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Biblioteca

MARC Lista (tabellare)
Data science and intelligent systems Proceedings of 5th Computational Methods in Systems and Software 2021 . Volume 2 / / Radek Silhavy, Petr Silhavy, Zdenka Prokopova, editors
Data science and intelligent systems Proceedings of 5th Computational Methods in Systems and Software 2021 . Volume 2 / / Radek Silhavy, Petr Silhavy, Zdenka Prokopova, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (1073 pages)
Disciplina 006.3
Collana Lecture notes in networks and systems
Soggetto topico Artificial intelligence
Control theory
Internet of things
Intel·ligència artificial
Teoria de control
Internet de les coses
Enginyeria de programari
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-90321-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910510569503321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Development and future of internet of drones (IoD) : insights, trends and road ahead / / Rajalakshmi Krishnamurthi, Anand Nayyar, Aboul Ella Hassanien, editors
Development and future of internet of drones (IoD) : insights, trends and road ahead / / Rajalakshmi Krishnamurthi, Anand Nayyar, Aboul Ella Hassanien, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (275 pages)
Disciplina 004.678
Collana Studies in Systems, Decision and Control
Soggetto topico Internet of things
Drone aircraft - Automatic control
Aerial surveillance - Automatic control
Drons
Internet de les coses
Aprenentatge automàtic
Intel·ligència artificial
Soggetto genere / forma Llibres electrònics
ISBN 3-030-63339-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910483663903321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Further advances in Internet of Things in biomedical and cyber physical systems / / editors, Valentina E. Balas, Vijender Kumar Solanki, Raghvendra Kumar
Further advances in Internet of Things in biomedical and cyber physical systems / / editors, Valentina E. Balas, Vijender Kumar Solanki, Raghvendra Kumar
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (xxxi, 403 pages) : illustrations (some color)
Disciplina 004.678
Collana Intelligent Systems Reference Library
Soggetto topico Internet of things
Cooperating objects (Computer systems)
Artificial intelligence
Biomedical engineering
Computational intelligence
Internet de les coses
Enginyeria biomèdica
Estudi de casos
Soggetto genere / forma Llibres electrònics
ISBN 3-030-57835-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Distributed sensor networks Intelligent system design and applications IoT applications in biomedical engineering Cyber physical system framework and applications
Record Nr. UNINA-9910483525103321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The Global environmental effects during and beyond COVID-19 : intelligent computing solutions / / edited by Aboul Ella Hassanien [and four others]
The Global environmental effects during and beyond COVID-19 : intelligent computing solutions / / edited by Aboul Ella Hassanien [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (178 pages)
Disciplina 006.3
Collana Studies in Systems, Decision and Control
Soggetto topico Computational intelligence
Engineering - Data processing
Adaptation (Biology)
Ecology
Euthenics
Nature and nurture
Pandèmia de COVID-19, 2020-
Intel·ligència artificial en medicina
Impacte ambiental
Internet de les coses
Soggetto genere / forma Llibres electrònics
ISBN 3-030-72933-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910484102803321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Hybrid artificial intelligence and IoT in healthcare / / edited by Akash Kumar Bhoi, Pradeep Kumar Mallick, Mihir Narayana Mohanty
Hybrid artificial intelligence and IoT in healthcare / / edited by Akash Kumar Bhoi, Pradeep Kumar Mallick, Mihir Narayana Mohanty
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (341 pages)
Disciplina 060
