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Applications of Operational Research in Business and Industries [[electronic resource] ] : Proceedings of 54th Annual Conference of ORSI / / edited by Angappa Gunasekaran, Jai Kishore Sharma, Samarjit Kar
| Applications of Operational Research in Business and Industries [[electronic resource] ] : Proceedings of 54th Annual Conference of ORSI / / edited by Angappa Gunasekaran, Jai Kishore Sharma, Samarjit Kar |
| Autore | Gunasekaran Angappa |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (512 pages) |
| Disciplina | 658.4034 |
| Altri autori (Persone) |
SharmaJai Kishore
KarSamarjit |
| Collana | Lecture Notes in Operations Research |
| Soggetto topico |
Operations research
Mathematical optimization Big data Machine learning Artificial intelligence—Data processing Operations Research and Decision Theory Optimization Big Data Machine Learning Data Science |
| Soggetto non controllato |
Technology
Technology & Engineering |
| ISBN |
9789811980121
9789811980114 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Optimization of an inventory model with demand dependent on selling price and stock, nonlinear holding cost along with trade credit policy -- Chapter 2: Software Defect Prediction Through a Hybrid Approach Comprising of a Statistical Tool and a Machine Learning Model -- Chapter 3: Conservation of a prey species through optimal taxation: a model with Beddington-DeAngelis Functional Response -- Chapter 4: Investigate the reason for students' absenteeism in Engineering College in Fuzzy MCDM environment -- Chapter 5: Optimal inventory management policies for substitutable products considering non-instantaneous decay and cost of substitution. . |
| Record Nr. | UNINA-9910726276403321 |
Gunasekaran Angappa
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence for Smart Manufacturing [[electronic resource] ] : Methods, Applications, and Challenges / / edited by Kim Phuc Tran
| Artificial Intelligence for Smart Manufacturing [[electronic resource] ] : Methods, Applications, and Challenges / / edited by Kim Phuc Tran |
| Autore | Tran Kim Phuc |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (271 pages) |
| Disciplina | 670.28563 |
| Collana | Springer Series in Reliability Engineering |
| Soggetto topico |
Industrial engineering
Production engineering Statistics Machine learning Industrial and Production Engineering Applied Statistics Machine Learning |
| Soggetto non controllato |
Manufactures
Technology & Engineering |
| ISBN | 3-031-30510-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Introduction to Artificial Intelligence for Smart Manufacturing -- Chapter 2: Artificial Intelligence for Smart Manufacturing in Industry 5.0: Methods, Applications, and Challenges -- Chapter 3: Quality control for Smart Manufacturing in Industry 5.0 -- Chapter 4: Dynamic Process Monitoring Using Machine Learning Control Charts -- Chapter 5: Fault Prediction of Papermaking Process Based on Gaussian Mixture Model and Mahalanobis Distance -- Chapter 6: Multi-objective optimization of flexible flow-shop intelligent scheduling based on a hybrid intelligent algorithm -- Chapter 7: Personalized pattern recommendation system of men’s shirts -- Chapter 8: Efficient and Trustworthy Federated Learning-based Explainable Anomaly Detection: Challenges, Methods, and Future Directions -- Chapter 9: Multimodal machine learning in prognostics and health management of manufacturing systems -- Chapter 10: Explainable Artificial Intelligence for Cybersecurity in Smart Manufacturing -- Chapter 11: Wearable technology for Smart Manufacturing in Industry 5.0 -- Chapter 12: Benefits of using Digital Twin for online fault diagnosis of a manufacturing system. |
| Record Nr. | UNINA-9910728942103321 |
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Tran Kim Phuc
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Digital Watermarking for Machine Learning Model [[electronic resource] ] : Techniques, Protocols and Applications / / edited by Lixin Fan, Chee Seng Chan, Qiang Yang
| Digital Watermarking for Machine Learning Model [[electronic resource] ] : Techniques, Protocols and Applications / / edited by Lixin Fan, Chee Seng Chan, Qiang Yang |
