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Advances in aerial sensing and imaging / / edited by Sandeep Kumar [and five others]
| Advances in aerial sensing and imaging / / edited by Sandeep Kumar [and five others] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc. |
| Descrizione fisica | 1 online resource (xiv, 415 pages) : illustrations, charts |
| Soggetto topico |
Imaging systems
Drone aircraft in remote sensing Drone aircraft |
| ISBN |
1-394-17551-5
1-394-17550-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 A Systematic Study on Aerial Images of Various Domains: Competences, Applications, and Futuristic Scope -- 1.1 Introduction -- 1.2 Literature Work -- 1.2.1 Based on Camera Axis -- 1.2.2 Based on Scale -- 1.2.3 Based on Sensor -- 1.3 Challenges of Object Detection and Classification in Aerial Images -- 1.4 Applications of Aerial Imaging in Various Domains -- 1.5 Conclusions and Future Scope -- 1.5.1 Conclusions -- 1.5.2 Future Scope -- References -- Chapter 2 Oriental Method to Predict Land Cover and Land Usage Using Keras with VGG16 for Image Recognition -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Materials and Methods -- 2.3.1 Dataset -- 2.3.2 Model Implemented -- 2.4 Discussion -- 2.5 Result Analysis -- 2.6 Conclusion -- References -- Chapter 3 Aerial Imaging Rescue and Integrated System for Road Monitoring Based on AI/ML -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Number of Accidents, Fatalities, and Injuries: 2016-2022 -- 3.3.1 Accidents Statistics in India -- 3.3.2 Accidents Statistics in Haryana -- 3.4 Proposed Methodology -- 3.4.1 ROI and Line Selection -- 3.4.2 Motion Detection -- 3.4.3 Single-Stage Clustering -- 3.4.4 Feature Fusion Process -- 3.4.5 Second-Stage Clustering -- 3.4.6 Tracking Objects -- 3.4.7 Classification -- 3.5 Result Analysis -- 3.6 Conclusion -- References -- Chapter 4 A Machine Learning Approach for Poverty Estimation Using Aerial Images -- 4.1 Introduction -- 4.2 Background and Literature Review -- 4.3 Proposed Methodology -- 4.3.1 Data Acquisition -- 4.3.2 Pre-Processing -- 4.3.3 Feature Extraction -- 4.3.4 Data Integration -- 4.3.5 Model Development -- 4.3.6 Validation -- 4.3.7 Visualization and Analysis -- 4.3.8 Policy and Program Development -- 4.4 Result and Discussion -- 4.5 Conclusion and Future Scope -- References.
Chapter 5 Agriculture and the Use of Unmanned Aerial Vehicles (UAVs): Current Practices and Prospects -- 5.1 Introduction -- 5.2 UAVs Classification -- 5.2.1 Comparison of Various UAVs -- 5.3 Agricultural Use of UAVs -- 5.4 UAVs in Livestock Farming -- 5.5 Challenges -- 5.6 Conclusion -- References -- Chapter 6 An Introduction to Deep Learning-Based Object Recognition and Tracking for Enabling Defense Applications -- 6.1 Introduction -- 6.2 Related Work -- 6.2.1 Importance of Object Monitoring and Surveillance in Defense -- 6.2.2 Need for Object Monitoring and Surveillance in Defense -- 6.2.3 Object Detection Techniques -- 6.2.4 Object Tracking Techniques -- 6.3 Experimental Methods -- 6.3.1 Experimental Setup and Dataset -- 6.3.2 DataSetVISdrone 2019 -- 6.3.3 Experimental Setup -- 6.4 Results and Outcomes -- 6.4.1 Comparison Results -- 6.4.2 Training Results -- 6.5 Conclusion -- 6.6 Future Scope -- References -- Chapter 7 A Robust Machine Learning Model for Forest Fire Detection Using Drone Images -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Proposed Methodology -- 7.4 Result and Discussion -- 7.5 Conclusion and Future Scope -- References -- Chapter 8 Semantic Segmentation of Aerial Images Using Pixel Wise Segmentation -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Proposed