Collana Intelligent Systems Reference Library
Soggetto topico Internet of things
Artificial intelligence - Medical applications
Intel·ligència artificial en medicina
Internet de les coses
Soggetto genere / forma Llibres electrònics
ISBN 981-16-2972-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Editors and Contributors -- Hybrid Cloud/Fog Environment for Healthcare: An Exploratory Study, Opportunities, Challenges, and Future Prospects -- 1 Introduction -- 2 Applications of Cloud Computing in Smart Healthcare System -- 3 Applications of Fog Computing in Smart Healthcare System -- 4 Challenges of Cloud and Fog Computing in Smart Healthcare System -- 5 The Future Prospects of Cloud and Fog Computing -- 6 Conclusion and Future Research Directions -- References -- Hybrid Intelligent System for Medical Diagnosis in Health Care -- 1 Introduction -- 1.1 Intelligent Systems -- 1.2 Hybrid Intelligent System -- 1.3 Health Care -- 2 Need for Health Care's Intelligent Infrastructure for Medical Diagnosis -- 2.1 Basic Algorithm -- 2.2 Applications in Diagnosis -- 3 Hybrid Intelligent Medical Diagnosis System -- 3.1 Adaptive Neuro-Fuzzy Inference Systems (ANFIS) -- 3.2 Ensemble Approaches -- 3.3 Evolutionary Artificial Neural Network -- 3.4 Application of Hybrid Intelligent System in Health Care -- 4 Need of Hybrid Intelligent System in Medical Diagnosis -- 5 Conclusion -- References -- Remote Patient Monitoring Using IoT, Cloud Computing and AI -- 1 Cloud-Oriented IoT Using AI -- 1.1 Introduction to Internet of Things -- 1.2 Cloud Computing (CC) -- 1.3 Artificial Intelligence (AI) -- 1.4 Deep Learning Architecture -- 2 Wireless Body Networks (WBN) -- 2.1 Overview -- 2.2 Architecture and Applications -- 2.3 Hybrid Sensor-Based Healthcare Systems -- 3 Cloud Infrastructure and Processing -- 3.1 Overview -- 3.2 Topology and Network Protocol for Remote Monitoring -- 3.3 Cloud Infrastructure -- 3.4 Cloud Computing Components and Characteristics -- 4 Challenges in Cloud and AI-Based IoT on Remote Monitoring -- 4.1 Overview -- 4.2 Accessing Cloud with Validation -- 4.3 Block Chain-Oriented Healthcare Records.
4.4 Reliability and Complexity in Computational Intelligence -- 5 Case Studies -- 5.1 IoT-Based Remote Pain Monitoring System: From Device to Cloud Platform [27] -- 5.2 Internet of Things Sensor Assisted Security and Quality Analysis for Healthcare Datasets Using Artificial Intelligent-Based Heuristic Health Management System [28] -- 5.3 A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic [29] -- References -- An Analytical Study of the Role of M-IoT in Healthcare Domain -- 1 Introduction to IoT and Healthcare -- 2 Integration of M-IOT in Healthcare -- 3 Working Principle of M-IOT in Healthcare -- 4 Comparative Analysis of Existing M-IoT Technologies -- 5 Applications of M-IOT Devices in Healthcare -- 6 Benefits of M-IoT -- 7 Challenges of M-IoT -- 8 Relevant Studies on M-IoT in Healthcare -- 9 Discussion of the Use M-IoT in Cancer Detection -- 10 M-IoT in Blood Pressure Measurement from Heart Rate -- 11 Conclusion and Future Scope -- References -- Hybrid AI and IoT Approaches Used in Health Care for Patients Diagnosis -- 1 Introduction -- 2 Methodology Used -- 3 Conclusion -- References -- RADIoT: The Unifying Framework for IoT, Radiomics and Deep Learning Modeling -- 1 Introduction -- 2 The Internet of Things (IoT) in smart healthcare system -- 2.1 Internet of Things (IoT) for Radiomics -- 3 The Radiomics -- 3.1 Dataset Acquisition -- 3.2 Volume of Interests (VOIs) Segmentation -- 3.3 Feature Mining -- 3.4 Feature Selection -- 3.5 Model Development -- 4 Machine Learning Models for Radiomics -- 4.1 Traditional ML Models -- 4.2 Deep Learning (DL) Models -- 4.3 Performance Indicators for ML Models -- 5 Challenges, Open Issues and Opportunities -- 5.1 Challenges of Handicraft Radiomics -- 5.2 Challenges of Deep Learning for Radiomics Analysis -- 5.3 Challenges of IoT in Radiomics.
