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Advances in Machine Learning for Big Data Analysis / / edited by Satchidananda Dehuri, Yen-Wei Chen
| Advances in Machine Learning for Big Data Analysis / / edited by Satchidananda Dehuri, Yen-Wei Chen |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (xix, 239 pages) : illustrations (some color), charts |
| Disciplina | 780 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Computational intelligence
Machine learning Artificial intelligence - Data processing Big data Computational Intelligence Machine Learning Data Science Big Data |
| ISBN |
9789811689291
9811689296 9789811689307 981168930X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep Learning for Supervised Learning -- Deep Learning for Unsupervised Learning -- Support Vector Machine for Regression -- Support Vector Machine for Classification -- Decision Tree for Regression -- Higher Order Neural Networks -- Competitive Learning -- Semi-supervised Learning -- Multi-objective Optimization Techniques -- Techniques for Feature Selection/Extraction -- Techniques for Task Relevant Big Data Analysis -- Techniques for Post Processing Task in Big Data Analysis -- Customer Relationship Management. |
| Record Nr. | UNINA-9910743352903321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Biologically inspired techniques in many criteria decision making : proceedings of BITMDM 2021 / / edited by Satchidananda Dehuri [and three others]
| Biologically inspired techniques in many criteria decision making : proceedings of BITMDM 2021 / / edited by Satchidananda Dehuri [and three others] |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (718 pages) |
| Disciplina | 658.403 |
| Collana | Smart Innovation, Systems and Technologies |
| Soggetto topico |
Bioinformatics
Artificial intelligence Data mining |
| ISBN |
981-16-8738-2
981-16-8739-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743225103321 |
| Gateway East, Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Biologically Inspired Techniques in Many Criteria Decision-Making : Proceedings of BITMDM 2024 / / edited by Satchidananda Dehuri, Sujata Dash, Ruppa K. Thulasiram, Rohen H. Singh, Margarita Favorskaya
| Biologically Inspired Techniques in Many Criteria Decision-Making : Proceedings of BITMDM 2024 / / edited by Satchidananda Dehuri, Sujata Dash, Ruppa K. Thulasiram, Rohen H. Singh, Margarita Favorskaya |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (XIV, 479 p. 223 illus., 187 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Learning and Analytics in Intelligent Systems |
| Soggetto topico |
Computational intelligence
Artificial intelligence Automatic control Robotics Automation Computational Intelligence Artificial Intelligence Control, Robotics, Automation |
| ISBN | 3-031-82706-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | -- Evaluating the top Machine Learning Classifiers Used in Diabetes Prediction -- A Machine Learning-Based Approach to Enhance Fraud Detection Using Decision Tree -- Oropharyngeal Cancer Detection with Machine Learning for Precision Diagnosis -- A Deep Learning Framework for Crime Detection -- Deep Learning and Bio-Inspired Algorithm Based Chat Bot, etc. |
| Record Nr. | UNINA-9910987782503321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Biologically Inspired Techniques in Many-Criteria Decision Making : International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019) / / edited by Satchidananda Dehuri, Bhabani Shankar Prasad Mishra, Pradeep Kumar Mallick, Sung-Bae Cho, Margarita N. Favorskaya
| Biologically Inspired Techniques in Many-Criteria Decision Making : International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019) / / edited by Satchidananda Dehuri, Bhabani Shankar Prasad Mishra, Pradeep Kumar Mallick, Sung-Bae Cho, Margarita N. Favorskaya |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xv, 258 pages) |
| Disciplina | 658.403 |
| Collana | Learning and Analytics in Intelligent Systems |
