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Computer Vision in Smart Agriculture and Crop Management
| Computer Vision in Smart Agriculture and Crop Management |
| Autore | Dhanaraj Rajesh Kumar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (402 pages) |
| Disciplina | 338.10285 |
| Altri autori (Persone) |
BalusamyBalamurugan
SamuelPrithi SathyamoorthyMalathy BashirAli Kashif |
| Soggetto topico |
Precision farming
Sustainable agriculture |
| ISBN |
9781394186662
1394186665 9781394186686 1394186681 9781394186679 1394186673 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Computer Vision-Based Innovations for Smart Agriculture and Crop Surveillance: Evolution, Trends, and Future Challenges -- 1.1 Introduction -- 1.2 Artificial Intelligence in Agriculture -- 1.3 Evolution of Smart Agriculture -- 1.4 AI Technology Trends in Computer Vision -- 1.5 Benefits of Artificial Intelligence in Agriculture -- 1.5.1 Improving the Whole Supply Chain -- 1.5.2 Agricultural Robotics -- 1.5.3 Policy, Governance and Market Access -- 1.5.4 Early Warning System -- 1.5.5 Food Safety and Traceability -- 1.5.6 Financial Inclusion and Risk Management -- 1.5.7 Capacity Building and Empowerment -- 1.5.8 Growth Driven by IoT -- 1.5.9 Image-Dependent Insight Generation -- 1.5.10 Identification of Optimal Mix for Agronomic Products -- 1.5.11 Monitoring of Crops and Soil Health -- 1.5.12 Automation Techniques in Irrigation and Enabling Farmers -- 1.5.13 Drones: The New Buzz in AI-Driven Agriculture -- 1.6 Precision Farming -- 1.7 Future Challenges -- 1.8 Conclusion -- References -- Chapter 2 Cyber Biosecurity Solutions for Protecting Smart Agriculture and Precision Farming |
| Record Nr. | UNINA-9911019976603321 |
Dhanaraj Rajesh Kumar
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep Sciences for Computing and Communications [[electronic resource] ] : First International Conference, IconDeepCom 2022, Chennai, India, March 17–18, 2022, Revised Selected Papers / / edited by Kottilingam Kottursamy, Ali Kashif Bashir, Utku Kose, Annie Uthra
| Deep Sciences for Computing and Communications [[electronic resource] ] : First International Conference, IconDeepCom 2022, Chennai, India, March 17–18, 2022, Revised Selected Papers / / edited by Kottilingam Kottursamy, Ali Kashif Bashir, Utku Kose, Annie Uthra |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (XIII, 368 p. 223 illus., 161 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Education—Data processing Social sciences—Data processing Image processing—Digital techniques Computer vision Computer networks Artificial Intelligence Computers and Education Computer Application in Social and Behavioral Sciences Computer Imaging, Vision, Pattern Recognition and Graphics Computer Communication Networks |
| ISBN | 3-031-27622-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Classification and Regression Problems for Communication Paradigms -- Deep Learning and Vision Computing -- Deep- Recurrent Neural network (RNN) for Industrial informatics -- Extended AI for heterogeneous Edge. |
| Record Nr. | UNISA-996547971703316 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Deep Sciences for Computing and Communications : First International Conference, IconDeepCom 2022, Chennai, India, March 17–18, 2022, Revised Selected Papers / / edited by Kottilingam Kottursamy, Ali Kashif Bashir, Utku Kose, Annie Uthra
| Deep Sciences for Computing and Communications : First International Conference, IconDeepCom 2022, Chennai, India, March 17–18, 2022, Revised Selected Papers / / edited by Kottilingam Kottursamy, Ali Kashif Bashir, Utku Kose, Annie Uthra |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (XIII, 368 p. 223 illus., 161 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Education - Data processing Social sciences - Data processing Image processing - Digital techniques Computer vision Computer networks Artificial Intelligence Computers and Education Computer Application in Social and Behavioral Sciences Computer Imaging, Vision, Pattern Recognition and Graphics Computer Communication Networks Aprenentatge automàtic Sistemes de telecomunicació |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN |
9783031276224
3031276221 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Classification and Regression Problems for Communication Paradigms -- Deep Learning and Vision Computing -- Deep- Recurrent Neural network (RNN) for Industrial informatics -- Extended AI for heterogeneous Edge. |