| Autore | Fan Lixin |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (233 pages) |
| Disciplina | 005.82 |
| Altri autori (Persone) |
ChanChee Seng
YangQiang |
| Soggetto topico |
Machine learning
Data protection Image processing—Digital techniques Computer vision Image processing Machine Learning Data and Information Security Computer Imaging, Vision, Pattern Recognition and Graphics Image Processing |
| Soggetto non controllato |
Engineering
Technology & Engineering |
| ISBN | 981-19-7554-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I. Preliminary -- Chapter 1. Introduction -- Chapter 2. Ownership Verification Protocols for Deep Neural Network Watermarks -- Part II Techniques -- Chapter 3. ModelWatermarking for Image Recovery DNNs -- Chapter 4. The Robust and Harmless ModelWatermarking -- Chapter 5. Protecting Intellectual Property of Machine Learning Models via Fingerprinting the Classification Boundary -- Chapter 6. Protecting Image Processing Networks via Model Water -- Chapter 7. Watermarks for Deep Reinforcement Learning -- Chapter 8. Ownership Protection for Image Captioning Models -- Chapter 9.Protecting Recurrent Neural Network by Embedding Key -- Part III Applications -- Chapter 10. FedIPR: Ownership Verification for Federated Deep Neural Network Models -- Chapter 11. Model Auditing For Data Intellectual Property . |
| Record Nr. | UNISA-996546839603316 |
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Fan Lixin
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Digital Watermarking for Machine Learning Model [[electronic resource] ] : Techniques, Protocols and Applications / / edited by Lixin Fan, Chee Seng Chan, Qiang Yang
| Digital Watermarking for Machine Learning Model [[electronic resource] ] : Techniques, Protocols and Applications / / edited by Lixin Fan, Chee Seng Chan, Qiang Yang |
| Autore | Fan Lixin |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (233 pages) |
| Disciplina | 005.82 |
| Altri autori (Persone) |
ChanChee Seng
YangQiang |
| Soggetto topico |
Machine learning
Data protection Image processing—Digital techniques Computer vision Image processing Machine Learning Data and Information Security Computer Imaging, Vision, Pattern Recognition and Graphics Image Processing |
| Soggetto non controllato |
Engineering
Technology & Engineering |
| ISBN | 981-19-7554-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I. Preliminary -- Chapter 1. Introduction -- Chapter 2. Ownership Verification Protocols for Deep Neural Network Watermarks -- Part II Techniques -- Chapter 3. ModelWatermarking for Image Recovery DNNs -- Chapter 4. The Robust and Harmless ModelWatermarking -- Chapter 5. Protecting Intellectual Property of Machine Learning Models via Fingerprinting the Classification Boundary -- Chapter 6. Protecting Image Processing Networks via Model Water -- Chapter 7. Watermarks for Deep Reinforcement Learning -- Chapter 8. Ownership Protection for Image Captioning Models -- Chapter 9.Protecting Recurrent Neural Network by Embedding Key -- Part III Applications -- Chapter 10. FedIPR: Ownership Verification for Federated Deep Neural Network Models -- Chapter 11. Model Auditing For Data Intellectual Property . |
| Record Nr. | UNINA-9910728383303321 |
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Fan Lixin
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Emerging Smart Technologies for Critical Infrastructure [[electronic resource] /] / edited by Shantanu Pal, Zahra Jadidi, Ernest Foo, Subhas C. Mukhopadhyay
| Emerging Smart Technologies for Critical Infrastructure [[electronic resource] /] / edited by Shantanu Pal, Zahra Jadidi, Ernest Foo, Subhas C. Mukhopadhyay |
| Autore | Pal Shantanu |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (172 pages) |
| Disciplina | 004.678 |
| Altri autori (Persone) |
JadidiZahra
FooErnest MukhopadhyaySubhas C |
| Collana | Smart Sensors, Measurement and Instrumentation |
| Soggetto topico |
Internet of things
Machine learning Materials Detectors Engineering—Data processing Cooperating objects (Computer systems) Internet of Things Machine Learning Sensors and biosensors Data Engineering Cyber-Physical Systems |
| Soggetto non controllato |
Engineering
Technology & Engineering |