5.4 Open Issues and Opportunities -- 6 Implementation -- 6.1 The RADIoT Unifying Radiomics Framework -- 6.2 Feature Selection -- 6.3 Classification Results and Discussion -- 7 Conclusion and Future Research Directions -- References -- Hybrid Artificial Intelligence and IoT in Health care for Cardiovascular Patient in Decision-Making System -- 1 Introduction -- 1.1 Comprehensive Health Care Systems -- 1.2 Connected eHealth Mobile Applications -- 1.3 Artificial Intelligence -- 2 Data Source -- 2.1 Analysis of Data -- 3 Materials and Methods -- 3.1 Data Gathering -- 3.2 Feature Selection -- 3.3 Classification -- 4 Various Machine Learning Algorithms -- 4.1 Logistic Regression -- 4.2 Naïve Bayes -- 4.3 Random Forest -- 4.4 Support Vector Machine -- 4.5 Gradient Boosting -- 4.6 Accuracy Module -- 5 Results and Discussion -- 6 Conclusion -- References -- A Smart Assistive System for Visually Impaired to Inform Acquaintance Using Image Processing (ML) Supported by IoT -- 1 Introduction -- 2 Related Work -- 3 System Design -- 4 Results and Discussion -- 5 Conclusion and Future Work -- References -- Internet of Things in Health Care: A Survey -- 1 Introduction -- 2 Classification and Overview -- 2.1 Based on Privacy and Security Techniques -- 2.2 Based on e-Health and m-Health -- 2.3 Based on Cloud, Fog, and Evolutionary Computing -- 2.4 Based on Network and Communication Techniques -- 2.5 Based on System Design and Architecture -- 3 Classification Based on Optimization Goal and Evaluation Platform -- 4 IoT Techniques -- 4.1 Access and Authentication -- 4.2 Compression and Encryption -- 4.3 E-health and M-health -- 4.4 Big Data and Cloud Computing -- 4.5 Evolutionary Computing Algorithms -- 4.6 Fog and Cloud Computing -- 4.7 Network and Communication -- 4.8 System Design and Architecture -- 5 Conclusion and Future Outlook -- References.
Disease Diagnosis System for IoT-Based Wearable Body Sensors with Machine Learning Algorithm -- 1 Introduction -- 2 The General Overview of IoT-Based Applications in Smart Healthcare System -- 3 The Applications of Wearable Body Sensors in Smart Healthcare System -- 4 IoT-Wearable Body Sensors-Based Framework with Machine Learning Algorithm for Disease Diagnosis -- 5 The Application of Machine Learning for the Diagnosis of Heart Diseases as Case Study -- 5.1 The Heart Disease Dataset Characteristics -- 5.2 Performance Evaluation Metrics -- 6 Results and Discussion -- 6.1 The Precision-Recall Curve (PRC) -- 6.2 Confusion Matrix -- 7 Conclusion and Future Research Directions -- References -- Integration of Machine Learning and IoT in Healthcare Domain -- 1 Healthcare Viewpoint -- 1.1 Machine Learning in Health Care -- 1.2 IoT in Health Care -- 2 Renowned Machine Learning Application in the Field of Health Care -- 2.1 Identifying Disease and Diagnosis -- 2.2 Machine Learning in Radiology -- 2.3 Clinical Trial and Research -- 2.4 Outbreak Prediction -- 3 Internet of Things (IoT) Applications in Clinical Domain -- 3.1 Depression Monitoring Apple Watch App -- 3.2 Coagulation Testing -- 3.3 Medical Information Distribution -- 3.4 Emergency Care -- 4 A General Architecture for IoMT Systems -- 5 Various Extensive Studies Conducted -- 6 Review of IoMT Monitoring Solutions -- 6.1 Physiological Analysis -- 6.2 IoMT Solutions in Rehabilitation Systems -- 6.3 Assessing of Diet Intake and Skin Pathology -- 6.4 Treatments Pertaining to the Spread of Epidemics and Their Diagnosis -- 6.5 Diagnosis and Treatment of Diabetes -- 7 Trends and Discussions About Applications -- 8 A Smart Predictive Framework for Disease Risk Factors Detection -- 9 Summary -- References -- Managing Interstitial Lung Diseases with Computer-Aided Visualization -- 1 Introduction.
