Info
AI and Data Analytics in Precision Agriculture for Sustainable Development / / edited by Sita Rani, Soumi Dutta, Álvaro Rocha, Korhan Cengiz
| AI and Data Analytics in Precision Agriculture for Sustainable Development / / edited by Sita Rani, Soumi Dutta, Álvaro Rocha, Korhan Cengiz |
| Autore | Rani Sita |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (438 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
DuttaSoumi
RochaÁlvaro CengizKorhan |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Engineering - Data processing Agriculture Artificial intelligence Computational Intelligence Data Engineering Artificial Intelligence |
| ISBN | 3-031-93087-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Precision Agriculture for Sustainable Development: Concepts and Applications -- AI4AI: Revolutionizing Agriculture with Artificial Intelligence -- Evolving Role of Applied AI in Crop Farming -- Role of Artificial Intelligence in Precision Farming -- Precision Agriculture: Fuzzy Logic or Deep Neural Network Models for Robust Crop Disease Screening -- Machine Learning Models for Crop Management -- Enhancing Agricultural Climate Resilience through Machine Learning Models and Hyper-Parameter Tuning -- Enhancing Crop Resilience to Climate Change with AI -- AI-Based Smart Irrigation Systems for Water Conservation -- Sensor Technologies and Blockchain Integration for Data-Driven Precision Agriculture -- Satellite Imagery and GIS Applications in Precision Agriculture -- Leveraging GIS and Remote Sensing for Agricultural Advancement and Economic Development -- Projecting Ecological Footprint and Biocapacity: Insights into Sustainable Agriculture and Development -- Projecting Ecological Footprint and Biocapacity: Insights into Sustainable Agriculture and Development -- Evaluating Sentinel-2 and Sentinel-1 for Land Use / Land Cover Classification in a Rice-Producing Region of Uruguay -- Enhancing Potato Crop Health: A CNN-Based System for Early Detection of Early and Late Blight -- Development of a Neutrosophic Set Theory based Feature Selection method for Classification of Paddy Seed -- Optimizing Potato Crop Water Quality: A Comparative Analysis of Machine Learning Techniques and Gradient Boosting Approaches -- EFFECTS OF THE FREQUENCY EMISSIONS OF THE Tadarita brasiliensis SIMULATED USING ELECTRONIC DEVICE FOR REPELLING Copitarsia decolora (Lepidoptera: Noctuidae) ADULTS. |
| Record Nr. | UNINA-9911015865903321 |
Rani Sita
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Design and Forecasting Models for Disease Management
| Design and Forecasting Models for Disease Management |
| Autore | Dutta Pijush |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (325 pages) |
| Disciplina | 616.00285631 |
| Altri autori (Persone) |
MandalSudip
CengizKorhan SadhuArindam JanaGour Gopal |
| ISBN |
9781394234059
1394234058 9781394234073 1394234074 9781394234066 1394234066 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Part 1: Safety and Regulatory Aspects for Disease Pre-Screening -- Chapter 1 A Study of Possible AI Aversion in Healthcare Consumers -- 1.1 Introduction to AI in Healthcare -- 1.1.1 The Role of AI in Transforming Healthcare -- 1.1.2 The Unfolding Paradigm: Potential Benefits and Challenges of AI Implementation in Healthcare -- 1.1.3 Overview of Consumer Receptivity Towards AI in Medicine: A Comparative Analysis -- 1.2 Consumer Reluctance to Utilize AI in Healthcare: Present Scenario -- 1.2.1 Top Factors Influencing Consumer Resistance to Medical AI -- 1.2.2 Uncovering the Psychological Barriers and Concerns Associated with AI Adoption in Healthcare -- 1.2.3 Case Studies and Research Findings on Consumer Aversion to AI-Based Healthcare Services -- 1.2.4 Impact on Consumer Decision-Making -- 1.2.5 Effects of AI Aversion on Consumer Decision-Making Processes: An Analysis -- 1.2.6 Understanding How Consumer Perceptions Influence Their Choice Between Human and AI Healthcare Providers -- 1.2.7 Exploring Role of Trust, Perceived Competence and Empathy in Consumer Preferences -- 1.3 Economic Implications of AI Aversion -- 1.3.1 Investigating Influence of AI Aversion on Consumer Willingness to Pay for Healthcare Services -- 1.3.2 Influence of Patient Education on AI Aversion in Healthcare -- 1.3.3 Influence of Patient Awareness on AI Aversion in Healthcare -- 1.3.4 Influence of Age of Patient on AI Aversion in Healthcare -- 1.4 Overcoming Resistance to Medical AI -- 1.4.1 Strategies for Enhancing Consumer Trust and Acceptance of AI in Healthcare -- 1.4.2 Approaches to Alleviate Consumer Concerns and Misconceptions: Communication and Education -- 1.4.3 Cases of Successful Implementation of AI Technologies in Healthcare and Lessons Learned.