| Record Nr. | UNINA-9910682560403321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Digital Twins in Industrial Production and Smart Manufacturing : An Understanding of Principles, Enhancers, and Obstacles
| Digital Twins in Industrial Production and Smart Manufacturing : An Understanding of Principles, Enhancers, and Obstacles |
| Autore | Dhanaraj Rajesh Kumar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (444 pages) |
| Altri autori (Persone) |
BalusamyBalamurugan
SamuelPrithi BashirAli Kashif KadrySeifedine |
| ISBN |
1-394-19533-8
1-394-19532-X 1-394-19531-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910889695103321 |
|
Dhanaraj Rajesh Kumar
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Environmental Monitoring Using Artificial Intelligence
| Environmental Monitoring Using Artificial Intelligence |
| Autore | Suresh A |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (423 pages) |
| Disciplina | 363.7063028563 |
| Altri autori (Persone) |
DeviT
DeepaN BashirAli Kashif |
| Soggetto topico |
Artificial intelligence
Environmental monitoring |
| ISBN |
9781394270392
1394270399 9781394270378 1394270372 9781394270385 1394270380 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Transformative Trends in AI for Environmental Monitoring: Challenges, Applications -- 1.1 Introduction -- 1.2 Literature Verticals -- 1.3 Key Methodologies in Literature Review -- 1.4 Most Common Methods in Environmental Monitoring -- 1.5 AI Architectures for Environmental Monitoring -- 1.6 Applications of AI in Environmental Monitoring -- 1.7 Challenges and Limitations of Using AI in Environment Modeling -- 1.8 Future Directions -- 1.9 Conclusion -- Acknowledgements -- References -- Chapter 2 Fundamentals of AI and NLP in Environmental Analysis -- 2.1 Introduction -- 2.2 AI and NLP Techniques -- 2.2.1 Artificial Neural Network (ANN) -- 2.2.2 Support Vector Machine (SVM) -- 2.2.3 Linear Regression (LR) -- 2.2.4 Random Forests (RF) and Decision Trees (DT) -- 2.2.5 Adaptive Neuro-Fuzzy Inference System (ANFIS) -- 2.2.6 Batch-Normalization (BN) -- 2.2.7 Convolutional Neural Networks (CNNs) -- 2.2.8 Deep Neural Networks (DNNs) -- 2.2.9 Genetic Algorithm (GA) |
| Record Nr. | UNINA-9911020213203321 |
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Suresh A
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Medical Imaging Informatics : Machine Learning, Deep Learning and Big Data Analytics
| Medical Imaging Informatics : Machine Learning, Deep Learning and Big Data Analytics |
| Autore | Rashid Mamoon |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Stevenage : , : Institution of Engineering & Technology, , 2023 |
| Descrizione fisica | 1 online resource (383 pages) |
| Disciplina | 616.07540285 |
| Altri autori (Persone) |
GoyalVishal
BashirAli Kashif HakakSaqib |
| Collana | Healthcare Technologies Series |
| Soggetto topico |
Machine learning
Deep learning (Machine learning) |
| ISBN |
1-83724-416-2
1-5231-6311-9 1-83953-744-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Contents -- About the editors -- Section 1: Medical image analysis using artificial intelligence -- 1. Intervention of medical images for disease prediction | Neha Singh, Shilpi Birla and Neeraj Kumar Shukla -- 2. Breast cancer detection in pathological imaging using deep learning methods | Bharati Ainapure and Reshma Pise -- 3. Detection of autism spectrum disorder using artificial intelligence | Kaushal Oza, Shree Udavant and Mamoon Rashid -- 4. Lossless medical image compression and noise removal using deep learning models | N. Karthikeyan, N. Pooranam, D. Surendran and M. Sivakumar -- 5. Prediction of diabetes using voting classification algorithms | Sunil Gupta, Neha Sharma, Gagandeep Kaur and Anita Sardana -- 6. Use of deep learning approaches for the prediction of diseases from medical images | Amrita Thakur, Kushagra Nagori, Ayushman Rao and Neeta Rai -- 7. Deep learning approach for the prediction of diseases in medical images | Sabeena Hussain and Sonali Powar -- 8. Detection of food allergy using deep learning | Abdul Majid Soomro, Sanjoy Kumar Debnath, Awad Bin Naeem, Susama Bagchi, Neha Sharma, Kamal Saluja and Sunil Gupta -- Section 2: Use of AI-enabled IoT in healthcare -- 9. Design and development of Internet of Things and artificial intelligence-based medical imaging system | Rupali A. Mahajan, Smita Chavan, Prajakta Ajay Khadkikar, Gayatri Mahendra Bhandari and Jyoti Manish Shinde -- 10. Internet of Things and medical imaging AI systems | Rajakumar Arul, Kalaipriyan Thirugnanasambandam and M. Kiruthigga -- 11. Role of artificial intelligence in medical IoT devices | Ankita Wadhawan, Nagnath Aherwadi and Usha Mittal -- Section 3: Applications of artificial intelligence in healthcare -- 12. Internet automation indulgence of virtual reality in psychiatric health disorder | Shweta Pandey, Samta Kathuria, Rajesh Singh and Anita Gehlot -- 13. Role of big data analytics in healthcare systems | Prathamesh Suhas Uravane, Vedant Vinay Ganthade, Adityaraj Sanjay Belhe, Abhiraj Sandeep Gadade and Mamoon Rashid -- Index |