2 ILD Diagnosis -- 2.1 HRCT Patterns -- 2.2 ILD Diagnosis Summary -- 2.3 ILD Treatment Algorithms -- 3 Computer-Aided Techniques -- 3.1 Regression -- 3.2 Hidden Markov Models -- 3.3 Neural Networks -- 3.4 Complex Networks -- 3.5 Layout Algorithm Selection -- 4 Conclusion -- References -- Use of Machine Learning Algorithms to Identify Sleep Phases Starting from ECG Signals -- 1 Introduction -- 2 Related Works -- 3 The Database -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Numerical Results -- 4.3 Statistical Analysis -- 5 Summary/Conclusion -- References -- Emerging Technologies for Pandemic and Its Impact -- 1 Introduction -- 2 Surveillance -- 2.1 Location Data -- 2.2 Health Tracking Mobile Applications -- 2.3 Robotic Diagnostic System -- 2.4 Robotic Patrolling System -- 3 Healthcare -- 3.1 3D Printing Supplies -- 3.2 Advanced Isolation Cubicles with Automation -- 3.3 Autonomous Vehicles -- 3.4 Cobotics for Treatment -- 4 Economy -- 4.1 3D Remote Work with XR (AR or VR) -- 4.2 Sanitization Systems for Essential Workers -- 5 Lockdown -- 5.1 Drones -- 5.2 AI Based Entertainment Streaming -- 5.3 Automation and Innovation in Cleaning -- 6 Education -- 6.1 Interactive Mixed Reality (MR) Classrooms -- 6.2 AI for Analyzing Student Mental Health -- 7 Conclusion -- References -- Impact of Artificial Intelligence in Health care: A Study -- 1 Introduction -- 1.1 Autonomous Vehicles -- 1.2 Cybersecurity -- 1.3 Agriculture -- 1.4 Social Media and Gaming -- 1.5 Military -- 1.6 Finance and Business -- 2 AI in Health care -- 3 Existing Applications Integrating AI in the Healthcare Sector -- 3.1 Virtual Nurses and Digital Consultation -- 3.2 Robots -- 3.3 Cybersecurity -- 3.4 Administration and Workflow -- 3.5 Dosage and Treatment Design -- 3.6 Fraud Detection -- 3.7 Health Monitoring -- 3.8 Drug Creation and Clinical Trial Participation.
3.9 Treatment Design and Precision Medicine.
Record Nr. UNINA-9910495204203321
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Information and communication technologies for agriculture theme II : data / / edited by Dionysis D. Bochtis [and four others]
Information and communication technologies for agriculture theme II : data / / edited by Dionysis D. Bochtis [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (296 pages)
Disciplina 630.2085
Collana Springer Optimization and Its Applications
Soggetto topico Agricultural informatics
Enginyeria agronòmica
Innovacions agrícoles
Internet de les coses
Aplicacions industrials
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-84148-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Part I: Data Technologies -- You Got Data Now What: Building the Right Solution for the Problem -- 1 Introduction -- 2 Sensors and Their Readings -- 3 Networks of Sensors -- 3.1 In-Field Crop Production -- 3.2 Intensive Crop Production -- 3.3 Intensive Animal Production -- 4 Using Machine Learning -- 5 Remaining Challenges and Opportunities -- References -- Data Fusion and Its Applications in Agriculture -- 1 Introduction -- 2 Data Fusion -- 2.1 Introduction -- 2.2 The ``Whys´´ and ``Wherefores´´ of Information Fusion -- 2.3 Information Fusion: Methods, Techniques, and Algorithms -- 2.4 Models of Data Fusion -- Architectures and Performance Aspects -- Data Alignment and Fusion of Attributes -- 2.5 Applications of Information Fusion in Agriculture -- Remote Sensing Image Preprocessing -- Restoration and Denoising -- Pixel-based Classification -- Spectral Feature Classification -- Classification with Spatial Information -- Target Recognition -- Scene Understanding -- 2.6 Data Mining and Artificial Intelligence in Agriculture -- Yield Prediction -- Disease Detection -- Weed Detection -- Species Recognition -- 3 Conclusions and Future Challenges -- References -- Machine Learning Technology and Its Current Implementation in Agriculture -- 1 Introduction -- 2 Machine Learning Versus Conventional Programming -- 3 Fundamental Features of Machine Learning -- 4 Types of Machine Learning Methods -- 4.1 Supervised Learning -- Regression -- Classification -- 4.2 Unsupervised Learning -- Clustering -- Dimensionality Reduction -- Association -- 4.3 Reinforcement Learning -- Classification -- Control -- 4.4 Recommender Systems (Active Learning) -- Content-based -- Collaborative Filtering -- 5 Families of Machine Learning Algorithms -- 5.1 Regression -- 5.2 Regularization -- 5.3 Bayesian -- 5.4 Instance-based -- 5.5 Decision Tree.