1.5 Ethical Considerations and Governance -- 1.5.1 Regulatory Frameworks for Ethical AI Operations to Fight Aversion in Healthcare Consumers -- 1.5.2 Addressing the Potential Cost-Effectiveness and Affordability Concerns Associated with AI-Based Healthcare Solutions -- 1.5.3 Balancing Privacy, Data Protection and Need for Transparency in AI Healthcare Applications -- 1.6 Future Outlook and Opportunities -- 1.6.1 The Future of AI in Healthcare and Its Impact on Consumer Aversion -- 1.6.2 Exploring Emerging Technologies and Trends That May Alleviate Consumer Concerns -- 1.6.3 Opportunities for Collaboration Between AI Developers, Healthcare Providers, and Consumers -- 1.6.4 Summary of Key Findings on Consumer Aversion to AI in Healthcare -- 1.6.5 Implications for Healthcare Practitioners, Policymakers and Researchers -- 1.7 Conclusion -- References -- Chapter 2 A Study of AI Application Through Integrated and Systematic Moral Cognitive Therapy in the Healthcare Sector -- 2.1 Introduction -- 2.1.1 Understanding the Role of AI in Healthcare -- 2.1.2 Advantages of AI in Healthcare -- 2.1.3 Moral Dilemmas and AI-Based Healthcare -- 2.2 What is Integrated and Systematic Moral Cognitive Therapy (ISMCT)? -- 2.2.1 Integrating Moral Cognitive Therapy with AI -- 2.2.2 Alignment of Moral Cognitive Therapy Principles with AI Applications -- 2.2.3 Benefits of Integrated and Systematic Moral Cognitive Therapy -- 2.2.4 Applications of AI-Integrated Moral Cognitive Therapy in Healthcare -- 2.3 The Role of AI in Healthcare: A Fine Balance Between Ethics and Innovation -- 2.3.1 Humanizing Healthcare: Towards an AI-ISMCT -- 2.3.2 Synergized AI and ISMCT -- 2.3.3 Case Study and Success Stories -- 2.4 Advancing Research in AI-Integrated Moral Cognitive Therapy -- 2.4.1 Collaborative Efforts Between Healthcare Professionals and AI Developers. 2.4.2 Implications for Policy and Regulatory Frameworks -- 2.5 Conclusion -- References -- Chapter 3 A Strategic Model to Control Non-Communicable Diseases -- 3.1 Introduction -- 3.1.1 India and NCDs -- 3.2 Survey of Literature -- 3.2.1 Factors Contributing to the Growth of NCDs -- 3.2.2 Lifestyle Modification - A Strategic Role in Mitigation of NCD -- 3.2.3 Policy to Control NCDs -- 3.3 Proposed Model -- 3.3.1 Registration and Information Centre (RIC) -- 3.3.2 Integration Centre (IIC) -- 3.3.3 Strategic Review Centre (SRC) -- 3.3.4 Expected Outcome of the Proposed Model -- 3.4 Conclusion -- References -- Chapter 4 Image Compression Technique Using Color Filter Array (CFA) for Disease Diagnosis and Treatment -- 4.1 Introduction -- 4.1.1 Color Filter Array -- 4.1.2 Electronic Health Record (EHR) -- 4.2 Related Works -- 4.3 Proposed Model -- 4.4 Implementation -- 4.5 Results -- 4.6 Conclusion -- References -- Chapter 5 Research in Image Processing for Medical Applications Using the Secure Smart Healthcare Technique -- 5.1 Introduction -- 5.1.1 Imaging Systems -- 5.1.2 The Digital Image Processing System -- 5.1.3 Image Enhancement -- 5.2 Classification of Digital Images -- 5.2.1 Utilizations of Digital Image Processing (DIP) -- 5.2.1.1 Medicine -- 5.2.1.2 Forensics -- 5.2.2 Medical Image Analysis -- 5.2.3 Max-Variance Automatic Cut-Off Method -- 5.2.4 Medical Imaging Segmentation -- 5.2.5 Image-Based on Edge Detection -- 5.2.5.1 Robert's Kernel Method -- 5.2.5.2 Prewitt Kernel -- 5.2.5.3 Sobel Kernel -- 5.2.5.4 k-Means Segmentation -- 5.2.6 Images from .