| Record Nr. | UNINA-9911007185403321 |
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Rashid Mamoon
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| Stevenage : , : Institution of Engineering & Technology, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of international conference on computing and communication networks : ICCCN 2021 / / Ali Kashif Bashir [and three others] editors
| Proceedings of international conference on computing and communication networks : ICCCN 2021 / / Ali Kashif Bashir [and three others] editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (590 pages) |
| Disciplina | 004.6 |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Computer networks
Computer systems |
| ISBN | 981-19-0604-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910584601103321 |
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of the 1st International Conference on Intelligent Healthcare and Computational Neural Modelling : Select Articles from ICIHCNN 2022, 30 November, Dehradun, India / / edited by Ninni Singh, Ali Kashif Bashir, Seifedine Kadry, Yu-Chen Hu
| Proceedings of the 1st International Conference on Intelligent Healthcare and Computational Neural Modelling : Select Articles from ICIHCNN 2022, 30 November, Dehradun, India / / edited by Ninni Singh, Ali Kashif Bashir, Seifedine Kadry, Yu-Chen Hu |
| Autore | Singh Ninni |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (973 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
BashirAli Kashif
KadrySeifedine HuYu-Chen |
| Collana | Advanced Technologies and Societal Change |
| Soggetto topico |
Artificial intelligence
Cognitive neuroscience Medical care Machine learning Computational intelligence Artificial Intelligence Cognitive Neuroscience Health Care Machine Learning Computational Intelligence |
| ISBN |
9789819928323
981992832X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Comparative study on Melanoma detection using machine learning paradigm -- Crop Recommendation System Using Machine Learning Algorithms -- Movie Recommendation System Using Deep Learning -- A New Switched Capacitor based Five-Level Inverter for PV Applications -- Multilingual Text Identification using NLP and Machine Learning -- A Single Source based Switched capacitor Nine-Level Boost Inverter with a Low Switch Count and SelfBalanced Capacitors -- IoT based Automatic Forest Fire Extinguisher -- Modeling of Voltage Droop Control in DC Microgrid for Voltage Profile Improvement -- Compact Transformer Neural Network for Pulmonary Disease Classification from Radiological Imaging -- Performance Investigation of Five Level Cascaded Multilevel Inverter based Solar PV System with Charge Balance Control -- Power System Stability Constrained Contingency Ranking using Game Theory -- Artificial Neural Network Based Contingency Ranking Using Line Voltage Stability Index -- Techniques of Applied Linguistics for Language Teaching – Learning -- Methodology for Sustainable Equity of Livelihood and Right-of-Way to Technology -- Automatic Brain Tumor Classification using Transfer Learning -- Early Disease Prediction Detection of Blast in Oryza Sativa -- Octave and Level Sampling based Medical Image Registration using Maximum Dominance -- Deep Learning for Kidney MRI Image Analysis -- Parkinson’s Disease Detection and Classification Through Gait Analysis -- Analysis of the Performance of a PMDC-Based EV during Regenerative Breaking -- Reduced Switch Count Multilevel inverters -- Design and Analysis of Six Phase Induction Motor using Wind Driven Optimization -- Intelligent Shopping Carts for Mall Automation -- Block chain-Embellished Cloud Data Security -- Implementation of Pectoral removal with Hybridization of MSER Pectoral and Hough Pectoral methods -- Attention-based Gated Recurrent Networks for Parkinson’s Disease Detection through Voice Patterns -- Classification of Brain Tumors from MRI Images Utilizing a Convolutional Neural Network -- Non-Gaussian Noise Detection by Machine learning Algorithm for Multispectral Satellite Images -- FPGA-based IC design for ECG monitoring signals generation -- Novel synchronous counters using flip flops for low power applications -- Intelligent Seasonal Palm Agriculture Systems for Better Health -- Privacy Preserving and Detection of EEG signals using Machine Learning methods -- Secure Communication Protocol for Maintaining Health care Data in Modern IoT Applications -- Convolutional neural network for Classification of Image-based malware: A Deep Learning -- Prophesising of Cardiovascular Disease Using Decision Tree Classification Algorithm -- Analysing Tweets Based On Emotions -- A Comparative Study of Deep Learning Models with Transfer Learning for Liver Tumor Classification -- The Prevalence of Emotional Stress among Undergraduates during Online Sessions at Government Colleges, Telangana- Analytical Study -- The Impact of Self-Concept among the Secondary School Students with Intellectual Disabilities -- Breast Cancer Detection using Ensemble Learning Model -- Design and Simulation of Glass Shaped Patch Antenna Array with Circular Slots for Wireless Applications. . |
| Record Nr. | UNINA-9910921010203321 |
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Singh Ninni
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Resource Management in Advanced Wireless Networks
| Resource Management in Advanced Wireless Networks |
| Autore | Suresh A |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2023 |
| Descrizione fisica | 1 online resource (337 pages) |
| Altri autori (Persone) |
RamkumarJ
BaskarM BashirAli Kashif |
| ISBN |
1-119-82760-4
1-119-82759-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Next Generation Networks 5G and Beyond -- 1.1 Introduction -- 1.1.1 Benefits of 5G Networks -- 1.2 5G Evolution -- 1.2.1 1G-Analog Cellular Networks -- 1.2.2 2G - Digital Mobile Networks -- 1.2.3 3G-High-Speed Data Networks -- 1.2.4 4G Expansion of Mobile Broadband -- 1.2.5 5G-Design Innovation -- 1.3 5G - Exclusiveness -- 1.4 Challenges of Networks -- 1.5 What Will 5G Accomplish? -- 1.6 Progressing Societies -- 1.7 Transforming Industries -- 1.8 The Invention of 5G -- 1.8.1 Technologies of 5G -- 1.8.2 Misinterpretations of 3GPP -Addressed -- 1.8.3 3GPP'S Real-Time Progress Indicator -- 1.9 Role of 5G -- 1.9.1 Impact on an Individual -- 1.9.2 Impact on Industries -- 1.10 Is a New Phone Required for 5G? -- 1.11 Summary -- Bibliography -- Chapter 2 Architecture and Future Trends on Next Generation Networks -- 2.1 Introduction -- 2.2 Topology Characterisitics -- 2.2.1 Network Structure -- 2.2.2 Modeling Network Layers -- 2.2.3 Research Topology Challenges -- 2.3 Current Network Topologies -- 2.3.1 Bus Topology -- 2.3.2 Ring Topology -- 2.3.3 Star Topology -- 2.3.4 Mesh Topology -- 2.4 Trends of Modern Networks -- 2.4.1 Network Link and Collabration -- 2.4.2 Disruptive Technologies -- 2.5 Architecture of Next Generation Networks -- 2.5.1 Functionality of NGN Network -- 2.5.2 Transport Control and Management -- 2.5.3 Service Control Management -- 2.6 Challenges in Network Structure -- 2.6.1 Implementing Dynamic Topologies -- 2.6.2 Management Questions -- 2.6.3 Consideration of Traffic Control -- 2.6.4 Cost Management System -- 2.6.5 Web Service Management -- 2.7 Network Analysis and Routing Protocols -- 2.7.1 Destination Sequenced Distance Vector Protocol -- 2.7.2 Dynamic Source Routing -- 2.7.3 AdHoc on Demand Distance Vector Routing.
2.8 Evolution of Networks and Services Towards NGN -- 2.8.1 Significant Stages in Network and Services -- 2.8.2 Network Convergence and Development -- 2.8.3 IP Base Network Management System -- 2.8.4 Service Extension and Network Integration -- 2.9 Advanced Network and Service Management Technologies -- 2.9.1 Technology of Artificial Intelligence -- 2.9.2 Smart Agent Technology -- 2.10 Conclusion -- References -- Chapter 3 Evolution of Next Generation Networks and Its Contribution Towards Industry 5.0 -- 3.1 Introduction -- 3.2 Networks of 4G -- 3.3 5G and its Vision -- 3.3.1 Requirements for 5G -- 3.3.2 Network Architecture -- 3.3.3 Economic Contributions and Networks Intelligent Automation of 5G -- 3.3.3.1 Internet of Things (IoT): Artificial Intelligent Plus 5Generation is a Smart IoT -- 3.3.3.2 Autonomous Vehicles, Smart Cars -- 3.3.3.3 Manufacturing Sector and Smart Factory -- 3.3.3.4 Healthcare Industry -- 3.3.3.5 Smart Grids and Cities -- 3.4 A Vision and a Need for 6G Communications -- 3.4.1 Development