Method -- 8.3.1 Pixelwise Classification Method -- 8.3.2 Morphological Processing -- 8.4 Datasets -- 8.5 Results and Discussion -- 8.5.1 Analysis of the Proposed Method -- 8.6 Conclusion -- References -- Chapter 9 Implementation Analysis of Ransomware and Unmanned Aerial Vehicle Attacks: Mitigation Methods and UAV Security Recommendations -- 9.1 Introduction -- 9.2 Types of Ransomwares -- 9.3 History of Ransomware -- 9.4 Notable Ransomware Strains and Their Impact -- 9.4.1 CryptoLocker (2013) -- 9.4.2 CryptoWall (2014) -- 9.4.3 TeslaCrypt (2015) -- 9.4.4 Locky (2016). 9.4.5 WannaCry (2017) -- 9.4.6 NotPetya (2017) -- 9.4.7 Ryuk (2018) -- 9.4.8 REvil (2019) -- 9.4.9 Present-Day Ransomware Families -- 9.5 Mitigation Methods for Ransomware Attacks -- 9.6 Cybersecurity in UAVs (Unmanned Aerial Vehicles) -- 9.6.1 Introduction on FANETS -- 9.6.2 Network Security Concerning FANETs -- 9.6.3 UAV Security Enhancement -- 9.6.4 Limitations in UAVs -- 9.6.5 Future Scope -- 9.7 Experimental analysis of Wi-Fi Attack on Ryze Tello UAVs -- 9.7.1 Introduction -- 9.7.2 Methodology -- 9.8 Results and Discussion -- 9.9 Conclusion and Future Scope -- References -- Chapter 10 A Framework for Detection of Overall Emotional Score of an Event from the Images Captured by a Drone -- 10.1 Introduction -- 10.1.1 Need for Emotion Recognition -- 10.1.2 Applications of Drones in Deep Learning -- 10.2 Literature Review -- 10.3 Proposed Work -- 10.3.1 Extraction of Images from a Drone -- 10.3.2 Proposed CNN Model -- 10.4 Experimentation and Results -- 10.4.1 Dataset Description -- 10.5 Future Work and Conclusion -- References -- Chapter 11 Drone-Assisted Image Forgery Detection Using Generative Adversarial Net-Based Module -- 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Proposed System -- 11.3.1 Common Forged Feature Network -- 11.3.2 Features Extraction -- 11.3.3 Features Classification and Classification Network -- 11.3.4 Label Prediction -- 11.3.5 Contrastive Learning -- 11.3.6 Binary Cross-Entropy Loss -- 11.4 Results -- 11.4.1 Experimental Settings -- 11.4.2 Performance Comparison -- 11.4.3 LBP Visualized Results -- 11.4.4 Training Convergence -- 11.5 Conclusion -- References -- Chapter 12 Optimizing the Identification and Utilization of Open Parking Spaces Through Advanced Machine Learning -- 12.1 Introduction -- 12.2 Proposed Framework Optimized Parking Space Identifier (OPSI) -- 12.2.1 Framework Components. 12.2.2 Learning Module: Adaptive Prediction of Parking Space Availability -- 12.2.3 System Design -- 12.2.4 Tools and Usage -- 12.2.5 Architecture -- 12.2.6 Implementation Techniques and Algorithms -- 12.2.7 Existing Methods and Workflow Model -- 12.2.8 Hyperparameter for OPSI -- 12.3 Potential Impact -- 12.3.1 Claims for the Accurate Detection of Fatigue -- 12.3.2 Similar Study and Results Analysis -- 12.4 Application and Results -- 12.4.1 Algorithm and Results -- 12.4.2 Implementation Using Python Modules -- 12.5 Discussion and Limitations -- 12.5.1 Discussion -- 12.5.2 Limitations -- 12.6 Future Work -- 12.6.1 Integration with Autonomous Vehicles -- 12.6.2 Real-Time Data Analysis -- 12.6.3 Integration with Smart Cities -- 12.7 Conclusion -- References -- Chapter 13 Graphical Password Authentication Using Python for Aerial Devices/Drones -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Methodology -- 13.4 A Brief Overview of a Drone and Authentication -- 13.4.1 Password Authentication -- 13.4.2 Types of Password Authentication Systems -- 13.4.3 Graphical Password Authentication -- 13.4.4 Advantages and Disadvantages of Graphical Passwords -- 13.5 Password Cracking -- 