-Rays -- 5.2.6.1 Non-Ionizing Radiation -- 5.2.6.2 Magnetic Resonance Imaging -- 5.2.6.3 Segmentation Using Multiple Images Acquired by Different Imaging Techniques -- 5.3 Methods -- 5.3.1 k-Means Approach -- 5.3.2 Bayesian Objective Function. 5.4 Segmentation and Database Extraction with Neural Networks -- 5.4.1 Artificial Neural Network -- 5.4.2 Bayesian Belief Networks -- 5.5 Applications in Medical Image Analysis -- 5.5.1 Using Artificial Neural Network for Better Optimization and Detection in Medical Imaging -- 5.5.1.1 Opportunities -- 5.6 Standardize Analytics Pipeline for the Health Sector -- 5.7 Feature Extraction/Selection -- 5.7.1 Significance of Machine Learning for Medical Image Processing -- 5.7.2 Significance of Deep Learning for Medical Image Processing -- 5.8 Image-Based Forecasting Using Internet of Things (IoT) in Smart Healthcare System -- 5.9 IoT Monitoring Applications Based on Image Processing -- 5.10 Significance of Computer-aided Big Healthcare Data (BHD) for Medical Image Processing -- 5.11 Applications of Big Data -- 5.11.1 Big Data Analytics in Health Sector -- 5.11.2 Computer-Aided Diagnosis in Mammography -- 5.11.3 Tumor Imaging and Treatment -- 5.11.4 Molecular Imaging -- 5.11.5 Surgical Interventions -- 5.12 Conclusion -- References -- Chapter 6 Comparative Study on Image Enhancement Techniques for Biomedical Images -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Theoretical Concepts -- 6.3.1 Logarithmic Transformation -- 6.3.1.1 Advantages of Log Transformation -- 6.3.1.2 Limitations of Log Transformation -- 6.3.2 Power Law Transformation or Gamma Correction -- 6.3.2.1 Advantages of Gamma Correction -- 6.3.2.2 Limitations of Gamma Correction -- 6.3.3 Piecewise Linear Transformation or Contrast Stretching -- 6.3.3.1 Advantages of Contrast Stretching -- 6.3.3.2 Limitations of Contrast Stretching -- 6.3.4 Histogram Equalization -- 6.3.4.1 Advantages of Histogram Equalization -- 6.3.4.2 Limitations of Histogram Equalization -- 6.3.5 Contrast-Limited Adaptive Histogram Equalization (CLAHE) -- 6.3.5.1 Advantages of CLAHE -- 6.3.5.2 Limitation of CLAHE. 6.3.6 Adjustment Function -- 6.4 Results and Discussion -- 6.4.1 Images and Histograms for Different Images Using Different Enhancement Methods -- 6.4.2 Comparison for Different Image Enhancement Techniques -- 6.5 Conclusion -- References -- Chapter 7 Exploring Parkinson's Disease Progression and Patient Variability: Insights from Clinical and Molecular Data Analysis -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Data Review -- 7.3.1 Clinical Data -- 7.3.2 Peptides Data -- 7.3.3 Protein Data -- 7.4 Parkinson's Dynamic for Patients in Train -- 7.5 Conclusion -- References -- Chapter 8 A Survey-Based Comparative Study on Machine Learning Techniques for Early Detection of Mental Illness -- 8.1 Introduction -- 8.2 Background -- 8.3 Review of Previous Works -- 8.3.1 Standard Questionnaire -- 8.3.2 Social Media Content -- 8.4 Comparative Result -- 8.5 Discussion -- 8.6 Conclusion -- References -- Part 2: Clinical Decision Support System for Early Disease Detection and Management -- Chapter 9 Diagnostics and Classification of Alzheimer's Diseases Using Improved Deep Learning Architectures -- 9.1 Introduction -- 9.2 Related Works -- 9.3 Method -- 9.3.1 Data Description -- 9.4 Result Analysis -- 9.4.1 Performance Metrics -- 9.4.2 Experimental Setup -- 9.5 Conclusion -- Data Availability -- References -- Chapter 10 Perform a Comparative Study Based on Conventional Machine Learning Approaches for Human Stress Level Detection -- 10.1 Introduction -- 10.2 Related Work -- 10.3 Architecture Design -- 10.3.1 Body Temperature -- 10.3.2 Humidity Analysis -- 10.3.3 Step Count Analysis -- 10.3.4 Dataset -- 10.4 Experiment -- 10.4.1 Performance Matrices -- 10.5 Result Analysis -- 10.6 Conclusion -- References -- Chapter 11 Diabetes Prediction Using a Hybrid PCA-Based Feature Selection and Computational Machine Learning Algorithm -- 11.1 Introduction. 11.2 Related Work. |