Projects -- 3.4.2 System Architecture of 6G -- 3.4.3 Satellite Network of 6G -- 3.5 Emerging of 7G -- Findings -- 3.6 Conclusions -- References -- Chapter 4 Understanding the Salient Features Related To Resource Management in Broadband Wireless Networks -- 4.1 Introduction -- 4.1.1 Network Topology Related to Wireless Networks -- 4.1.2 Challenges in Wireless Broadband Network -- 4.1.3 Radio Channel -- 4.1.4 Scarcity in the Spectrum Utilization -- 4.1.5 Quality of Service (QoS) -- 4.1.6 OFDM with WiMAX Technology -- 4.1.7 Advanced Modulation and Coding Techniques in WiMAX -- 4.1.8 Conclusion -- References -- Chapter 5 Network Routing and Its Real-Time Practice in Broadband Wireless Networks -- 5.1 Introduction -- 5.2 Outline of Broadband Wireless Networking -- 5.2.1 Type of Broadband Wireless Networks -- 5.2.1.1 Fixed Networks -- 5.2.1.2 WiMAX. 5.2.1.3 The Broadband Mobile Wireless Networks -- 5.2.2 BWN Network Structure -- 5.2.3 Wireless Broadband Applications -- 5.2.3.1 Digital Telephone -- 5.2.3.2 Broadband Data Connections -- 5.2.3.3 Digital Television -- 5.2.4 Promising Approaches Beyond BWN -- 5.3 Routing Mechanisms -- 5.3.1 Distance Vector (DV) -- 5.3.2 Link State (LS) -- 5.4 Security Issues and Mechanisms in BWN -- 5.4.1 DoS Attack -- 5.4.2 Distributed Flooding DoS -- 5.4.3 Rogue and Selfish Backbone Devices -- 5.4.4 Authorization Flooding on Backbone Devices -- 5.4.5 Node Deprivation Attack -- 5.5 Conclusion -- References -- Chapter 6 Routing Mechanism in Broadband Wireless Network -- 6.1 Classification of BWN Routing Protocols -- 6.1.1 Routing -- 6.1.2 Routing-Protocols -- 6.1.2.1 Types of Routing Protocols for Broadband Wireless Network -- 6.2 Routing Mechanism in WiMAX Mesh Network -- 6.2.1 Routing Protocols - IEEE 802.16 Mesh Network -- 6.2.2 Architecture of WiMAX Protocol -- 6.3 Routing Mechanism in Mobile Networks -- 6.3.1 4G Mobile Network -- 6.3.2 5G Mobile Network -- 6.3.3 Convergence Communication Issue -- 6.3.4 Multi-Hop Dynamic Routing Issue -- 6.4 Service Specific Routing Protocols -- 6.4.1 Protocols Based on Topology and Position -- 6.4.2 Protocols Based on Proactive and Reactive Nature -- 6.4.3 Protocols Based on Distance Vector and Link State Routing -- 6.4.4 Protocols Based on Hop-by-Hop Routing and Source Routing -- 6.4.5 Protocols Based on Flat and Hierarchical Infrastructure -- 6.4.6 Protocols Based on Single-Path and Multipath -- 6.5 Novel Approaches and Algorithms on Broadband Routing -- 6.5.1 Approaches for efficient Performance in Broadband Routing -- 6.5.2 Algorithms for Broadband Routing -- 6.6 Conclusion -- References -- Chapter 7 Interference Problem in 5G with Radio Access Network -- 7.1 Introduction -- 7.1.1 Interface Management. 7.1.2 Management of Conflict in Agile RM Framework -- 7.1.2.1 A Holistic View of Technology -- 7.1.3 Ratings and Context Information -- 7.1.4 Dynamic Traffic Steering -- 7.1.4.1 Reduced-Overhead Interference Mitigation -- 7.1.5 Reducing Overhead Interference Mitigation -- 7.1.5.1 RIM and 5G NR -- 7.1.5.2 Contributions and Differences -- 7.1.6 High-Level 5NR - RIM Framework -- 7.1.7 OS RIM-RS -- 7.1.7.1 OS RIM-RS -- 7.1.8 Radio-Access Network -- 7.1.9 Improve RAN Coverage -- 7.1.9.1 Improving RAN Coverage Can Take Many Forms -- 7.1.10 Related Work -- 7.1.11 Conclusion -- References -- Chapter 8 Interference Techniques Based on Deep Learning in Wireless Networks -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Suppression Techniques -- 8.3.1 Deep Learning Based Approach -- 8.3.2 Adjacent Channel Interference (ACI) -- 8.3.3 Co-Channel Interference -- 8.3.4 Self-Interference -- 8.3.5 Homogeneous Technology Interference -- 8.3.6 Heterogeneous Technology Interference -- 8.4 Classification of Interference Suppression Techniques -- 8.5 A Blind Approach of Interference Cancellation Using Neural Networks -- 8.6 Interference Estimation -- 8.6.1 Feature Extraction -- 8.6.2 Channel Usage Ratio -- 8.7 Machine Learning Model-Decision Tree -- 8.8 Lookup Table -- 8.9 Conclusion -- References -- Chapter 9 Implementing SDN Process-Based MRMC Wireless Networks -- 9.1 Multi-Radio Multi Channel Network in Wireless Network -- 9.1.1 The Concept of QoS inWN -- 9.2 QoS Challenges in MRMC Based Wireless Networks -- 9.2.1 Bandwidth Limitation -- 9.2.2 Removal of Redundancy -- 9.2.3 Energy and Delay Trade-Off -- 9.2.4 Buffer Size Limitation -- 9.3 Opportunistic Routing -- 9.4 The Need for Software-Defined Network in WSN for Enhancing QoS -- 9.4.1 QoS Management -- 9.5 ILP Problem Design -- 9.5.1 Network Model -- 9.5.2 Interference Model -- 9.5.3 VL Request Design. 