5.6 Ensemble -- 5.7 Clustering -- 5.8 Dimensionality Reduction -- 5.9 Association Rule -- 5.10 Artificial Neural Networks -- 5.11 Deep Neural Networks -- 6 Machine Learning in Agriculture -- 6.1 Yield Prediction -- 6.2 Crop Disease Detection -- 6.3 Weed Detection -- 6.4 Quality Assessment -- 7 Summary of the Basic Aspects of the Reviewed Studies -- 8 Conclusions -- References -- Part II: Applications -- Application Possibilities of IoT-based Management Systems in Agriculture -- 1 Introduction -- 1.1 Data Acquisition and Management in Agriculture -- 2 Methodology -- 3 Progression and Evaluation of the System -- 3.1 The Main Characteristics Based on the Literature -- 3.2 Determining the Possibilities from a Practical Standpoint -- Data Acquisition Systems -- Data Management Methods and Applications -- Data Utilization -- 4 Discussion -- 5 Conclusions -- References -- Plant Species Detection Using Image Processing and Deep Learning: A Mobile-Based Application -- 1 Introduction -- 2 Background Research -- 2.1 Deep Learning -- 3 Methodology -- 3.1 Dataset and Data Preparation -- Background Removal -- Data Augmentation -- 4 Software Development and Analysis -- 5 Detailed Design and Software Implementation -- 5.1 Developing Convolutional Neural Network -- 5.2 Online Classification System App -- 6 Testing and Evaluation -- 7 Discussion and Future Work -- 8 Conclusions -- References -- Computer Vision-based Detection and Tracking in the Olive Sorting Pipeline -- 1 Introduction -- 1.1 Industrial Sorters -- 2 Problem Description -- 2.1 Related Work -- 3 The Proposed Olive Separation Approach -- 3.1 Image Binarization -- 3.2 Distance Transform -- 3.3 Watershed Transform -- 3.4 Centroid Extraction -- 3.5 Multiple Object Tracking -- 4 The Unscented Kalman Filter -- 4.1 Prediction Phase of the UKF -- 4.2 Update Phase of the UKF.
5 The Kuhn-Munkres (Hungarian) Algorithm -- 6 Results -- 6.1 Sample Collection -- 6.2 Simulation Design -- 6.3 Results Using Kalman Filtering -- 7 Evaluation of the Results -- 8 Conclusions -- References -- Integrating Spatial with Qualitative Data to Monitor Land Use Intensity: Evidence from Arable Land - Animal Husbandry Systems -- 1 Introduction -- 1.1 Land Use Intensity and Farming Systems -- 1.2 Land Use/Land Cover (LULC) Extraction -- 2 Methodology -- 2.1 Study Area -- 2.2 Materials and Methods -- Timeline of Changes -- Remote Sensing Data -- 2.3 Participatory Workshop -- 3 Results and Discussion -- 3.1 Image Processing -- 3.2 Land Cover Type Extraction and Change Detection -- 3.3 Land Conversions -- 3.4 Results from Qualitative Methods -- 3.5 Comparison and Synthesis of Results -- 3.6 Farming Systems and Land Use Intensity -- 4 Conclusion: Ways Forward in Integrating Qualitative Data in Land Use Intensity -- References -- Air drill Seeder Distributor Head Evaluation: A Comparison between Laboratory Tests and Computational Fluid Dynamics Simulatio... -- 1 Introduction -- 2 Materials and Methods -- 2.1 Tested Model Description -- 2.2 Description of Distributor Head´s Test Bench -- 2.3 Experiment Design -- 2.4 Numerical Simulations -- Air-Seeds Mixture Flow -- Air Flow -- Particles Trajectory -- Discrete Phase Model Setup -- 3 Results -- 3.1 Experimental Results -- 3.2 Numerical Results -- 3.3 Validation of the Numerical Model -- 4 Conclusions and Perspectives -- References -- Part III: Value Chain -- Data-Based Agricultural Business Continuity Management Policies -- 1 Introduction -- 2 Motivation -- 2.1 Business Intelligence Tools as Business Continuity Solutions in the Modern Era -- 2.2 Business Continuity and Big Data Challenges in the Agricultural Domain -- 2.3 Research Steps -- 3 Tools and Methods -- 3.1 Formulation of Datasets.
