapter 10. A robust approach with text analytics for bengalir digit recgnition using mechine learning -- Chapter 11. Internet of Things Based Security Model and Solutions for Educational Systems. |
| Record Nr. | UNINA-9910484574303321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Next generation of Internet of things : proceedings of ICNGIoT 2022 / / edited by Raghvendra Kumar, Prasant Kumar Pattnaik, and João Manuel R. S. Tavares
| Next generation of Internet of things : proceedings of ICNGIoT 2022 / / edited by Raghvendra Kumar, Prasant Kumar Pattnaik, and João Manuel R. S. Tavares |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (708 pages) |
| Disciplina | 004.678 |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico | Internet of things |
| ISBN | 981-19-1412-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Editors and Contributors -- IoT-Assisted Crop Monitoring Using Machine Learning Algorithms for Smart Farming -- 1 Introduction -- 2 Literature Survey -- 3 Research Questions -- 4 Proposed Model -- 4.1 Optical Sensors -- 4.2 HTE MIX Sensors -- 4.3 Motion Detector Sensors -- 5 Implementation Details -- 6 Conclusion and Future Scope -- References -- Behavioural Investigation and Analysis of Flux and Torque in Faulty Electrical Machines Using Finite Element Techniques -- 1 Introduction -- 2 Mechanical Fault Concept -- 3 Mathematical Model -- 4 Tests and Results -- 5 Conclusion -- References -- A Comprehensive Survey for Internet of Things (IoT)-Based Smart City Architecture -- 1 Introduction -- 2 Related Work -- 3 Recent Trends and Overview -- 4 Technologies Involved -- 4.1 Architecture of IoT -- 4.2 Analysis -- 4.3 Cloud Computation Techniques and Concepts -- 4.4 Models and Tools Used -- 5 Methodologies of Dl in IoT-Based Smart Cities -- 6 Comparative Analysis -- 7 Challenges -- 8 Conclusion -- References -- Vibration Analysis of Fluid Structure Interface for Rectangular Plate -- 1 The Preface -- 2 Vibroacoustic Analysis -- 3 Geometrical and Material Properties of the Fluid-Structure -- 4 ANSYS Model -- 5 Modal Analysis Procedure -- 6 Results and Discussion -- 7 Inference -- References -- IoT-Based Prediction of Chronic Kidney Disease Using Python and R Based on Machine and Deep Learning Algorithms -- 1 Introduction -- 2 Findings -- 3 The Dataset's Description -- 4 Stacked Autoencoder -- 5 Methodology -- 6 Human Role in the IoT-Based Glomerular Chronic Kidney Disease -- 7 Conclusion -- References -- Evaluating Various Classifiers for Iraqi Dialectic Sentiment Analysis -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Dataset -- 3.2 Preprocessing -- 3.3 Feature Selection -- 3.4 Training and Testing.