9.6 Problem Constraints -- 9.6.1 Genetic Algorithm -- 9.6.2 Balanced Source Distribution with DL Cost -- 9.7 Virtual Network Embedding -- 9.7.1 Specification of Virtual Network Embedding -- 9.7.2 System Model -- 9.7.3 Open Flow Enabled Network -- 9.7.4 Network Model -- 9.7.5 Interference Model -- 9.8 Algorithm on Interference Modeling and Channel Selection Process -- 9.8.1 Interference Aware Routing Algorithm -- 9.8.2 Channel Assignment Algorithm -- 9.8.3 The MCM Algorithm -- 9.9 Performance Evaluation -- 9.9.1 Network Model -- 9.9.2 Load Design Algorithm -- 9.9.3 Simulation Settings -- 9.9.4 Performance Metrics -- 9.10 Performance Results -- 9.10.1 Handling with WL Intervention -- 9.10.2 Evaluating the Multicast Gain -- 9.10.3 Clique Utilization Balancing -- 9.10.4 Analysis of Switch Resource Consumption -- 9.10.5 Embedding Method Selection: Integer Linear Programming Vs Genetic Algorithm -- 9.11 Conclusion -- References -- Chapter 10 Advanced Wireless Mobile Network on Financial Literacy -- 10.1 Introduction -- 10.2 Statement of the Problem -- 10.3 Objectives of the Study -- 10.4 Hypothesis -- 10.5 Sampling Design -- 10.6 Literature Review -- 10.7 Methodology -- 10.8 Measurement of Financial Literacy -- 10.9 Elements of Financial Literacy -- 10.10 Financial Literacy Among Scheduled Community -- 10.11 Age Wise Status of Financial Literacy -- 10.12 Financial Literacy Among Scheduled Communities of Different Age Group - ANOVA -- 10.12.1 Null Hypothesis -- 10.13 Financial Literacy and its Relationship with Gender -- 10.13.1 Null Hypothesis -- 10.14 Financial Literacy and its Relationship with Marital Status -- 10.14.1 Null Hypothesis -- 10.15 Financial Literacy and its Relationship with Religion -- 10.15.1 Null Hypothesis -- 10.16 Financial Literacy Among Scheduled Communities of Different Educational Qualification - ANOVA -- 10.16.1 Null Hypothesis. 10.17 Occupation Wise Status of Financial Literacy. |
| Record Nr. | UNINA-9910830910403321 |
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Suresh A
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| Newark : , : John Wiley & Sons, Incorporated, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Resource Management in Advanced Wireless Networks
| Resource Management in Advanced Wireless Networks |
| Autore | Suresh A |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2023 |
| Descrizione fisica | 1 online resource (337 pages) |
| Disciplina | 621.382 |
| Altri autori (Persone) |
RamkumarJ
BaskarM BashirAli Kashif |
| Soggetto topico | Radio resource management (Wireless communications) |
| ISBN |
9781119827603
1119827604 9781119827597 1119827590 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Next Generation Networks 5G and Beyond -- 1.1 Introduction -- 1.1.1 Benefits of 5G Networks -- 1.2 5G Evolution -- 1.2.1 1G-Analog Cellular Networks -- 1.2.2 2G - Digital Mobile Networks -- 1.2.3 3G-High-Speed Data Networks -- 1.2.4 4G Expansion of Mobile Broadband -- 1.2.5 5G-Design Innovation -- 1.3 5G - Exclusiveness -- 1.4 Challenges of Networks -- 1.5 What Will 5G Accomplish? -- 1.6 Progressing Societies -- 1.7 Transforming Industries -- 1.8 The Invention of 5G -- 1.8.1 Technologies of 5G -- 1.8.2 Misinterpretations of 3GPP -Addressed -- 1.8.3 3GPP'S Real-Time Progress Indicator -- 1.9 Role of 5G -- 1.9.1 Impact on an Individual -- 1.9.2 Impact on Industries -- 1.10 Is a New Phone Required for 5G? -- 1.11 Summary -- Bibliography -- Chapter 2 Architecture and Future Trends on Next Generation Networks -- 2.1 Introduction -- 2.2 Topology Characterisitics -- 2.2.1 Network Structure -- 2.2.2 Modeling Network Layers -- 2.2.3 Research Topology Challenges -- 2.3 Current Network Topologies -- 2.3.1 Bus Topology -- 2.3.2 Ring Topology -- 2.3.3 Star Topology -- 2.3.4 Mesh Topology -- 2.4 Trends of Modern Networks -- 2.4.1 Network Link and Collabration -- 2.4.2 Disruptive Technologies -- 2.5 Architecture of Next Generation Networks -- 2.5.1 Functionality of NGN Network -- 2.5.2 Transport Control and Management -- 2.5.3 Service Control Management -- 2.6 Challenges in Network Structure -- 2.6.1 Implementing Dynamic Topologies -- 2.6.2 Management Questions -- 2.6.3 Consideration of Traffic Control -- 2.6.4 Cost Management System -- 2.6.5 Web Service Management -- 2.7 Network Analysis and Routing Protocols -- 2.7.1 Destination Sequenced Distance Vector Protocol -- 2.7.2 Dynamic Source Routing -- 2.7.3 AdHoc on Demand Distance Vector Routing.