| Soggetto topico |
Computational intelligence
Engineering - Data processing Artificial intelligence Computational Intelligence Data Engineering Artificial Intelligence |
| ISBN | 3-030-39033-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Classification of Arrhythmia Using Artificial Neural Network with Grey Wolf Optimization -- Chapter 2: Multi-objective Biogeography-Based Optimization for Influence Maximization-Cost Minimization in Social Networks -- Chapter 3: Classification of Credit Dataset Using Improved Particle Swarm Optimization Tuned Radial Basis Function Neural Networks -- Chapter 4: Multi-verse Optimization of Multilayer Perceptrons (MV-MLPs) for Efficient Modeling and Forecasting of Crude Oil Prices Data -- Chapter 5: Application of machine learning to predict diseases based on symptoms in rural India -- Chapter 6: Classıfıcatıon of Real Tıme Noısy Fıngerprınt Images Usıng FLANN -- Chapter 7: Software Reliability Prediction with Ensemble Method and Virtual Data Point Incorporation -- Chapter 8: Hyperspectral Image Classification using Stochastic Gradient Descent based Support Vector Machine -- Chapter 9: A Survey on Ant Colony Optimization for Solving Some of the Selected NP-Hard Problem -- Chapter 10: Machine Learning Models for Stock Prediction using Real-Time Streaming Data -- Chapter 11: Epidemiology of Breast Cancer (BC) and its Early Identification via Evolving Machine Learning Classification Tools (MLCT)–A Study -- Chapter 12: Ensemble Classification Approach for Cancer Prognosis and Prediction -- Chapter 13: Extractive Odia Text Summarization System: An OCR based Approach -- Chapter 14: Predicting sensitivity of local news articles from Odia dailies -- Chapter 15: A systematic frame work using machine learning approaches in supply chain forecasting -- Chapter 16: An Intelligent system on computer-aided diagnosis for Parkinson’s disease with MRI using Machine Learning -- Chapter 17: Operations on Picture Fuzzy Numbers and their Application in Multi-Criteria Group Decision Making Problems -- Chapter 18: Some Generalized Results on Multi-Criteria Decision Making Model using Fuzzy TOPSIS Technique -- Chapter 19: A Survey on FP-Tree Based Incremental Frequent Pattern Mining -- Chapter 20: Improving Co-expressed Gene Pattern Finding Using Gene Ontology -- Chapter 21: Survey of Methods Used for Differential Expression Analysis on RNA Seq Data -- Chapter 22: Adaptive Antenna Tilt for Cellular Coverage Optimization in Suburban Scenario -- Chapter 23: A survey of the different itemset representation for candidate. |
| Record Nr. | UNINA-9910484374803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Boosting Software Development Using Machine Learning / / edited by Tirimula Rao Benala, Satchidananda Dehuri, Rajib Mall, Margarita N. Favorskaya
| Boosting Software Development Using Machine Learning / / edited by Tirimula Rao Benala, Satchidananda Dehuri, Rajib Mall, Margarita N. Favorskaya |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (XXII, 320 p. 66 illus., 41 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Artificial Intelligence-Enhanced Software and Systems Engineering |
| Soggetto topico |
Computational intelligence
Artificial intelligence Machine learning Computational Intelligence Artificial Intelligence Machine Learning |
| ISBN | 3-031-88188-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1.Transforming Software Development: From Traditional Methods to Generative Artificial Intelligence -- 2.Case Study: Transforming Operational and Organizational Efficiency Using Artificial Intelligence and Machine Learning -- 3.Revolutionizing Software Development: The Transformative Influence of Machine Learning Integrated SDLC Model -- 4.Generative Coding: Unlocking Ontological AI -- 5.Case Studies: Machine Learning Approaches for Software Development Effort Estimation -- 6.Hybridizing Metaheuristics and Analogy-based Methods with Ensemble Learning for Improved Software Cost Estimation -- 7.A Review on Detection