13.6 Data Analysis -- 13.7 Discussion -- 13.8 Conclusion and Future Scope -- References -- Chapter 14 A Study Centering on the Data and Processing for Remote Sensing Utilizing from Annoyed Aerial Vehicles -- 14.1 Introduction -- 14.2 An Acquisition Method for 3D Data Utilising Annoyed Aerial Vehicles -- 14.3 Background and Literature of Review -- 14.4 Research Gap -- 14.5 Methodology -- 14.6 Discussion -- 14.7 Conclusion -- References -- Chapter 15 Satellite Image Classification Using Convolutional Neural Network -- 15.1 Introduction -- 15.2 Literature Review -- 15.3 Objectives of this Research Work -- 15.3.1 Novelty of the Research Work -- 15.4 Description of the Dataset. 15.5 Theoretical Framework -- 15.6 Implementation and Results -- 15.6.1 Data Visualization -- 15.6.1.1 Class-Wise Data Count -- 15.6.1.2 Class-Wise Augmented Data Count -- 15.6.2 Implementation of MobileNetV3 -- 15.6.2.1 Visualization of a Sample of Training Images -- 15.6.2.2 Visualization of Executed Codes of MobileNetV3 -- 15.6.2.3 Training Results of MobileNetV3 -- 15.6.2.4 Classifications of Errors on Test Sets of MobileNetV3 -- 15.6.2.5 Confusion Matrix of MobileNetV3 -- 15.6.2.6 Classification Report of MobileNetV3 -- 15.6.3 Implementation of EfficientNetB0 -- 15.6.3.1 Visualization of a Sample of Training Images -- 15.6.3.2 Visualization of Executed Codes of EfficientNetB0 -- 15.6.3.3 Training Results of EfficientNetB0 -- 15.6.3.4 Classifications of Errors on Test Sets of EfficientNetB0 -- 15.6.3.5 Confusion Matrix of EfficientNetB0 -- 15.6.3.6 Classification Report of EfficientNetB0 -- 15.7 Conclusion and Future Scope -- References -- Chapter 16 Edge Computing in Aerial Imaging - A Research Perspective -- 16.1 Introduction -- 16.1.1 Edge Computing and Aerial Imaging -- 16.2 Research Applications of Aerial Imaging -- 16.2.1 Vehicle Imaging -- 16.2.2 Precision Agriculture -- 16.2.3 Environment Monitoring -- 16.2.4 Urban Planning and Development -- 16.2.5 Emergency Response -- 16.3 Edge Computing and Aerial Imaging -- 16.3.1 Research Perspective in Aerial Imaging -- 16.3.2 Edge Architectures -- 16.4 Comparative Analysis of the Aerial Imaging Algorithms and Architectures -- 16.5 Discussion -- 16.6 Conclusion -- References -- Chapter 17 Aerial Sensing and Imaging Analysis for Agriculture -- 17.1 Introduction -- 17.2 Experimental Methods and Techniques -- 17.3 Aerial Imaging and Sensing Applications in Agriculture -- 17.3.1 Assessing Yield and Fertilizer Response -- 17.3.2 Plant and Crop Farming -- 17.3.3 Soil and Field Analysis. 17.3.4 Weed Mapping and Management. |
| Record Nr. | UNINA-9910877032103321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc. | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Computing and Intelligent Systems : Proceedings of ICACM 2019 / / edited by Harish Sharma, Kannan Govindan, Ramesh C. Poonia, Sandeep Kumar, Wael M. El-Medany
| Advances in Computing and Intelligent Systems : Proceedings of ICACM 2019 / / edited by Harish Sharma, Kannan Govindan, Ramesh C. Poonia, Sandeep Kumar, Wael M. El-Medany |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xiv, 637 pages) : illustrations |
| Disciplina | 006.3 |
| Collana | Algorithms for Intelligent Systems |
| Soggetto topico |
Computational intelligence
Artificial intelligence Algorithms Pattern recognition Computational Intelligence Artificial Intelligence Algorithm Analysis and Problem Complexity Pattern Recognition |