| Record Nr. | UNINA-9911020138203321 |
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Dutta Pijush
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems
| A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems |
| Autore | Kumar Pardeep |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2021 |
| Descrizione fisica | 1 online resource (462 pages) |
| Altri autori (Persone) |
ObaidAhmed Jabbar
CengizKorhan KhannaAshish BalasValentina Emilia |
| Collana | Intelligent Systems Reference Library |
| Soggetto genere / forma | Electronic books. |
| ISBN | 3-030-76653-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910497108203321 |
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Kumar Pardeep
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| Cham : , : Springer International Publishing AG, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Intelligent Network Design Driven by Big Data Analytics, IoT, AI and Cloud Computing
| Intelligent Network Design Driven by Big Data Analytics, IoT, AI and Cloud Computing |
| Autore | Kumar Sunil |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Stevenage : , : Institution of Engineering & Technology, , 2022 |
| Descrizione fisica | 1 online resource (372 pages) |
| Disciplina | 004.6782 |
| Altri autori (Persone) |
MappGlenford
CengizKorhan |
| Collana | Computing and Networks |
| Soggetto topico |
Internet of things
Cloud computing |
| ISBN |
1-83724-485-5
1-5231-5346-6 1-83953-534-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Halftitle Page -- Series Page -- Title Page -- Copyright -- Contents -- About the Editors -- 1 Introduction to intelligent network design driven by big data analytics, IoT, AI and cloud computing -- Preface -- Chapter 2: Role of automation, Big Data, AI, ML IBN, and cloud computing in intelligent networks -- Chapter 3: An intelligent verification management approach for efficient VLSI computing system -- Chapter 4: Evaluation of machine learning algorithms on academic big dataset by using feature selection techniques -- Chapter 5: Accurate management and progression of Big Data analysis -- Chapter 6: Cram on data recovery and backup cloud computing techniques -- Chapter 7: An adaptive software defined networking (SDN) for load balancing in cloud computing -- Chapter 8: Emerging security challenges in cloud computing: An insight -- Chapter 9: Factors responsible and phases of speaker recognition system -- Chapter 10: IoT-based water quality assessment using fuzzy logic controller -- Chapter 11: Design and analysis of wireless sensor network for intelligent transportation and industry automation -- Chapter 12: A review of edge computing in healthcare Internet of Things: theories, practices, and challenges -- Chapter 13: Image processing for medical images on the basis of intelligence and bio computing -- Chapter 14: IoT-based architecture for smart health-care systems -- Chapter 15: IoT-based heart disease prediction system -- Chapter 16: DIAIF: detection of interest flooding using artificial intelligence-based framework in NDN android -- Chapter 17: Intelligent and cost-effective mechanism for monitoring road quality using machine learning -- References -- 2 Role of automation, Big Data, AI, ML IBN, and cloud computing in intelligent networks -- 2.1 Evolution of networks: everything is connected -- 2.1.1 Intelligent devices.