of Design Pattern in Source Code Using Machine Learning Techniques -- 8.Machine Learning Techniques for the Measurement of Software Attributes -- 9.An Effective Analysis of New Metaheuristic Algorithms and its Performance Comparison -- 10.Empowering Software Security: Leveraging Machine Learning for Anomaly Detection and Threat Prevention -- 11.Sentiment Analysis on Movie Reviews Using the Convolutional LSTM (Co-LSTM) Model -- 12.An Overview of AI Workload Optimization Techniques -- 13.Opportunity Discovery for Effective Innovation Using Artificial Intelligence -- 14.Applications of Machine Learning Algorithms in Open Innovation. |
| Record Nr. | UNINA-9911007493203321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Cloud Computing for Optimization: Foundations, Applications, and Challenges / / edited by Bhabani Shankar Prasad Mishra, Himansu Das, Satchidananda Dehuri, Alok Kumar Jagadev
| Cloud Computing for Optimization: Foundations, Applications, and Challenges / / edited by Bhabani Shankar Prasad Mishra, Himansu Das, Satchidananda Dehuri, Alok Kumar Jagadev |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (467 pages) |
| Disciplina | 004.6782 |
| Collana | Studies in Big Data |
| Soggetto topico |
Computational intelligence
Artificial intelligence Big data Computational Intelligence Artificial Intelligence Big Data |
| ISBN | 3-319-73676-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Nature Inspired Optimizations in Cloud Computing: Applications and Challenges.- Resource Allocation in Cloud Computing Using OptimizationTechniques.- Energy Aware Resource Allocation Model for IaaS Optimization.- A Game Theoretic Model for Cloud Federation -- Resource Provisioning Strategy for Scientific Workflows in Cloud Computing Environment -- Consolidation in Cloud Environment Using Optimization Techniques. |
| Record Nr. | UNINA-9910739444303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computational Intelligence for Big Data Analysis : Frontier Advances and Applications / / edited by D.P. Acharjya, Satchidananda Dehuri, Sugata Sanyal
| Computational Intelligence for Big Data Analysis : Frontier Advances and Applications / / edited by D.P. Acharjya, Satchidananda Dehuri, Sugata Sanyal |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (276 p.) |
| Disciplina | 620.00151 |
| Collana | Adaptation, Learning, and Optimization |
| Soggetto topico |
Computational intelligence
Data mining Artificial intelligence Computational Intelligence Data Mining and Knowledge Discovery Artificial Intelligence |
| ISBN | 3-319-16598-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | “Atrain Distributed System” (ADS) : An Infinitely Scalable Architecture for Processing Big Data of any 4Vs -- “Atrain Distributed System” (ADS) : An Infinitely Scalable Architecture for Processing Big Data of any 4Vs -- Learning Using Hybrid Intelligence Techniques -- Neutrosophic Sets and its Applications to Decision Making -- An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis -- Self Organizing Migrating Algorithm with Nelder Mead Crossover and Log-Logisti Mutation for Large Scale Optimization -- A Spectrum of Big Data Applications for Data Analytics -- Fundamentals of Brain Signals and its Medical Application Using Data Analysis Techniques -- BigData: Processing of Data Intensive Applications on Cloud -- Framework for Supporting Heterogenous Clouds using Model Driven Approach -- Cloud based Big Data Analytics:WAN Optimization Techniques and Solutions -- Cloud Based E-Governance Solution: A Case Study. |
| Record Nr. | UNINA-9910299836203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Integration of swarm intelligence and artificial neutral network [[electronic resource] /] / Satchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors
| Integration of swarm intelligence and artificial neutral network [[electronic resource] /] / Satchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors |
| Pubbl/distr/stampa | Hackensack, N.J. ; ; London, : World Scientific, 2011 |