| ISBN | 981-15-0222-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Intuitionistic Fuzzy Shannon Entropy Weight Based Multi Criteria Decision Model with TOPSIS to Analyse Security Risks and Select Online Transaction Method -- Chapter 2. Fermat Spiral Based Moth-Flame Optimization Algorithm for Object-Oriented Testing -- Chapter 3. A Comparative Study of Information Retrieval Using Machine Learning -- Chapter 4. Adaptive Background Subtraction Using Manual Approach for Static Images -- Chapter 5. Tweets Daily: Categorised News from Twitter -- Chapter 6. Compressing Meta Class Files through String Optimization -- Chapter 7. An Algorithm to Generate Largest Prime Number -- Chapter 8. Development of a Discretization Methodology for 2.5D Milling Toolpath optimization Using Genetic Algorithm -- Chapter 9. Machine Learning Based Prediction of PM 2.5 Pollution Level in Delhi -- Chapter 10. A Comparative Study of Load Balancing Algorithms in a Cloud Environment -- Chapter 11. Information Retrieval from Search Engine Using Particle Swarm Optimization -- Chapter 12. Genetic Algorithm Based Multi Objective Optimization Framework to Solve Travelling Salesman Problem -- Chapter 13. Design of Optimal PID Controller for Varied System Using Teaching-Learning-Based Optimization -- Chapter 14. Innovative Review on Artificial Bee Colony Algorithm and it’s Variants -- Chapter 15. Multi Linear Regression Model to Predict Correlation between IT Graduate Attributes for Employability using R. . |
| Record Nr. | UNINA-9910484185503321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Ambient Intelligence and Internet of Things : Convergent Technologies
| Ambient Intelligence and Internet of Things : Convergent Technologies |
| Autore | Mahmood Rashid |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (421 pages) |
| Altri autori (Persone) |
RajaRohit
KaurHarpreet KumarSandeep NagwanshiKapil Kumar |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9781119821830
9781119821236 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Ambient Intelligence and Internet of Things: An Overview -- 1.1 Introduction -- 1.2 Ambient Intelligent System -- 1.3 Characteristics of AmI Systems -- 1.4 Driving Force for Ambient Computing -- 1.5 Ambient Intelligence Contributing Technologies -- 1.6 Architecture Overview -- 1.7 The Internet of Things -- 1.8 IoT as the New Revolution -- 1.9 IoT Challenges -- 1.10 Role of Artificial Intelligence in the Internet of Things (IoT) -- 1.11 IoT in Various Domains -- 1.12 Healthcare -- 1.13 Home Automation -- 1.14 Smart City -- 1.15 Security -- 1.16 Industry -- 1.17 Education -- 1.18 Agriculture -- 1.19 Tourism -- 1.20 Environment Monitoring -- 1.21 Manufacturing and Retail -- 1.22 Logistics -- 1.23 Conclusion -- References -- Chapter 2 An Overview of Internet of Things Related Protocols, Technologies, Challenges and Application -- 2.1 Introduction -- 2.1.1 History of IoT -- 2.1.2 Definition of IoT -- 2.1.3 Characteristics of IoT -- 2.2 Messaging Protocols -- 2.2.1 Constrained Application Protocol -- 2.2.2 Message Queue Telemetry Transport -- 2.2.3 Extensible Messaging and Presence Protocol -- 2.2.4 Advance Message Queuing Protocol (AMQP) -- 2.3 Enabling Technologies -- 2.3.1 Wireless Sensor Network -- 2.3.2 Cloud Computing -- 2.3.3 Big Data Analytics -- 2.3.4 Embedded System -- 2.4 IoT Architecture -- 2.5 Applications Area -- 2.6 Challenges and Security Issues -- 2.7 Conclusion -- References -- Chapter 3 Ambient Intelligence Health Services Using IoT -- 3.1 Introduction -- 3.2 Background of AML -- 3.2.1 What is AML? -- 3.3 AmI Future -- 3.4 Applications of Ambient Intelligence -- 3.4.1 Transforming Hospitals and Enhancing Patient Care With the Help of Ambient Intelligence -- 3.4.2 With Technology, Life After the COVID-19 Pandemic -- 3.5 COVID-19 -- 3.5.1 Prevention.