3.5 Evaluation Metrics -- 4 Results and Discussion -- 5 Conclusion -- References -- Sentiment Analysis of Software Project Code Commits -- 1 Introduction -- 2 Related Works -- 3 Research Background: -- 4 Proposed Research Framework -- 5 Result Discussions -- 6 Conclusion -- References -- Internet of Robotic Things: Issues and Challenges in the Era of Industry 4.0 -- 1 Introduction -- 2 The Role of Smart Technologies in the Fourth Industrial Revolution (Industry 4.0) -- 3 Framework of CPSs -- 4 The Role of IoRT in Different Domains -- 5 Issues and Challenges -- 6 Conclusions -- References -- Security Issues and Vulnerabilities in Web Application -- 1 Introduction -- 2 Literature Review -- 3 Web Application in Different Sector -- 3.1 Health Care -- 3.2 Travel -- 3.3 Educational -- 3.4 Web Application for Home -- 4 Security Requirements -- 5 Vulnerabilities in Web Application -- 5.1 Injection Flaws -- 5.2 Broken Access Control -- 5.3 Insecure Configuration -- 5.4 Error Handling -- 5.5 Broken Authentication and Session Management -- 5.6 Parameter Modification -- 6 Security Solutions -- 7 Result Analysis -- 7.1 Broken Access Control -- 7.2 Session Management -- 7.3 Improper Error Handling -- 7.4 Invalid Input -- 7.5 Parameter Modification -- 8 Conclusion and Future Scope -- References -- A Systematic Review on Usability of mHealth Applications on Type 2 Diabetes Mellitus -- 1 Introduction -- 2 Review of Literature -- 3 Objectives of the Study -- 4 Usability Attributes -- 5 Results and Analysis -- 6 Conclusion and Future Work -- References -- An Effective Diagnostic Framework for COVID-19 Using an Integrated Approach -- 1 Introduction -- 2 Literature Review -- 3 Proposed Framework -- 4 Discussion -- 5 Conclusion -- References -- Diabetes Mellitus Prediction Through Interactive Machine Learning Approaches -- 1 Introduction. 1.1 Type of Diabetes with Symptoms -- 2 Literature Survey -- 3 Various Machine Learning Approaches -- 4 Methodology -- 4.1 Data set Description -- 4.2 Implementation and Design -- 5 Conclusion -- References -- IoT and RFID: Make Life Easier and Shake up E-commerce Processes with Smart Objects -- 1 Introduction -- 2 Background -- 2.1 IoT Applications -- 2.2 RFID Technology -- 3 Related Work -- 4 Problematic -- 5 Case Study and Proposed Solutions -- 6 Results -- 7 Discussion and Future Recommendation -- 8 Conclusion -- References -- Adopting a Blockchain-Based Algorithmic Model for Electronic Healthcare Records (EHR) in Nigeria -- 1 Introduction -- 2 Review of Literature -- 2.1 Electronic Health Records (EHR) -- 2.2 Interoperability -- 2.3 Challenges of EHR Interoperability -- 3 Nigerian Healthcare Structure -- 4 EHR Interoperability Blockchain Algorithm -- 5 Conclusion -- References -- Design and Development of IoT Wearable Device for Early Detection of COVID-19 and Monitoring Through Efficient Data Management Framework in Pre-pandemic Life -- 1 Introduction -- 2 Related Works -- 3 Proposed Framework Architecture -- 3.1 Cloud Data Repository -- 3.2 Prediction of Cases Analysis -- 3.3 Assessment Based on Performance -- 4 Work Flow -- 5 Experimental on Zone Level Setup -- 6 Results and Discussions -- 6.1 Calculation of Confusion Matrix -- 6.2 Performance Measurement Value -- 7 Conclusion -- 8 Future Enhancement -- References -- Computational Complexity and Analysis of Supervised Machine Learning Algorithms -- 1 Introduction -- 2 Complexity of Machine Learning Algorithms -- 2.1 K-nearest Neighbors (KNN) -- 2.2 Naive Bayes -- 2.3 Logistic Regression -- 2.4 Linear Regressions -- 2.5 Support Vector Machine (SVM) -- 2.6 Decision Tree -- 2.7 Ensemble Models -- 3 Analysis -- 3.1 Training Dataset Size -- 3.2 Accuracy and/or Interpretability of the Output. 