2.1.2 Intelligent devices connection with networks -- 2.2 Huge volume of data generation by intelligent devices -- 2.2.1 Issues and challenges of Big Data Analytics -- 2.2.2 Storage of Big Data -- 2.3 Need of data analysis by business -- 2.3.1 Sources of information -- 2.3.2 Data visualization -- 2.3.3 Analyzing Big Data for effective use of business -- 2.3.4 Intelligent devices thinking intelligently -- 2.4 Artificial intelligence and machine learning in networking -- 2.4.1 Role of ML in networks -- 2.5 Intent-based networking -- 2.6 Role of programming -- 2.6.1 Basic programming using Blockly -- 2.6.2 Blockly games -- 2.7 Role of technology to design a model -- 2.7.1 Electronic toolkits -- 2.7.2 Programming resources -- 2.8 Relation of AI, ML, and IBN -- 2.9 Business challenges and opportunities -- 2.9.1 The evolving job market -- 2.10 Security -- 2.10.1 Challenges to secure device and networks -- 2.11 Summary -- References -- 3 An intelligent verification management approach for efficient VLSI computing system -- 3.1 Introduction -- 3.2 Literature study -- 3.3 Verification management approach: Case Study 1 -- 3.3.1 The pseudo random number generator in a verification environment -- 3.3.2 Implementation of PRNG in higher abstraction language and usage of DPI -- 3.4 Verification management approach: Case Study 2 -- 3.5 Challenges and research direction -- 3.5.1 Challenges in intelligent systems -- 3.6 Conclusion -- References -- 4 Evaluation of machine learning algorithms on academic big dataset by using feature selection techniques -- 4.1 Introduction -- 4.1.1 EDM -- 4.1.2 EDM process -- 4.1.3 Methods and techniques -- 4.1.4 Application areas of data mining -- 4.2 Literature survey -- 4.3 Materials and methods -- 4.3.1 Dataset description -- 4.3.2 Classification algorithms -- 4.3.3 FS algorithms -- 4.3.4 Data preprocessing phase. 4.4 Implementation of the proposed algorithms -- 4.4.1 Model construction for the standard classifier -- 4.4.2 Implementation after attribute selection using ranker method -- 4.5 Result analysis and discussion -- 4.6 Conclusion -- References -- 5 Accurate management and progression of Big Data Analysis -- 5.1 Introduction -- 5.1.1 Examples of Big Data -- 5.2 Big Data Analysis -- 5.2.1 Life cycle of Big Data -- 5.2.2 Classification of the Big Data -- 5.2.3 Working of Big Data Analysis -- 5.2.4 Common flaws that undermine Big Data Analysis -- 5.2.5 Advantages of Big Data Analysis -- 5.3 Processing techniques -- 5.3.1 Traditional method -- 5.3.2 MapReduce -- 5.3.3 Advantages of MapReduce -- 5.4 Cyber crime -- 5.4.1 Different strategies in Big Data to help in various circumstances -- 5.4.2 Big Data Analytics and cybercrime -- 5.4.3 Security issues associated with Big Data -- 5.4.4 Big Data Analytics in digital forensics -- 5.5 Real-time edge analytics for Big Data in IoT -- 5.6 Conclusion -- References -- 6 Cram on data recovery and backup cloud computing techniques -- 6.1 Introduction -- 6.1.1 Origin of cloud -- 6.1.2 Sole features of cloud computing -- 6.1.3 Advantages of cloud computing -- 6.1.4 Disadvantages of cloud computing -- 6.2 Classification of data recovery and backup -- 6.2.1 Recovery -- 6.2.2 Backup -- 6.3 Study on data recovery and backup cloud computing techniques -- 6.3.1 Backup of real-time data and recovery using cloud computing -- 6.3.2 Data recovery and security in cloud -- 6.3.3 Amoeba: An autonomous backup and recovery solid-state drives for ransomware attack defense -- 6.3.4 A cloud-based automatic recovery and backup system for video compression -- 6.3.5 Efficient and reliable data recovery techniques in cloud computing -- 6.3.6 Cost-efficient remote backup services for enterprise cloud. 