3.5.2 Symptoms -- 3.6 Coronavirus Worldwide -- 3.7 Proposed Framework for COVID-19 -- 3.8 Hardware and Software -- 3.8.1 Hardware -- 3.8.2 Heartbeat Sensor -- 3.8.3 Principle -- 3.8.4 Working -- 3.8.5 Temperature Sensor -- 3.8.6 Principle -- 3.8.7 Working -- 3.8.8 BP Sensor -- 3.8.9 Principle -- 3.8.10 Working -- 3.9 Mini Breadboard -- 3.10 Node MCU -- 3.11 Advantages -- 3.12 Conclusion -- References -- Chapter 4 Security in Ambient Intelligence and Internet of Things -- 4.1 Introduction -- 4.2 Research Areas -- 4.3 Security Threats and Requirements -- 4.3.1 Ad Hoc Network Security Threats and Requirements -- 4.3.1.1 Availability -- 4.3.1.2 Confidentiality -- 4.3.1.3 Integrity -- 4.3.1.4 Key Management and Authorization -- 4.3.2 Security Threats and Requirements Due to Sensing Capability in the Network -- 4.3.2.1 Availability -- 4.3.2.2 Confidentiality -- 4.3.2.3 Integrity -- 4.3.2.4 Key Distribution and Management -- 4.3.2.5 Resilience to Node Capture -- 4.3.3 Security Threats and Requirements in AmI and IoT Based on Sensor Network -- 4.3.3.1 Availability -- 4.3.3.2 Confidentiality -- 4.3.3.3 Confidentiality of Location -- 4.3.3.4 Integrity -- 4.3.3.5 Nonrepudiation -- 4.3.3.6 Fabrication -- 4.3.3.7 Intrusion Detection -- 4.3.3.8 Confidentiality -- 4.3.3.9 Trust Management -- 4.4 Security Threats in Existing Routing Protocols that are Designed With No Focus on Security in AmI and IoT Based on Sensor Networks -- 4.4.1 Infrastructureless -- 4.4.1.1 Dissemination-Based Routing -- 4.4.1.2 Context-Based Routing -- 4.4.2 Infrastructure-Based -- 4.4.2.1 Network with Fixed Infrastructure -- 4.4.2.2 New Routing Strategy for Wireless Sensor Networks to Ensure Source Location Privacy -- 4.5 Protocols Designed for Security Keeping Focus on Security at Design Time for AmI and IoT Based on Sensor Network -- 4.5.1 Secure Routing Algorithms. 4.5.1.1 Identity-Based Encryption (I.B.E.) Scheme -- 4.5.1.2 Policy-Based Cryptography and Public Encryption with Keyword Search -- 4.5.1.3 Secure Content-Based Routing -- 4.5.1.4 Secure Content-Based Routing Using Local Key Management Scheme -- 4.5.1.5 Trust Framework Using Mobile Traces -- 4.5.1.6 Policy-Based Authority Evaluation Scheme -- 4.5.1.7 Optimized Millionaire's Problem -- 4.5.1.8 Security in Military Operations -- 4.5.1.9 A Security Framework Application Based on Wireless Sensor Networks -- 4.5.1.10 Trust Evaluation Using Multifactor Method -- 4.5.1.11 Prevention of Spoofing Attacks -- 4.5.1.12 QoS Routing Protocol -- 4.5.1.13 Network Security Virtualization -- 4.5.2 Comparison of Routing Algorithms and Impact on Security -- 4.5.3 Inducing Intelligence in IoT Networks Using Artificial Intelligence -- 4.5.3.1 Fuzzy Logic-1 -- 4.5.3.2 Fuzzy Logic-2 -- 4.6 Introducing Hybrid Model in Military Application for Enhanced Security -- 4.6.1 Overall System Architecture -- 4.6.2 Best Candidate Selection -- 4.6.3 Simulation Results in Omnet++ -- 4.6 Conclusion -- References -- Chapter 5 Futuristic AI Convergence of Megatrends: IoT and Cloud Computing -- 5.1 Introduction -- 5.1.1 Our Contribution -- 5.2 Methodology -- 5.2.1 Statistical Information -- 5.3 Artificial Intelligence of Things -- 5.3.1 Application Areas of IoT Technologies -- 5.3.1.1 Energy Management -- 5.3.1.2 5G/Wireless Systems -- 5.3.1.3 Risk Assessment -- 5.3.1.4 Smart City -- 5.3.1.5 Health Sectors -- 5.4 AI Transforming Cloud Computing -- 5.4.1 Application Areas of Cloud Computing -- 5.4.2 Energy/Resource Management -- 5.4.3 Edge Computing -- 5.4.4 Distributed Edge Computing and Edge-of-Things (EoT) -- 5.4.5 Fog Computing in Cloud Computing -- 5.4.6 Soft Computing and Others -- 5.5 Conclusion -- References. Chapter 6 Analysis of Internet of Things Acceptance Dimensions in Hospitals -- 6.1 Introduction -- 6.2 Literature Review -- 6.2.1 Overview of Internet of Things -- 6.2.2 Internet of Things in Healthcare -- 6.2.3 Research Hypothesis -- 6.2.3.1 Technological Context (TC) -- 6.2.3.2 Organizational Context (OC) -- 6.2.3.3 Environmental Concerns (EC) -- 6.3 Research Methodology -- 6.3.1 Demographics of the Respondents -- 6.4 Data Analysis -- 6.4.1 Reliability and Validity -- 6.4.1.1 Cronbach's Alpha -- 6.4.1.2 Composite Reliability -- 6.4.2 Exploratory Factor Analysis (EFA) -- 6.4.3 Confirmatory Factor Analysis Results -- 6.4.3.1 Divergent or Discriminant Validity -- 6.4.4 Structural Equation Modeling -- 6.5 Discussion -- 6.5.1 Technological Context -- 6.5.2 Organizational Context -- 6.5.3 Environmental Context -- 6.6 Conclusion -- References -- Chapter 7 Role of IoT in Sustainable Healthcare Systems -- 7.1 Introduction -- 7.2 Basic Structure of IoT Implementation in the Healthcare Field -- 7.3 Different Technologies of IoT for the Healthcare Systems -- 7.3.1 On the Basis of the Node Identification -- 7.3.2 On the Basis of the Communication Method -- 7.3.3 Depending on the Location of the Object -- 7.4 Applications and Examples of IoT in the Healthcare Systems -- 7.4.1 IoT-Based Healthcare System to Encounter COVID-19 Pandemic Situations -- 7.4.2 Wearable Devices -- 7.4.3 IoT-Enabled Patient Monitoring Devices From Remote Locations -- 7.4.3.1 Pulse Rate Sensor -- 7.4.3.2 Respiratory Rate Sensors -- 7.4.3.3 Body Temperature Sensors -- 7.4.3.4 Blood Pressure Sensing -- 7.4.3.5 Pulse Oximetry Sensors -- 7.5 Companies Associated With IoT and Healthcare Sector Worldwide -- 7.6 Conclusion and Future Enhancement in the Healthcare System With IoT -- References -- Chapter 8 Fog Computing Paradigm for Internet of Things Applications -- 8.1 Introduction. 8.2 Challenges -- 8.3 Fog Computing: The Emerging Era of Computing Paradigm -- 8.3.1 Definition of Fog Computing -- 8.3.2 Fog Computing Characteristic -- 8.3.3 Comparison Between Cloud and Fog Computing Paradigm -- 8.3.4 When to Use Fog Computing -- 8.3.5 Fog Computing Architecture for Internet of Things -- 8.3.6 Fog Assistance to Address the New IoT Challenges -- 8.3.7 Devices Play a Role of Fog Computing Node -- 8.4 Related Work -- 8.5 Fog Computing Challenges -- 8.6 Fog Supported IoT Applications -- 8.7 Summary and Conclusion -- References -- Chapter 9 Application of Internet of Things in Marketing Management -- 9.1 Introduction -- 9.2 Literature Review -- 9.2.1 Customer Relationship Management -- 9.2.2 Product Life Cycle (PLC) -- 9.2.3 Business Process Management (BPM) -- 9.2.4 Ambient Intelligence (AmI) -- 9.2.5 IoT and CRM Integration -- 9.2.6 IoT and BPM Integration -- 9.2.7 IoT and Product Life Cycle -- 9.2.8 IoT in MMgnt -- 9.2.9 Impacts of AmI on Marketing Paradigms -- 9.3 Research Methodology -- 9.4 Discussion -- 9.4.1 Research Proposition 1 -- 9.4.2 Research Proposition 2 -- 9.4.3 Research Proposition 3 -- 9.4.4 Research Proposition 4 -- 9.4.5 Research Proposition 5 -- 9.5 Results -- 9.4 Conclusions -- References -- Chapter 10 Healthcare Internet of Things: A New Revolution -- 10.1 Introduction -- 10.2 Healthcare IoT Architecture (IoT) -- 10.3 Healthcare IoT Technologies -- 10.3.1 Technology for Identification -- 10.3.2 Location Technology -- 10.3.2.1 Mobile-Based IoT -- 10.3.2.2 Wearable Devices -- 10.3.2.3 Ambient-Assisted Living (AAL) -- 10.3.3 Communicative Systems -- 10.3.3.1 Radiofrequency Identification -- 10.3.3.2 Bluetooth -- 10.3.3.3 Zigbee -- 10.3.3.4 Near Field Communication -- 10.3.3.5 Wireless Fidelity (Wi-Fi) -- 10.3.3.6 Satellite Communication -- 10.4 Community-Based Healthcare Services -- 10.5 Cognitive Computation. 10.6 Adverse Drug Reaction. |
| Record Nr. | UNINA-9910646196503321 |
Mahmood Rashid
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence and Sustainable Computing : Proceedings of ICSISCET 2023 / / edited by Manjaree Pandit, M. K. Gaur, Sandeep Kumar
| Artificial Intelligence and Sustainable Computing : Proceedings of ICSISCET 2023 / / edited by Manjaree Pandit, M. K. Gaur, Sandeep Kumar |
| Autore | Pandit Manjaree |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (714 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