3.2 Business Intelligence Multidimensional Data Models - Preliminary Concepts -- 3.3 Business Process Modelling Notation (BPMN) for Supporting Business Decisions Based on Multidimensional Data -- 3.4 A Robust Machine Learning Agricultural Business Continuity Classifier -- 4 Results -- 4.1 The Multidimensional Data Models for Supporting Agricultural Business Continuity Management Decisions -- Model 1: The Criticality Levels Multidimensional Model -- Model 2: The Risks/Hazards Multidimensional Model -- 4.2 Machine Learning Predictive Analytics Based on the Proposed Multidimensional Schemas -- Data Preprocessing -- Risk Exposure Classification Based on Decision Tree Induction -- Boosting the Risk Exposure classifier´s Predictive Power with the 10-Fold Cross-Validation and the Random Forest Techniques -- 5 Discussion -- 6 Conclusions -- References -- Soybean Price Trend Forecast Using Deep Learning Techniques Based on Prices and Text Sentiments -- 1 Introduction -- 2 Related Work -- 2.1 Price Prediction of Agricultural Commodities -- 2.2 Deep Learning for Price Trend Prediction -- 2.3 Deep Learning for Text Sentiment Analysis -- 3 Methodology -- 4 Results -- 4.1 Models Considering Only Prices -- 4.2 Models Considering Only Text Sentiments -- 4.3 Ensemble Model Considering Prices and Text Sentiments -- 5 Discussion -- 5.1 Benefits of Deep Learning for Agricultural Price Prediction -- 5.2 Adaptation and Uses for Other Products -- 6 Conclusions -- References -- Use of Unsupervised Machine Learning for Agricultural Supply Chain Data Labeling -- 1 Introduction -- 2 Unsupervised Machine Learning in Agriculture -- 3 Methodology -- 4 Results -- 5 Discussion -- 5.1 Training Time for Each Model -- 5.2 Implementation Difficulties -- 5.3 Benefits for SC Traceability -- 5.4 Adaptation to Other SCs -- 6 Conclusions -- References.
Record Nr. UNISA-996466419403316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Information and communication technologies for agriculture theme II : data / / edited by Dionysis D. Bochtis [and four others]
Information and communication technologies for agriculture theme II : data / / edited by Dionysis D. Bochtis [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (296 pages)
Disciplina 630.2085
Collana Springer Optimization and Its Applications
Soggetto topico Agricultural informatics
Enginyeria agronòmica
Innovacions agrícoles
Internet de les coses
Aplicacions industrials
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-84148-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Part I: Data Technologies -- You Got Data Now What: Building the Right Solution for the Problem -- 1 Introduction -- 2 Sensors and Their Readings -- 3 Networks of Sensors -- 3.1 In-Field Crop Production -- 3.2 Intensive Crop Production -- 3.3 Intensive Animal Production -- 4 Using Machine Learning -- 5 Remaining Challenges and Opportunities -- References -- Data Fusion and Its Applications in Agriculture -- 1 Introduction -- 2 Data Fusion -- 2.1 Introduction -- 2.2 The ``Whys´´ and ``Wherefores´´ of Information Fusion -- 2.3 Information Fusion: Methods, Techniques, and Algorithms -- 2.4 Models of Data Fusion -- Architectures and Performance Aspects -- Data Alignment and Fusion of Attributes -- 2.5 Applications of Information Fusion in Agriculture -- Remote Sensing Image Preprocessing -- Restoration and Denoising -- Pixel-based Classification -- Spectral Feature Classification -- Classification with Spatial Information -- Target Recognition -- Scene Understanding -- 2.6 Data Mining and Artificial Intelligence in Agriculture -- Yield Prediction -- Disease Detection -- Weed Detection -- Species Recognition -- 3 Conclusions and Future Challenges -- References -- Machine Learning Technology and Its Current Implementation in Agriculture -- 1 Introduction -- 2 Machine Learning Versus Conventional Programming -- 3 Fundamental Features of Machine Learning -- 4 Types of Machine Learning Methods -- 4.1 Supervised Learning -- Regression -- Classification -- 4.2 Unsupervised Learning -- Clustering -- Dimensionality Reduction -- Association -- 4.3 Reinforcement Learning -- Classification -- Control -- 4.4 Recommender Systems (Active Learning) -- Content-based -- Collaborative Filtering -- 5 Families of Machine Learning Algorithms -- 5.1 Regression -- 5.2 Regularization -- 5.3 Bayesian -- 5.4 Instance-based -- 5.5 Decision Tree.
