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Innovations in Machine and Deep Learning [[electronic resource] ] : Case Studies and Applications / / edited by Gilberto Rivera, Alejandro Rosete, Bernabé Dorronsoro, Nelson Rangel-Valdez
| Innovations in Machine and Deep Learning [[electronic resource] ] : Case Studies and Applications / / edited by Gilberto Rivera, Alejandro Rosete, Bernabé Dorronsoro, Nelson Rangel-Valdez |
| Autore | Rivera Gilberto |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (506 pages) |
| Disciplina | 620.00285 |
| Altri autori (Persone) |
RoseteAlejandro
DorronsoroBernabé Rangel-ValdezNelson |
| Collana | Studies in Big Data |
| Soggetto topico |
Engineering - Data processing
Computational intelligence Big data Data Engineering Computational Intelligence Big Data |
| ISBN | 3-031-40688-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Analytics-Oriented Applications -- Recursive Multi-step Time-Series Forecasting for Residual-Feedback Artificial Neural Networks: A Survey -- 1 Introduction -- 2 Residual-Feedback ANNs: A Systematic Review -- 2.1 Systematic Review Planning and Execution -- 2.2 Overview of the Systematic Review Findings -- 3 The Existing Recursive Multi-step Forecast Strategy Solution -- 4 Limitation -- 5 Conclusions and Future Works -- References -- Feature Selection: Traditional and Wrapping Techniques with Tabu Search -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Description -- 3.2 Entropy-Based Feature Selection -- 3.3 Feature Selection Using Principal Component Analysis -- 3.4 Correlation-Based Feature Selection -- 4 Tabu Search -- 4.1 Initial Solution -- 4.2 Neighborhood -- 4.3 Objective Function -- 4.4 Memory Structures -- 5 Results -- 6 Discussion -- 7 Conclusions and Future Work -- References -- Pattern Classification with Holographic Neural Networks: A New Tool for Feature Selection -- 1 Introduction -- 2 Holographic Neural Networks -- 2.1 Basic Theory -- 2.2 Learning and Prediction Methods -- 2.3 red Explainability and Optimization of Holographic Models -- 3 Feature Selection with Holographic Neural Neworks -- 3.1 Previous Works -- 3.2 Pythagorean Membership Grades -- 4 Pattern Classification -- 4.1 Iris Dataset -- 4.2 red NIPS Feature Selection Challenge -- 5 red Conclusions and Future Works -- References -- Reusability Analysis of K-Nearest Neighbors Variants for Classification Models -- 1 Introduction -- 2 The K-Nearest Neighbors Algorithm -- 3 The Parameter K -- 4 Closeness Metrics -- 5 Analysis of KNN Variants -- 5.1 Heuristics for Class Assignment -- 5.2 Reduction of Dataset Records -- 5.3 Estimation of Dataset Variables -- 5.4 Discussion -- 6 Conclusions -- References.