6.3.7 DR-cloud: Multi-cloud-based disaster recovery service -- 6.4 Conclusion -- References -- 7 An adaptive software-defined networking (SDN) for load balancing in cloud computing -- 7.1 Introduction -- 7.2 Related works -- 7.3 Architecture overview of SDN -- 7.4 Load-balancing framework in SDN -- 7.4.1 Classification of SDN controller architectures -- 7.5 Problem statement -- 7.5.1 Selection strategy of controller head -- 7.5.2 Network setup -- 7.6 Illustration -- 7.7 Results and discussion -- 7.7.1 Comparison of throughput -- 7.7.2 Comparison of PTR -- 7.7.3 Comparison of number of packet loss -- 7.8 Conclusion -- References -- 8 Emerging security challenges in cloud computing: an insight -- 8.1 Introduction -- 8.1.1 An introduction to cloud computing and its security -- 8.2 The security issues in different cloud models and threat management techniques -- 8.2.1 Five most indispensable characteristics -- 8.2.2 The security issues in cloud service model -- 8.2.3 Security issues in cloud deployment models -- 8.2.4 Security challenges in the cloud environment -- 8.2.5 The threat management techniques -- 8.3 Review on existing proposed models -- 8.3.1 SeDaSC -- 8.3.2 The 'SecCloud' protocol -- 8.3.3 Data accountability and auditing for secure cloud data storage -- 8.4 Conclusion and future prospectives -- References -- 9 Factors responsible and phases of speaker recognition system -- 9.1 Study of related research -- 9.2 Phases of speaker recognition system -- 9.2.1 Speaker database collection -- 9.2.2 Feature extraction -- 9.2.3 Feature mapping -- 9.3 Basics of speech signals -- 9.3.1 Speech production system -- 9.3.2 Speech perception -- 9.3.3 Speech signals -- 9.3.4 Properties of the sinusoids -- 9.3.5 Windowing signals -- 9.3.6 Zero-crossing rate -- 9.3.7 Autocorrelation -- 9.4 Features of speech signals -- 9.4.1 Physical features. 9.4.2 Perceptual features -- 9.4.3 Signal features -- 9.5 Localization of speaker -- 9.6 Conclusion -- References -- 10 IoT-based water quality assessment using fuzzy logic controller -- 10.1 Introduction -- 10.2 Experimental procedures -- 10.3 Working -- 10.4 Results and discussions -- 10.5 Conclusion -- References -- 11 Design and analysis of wireless sensor network for intelligent transportation and industry automation -- 11.1 Introduction -- 11.2 Wireless sensor network -- 11.3 WSN application -- 11.4 Limitations of WSN -- 11.5 Literature survey -- 11.6 Related work -- 11.7 Methodology -- 11.7.1 Throughput -- 11.7.2 Delay -- 11.7.3 Packet delivery ratio -- 11.7.4 Design of WiMAX-based WSN system -- 11.8 Related results -- 11.9 Conclusion -- 11.10 Future scope -- References -- 12 A review of edge computing in healthcare Internet of things: theories, practices and challenges -- 12.1 Introduction -- 12.2 Cloud computing in healthcare and its limitations -- 12.2.1 Public cloud -- 12.2.2 Private cloud -- 12.2.3 Hybrid cloud -- 12.2.4 Community cloud -- 12.3 Edge computing and its advantages over cloud computing -- 12.3.1 Advantages of edge/fog computing -- 12.3.2 Disadvantages of edge/fog