5.6 Ensemble -- 5.7 Clustering -- 5.8 Dimensionality Reduction -- 5.9 Association Rule -- 5.10 Artificial Neural Networks -- 5.11 Deep Neural Networks -- 6 Machine Learning in Agriculture -- 6.1 Yield Prediction -- 6.2 Crop Disease Detection -- 6.3 Weed Detection -- 6.4 Quality Assessment -- 7 Summary of the Basic Aspects of the Reviewed Studies -- 8 Conclusions -- References -- Part II: Applications -- Application Possibilities of IoT-based Management Systems in Agriculture -- 1 Introduction -- 1.1 Data Acquisition and Management in Agriculture -- 2 Methodology -- 3 Progression and Evaluation of the System -- 3.1 The Main Characteristics Based on the Literature -- 3.2 Determining the Possibilities from a Practical Standpoint -- Data Acquisition Systems -- Data Management Methods and Applications -- Data Utilization -- 4 Discussion -- 5 Conclusions -- References -- Plant Species Detection Using Image Processing and Deep Learning: A Mobile-Based Application -- 1 Introduction -- 2 Background Research -- 2.1 Deep Learning -- 3 Methodology -- 3.1 Dataset and Data Preparation -- Background Removal -- Data Augmentation -- 4 Software Development and Analysis -- 5 Detailed Design and Software Implementation -- 5.1 Developing Convolutional Neural Network -- 5.2 Online Classification System App -- 6 Testing and Evaluation -- 7 Discussion and Future Work -- 8 Conclusions -- References -- Computer Vision-based Detection and Tracking in the Olive Sorting Pipeline -- 1 Introduction -- 1.1 Industrial Sorters -- 2 Problem Description -- 2.1 Related Work -- 3 The Proposed Olive Separation Approach -- 3.1 Image Binarization -- 3.2 Distance Transform -- 3.3 Watershed Transform -- 3.4 Centroid Extraction -- 3.5 Multiple Object Tracking -- 4 The Unscented Kalman Filter -- 4.1 Prediction Phase of the UKF -- 4.2 Update Phase of the UKF.
5 The Kuhn-Munkres (Hungarian) Algorithm -- 6 Results -- 6.1 Sample Collection -- 6.2 Simulation Design -- 6.3 Results Using Kalman Filtering -- 7 Evaluation of the Results -- 8 Conclusions -- References -- Integrating Spatial with Qualitative Data to Monitor Land Use Intensity: Evidence from Arable Land - Animal Husbandry Systems -- 1 Introduction -- 1.1 Land Use Intensity and Farming Systems -- 1.2 Land Use/Land Cover (LULC) Extraction -- 2 Methodology -- 2.1 Study Area -- 2.2 Materials and Methods -- Timeline of Changes -- Remote Sensing Data -- 2.3 Participatory Workshop -- 3 Results and Discussion -- 3.1 Image Processing -- 3.2 Land Cover Type Extraction and Change Detection -- 3.3 Land Conversions -- 3.4 Results from Qualitative Methods -- 3.5 Comparison and Synthesis of Results -- 3.6 Farming Systems and Land Use Intensity -- 4 Conclusion: Ways Forward in Integrating Qualitative Data in Land Use Intensity -- References -- Air drill Seeder Distributor Head Evaluation: A Comparison between Laboratory Tests and Computational Fluid Dynamics Simulatio... -- 1 Introduction -- 2 Materials and Methods -- 2.1 Tested Model Description -- 2.2 Description of Distributor Head´s Test Bench -- 2.3 Experiment Design -- 2.4 Numerical Simulations -- Air-Seeds Mixture Flow -- Air Flow -- Particles Trajectory -- Discrete Phase Model Setup -- 3 Results -- 3.1 Experimental Results -- 3.2 Numerical Results -- 3.3 Validation of the Numerical Model -- 4 Conclusions and Perspectives -- References -- Part III: Value Chain -- Data-Based Agricultural Business Continuity Management Policies -- 1 Introduction -- 2 Motivation -- 2.1 Business Intelligence Tools as Business Continuity Solutions in the Modern Era -- 2.2 Business Continuity and Big Data Challenges in the Agricultural Domain -- 2.3 Research Steps -- 3 Tools and Methods -- 3.1 Formulation of Datasets.