| Descrizione fisica | 1 online resource (352 p.) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
DehuriSatchidananda
GhoshSusmita ChoSung-Bae |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Swarm intelligence
Neural networks (Computer science) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-43330-3
9786613433305 981-4280-15-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; Chapter 1 Swarm Intelligence and Neural Networks; 1.1. Introduction; 1.2. Swarm Intelligence; 1.2.1. Particle Swarm Optimization; 1.2.2. Ant Colony Optimization; 1.2.3. Bee Colony Optimization; 1.3. Neural Networks; 1.3.1. Evolvable Neural Network; 1.3.2. Higher Order Neural Network; 1.3.3. Pi (Π)-Sigma (Σ) Neural Networks; 1.3.4. Functional Link Artificial Neural Network; 1.3.5. Ridge Polynomial Neural Networks (RPNNs); 1.4. Summary and Discussion; References; Chapter 2 Neural Network and Swarm Intelligence for Data Mining; 2.1. Introduction; 2.2. Testbeds for Data Mining
2.2.1. Fisher Iris Data2.2.2. Pima - Diabetes Data; 2.2.3. Shuttle Data; 2.2.4. Classification Efficiency; 2.3. Neural Network for Data Mining; 2.3.1. Multi-Layer Perceptron (MLP); 2.3.2. Radial Basis Function Network; 2.4. Swarm Intelligence for Data Mining; 2.4.1. Ant Miner; 2.4.2. Artificial Bee Colony; 2.4.3. Particle Swarm Optimization; 2.5. Comparative Study; 2.6. Conclusions and Outlook; Acknowledgments; References; Chapter 3 Multi-Objective Ant Colony Optimization: A Taxonomy and Review of Approaches; 3.1. Introduction; 3.2. Ant Colony Optimization 3.3. Basic Concepts of Multi-Objective Optimization3.4. The ACO Metaheuristic for MOOPs in the Literature; 3.5. ACO Variants for MOOP: A Refined Taxonomy; 3.6. Promising Research Areas; 3.7. Conclusions; Acknowledgments; References; Chapter 4 Recurrent Neural Networks with Discontinuous Activation Functions for Convex Optimization; 4.1. Introduction; 4.2. Related Definitions and Lemmas; 4.3. For Linear Programming; 4.3.1. Model Description and Convergence Results; 4.3.2. Simulation Results; 4.4. For Quadratic Programming; 4.4.1. Model Description; 4.4.2. Convergence Results 4.4.3. Simulation Results4.5. For Non-Smooth Convex Optimization Subject to Linear Equality Constraints; 4.5.1. Model Description and Convergence Results; 4.5.2. Constrained Least Absolute Deviation; 4.6. Application to k-Winners-Take-All; 4.6.1. LP-Based Model; 4.6.2. QP-Based Model; 4.6.3. Simulation Results; 4.7. Concluding Remarks; Acknowledgments; References; Chapter 5 Automated Power Quality Disturbance Classification Using Evolvable Neural Network; 5.1. Introduction; 5.2. Wavelet Transform (WT); 5.3. Brief Overview of Neural Network Classifiers 5.4. Overview of Particle Swarm Optimization5.5. Signal Generation, Feature Extraction and Classification; 5.6. Results and Discussion; 5.7. Conclusions; References; Chapter 6 Condition Monitoring and Fault Diagnosis Using Intelligent Techniques; 6.1. Introduction; 6.2. Methodology; 6.2.1. Hardware Specification, System Setup and Audio Data Generation; 6.2.2. Data Pre-Processing; 6.2.3. Data Classification Techniques; 6.2.4. Signal Segregation using Independent Component Analysis; 6.3. Experimental Details; 6.3.1. Pre-Processing 6.3.2. Method 1: Artificial Neural Network Setup for Engine Classification |
| Record Nr. | UNINA-9910464493103321 |
| Hackensack, N.J. ; ; London, : World Scientific, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Integration of swarm intelligence and artificial neutral network [[electronic resource] /] / Satchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors
| Integration of swarm intelligence and artificial neutral network [[electronic resource] /] / Satchidananda Dehuri, Susmita Ghosh, Sung-bae Cho, editors |
| Pubbl/distr/stampa | Hackensack, N.J. ; ; London, : World Scientific, 2011 |
| Descrizione fisica | 1 online resource (352 p.) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
DehuriSatchidananda
GhoshSusmita ChoSung-Bae |