computing -- 12.4 IoT in healthcare -- 12.5 Edge computing in healthcare -- 12.6 Machine learning in healthcare -- 12.7 Integrated role of IOT, ML and edge computing in healthcare -- 12.7.1 Patient care during surgical procedure -- 12.7.2 Patient care at home -- 12.7.3 Patient care in ambulance -- 12.8 Modelling and simulation tools for edge/fog computing -- 12.9 Edge computing in Covid-19 pandemic era -- 12.10 Challenges of edge computing -- 12.11 Conclusion -- References -- 13 Image Processing for medical images on the basis of intelligence and biocomputing -- 13.1 Introduction -- 13.1.1 What is an image? -- 13.2 Image processing. 13.2.1 Equivalent image processing. |
| Record Nr. | UNINA-9911004828103321 |
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Kumar Sunil
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| Stevenage : , : Institution of Engineering & Technology, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Micro-Electronics and Telecommunication Engineering : Proceedings of 3rd ICMETE 2019 / / edited by Devendra Kumar Sharma, Valentina Emilia Balas, Le Hoang Son, Rohit Sharma, Korhan Cengiz
| Micro-Electronics and Telecommunication Engineering : Proceedings of 3rd ICMETE 2019 / / edited by Devendra Kumar Sharma, Valentina Emilia Balas, Le Hoang Son, Rohit Sharma, Korhan Cengiz |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XVI, 735 p. 413 illus., 305 illus. in color.) |
| Disciplina | 621.381 |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Electronics
Microelectronics Power electronics Electrical engineering Artificial intelligence Electronics and Microelectronics, Instrumentation Power Electronics, Electrical Machines and Networks Communications Engineering, Networks Artificial Intelligence |
| ISBN | 981-15-2329-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Radio Direction Finding techniques for an Unmanned Aerial Vehicle -- Translation into Pali language from Brahmi Script -- A Fractal Boundary Wideband Antenna with DGS for X-Band Application -- An Approach to Automated Spam Detection Using Deep Neural Network and Machine Learning Classifiers -- Handling Sparsity in Cross Domain Recommendation Systems: Review -- Daily Rainfall Prediction Using Nonlinear Autoregressive Neural Network -- Adiabatic Design Implementation of Digital Circuits for Low Power Applications -- Parametric Classification of Dynamic Community Detection Techniques -- Survey on the Impact of FSM Design for High-performance Architecture Evaluation -- Design of Configurable Analog Block based Oscillator and Possible Applications -- Classification of Pre-diabetes and Healthy Subjects in Plantar Infrared Thermal Imaging Using Various Machine Learning Algorithms. |
| Record Nr. | UNINA-9910383834003321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Paradigms of Smart and Intelligent Communication, 5G and Beyond [[electronic resource] /] / edited by Amrita Rai, Dinesh Kumar Singh, Amit Sehgal, Korhan Cengiz
| Paradigms of Smart and Intelligent Communication, 5G and Beyond [[electronic resource] /] / edited by Amrita Rai, Dinesh Kumar Singh, Amit Sehgal, Korhan Cengiz |
| Autore | Rai Amrita |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (297 pages) |
| Disciplina | 621.382028563 |
| Altri autori (Persone) |
Kumar SinghDinesh
SehgalAmit CengizKorhan |
| Collana | Transactions on Computer Systems and Networks |
| Soggetto topico |
Artificial intelligence
Machine learning Telecommunication Artificial Intelligence Machine Learning Communications Engineering, Networks |