GaurM. K
KumarSandeep |
| Collana | Algorithms for Intelligent Systems |
| Soggetto topico |
Computational intelligence
Electronic circuits Cooperating objects (Computer systems) Internet of things Machine learning Computational Intelligence Electronic Circuits and Systems Cyber-Physical Systems Internet of Things Machine Learning |
| ISBN | 981-9703-27-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Contents -- About the Editors -- 1 A Novel Intelligence System for Hybrid Crop Suitable Landform Prediction Using Machine Learning Techniques and IoT -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Dataset Description -- 5 Feature Engineering -- 6 Experiments -- 6.1 Logistic Regression -- 6.2 K-Nearest Neighbours (KNN) -- 6.3 Extreme Gradient Boosting (XGBoost) -- 6.4 Implementation in Cloud -- 7 Results and Discussion -- 8 Conclusion -- 9 Future Work -- References -- 2 Indian Annual Report Assessment Using Large Language Models -- 1 Introduction -- 1.1 Problem Statement -- 1.2 Objective -- 1.3 Contribution -- 2 Related Work -- 3 Methodology -- 3.1 Dataset Preparation -- 3.2 Class Labels -- 4 Results -- 4.1 Fine-Tuning Language Model -- 4.2 Sentence Transformers |
| Record Nr. | UNINA-9910851982603321 |
|
Pandit Manjaree
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial Intelligence and Sustainable Computing : Proceedings of ICSISCET 2022 / / edited by Manjaree Pandit, M. K. Gaur, Sandeep Kumar
| Artificial Intelligence and Sustainable Computing : Proceedings of ICSISCET 2022 / / edited by Manjaree Pandit, M. K. Gaur, Sandeep Kumar |
| Autore | Pandit Manjaree |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (737 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
GaurM. K
KumarSandeep |
| Collana | Algorithms for Intelligent Systems |
| Soggetto topico |
Computational intelligence
Embedded computer systems Internet of things Machine learning Computational Intelligence Embedded Systems Internet of Things Machine Learning |
| ISBN | 981-9914-31-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Features Extraction and Analysis of Electro-Myogram Signals using Time, Frequency, and Wavelet Transform Methods -- Lycopersicon Crop Leaf Disease Identification using Deep Learning -- Performance Analysis of Satellite Image Classification Using Deep Learning Neural Network -- Text Visualization of Entire Corpus through Single document Input Tools. |
| Record Nr. | UNINA-9910746996503321 |
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Pandit Manjaree
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial Intelligence-based Healthcare Systems / / edited by Manju, Sandeep Kumar, Sardar M. N. Islam
| Artificial Intelligence-based Healthcare Systems / / edited by Manju, Sandeep Kumar, Sardar M. N. Islam |
| Autore | Manju |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (208 pages) |
| Disciplina | 362.1028563 |
| Altri autori (Persone) |
KumarSandeep
IslamSardar M. N |
| Collana | The Springer Series in Applied Machine Learning |
| Soggetto topico |
Artificial intelligence
Machine learning Internet of things Medical care Artificial Intelligence Machine Learning Internet of Things Health Care |
| ISBN | 3-031-41925-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Schedule and Routing In Home Healthcare System Using Clustering Analysis and Multi-Objective Optimization -- Obesity level prediction using Multinomial Logistic Regression -- Importance of Feature Selection methods in Machine Learning-based Obesity Prediction -- A Clinical Decision Support System Using Machine Learning To Forecast The Risk Of Chronic Pulmonary Disease And Anthracosis -- Smart Healthcare: A Breakthrough in the growth of technologies -- A Multidisciplinary Explanation of Healthcare AI Uses, Trends and Possibilities -- Optimum Utilization Of Bed Resources In Hospitals-A Stochastic Approach -- Early-Detection of Diabetic Retinopathy using Deep Learning -- Performance Analysis of Memory-Efficient Vision Transformers in Brain Tumor Segmentation -- Unlocking New Possibilities in Drug Discovery: A GAN-based Approach -- A Systematic Review on ECG and EMG Biomedical Signal using Deep Learning Approaches -- Smart AI bot for healthcare Assistance -- AI-Driven Hospital Readmission Predictor for Diabetic Patients -- Gleason Grading System for Prostate Cancer diagnosis. |