2.8 Evolution of Networks and Services Towards NGN -- 2.8.1 Significant Stages in Network and Services -- 2.8.2 Network Convergence and Development -- 2.8.3 IP Base Network Management System -- 2.8.4 Service Extension and Network Integration -- 2.9 Advanced Network and Service Management Technologies -- 2.9.1 Technology of Artificial Intelligence -- 2.9.2 Smart Agent Technology -- 2.10 Conclusion -- References -- Chapter 3 Evolution of Next Generation Networks and Its Contribution Towards Industry 5.0 -- 3.1 Introduction -- 3.2 Networks of 4G -- 3.3 5G and its Vision -- 3.3.1 Requirements for 5G -- 3.3.2 Network Architecture -- 3.3.3 Economic Contributions and Networks Intelligent Automation of 5G -- 3.3.3.1 Internet of Things (IoT): Artificial Intelligent Plus 5Generation is a Smart IoT -- 3.3.3.2 Autonomous Vehicles, Smart Cars -- 3.3.3.3 Manufacturing Sector and Smart Factory -- 3.3.3.4 Healthcare Industry -- 3.3.3.5 Smart Grids and Cities -- 3.4 A Vision and a Need for 6G Communications -- 3.4.1 Development Projects -- 3.4.2 System Architecture of 6G -- 3.4.3 Satellite Network of 6G -- 3.5 Emerging of 7G -- Findings -- 3.6 Conclusions -- References -- Chapter 4 Understanding the Salient Features Related To Resource Management in Broadband Wireless Networks -- 4.1 Introduction -- 4.1.1 Network Topology Related to Wireless Networks -- 4.1.2 Challenges in Wireless Broadband Network -- 4.1.3 Radio Channel -- 4.1.4 Scarcity in the Spectrum Utilization -- 4.1.5 Quality of Service (QoS) -- 4.1.6 OFDM with WiMAX Technology -- 4.1.7 Advanced Modulation and Coding Techniques in WiMAX -- 4.1.8 Conclusion -- References -- Chapter 5 Network Routing and Its Real-Time Practice in Broadband Wireless Networks -- 5.1 Introduction -- 5.2 Outline of Broadband Wireless Networking -- 5.2.1 Type of Broadband Wireless Networks -- 5.2.1.1 Fixed Networks -- 5.2.1.2 WiMAX. 5.2.1.3 The Broadband Mobile Wireless Networks -- 5.2.2 BWN Network Structure -- 5.2.3 Wireless Broadband Applications -- 5.2.3.1 Digital Telephone -- 5.2.3.2 Broadband Data Connections -- 5.2.3.3 Digital Television -- 5.2.4 Promising Approaches Beyond BWN -- 5.3 Routing Mechanisms -- 5.3.1 Distance Vector (DV) -- 5.3.2 Link State (LS) -- 5.4 Security Issues and Mechanisms in BWN -- 5.4.1 DoS Attack -- 5.4.2 Distributed Flooding DoS -- 5.4.3 Rogue and Selfish Backbone Devices -- 5.4.4 Authorization Flooding on Backbone Devices -- 5.4.5 Node Deprivation Attack -- 5.5 Conclusion -- References -- Chapter 6 Routing Mechanism in Broadband Wireless Network -- 6.1 Classification of BWN Routing Protocols -- 6.1.1 Routing -- 6.1.2 Routing-Protocols -- 6.1.2.1 Types of Routing Protocols for Broadband Wireless Network -- 6.2 Routing Mechanism in WiMAX Mesh Network -- 6.2.1 Routing Protocols - IEEE 802.16 Mesh Network -- 6.2.2 Architecture of WiMAX Protocol -- 6.3 Routing Mechanism in Mobile Networks -- 6.3.1 4G Mobile Network -- 6.3.2 5G Mobile Network -- 6.3.3 Convergence Communication Issue -- 6.3.4 Multi-Hop Dynamic Routing Issue -- 6.4 Service Specific Routing Protocols -- 6.4.1 Protocols Based on Topology and Position -- 6.4.2 Protocols Based on Proactive and Reactive Nature -- 6.4.3 Protocols Based on Distance Vector and Link State Routing -- 6.4.4 Protocols Based on Hop-by-Hop Routing and Source Routing -- 6.4.5 Protocols Based on Flat and Hierarchical Infrastructure -- 6.4.6 Protocols Based on Single-Path and Multipath -- 6.5 Novel Approaches and Algorithms on Broadband Routing -- 6.5.1 Approaches for efficient Performance in Broadband Routing -- 6.5.2 Algorithms for Broadband Routing -- 6.6 Conclusion -- References -- Chapter 7 Interference Problem in 5G with Radio Access Network -- 7.1 Introduction -- 7.1.1 Interface Management. 