| Soggetto non controllato |
Artificial Intelligence
Telecommunication Computers Technology & Engineering |
| ISBN | 981-9901-09-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Artificial Cognitive Computing for Smart Communications -- Green IoT using Machine Learning, Deep Learning Models for 5G Networks -- Integration of IOT and 5G Communication -- Role of IoT in Smart Communication and 5G using Antenna Array -- Applications of Deep Reinforcement Learning in Wireless Networks -- Detection of Consumption of Alcohol Using Artificial Intelligence -- Analysis of Finger Vein Pattern Recognition Technique using Machine Learning -- Machine Learning Techniques for Anomaly Detection -- Application of AI & ML in 5G Communication -- Software Defined Network-based Management Architecture for 5G Network -- Reversible Logic Based Single Layer Flip Flops and Shift Registers in QCA Framework for the Application of Nano-communication -- Machine Learning Technique for Few-mode Fiber Design with Inverse Modelling for 5G and Beyond -- IoT for Landslides: Applications, Technologies and Challenges -- A Review: Dust Cleaning Approach of Solar Photovoltaic System Using Emerging Technology -- Prediction of Heart Disease Using Hybrid Machine Learning Technique. |
| Record Nr. | UNISA-996546835703316 |
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Rai Amrita
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Paradigms of Smart and Intelligent Communication, 5G and Beyond / / edited by Amrita Rai, Dinesh Kumar Singh, Amit Sehgal, Korhan Cengiz
| Paradigms of Smart and Intelligent Communication, 5G and Beyond / / edited by Amrita Rai, Dinesh Kumar Singh, Amit Sehgal, Korhan Cengiz |
| Autore | Rai Amrita |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (297 pages) |
| Disciplina | 621.382028563 |
| Altri autori (Persone) |
Kumar SinghDinesh
SehgalAmit CengizKorhan |
| Collana | Transactions on Computer Systems and Networks |
| Soggetto topico |
Artificial intelligence
Machine learning Telecommunication Artificial Intelligence Machine Learning Communications Engineering, Networks |
| ISBN |
9789819901098
981990109X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Artificial Cognitive Computing for Smart Communications -- Green IoT using Machine Learning, Deep Learning Models for 5G Networks -- Integration of IOT and 5G Communication -- Role of IoT in Smart Communication and 5G using Antenna Array -- Applications of Deep Reinforcement Learning in Wireless Networks -- Detection of Consumption of Alcohol Using Artificial Intelligence -- Analysis of Finger Vein Pattern Recognition Technique using Machine Learning -- Machine Learning Techniques for Anomaly Detection -- Application of AI & ML in 5G Communication -- Software Defined Network-based Management Architecture for 5G Network -- Reversible Logic Based Single Layer Flip Flops and Shift Registers in QCA Framework for the Application of Nano-communication -- Machine Learning Technique for Few-mode Fiber Design with Inverse Modelling for 5G and Beyond -- IoT for Landslides: Applications, Technologies and Challenges -- A Review: Dust Cleaning Approach of Solar Photovoltaic System Using Emerging Technology -- Prediction of Heart Disease Using Hybrid Machine Learning Technique. |
| Record Nr. | UNINA-9910726277703321 |
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Rai Amrita
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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