| ISBN | 3-031-29845-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910728929703321 |
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Pal Shantanu
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Engineering of Additive Manufacturing Features for Data-Driven Solutions [[electronic resource] ] : Sources, Techniques, Pipelines, and Applications / / by Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao
| Engineering of Additive Manufacturing Features for Data-Driven Solutions [[electronic resource] ] : Sources, Techniques, Pipelines, and Applications / / by Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao |
| Autore | Safdar Mutahar |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (151 pages) |
| Disciplina | 621.988 |
| Altri autori (Persone) |
LamoucheGuy
PaulPadma Polash WoodGentry ZhaoYaoyao (Fiona) |
| Collana | SpringerBriefs in Applied Sciences and Technology |
| Soggetto topico |
Industrial engineering
Production engineering Engineering—Data processing Artificial intelligence Machine learning Education Industrial and Production Engineering Data Engineering Artificial Intelligence Machine Learning |
| Soggetto non controllato |
Manufactures
Technology & Engineering |
| ISBN | 3-031-32154-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Feature Engineering in AM -- Applications in Data-driven AM -- Analyzing AM Feature Spaces -- Challenges and Opportunities in AM Data Preparation -- Summary. |
| Record Nr. | UNINA-9910728930303321 |
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Safdar Mutahar
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning for Advanced Functional Materials [[electronic resource] /] / edited by Nirav Joshi, Vinod Kushvaha, Priyanka Madhushri
| Machine Learning for Advanced Functional Materials [[electronic resource] /] / edited by Nirav Joshi, Vinod Kushvaha, Priyanka Madhushri |
| Autore | Joshi Nirav |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (306 pages) |
| Disciplina | 620.110285631 |
| Altri autori (Persone) |
KushvahaVinod
MadhushriPriyanka |
| Soggetto topico |
Optics
Machine learning Materials Detectors Tumor markers Photonics Optical engineering Optics and Photonics Machine Learning Sensors and biosensors Tumour Biomarkers Photonics and Optical Engineering Photonic Devices |
| Soggetto non controllato |
Artificial Intelligence
Materials Oncology Microwaves Optics Computers Technology & Engineering Medical Science |
| ISBN | 981-9903-93-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Solar Cells and Relevant Machine Learning -- Machine learning-driven gas identification in gas sensors -- Recent advances in Machine Learning for electrochemical, optical, and gas sensors -- Machine Learning in Wearable Healthcare Devices -- A Machine Learning approach in wearable Technologies -- The application of novel functional materials to machine learning -- Potential of Machine Learning Algorithms in Material Science: Predictions in design, properties and applications of novel functional materials -- Perovskite Based Materials for Photovoltaic Applications: A Machine Learning Approach -- A review of the high-performance gas sensors using machine learning -- Machine Learning For Next‐Generation Functional Materials -- Contemplation of Photocatalysis Through Machine Learning -- Discovery of Novel Photocatalysts using Machine Learning Approach -- Machine Learning In Impedance Based Sensors. |
| Record Nr. | UNINA-9910726286403321 |
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Joshi Nirav
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy [[electronic resource] ] : Proceedings of the Third International Conference, MMCITRE 2022 / / edited by Manoj Sahni, José M. Merigó, Walayat Hussain, Ernesto León-Castro, Raj Kumar Verma, Ritu Sahni
| Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy [[electronic resource] ] : Proceedings of the Third International Conference, MMCITRE 2022 / / edited by Manoj Sahni, José M. Merigó, Walayat Hussain, Ernesto León-Castro, Raj Kumar Verma, Ritu Sahni |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (474 pages) |
| Disciplina | 006.3 |
| Collana | Advances in Intelligent Systems and Computing |
| Soggetto topico |
Computational intelligence