3.2 Business Intelligence Multidimensional Data Models - Preliminary Concepts -- 3.3 Business Process Modelling Notation (BPMN) for Supporting Business Decisions Based on Multidimensional Data -- 3.4 A Robust Machine Learning Agricultural Business Continuity Classifier -- 4 Results -- 4.1 The Multidimensional Data Models for Supporting Agricultural Business Continuity Management Decisions -- Model 1: The Criticality Levels Multidimensional Model -- Model 2: The Risks/Hazards Multidimensional Model -- 4.2 Machine Learning Predictive Analytics Based on the Proposed Multidimensional Schemas -- Data Preprocessing -- Risk Exposure Classification Based on Decision Tree Induction -- Boosting the Risk Exposure classifier´s Predictive Power with the 10-Fold Cross-Validation and the Random Forest Techniques -- 5 Discussion -- 6 Conclusions -- References -- Soybean Price Trend Forecast Using Deep Learning Techniques Based on Prices and Text Sentiments -- 1 Introduction -- 2 Related Work -- 2.1 Price Prediction of Agricultural Commodities -- 2.2 Deep Learning for Price Trend Prediction -- 2.3 Deep Learning for Text Sentiment Analysis -- 3 Methodology -- 4 Results -- 4.1 Models Considering Only Prices -- 4.2 Models Considering Only Text Sentiments -- 4.3 Ensemble Model Considering Prices and Text Sentiments -- 5 Discussion -- 5.1 Benefits of Deep Learning for Agricultural Price Prediction -- 5.2 Adaptation and Uses for Other Products -- 6 Conclusions -- References -- Use of Unsupervised Machine Learning for Agricultural Supply Chain Data Labeling -- 1 Introduction -- 2 Unsupervised Machine Learning in Agriculture -- 3 Methodology -- 4 Results -- 5 Discussion -- 5.1 Training Time for Each Model -- 5.2 Implementation Difficulties -- 5.3 Benefits for SC Traceability -- 5.4 Adaptation to Other SCs -- 6 Conclusions -- References.
Record Nr. UNINA-9910552715003321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Information and communication technologies for agriculture-Theme IV : actions / / edited by Dionysis D. Bochtis [and four others]
Information and communication technologies for agriculture-Theme IV : actions / / edited by Dionysis D. Bochtis [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (293 pages)
Disciplina 630.2085
Collana Springer Optimization and Its Applications
Soggetto topico Agricultural informatics
Innovacions agrícoles
Internet de les coses
Aplicacions industrials
Soggetto genere / forma Llibres electrònics
ISBN 3-030-84156-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996466555703316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Information and communication technologies for agriculture-Theme IV : actions / / edited by Dionysis D. Bochtis [and four others]
Information and communication technologies for agriculture-Theme IV : actions / / edited by Dionysis D. Bochtis [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (293 pages)
Disciplina 630.2085
Collana Springer Optimization and Its Applications
Soggetto topico Agricultural informatics
Innovacions agrícoles
Internet de les coses
Aplicacions industrials
Soggetto genere / forma Llibres electrònics
ISBN 3-030-84156-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910552711703321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Internet of medical things : remote healthcare systems and applications / / D. Jude Hemanth, J. Anitha, George A. Tsihrintzis, editors
Internet of medical things : remote healthcare systems and applications / / D. Jude Hemanth, J. Anitha, George A. Tsihrintzis, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (274 pages)
Disciplina 025.040256155
Collana Internet of Things, Technology, Communications and Computing
Soggetto topico Internet in medicine
Internet of things
Artificial intelligence - Medical applications
Internet de les coses
Internet en medicina
Assistència sanitària
Soggetto genere / forma Llibres electrònics
ISBN 3-030-63937-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910484752303321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui