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3D point cloud analysis : traditional, deep learning, and explainable machine learning methods / / Shan Liu
| 3D point cloud analysis : traditional, deep learning, and explainable machine learning methods / / Shan Liu |
| Autore | Liu Songbin |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (156 pages) |
| Disciplina | 006.37 |
| Soggetto topico |
Visió per ordinador
Aprenentatge automàtic Machine learning |
| ISBN | 3-030-89180-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910513688503321 |
Liu Songbin
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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3D point cloud analysis : traditional, deep learning, and explainable machine learning methods / / Shan Liu
| 3D point cloud analysis : traditional, deep learning, and explainable machine learning methods / / Shan Liu |
| Autore | Liu Songbin |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (156 pages) |
| Disciplina | 006.37 |
| Soggetto topico |
Visió per ordinador
Aprenentatge automàtic Machine learning |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-89180-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466555003316 |
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Liu Songbin
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Advanced machine learning approaches in cancer prognosis : challenges and applications / / Janmenjoy Nayak [and four others] editors
| Advanced machine learning approaches in cancer prognosis : challenges and applications / / Janmenjoy Nayak [and four others] editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (461 pages) |
| Disciplina | 006.31 |
| Collana | Intelligent systems reference library ; Volume 204. |
| Soggetto topico |
Cancer - Prognosis - Technological innovations
Machine learning Artificial intelligence - Medical applications Càncer Pronòstic mèdic Innovacions tecnològiques Intel·ligència artificial en medicina Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-71975-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910482988603321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced machine learning technologies and applications : proceedings of AMLTA 2021 / / edited by Aboul-Ella Hassanien, Kuo-Chi Chang, Tang Mincong
| Advanced machine learning technologies and applications : proceedings of AMLTA 2021 / / edited by Aboul-Ella Hassanien, Kuo-Chi Chang, Tang Mincong |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (1,144 pages) : illustrations |
| Disciplina | 006.31 |
| Collana | Advances in Intelligent Systems and Computing |
| Soggetto topico |
Machine learning
Aprenentatge automàtic COVID-19 Intel·ligència artificial en medicina |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-69717-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484064003321 |
| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in Artificial Systems for Logistics Engineering : 2021 International Conference on Artificial Intelligence and Logistics Engineering (ICAILE2021) ; Kyiv, Ukraine, 22-24 January 2021 / / Zhengbing Hu, Qingying Zhang, Sergey Petoukhov, Matthew He, editors
| Advances in Artificial Systems for Logistics Engineering : 2021 International Conference on Artificial Intelligence and Logistics Engineering (ICAILE2021) ; Kyiv, Ukraine, 22-24 January 2021 / / Zhengbing Hu, Qingying Zhang, Sergey Petoukhov, Matthew He, editors |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2021 |
| Descrizione fisica | 1 online resource (384 pages) |
| Collana | Lecture Notes on Data Engineering and Communications Technologies |
| Soggetto topico |
Machine learning
Artificial intelligence Logistics - Data processing Logística industrial Intel·ligència artificial Aprenentatge automàtic |
| Soggetto genere / forma |
Llibres electrònics
Congressos |
| ISBN | 3-030-80475-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I. Mathematical Advances in Schemes for Logistics Engineering --Deep Learning for Grasp-and-Lift Movement Forecasting based on Electroencephalography by Brain-computer Interface --Inland Waterborne Commerce Study based on Variance Decomposition and Cross-spectral Analysis --RBF-based Input Doubling Method for Small Medical Data Processing --Vehicles and Cargos Two-sided Matching based on Similarity Calculation --Improving pedestrian detection methods by architecture and hyperparameter modification of deep neural networks --Warehousing Process Mining Research based on Petri Net --Mathematical Methods of Algebraic Biology Forecast and Analysis of Domestic Iron Ore Shipping Pricing Based on BP Neural Network --On Trilateral Evolutionary Game and Simulation Analysis of College-Enterprise-Government Collaborative Digital Talent Cultivation in the Context of Digital Economy --Design and Development of Mobile Learning Platform Based on WeChat Mini Programs --A New Fog Enabled Sensor Cloud Platform for Smart Logistics Park --The Location of Company's Logistics Hub Based on Principal Component Analysis --Part II. Advances in Technological and Educational Approaches --Deep Learning for Melanoma Detection with Testing Time Data Augmentation --Research and Development of Interactive Virtual Disassembly Teaching System Considering Difficulty Gradient --Teaching Reform and Empirical Research of Logistics Professional English Based on Super Star Learning APP Platform --Integrated Operation Mode of Warehousing and Distribution in the O2O Environment --A Comparison of Machine Learning Algorithms for Prediction Higher Education Institution's Entrants Admissions. |
| Altri titoli varianti | ICAILE 2021 |
| Record Nr. | UNINA-9910495168103321 |
| Cham : , : Springer International Publishing AG, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in data science and information engineering : proceedings from ICDATA 2020 and IKE 2020 / / Robert Stahlbock [and five others] (editors)
| Advances in data science and information engineering : proceedings from ICDATA 2020 and IKE 2020 / / Robert Stahlbock [and five others] (editors) |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (965 pages) |
| Disciplina | 006.3 |
| Collana | Transactions on computational science and computational intelligence |
| Soggetto topico |
Data mining
Mineria de dades Aprenentatge automàtic Sistemes d'informació Machine learning Information storage and retrieval systems |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-71704-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466406103316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Advances in data science and information engineering : proceedings from ICDATA 2020 and IKE 2020 / / Robert Stahlbock [and five others] (editors)
| Advances in data science and information engineering : proceedings from ICDATA 2020 and IKE 2020 / / Robert Stahlbock [and five others] (editors) |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (965 pages) |
| Disciplina | 006.3 |
| Collana | Transactions on computational science and computational intelligence |
| Soggetto topico |
Data mining
Mineria de dades Aprenentatge automàtic Sistemes d'informació Machine learning Information storage and retrieval systems |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-71704-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910508474303321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in learning automata and intelligent optimization / / Javidan Kazemi Kordestani [and three others], editors
| Advances in learning automata and intelligent optimization / / Javidan Kazemi Kordestani [and three others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (355 pages) |
| Disciplina | 006.31 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Aprenentatge automàtic
Optimització matemàtica Mathematical optimization |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-76291-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Authors -- Abbreviations -- 1 An Introduction to Learning Automata and Optimization -- 1.1 Introduction -- 1.2 Learning Automata -- 1.2.1 Learning Automata Variants -- 1.2.2 Recent Applications of Learning Automata -- 1.3 Optimization -- 1.3.1 Evolutionary Algorithms and Swarm Intelligence -- 1.4 Reinforcement Learning and Optimization Methods -- 1.4.1 Static Optimization -- 1.4.2 Dynamic Optimization -- 1.5 LA and Optimization Timeline -- 1.6 Chapter Map -- 1.7 Conclusion -- References -- 2 Learning Automaton and Its Variants for Optimization: A Bibliometric Analysis -- 2.1 Introduction -- 2.2 Learning Automata Models and Optimization -- 2.3 Material and Method -- 2.3.1 Data Collection and Initial Results -- 2.3.2 Refining the Initial Results -- 2.4 Analyzing the Results -- 2.4.1 Initial Result Statistics -- 2.4.2 Top Journals -- 2.4.3 Top Researchers -- 2.4.4 Top Papers -- 2.4.5 Top Affiliations -- 2.4.6 Top Keywords -- 2.5 Conclusion -- References -- 3 Cellular Automata, Learning Automata, and Cellular Learning Automata for Optimization -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Cellular Automata -- 3.2.2 Learning Automata -- 3.2.3 Cellular Learning Automata -- 3.3 CA, CLA, and LA Models for Optimization -- 3.3.1 Cellular Learning Automata-Based Evolutionary Computing (CLA-EC) -- 3.3.2 Cooperative Cellular Learning Automata-Based Evolutionary Computing (CLA-EC) -- 3.3.3 Recombinative Cellular Learning Automata-Based Evolutionary Computing (RCLA-EC) -- 3.3.4 CLA-EC with Extremal Optimization (CLA-EC-EO) -- 3.3.5 Cellular Learning Automata-Based Differential Evolution (CLA-DE) -- 3.3.6 Cellular Particle Swarm Optimization (Cellular PSO) -- 3.3.7 Firefly Algorithm Based on Cellular Learning Automata (CLA-FA) -- 3.3.8 Harmony Search Algorithm Based on Learning Automata (LAHS).
3.3.9 Learning Automata Based Butterfly Optimization Algorithm (LABOA) -- 3.3.10 Grey Wolf Optimizer Based on Learning Automata (GWO-LA) -- 3.3.11 Learning Automata Models with Multiple Reinforcements (MLA) -- 3.3.12 Cellular Learning Automata Models with Multiple Reinforcements (MCLA) -- 3.3.13 Multi-reinforcement CLA with the Maximum Expected Rewards (MCLA) -- 3.3.14 Gravitational Search Algorithm Based on Learning Automata (GSA-LA) -- 3.4 Conclusion -- References -- 4 Learning Automata for Behavior Control in Evolutionary Computation -- 4.1 Introduction -- 4.2 Types of Parameter Adjustment in EC Community -- 4.2.1 EC with Constant Parameters -- 4.2.2 EC with Time-Varying Parameters -- 4.3 Differential Evolution -- 4.3.1 Initialization -- 4.3.2 Difference-Vector Based Mutation -- 4.3.3 Repair Operator -- 4.3.4 Crossover -- 4.3.5 Selection -- 4.4 Learning Automata for Adaptive Control of Behavior in Differential Evolution -- 4.4.1 Behavior Control in DE with Variable-Structure Learning Automaton -- 4.4.2 Behavior Control in DE with Fixed-Structure Learning Automaton -- 4.5 Experimental Setup -- 4.5.1 Benchmark Functions -- 4.5.2 Algorithm's Configuration -- 4.5.3 Simulation Settings and Results -- 4.5.4 Experimental Results -- 4.6 Conclusion -- References -- 5 A Memetic Model Based on Fixed Structure Learning Automata for Solving NP-Hard Problems -- 5.1 Introduction -- 5.2 Fixed Structure Learning Automata and Object Migrating Automata -- 5.2.1 Fixed Structure Learning Automata -- 5.2.2 Object Migration Automata -- 5.3 GALA -- 5.3.1 Global Search in GALA -- 5.3.2 Crossover Operator -- 5.3.3 Mutation Operator -- 5.3.4 Local Learning in GALA -- 5.3.5 Applications of GALA -- 5.4 The New Memetic Model Based on Fixed Structure Learning Automata -- 5.4.1 Hybrid Fitness Function -- 5.4.2 Mutation Operators -- 5.4.3 Crossover Operators. 5.5 The OneMax Problem -- 5.5.1 Local Search for OneMax -- 5.5.2 Experimental Results -- 5.6 Conclusion -- References -- 6 The Applications of Object Migration Automaton (OMA)-Memetic Algorithm for Solving NP-Hard Problems -- 6.1 Introduction -- 6.2 The Equipartitioning Problem -- 6.2.1 Local Search for EPP -- 6.2.2 Experimental Results -- 6.3 The Graph Isomorphism Problem -- 6.3.1 The Local Search in the Graph Isomorphism Problem -- 6.3.2 Experimental Results -- 6.4 Assignment of Cells to Switches Problem (ACTSP) in Cellular Mobile Network -- 6.4.1 Background and Related Work -- 6.4.2 The OMA-MA for Assignment of Cells to Switches Problem -- 6.4.3 The Framework of the OMA-MA Algorithm -- 6.4.4 Experimental Result -- 6.5 Conclusion -- References -- 7 An Overview of Multi-population Methods for Dynamic Environments -- 7.1 Introduction -- 7.2 Moving Peaks Benchmark -- 7.2.1 Extended Versions of MPB -- 7.3 Performance Measurement -- 7.4 Types of Multi-population Methods -- 7.4.1 Methods with a Fixed Number of Populations -- 7.4.2 Methods with a Variable Number of Populations -- 7.4.3 Methods Based on Population Clustering -- 7.4.4 Self-adapting the Number of Populations -- 7.5 Numerical Results -- 7.6 Conclusions -- References -- 8 Learning Automata for Online Function Evaluation Management in Evolutionary Multi-population Methods for Dynamic Optimization Problems -- 8.1 Introduction -- 8.2 Preliminaries -- 8.2.1 Waste of FEs Due to Change Detection -- 8.2.2 Waste of FEs Due to the Excessive Number of Sub-populations -- 8.2.3 Waste of FEs Due to Overcrowding of Subpopulations in the Same Area of the Search Space -- 8.2.4 Waste of FEs Due to Exclusion Operator -- 8.2.5 Allocation of FEs to Unproductive Populations -- 8.2.6 Unsuitable Parameter Configuration of the EC Methods -- 8.2.7 Equal Distribution of FEs Among Sub-populations. 8.3 Theory of Learning Automata -- 8.3.1 Fixed Structure Learning Automata -- 8.3.2 Variable Structure Learning Automata -- 8.4 EC Techniques under Study -- 8.4.1 Particle Swarm Optimization -- 8.4.2 Firefly Algorithm -- 8.4.3 Jaya -- 8.5 LA-Based FE Management Model for MP Evolutionary Dynamic Optimization -- 8.5.1 Initialization of Sub-populations -- 8.5.2 Detection and Response to Environmental Changes -- 8.5.3 Choose a Sub-population for Execution -- 8.5.4 Evaluate the Search Progress of Populations and Generate the Reinforcement Signal -- 8.5.5 Exclusion -- 8.6 FE-Management in MP Method with a Fixed Number of Populations -- 8.6.1 VSLA-Based FE Management Strategy -- 8.6.2 FSLA-Based FE Management Strategies -- 8.7 Experimental Study -- 8.7.1 Experimental Setup -- 8.7.2 Experimental Results and Discussion -- 8.8 Conclusion -- References -- 9 Function Management in Multi-population Methods with a Variable Number of Populations: A Variable Action Learning Automaton Approach -- 9.1 Introduction -- 9.2 Main Framework of Clustering Particle Swarm Optimization -- 9.2.1 Creating Multiple Sub-swarms from the Cradle Swarm -- 9.2.2 Local Search by PSO -- 9.2.3 Status of Sub-swarms -- 9.2.4 Detection and Response to Environmental Changes -- 9.3 Variable Action-Set Learning Automata -- 9.4 FEM in MP Methods with a Variable Number of Populations -- 9.5 Experimental Study -- 9.5.1 Dynamic Test Function -- 9.5.2 Performance Measure -- 9.5.3 Experimental Settings -- 9.5.4 Experimental Results -- 9.6 Conclusions -- References. |