3.3 Speed Versus Training Time -- 3.4 Linearity Versus Nonlinearity -- 3.5 Multiple Features -- 4 Conclusion and Future Scope -- References -- An Intelligent Iris Recognition Technique -- 1 Introduction -- 2 Review of Related Studies -- 3 The Proposed Iris Recognition System -- 3.1 Image Acquisition -- 3.2 Preprocessing -- 4 Experimental Results and Analysis -- 5 Conclusions -- References -- IoT-Based Smart Doorbell: A Review on Technological Developments -- 1 Introduction -- 2 Preliminaries -- 2.1 Internet of Things -- 2.2 Home Automation -- 2.3 Video Analytics -- 2.4 Artificial Intelligence, Machine Learning, and Deep Learning -- 3 Review of Smart Doorbell Systems -- 3.1 Doorbell Evolution -- 3.2 System Architecture -- 3.3 Connectivity Technologies -- 3.4 Hardware Technology -- 3.5 System Features -- 4 Challenges and Future Enhancements -- 5 Conclusion -- References -- Development of Student's Enrolment System Using Depth-first Search Algorithm -- 1 Introduction -- 2 Practical Part -- 2.1 Enrolment System -- 2.2 Database Design -- 2.3 Depth-first Search Algorithm -- 2.4 Statistical Analyses -- 3 Results -- 3.1 Data Test -- 3.2 Analyses Static -- 4 Conclusion -- References -- Merging of Internet of Things and Cloud Computing (SmartCIOT): Open Issues and Challenges -- 1 Introduction -- 2 Concepts About IoT, Cloud Computing, and Cloud-Based IoT -- 2.1 Internet of Things -- 2.2 Cloud Computing -- 3 Smart Combination of Cloud Computing and Internet of Things (SmartCIOT) -- 4 Merits of SmartCIOT -- 5 Structural Diagram of Smart Merger, i.e., SmartCIOT -- 6 Applications of Smart Merger SmartCIOT -- 7 Challenges Faced in SmartCIOT -- 8 SmartCIOT Open Issues and Future Guidelines -- 9 Conclusion -- References -- Patient Privacy: A Secure Medical Care by Collection, Preservation, and Secure Utilization of Medicinal e-Records Based on IoMT -- 1 Introduction. 2 Related Literature -- 3 Suggested Methodology -- 3.1 Architecture -- 3.2 The Suggested Architecture Algorithm -- 3.3 Advantages of the Suggested Method -- 3.4 Comparison -- 4 Experimental Findings -- 4.1 Analyzing of Security -- 4.2 Analyzing of Computational Cost (ComCos) -- 4.3 Analyzing of Implementation Complexity -- 4.4 Analyzing of Design Complexity -- 4.5 Processing Need Analyzing -- 4.6 Computational Need Analysis -- 4.7 Memory Consumption Analysis -- 4.8 Time Consumption Analyzing -- 5 Conclusion -- References -- Intelligent Cloud and IoT-Based Voice-Controlled Car -- 1 Introduction -- 2 Methodology -- 2.1 Component Description -- 3 Proposed System and Description -- 4 Performance Analysis and Result Discussion -- 5 Conclusion and Future Scope -- References -- Software Testability (Its Benefits, Limitations, and Facilitation) -- 1 Introduction -- 2 Software Testing Overview -- 3 Related Work -- 4 Conclusion -- References -- Development of Sign Language Recognition Application Using Deep Learning -- 1 Introduction -- 1.1 Literature Survey -- 1.2 Proposed System Architecture -- 2 Data Preprocessing -- 2.1 Feature Extraction -- 2.2 Basic CNN Components -- 3 Implementation -- 4 Results and Discussion -- 5 Conclusion -- References -- Changing Many Design Parameters in the Performance of Single-Sided Linear Induction Motor (SLIM) for Improved Efficiency and Power Factor -- 1 Introduction -- 2 Physical Structure and Magnetic Equivalent Circuit Models -- 3 Thrust and Efficiency -- 4 Voltage Source Inverter (VSI) Technique -- 5 SLIM Design and Optimization -- 6 Simulation Results Using PID Controller -- 7 Result -- 7.1 Simulation Results for Speed Control Using VSI -- 7.2 Simulation Results Using PID Controller and Comparing with VSI -- 7.3 Result Analysis -- 8 Conclusion -- References. An Approach for Potato Yield Prediction Using Machine Learning Regression Algorithms. |
| Record Nr. | UNINA-9910627269703321 |
| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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