| Record Nr. | UNINA-9910755082503321 |
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Manju
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Broadening the Genetic Base of Grain Cereals [[electronic resource] /] / edited by Mohar Singh, Sandeep Kumar
| Broadening the Genetic Base of Grain Cereals [[electronic resource] /] / edited by Mohar Singh, Sandeep Kumar |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | New Delhi : , : Springer India : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (IX, 275 p. 16 illus., 13 illus. in color.) |
| Disciplina | 630 |
| Soggetto topico |
Agriculture
Plant genetics Plant Genetics and Genomics |
| ISBN | 81-322-3613-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. Wheat -- 3. Rice -- 4. Maize -- 5. Barley -- 6. Oats -- 7. Sorghum -- 8. Pearl Millet -- 9. Finger Millet -- 10. Foxtail and Barnyard Millets. |
| Record Nr. | UNINA-9910253892003321 |
| New Delhi : , : Springer India : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cognitive behavior and human computer interaction based on machine learning algorithms / / editors, Sandeep Kumar Panda [et al.]
| Cognitive behavior and human computer interaction based on machine learning algorithms / / editors, Sandeep Kumar Panda [et al.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley-Scrivener, , [2022] |
| Descrizione fisica | 1 online resource (416 pages) |
| Disciplina | 004.019 |
| Soggetto topico |
Algorithms
Human-computer interaction Human behavior Cognition |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-79208-8
1-119-79210-X 1-119-79209-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910555075503321 |
| Hoboken, NJ : , : Wiley-Scrivener, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cognitive behavior and human computer interaction based on machine learning algorithms / / editors, Sandeep Kumar Panda [et al.]
| Cognitive behavior and human computer interaction based on machine learning algorithms / / editors, Sandeep Kumar Panda [et al.] |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley-Scrivener, , [2022] |
| Descrizione fisica | 1 online resource (416 pages) |
| Disciplina | 004.019 |
| Soggetto topico | Human-computer interaction |
| ISBN |
1-119-79208-8
1-119-79210-X 1-119-79209-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910830652603321 |
| Hoboken, NJ : , : Wiley-Scrivener, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fault Prediction Modeling for the Prediction of Number of Software Faults / / by Santosh Singh Rathore, Sandeep Kumar
| Fault Prediction Modeling for the Prediction of Number of Software Faults / / by Santosh Singh Rathore, Sandeep Kumar |
| Autore | Rathore Santosh Singh |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XIII, 78 p. 8 illus., 1 illus. in color.) |
| Disciplina | 005.1 |
| Collana | SpringerBriefs in Computer Science |
| Soggetto topico |
Software engineering
Computer industry Software Engineering The Computer Industry |
| ISBN | 981-13-7131-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Techniques used for the Prediction of Number of Faults -- Homogeneous Ensemble Methods for the Prediction of Number of Faults -- Linear Rule based Ensemble Methods for the prediction of Number of Faults -- Non-Linear Rule based Ensemble Methods for the prediction of Number of Faults -- Conclusions. |
| Record Nr. | UNINA-9910350229603321 |
|
Rathore Santosh Singh
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| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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