| Record Nr. | UNINA-9910488695403321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI and machine learning paradigms for health monitoring system : intelligent data analytics / / Hasmat Malik, Nuzhat Fatema, Jafar A. Alzubi, editors
| AI and machine learning paradigms for health monitoring system : intelligent data analytics / / Hasmat Malik, Nuzhat Fatema, Jafar A. Alzubi, editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (522 pages) |
| Disciplina | 006.3 |
| Collana | Studies in Big Data |
| Soggetto topico |
Computational intelligence
Artificial intelligence Machine learning Intel·ligència artificial Aprenentatge automàtic Intel·ligència computacional |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-334-412-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484882003321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI in Learning: Designing the Future / / edited by Hannele Niemi, Roy D. Pea, Yu Lu
| AI in Learning: Designing the Future / / edited by Hannele Niemi, Roy D. Pea, Yu Lu |
| Autore | Niemi Hannele |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2023 |
| Descrizione fisica | 1 online resource (XXV, 344 p. 49 illus., 42 illus. in color.) |
| Disciplina | 150 |
| Soggetto topico |
Psychology
Social sciences—Data processing Education Cognitive science Teaching Artificial intelligence Behavioral Sciences and Psychology Computer Application in Social and Behavioral Sciences Cognitive Science Pedagogy Artificial Intelligence Intel·ligència artificial Ensenyament Aprenentatge Educació Ètica Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
artificial intelligence
life-long learning tutoring virtual learning learning analytics well-being simulations games intelligent digital tools deep learning robotics human-machine interaction |
| ISBN | 3-031-09687-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1.Introduction to AI in Learning – Designing the Future -- Part I: AI expanding learning and wellbeing throughout life -- 2.Artificial Intelligence Innovations for Multimodal Learning, Interfaces, and Analytics -- 3.Curiosity and Interactive Learning in Artificial Systems -- 4.Assessing and Tracking Students’ Wellbeing through an Automated Scoring System: Schoolday Wellbeing Model -- 5.Learning from Intelligent Social Agents as Social and Intellectual Mirrors -- 6.An AI-Powered Teacher Assistant for Student Problem Behavior Diagnosis -- 7.Analysis and Improvement of Classroom Teaching Based on Artificial Intelligence -- Part II. AI in Games and Simulations -- 8.Perspectives and Metaphors of Learning: A Commentary on James Lester’s Narrative-centered AI-based Environments -- 9.Learning Career Knowledge: Can AI Simulation and Machine Learning Improve Career Plans and Educational Expectations? -- 10.Learning clinical reasoning through gaming in nursing education – Future scenarios of game metrics and AI -- 11.AI-Supported Simulation-Based Learning: Learners’ Emotional Experiences and Self-Regulation in Challenging Situations -- Part III. AI Technologies for education and Intelligent Tutoring Systems -- 12.Training Hard Skills in Virtual Reality: Developing a Theoretical Framework for AI-based Immersive Learning.-13.Multiple users’ experiences of an AI-aided educational platform for teaching and learning. 14.Deep Learning in Automatic Math Word Problem Solvers. 15.Recent Advances in Intelligent Textbooks for Better Learning -- Part IV. AI and Ethical Challenges in New Learning Environments -- 16.Ethical Guidelines for Artificial Intelligence-based Learning: A Transnational Study between in China and Finland -- 17.Artificial Intelligence Ethics from the Perspective of Educational Technology Companies and Schools -- 18.Artificial Intelligence in Education as a Rawlsian Massively Multiplayer Game: A thought experiment on AI Ethics -- 19.Four surveillance technologies creating challenges for education -- 20.Reflections on the contributions and future scenarios in AI-based learning. |
| Record Nr. | UNINA-9910632469503321 |
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Niemi Hannele
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| Cham, : Springer Nature, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Soggetto topico
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Aprenentatge automàtic
(96)
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- Intel·ligència artificial (14)
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