Artificial intelligence Machine learning Computer simulation Renewable energy sources Computational Intelligence Artificial Intelligence Machine Learning Computer Modelling Renewable Energy |
| Soggetto non controllato |
Artificial Intelligence
Computer Simulation Renewable Energy Sources Engineering Computers Technology & Engineering |
| ISBN | 981-19-9906-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A New Result Using Quasi-β-Power Increasing Sequence -- (C, 1, 1)-Quasinormal convergence of double sequence of functions -- Fixed Point Theorems in Neutrosophic Soft Metric Space -- Existence of Best Proximity Points on (����, ����) Contractions in RMS -- Approximation of signals by El1El1 product summability means of Fourier-Laguerre expansion -- Approximation of signal belongs to generalized W′(Lr, ξ (t)) class by (C, α, η) A – matrix summability of Fourier series -- A New connection on Generalised Tangent Bundle -- Analysis of third order resonant periodic orbits in perturbed circular restricted three body problem -- Exergy Optimisation in Closed-Loop Spray Drying -- Simulation and Modelling of Linear and Nonlinear PID Controller. |
| Record Nr. | UNINA-9910720098403321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Neural Text-to-Speech Synthesis [[electronic resource] /] / by Xu Tan
| Neural Text-to-Speech Synthesis [[electronic resource] /] / by Xu Tan |
| Autore | Tan Xu |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (214 pages) |
| Disciplina | 006.54 |
| Collana | Artificial Intelligence: Foundations, Theory, and Algorithms |
| Soggetto topico |
Natural language processing (Computer science)
Speech processing systems Signal processing Machine learning Artificial intelligence Natural Language Processing (NLP) Speech and Audio Processing Machine Learning Artificial Intelligence |
| Soggetto non controllato |
Electronics
Artificial Intelligence Computer Sound Processing Technology & Engineering Computers |
| ISBN |
9789819908271
9789819908264 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Part 1. Preliminary -- Chapter 2. Basics of Spoken Language Processing -- Chapter 3. Basics of Deep Learning -- Part 2. Key Components in TTS -- Chapter 4. Text Analyses -- Chapter 5. Acoustic Models -- Chapter 6. Vocoders -- Chapter 7. Fully End-to-End TTS -- Part 3. Advanced Topics in TTS -- Chapter 8. Expressive and Controllable TTS -- Chapter 9. Robust TTS -- Chapter 10. Model-Efficient TTS -- Chapter 11. Data-Efficient TTS -- Chapter 12. Beyond Text-to-Speech Synthesis -- Part 4. Summary and Outlook -- Chapter 13. Summary and Outlook. |
| Record Nr. | UNINA-9910739431403321 |
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Tan Xu
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Neural Text-to-Speech Synthesis [[electronic resource] /] / by Xu Tan
| Neural Text-to-Speech Synthesis [[electronic resource] /] / by Xu Tan |
| Autore | Tan Xu |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (214 pages) |
| Disciplina | 006.54 |
| Collana | Artificial Intelligence: Foundations, Theory, and Algorithms |
| Soggetto topico |
Natural language processing (Computer science)
Speech processing systems Signal processing Machine learning Artificial intelligence Natural Language Processing (NLP) Speech and Audio Processing Machine Learning Artificial Intelligence |
| Soggetto non controllato |
Electronics
Artificial Intelligence Computer Sound Processing Technology & Engineering Computers |
| ISBN |
9789819908271
9789819908264 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Part 1. Preliminary -- Chapter 2. Basics of Spoken Language Processing -- Chapter 3. Basics of Deep Learning -- Part 2. Key Components in TTS -- Chapter 4. Text Analyses -- Chapter 5. Acoustic Models -- Chapter 6. Vocoders -- Chapter 7. Fully End-to-End TTS -- Part 3. Advanced Topics in TTS -- Chapter 8. Expressive and Controllable TTS -- Chapter 9. Robust TTS -- Chapter 10. Model-Efficient TTS -- Chapter 11. Data-Efficient TTS -- Chapter 12. Beyond Text-to-Speech Synthesis -- Part 4. Summary and Outlook -- Chapter 13. Summary and Outlook. |
| Record Nr. | UNISA-996546838503316 |
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Tan Xu
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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