7.1.2 Management of Conflict in Agile RM Framework -- 7.1.2.1 A Holistic View of Technology -- 7.1.3 Ratings and Context Information -- 7.1.4 Dynamic Traffic Steering -- 7.1.4.1 Reduced-Overhead Interference Mitigation -- 7.1.5 Reducing Overhead Interference Mitigation -- 7.1.5.1 RIM and 5G NR -- 7.1.5.2 Contributions and Differences -- 7.1.6 High-Level 5NR - RIM Framework -- 7.1.7 OS RIM-RS -- 7.1.7.1 OS RIM-RS -- 7.1.8 Radio-Access Network -- 7.1.9 Improve RAN Coverage -- 7.1.9.1 Improving RAN Coverage Can Take Many Forms -- 7.1.10 Related Work -- 7.1.11 Conclusion -- References -- Chapter 8 Interference Techniques Based on Deep Learning in Wireless Networks -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Suppression Techniques -- 8.3.1 Deep Learning Based Approach -- 8.3.2 Adjacent Channel Interference (ACI) -- 8.3.3 Co-Channel Interference -- 8.3.4 Self-Interference -- 8.3.5 Homogeneous Technology Interference -- 8.3.6 Heterogeneous Technology Interference -- 8.4 Classification of Interference Suppression Techniques -- 8.5 A Blind Approach of Interference Cancellation Using Neural Networks -- 8.6 Interference Estimation -- 8.6.1 Feature Extraction -- 8.6.2 Channel Usage Ratio -- 8.7 Machine Learning Model-Decision Tree -- 8.8 Lookup Table -- 8.9 Conclusion -- References -- Chapter 9 Implementing SDN Process-Based MRMC Wireless Networks -- 9.1 Multi-Radio Multi Channel Network in Wireless Network -- 9.1.1 The Concept of QoS inWN -- 9.2 QoS Challenges in MRMC Based Wireless Networks -- 9.2.1 Bandwidth Limitation -- 9.2.2 Removal of Redundancy -- 9.2.3 Energy and Delay Trade-Off -- 9.2.4 Buffer Size Limitation -- 9.3 Opportunistic Routing -- 9.4 The Need for Software-Defined Network in WSN for Enhancing QoS -- 9.4.1 QoS Management -- 9.5 ILP Problem Design -- 9.5.1 Network Model -- 9.5.2 Interference Model -- 9.5.3 VL Request Design. 9.6 Problem Constraints -- 9.6.1 Genetic Algorithm -- 9.6.2 Balanced Source Distribution with DL Cost -- 9.7 Virtual Network Embedding -- 9.7.1 Specification of Virtual Network Embedding -- 9.7.2 System Model -- 9.7.3 Open Flow Enabled Network -- 9.7.4 Network Model -- 9.7.5 Interference Model -- 9.8 Algorithm on Interference Modeling and Channel Selection Process -- 9.8.1 Interference Aware Routing Algorithm -- 9.8.2 Channel Assignment Algorithm -- 9.8.3 The MCM Algorithm -- 9.9 Performance Evaluation -- 9.9.1 Network Model -- 9.9.2 Load Design Algorithm -- 9.9.3 Simulation Settings -- 9.9.4 Performance Metrics -- 9.10 Performance Results -- 9.10.1 Handling with WL Intervention -- 9.10.2 Evaluating the Multicast Gain -- 9.10.3 Clique Utilization Balancing -- 9.10.4 Analysis of Switch Resource Consumption -- 9.10.5 Embedding Method Selection: Integer Linear Programming Vs Genetic Algorithm -- 9.11 Conclusion -- References -- Chapter 10 Advanced Wireless Mobile Network on Financial Literacy -- 10.1 Introduction -- 10.2 Statement of the Problem -- 10.3 Objectives of the Study -- 10.4 Hypothesis -- 10.5 Sampling Design -- 10.6 Literature Review -- 10.7 Methodology -- 10.8 Measurement of Financial Literacy -- 10.9 Elements of Financial Literacy -- 10.10 Financial Literacy Among Scheduled Community -- 10.11 Age Wise Status of Financial Literacy -- 10.12 Financial Literacy Among Scheduled Communities of Different Age Group - ANOVA -- 10.12.1 Null Hypothesis -- 10.13 Financial Literacy and its Relationship with Gender -- 10.13.1 Null Hypothesis -- 10.14 Financial Literacy and its Relationship with Marital Status -- 10.14.1 Null Hypothesis -- 10.15 Financial Literacy and its Relationship with Religion -- 10.15.1 Null Hypothesis -- 10.16 Financial Literacy Among Scheduled Communities of Different Educational Qualification - ANOVA -- 10.16.1 Null Hypothesis. 10.17 Occupation Wise Status of Financial Literacy. |
| Record Nr. | UNINA-9911020218903321 |
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Suresh A
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| Newark : , : John Wiley & Sons, Incorporated, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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