| Collana | Series in machine perception and artificial intelligence |
| Soggetto topico |
Swarm intelligence
Neural networks (Computer science) |
| ISBN |
1-283-43330-3
9786613433305 981-4280-15-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; Chapter 1 Swarm Intelligence and Neural Networks; 1.1. Introduction; 1.2. Swarm Intelligence; 1.2.1. Particle Swarm Optimization; 1.2.2. Ant Colony Optimization; 1.2.3. Bee Colony Optimization; 1.3. Neural Networks; 1.3.1. Evolvable Neural Network; 1.3.2. Higher Order Neural Network; 1.3.3. Pi (Π)-Sigma (Σ) Neural Networks; 1.3.4. Functional Link Artificial Neural Network; 1.3.5. Ridge Polynomial Neural Networks (RPNNs); 1.4. Summary and Discussion; References; Chapter 2 Neural Network and Swarm Intelligence for Data Mining; 2.1. Introduction; 2.2. Testbeds for Data Mining
2.2.1. Fisher Iris Data2.2.2. Pima - Diabetes Data; 2.2.3. Shuttle Data; 2.2.4. Classification Efficiency; 2.3. Neural Network for Data Mining; 2.3.1. Multi-Layer Perceptron (MLP); 2.3.2. Radial Basis Function Network; 2.4. Swarm Intelligence for Data Mining; 2.4.1. Ant Miner; 2.4.2. Artificial Bee Colony; 2.4.3. Particle Swarm Optimization; 2.5. Comparative Study; 2.6. Conclusions and Outlook; Acknowledgments; References; Chapter 3 Multi-Objective Ant Colony Optimization: A Taxonomy and Review of Approaches; 3.1. Introduction; 3.2. Ant Colony Optimization 3.3. Basic Concepts of Multi-Objective Optimization3.4. The ACO Metaheuristic for MOOPs in the Literature; 3.5. ACO Variants for MOOP: A Refined Taxonomy; 3.6. Promising Research Areas; 3.7. Conclusions; Acknowledgments; References; Chapter 4 Recurrent Neural Networks with Discontinuous Activation Functions for Convex Optimization; 4.1. Introduction; 4.2. Related Definitions and Lemmas; 4.3. For Linear Programming; 4.3.1. Model Description and Convergence Results; 4.3.2. Simulation Results; 4.4. For Quadratic Programming; 4.4.1. Model Description; 4.4.2. Convergence Results 4.4.3. Simulation Results4.5. For Non-Smooth Convex Optimization Subject to Linear Equality Constraints; 4.5.1. Model Description and Convergence Results; 4.5.2. Constrained Least Absolute Deviation; 4.6. Application to k-Winners-Take-All; 4.6.1. LP-Based Model; 4.6.2. QP-Based Model; 4.6.3. Simulation Results; 4.7. Concluding Remarks; Acknowledgments; References; Chapter 5 Automated Power Quality Disturbance Classification Using Evolvable Neural Network; 5.1. Introduction; 5.2. Wavelet Transform (WT); 5.3. Brief Overview of Neural Network Classifiers 5.4. Overview of Particle Swarm Optimization5.5. Signal Generation, Feature Extraction and Classification; 5.6. Results and Discussion; 5.7. Conclusions; References; Chapter 6 Condition Monitoring and Fault Diagnosis Using Intelligent Techniques; 6.1. Introduction; 6.2. Methodology; 6.2.1. Hardware Specification, System Setup and Audio Data Generation; 6.2.2. Data Pre-Processing; 6.2.3. Data Classification Techniques; 6.2.4. Signal Segregation using Independent Component Analysis; 6.3. Experimental Details; 6.3.1. Pre-Processing 6.3.2. Method 1: Artificial Neural Network Setup for Engine Classification |
| Record Nr. | UNINA-9910788961703321 |
| Hackensack, N.J. ; ; London, : World Scientific, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
A Journey Towards Bio-inspired Techniques in Software Engineering / / edited by Jagannath Singh, Saurabh Bilgaiyan, Bhabani Shankar Prasad Mishra, Satchidananda Dehuri
| A Journey Towards Bio-inspired Techniques in Software Engineering / / edited by Jagannath Singh, Saurabh Bilgaiyan, Bhabani Shankar Prasad Mishra, Satchidananda Dehuri |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (viii, 210 pages) : illustrations |
| Disciplina | 660.6 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Computational intelligence
Software engineering Computational Intelligence Software Engineering |
| ISBN | 3-030-40928-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483671703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