Speech Emotion Recognition Using Deep CNNs Trained on Log-Frequency Spectrograms -- 1 Introduction -- 2 Literature Survey -- 2.1 Motivation -- 2.2 Contributions -- 3 Proposed Methodology -- 3.1 Data Augmentation -- 3.2 Extraction of Log-Frequency Spectrograms -- 3.3 Motivation Behind Using Spectrograms -- 3.4 Log-Frequency Spectrogram Extraction -- 3.5 Understanding What a Spectrogram Conveys -- 4 The Deep Convolutional Neural Network -- 4.1 Architecture -- 4.2 Training -- 5 Observations -- 5.1 Dataset Used -- 5.2 Performance Metrics Used -- 5.3 Results Obtained -- 5.4 Comparison Study -- 6 Conclusion -- References -- Text Classifier of Sensationalist Headlines in Spanish Using BERT-Based Models -- 1 Introduction -- 2 Background -- 2.1 Sensationalism -- 2.2 BERT-Based Models -- 3 Related Work -- 4 Dataset and Methods -- 4.1 Data Gathering and Data Labeling -- 4.2 Data Analysis -- 4.3 Model Generation and Fine-Tuning -- 5 Results -- 6 Conclusion -- References -- Arabic Question-Answering System Based on Deep Learning Models -- 1 Introduction -- 2 Natural Language Processing (NLP) -- 2.1 Difficulties in NLP -- 2.2 Natural Language Processing Phases -- 3 Question Answer System -- 3.1 Usage Deep Learning Models in Questions Answering System -- 3.2 Different Questions Based on Bloom's Taxonomy -- 3.3 Question-Answering System Based on Types -- 3.4 Wh-Type Questions (What, Which, When, Who) -- 4 List-Based Questions -- 5 Yes/No Questions -- 6 Causal Questions [Why or How] -- 7 Hypothetical Questions -- 8 Complex Questions -- 8.1 Question Answering System Issues -- 9 Arabic Language Overview -- 9.1 Arabic Language Challenges -- 10 Related Work -- 11 Proposed Methodology -- 11.1 Recurrent Neural Networks (RNNs) -- 11.2 Long Short-Term Memory (LSTM) -- 11.3 Gated Recurrent Unit (GRU) -- 12 Prepare the Dataset -- 12.1 Collecting Data -- 13 Data Preprocessing. 14 Results and Discussion -- 15 Conclusion and Future Work -- References -- Healthcare-Oriented Applications -- Machine and Deep Learning Algorithms for ADHD Detection: A Review -- 1 Introduction -- 2 Research Methodology -- 3 Related Work -- 3.1 Machine Learning Approaches -- 3.2 Deep Learning Approaches -- 4 Approaches for ADHD Detection Using AI Algorithms -- 4.1 Machine Learning-Based Approaches -- 4.2 Deep Learning-Based Approaches -- 5 Datasets for ADHD Detection -- 5.1 Hyperaktiv -- 5.2 Working Memory and Reward in Children with and Without ADHD -- 5.3 Working Memory and Reward in Adults -- 5.4 Eeg Data for ADHD -- 6 Machine Learning and Deep Learning Classifiers for ADHD Detection -- 7 Trends and Challenges -- 7.1 New Types of Sensors or Biosensors -- 7.2 Multi-Modal Detection and/or Diagnosis of ADHD -- 7.3 The Use of Biomarkers as Variables for Diagnosis -- 7.4 Interpretability -- 7.5 Building of Standardized and Accurate Public Datasets -- 7.6 Different Classification Techniques -- 8 Conclusion -- References -- Mosquito on Human Skin Classification Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Dataset Description -- 3.2 Deep Convolutional Neural Networks and Transfer Learning -- 3.3 Hyperparameter Tuning -- 3.4 Proposed Workflow -- 4 Experiments and Results -- 5 Conclusion and Future Work -- References -- Analysis and Interpretation of Deep Convolutional Features Using Self-organizing Maps -- 1 Introduction -- 2 Materials -- 2.1 Convolutional Neural Networks -- 2.2 Self-organizing Maps -- 3 Proposed Method -- 3.1 Stage A: Training of CNN -- 3.2 Stage B: Extraction of Features -- 3.3 Stage C: SOM Training -- 3.4 Stage D: Analysis and Interpretation -- 4 Application Example -- 4.1 Experimental Setup -- 4.2 Result Analysis -- 5 Conclusions -- References. A Hybrid Deep Learning-Based Approach for Human Activity Recognition Using Wearable Sensors -- 1 Introduction -- 2 Literature Analysis -- 3 OPPORTUNITY Dataset -- 4 MHEALTH Dataset -- 5 HARTH Dataset -- 6 Materials and Methods -- 6.1 Some Preliminaries -- 6.2 Basic Architecture of CNN -- 7 Long-Short Term Memory (LSTM) -- 7.1 Working Principle of LSTM -- 8 Proposed Model Architecture -- 9 Dataset Description -- 9.1 MHEALTH Dataset -- 9.2 OPPORTUNITY Dataset -- 9.3 HARTH Dataset -- 10 Experimental Results -- 10.1 Evaluation Metrics Used -- 10.2 Results Analysis on MHEALTH Dataset -- 10.3 Results Analysis on OPPORTUNITY Dataset -- 10.4 Results Analysis on HARTH Dataset -- 10.5 Result Summary and Comparison -- 11 Conclusion and Future Works -- References -- Predirol: Predicting Cholesterol Saturation Levels Using Big Data, Logistic Regression, and Dissipative Particle Dynamics Simulation -- 1 Introduction -- 2 Related Works -- 2.1 Models for the Simulation of Fluids -- 2.2 Data Mining Application for Prevention of Cardiovascular Diseases -- 2.3 Comparative Analysis -- 3 PREDIROL Architecture -- 3.1 Big Data Model -- 3.2 Cholesterol Saturation Level Prediction Module -- 3.3 Cholesterol Levels Simulation Module with Dissipative Particle Dynamics -- 4 Case Study: Prediction of Cholesterol Levels of a Hospital Patients -- 5 Conclusions and Future Work -- References -- Convolutional Neural Network-Based Cancer Detection Using Histopathologic Images -- 1 Introduction -- 2 Image Processing Techniques -- 2.1 Statistical-Based Algorithms -- 2.2 Learning-Based Algorithms -- 2.3 Hyper-Parameters of CNN -- 2.4 Evaluation Metrics -- 2.5 Implementation -- 3 Stage 3: CNN Algorithm Training -- 3.1 Model Training Phase -- 3.2 Model Optimization Phase -- 4 Conclusion -- References. Artificial Neural Network-Based Model to Characterize the Reverberation Time of a Neonatal Incubator -- 1 Introduction -- 2 Materials and Methods -- 2.1 Artificial Neural Networks Using the Levenberg-Marquardt Algorithm -- 3 Results -- 3.1 Data Analysis -- 3.2 Artificial Neural Network-Based Model Training -- 4 Conclusions -- References -- A Comparative Study of Machine Learning Methods to Predict COVID-19 -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 Covid-19 -- 3.2 Machine Learning -- 4 Materials and Methods -- 4.1 Dataset Pre-processing -- 4.2 Machine Learning Models -- 5 Results and Discussions -- 6 Conclusions -- References -- Sustainability-Oriented Applications -- Multi-product Inventory Supply and Distribution Model with Non-linear CO2 Emission Model to Improve Economic and Environmental Aspects of Freight Transportation -- 1 Introduction -- 2 Literature Review and Contributions -- 3 Development of the Integrated Routing Model -- 3.1 Inventory Planning with Non-deterministic Demand and Multiple Products -- 3.2 Non-linear Emission for Heterogeneous Fleet -- 3.3 Association of Variables -- 4 Assessment of the Model -- 4.1 Numerical Data and Solving Method -- 4.2 Analysis of Results -- 5 Future Work -- 6 Statement -- References -- Convolutional Neural Networks for Planting System Detection of Olive Groves -- 1 Background -- 1.1 Evolution of Production Techniques in Olive Groves -- 1.2 Current Situation of Modern Olive Cultivation Systems -- 1.3 Application of Remote Sensing Techniques for Image Analysis -- 1.4 Scope of the Present Chapter -- 2 Materials and Experimental Methods -- 2.1 Area of Study and Image Acquisition -- 2.2 Methodology -- 3 Results and Discussion -- 4 Conclusions and Future Lines -- References -- A Conceptual Model for Analysis of Plant Diseases Through EfficientNet: Towards Precision Farming -- 1 Introduction. 2 Related Study. |
| Record Nr. | UNINA-9910746971103321 |
Rivera Gilberto
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Integrated computer-aided engineering
| Integrated computer-aided engineering |
| Pubbl/distr/stampa | [Amsterdam], : IOS Press |
| Disciplina | 620.00285 |
| Soggetto topico | Computer-aided engineering |
| ISSN | 1875-8835 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910338724603321 |
| [Amsterdam], : IOS Press | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Integrated Science in Digital Age [[electronic resource] ] : ICIS 2019 / / edited by Tatiana Antipova
| Integrated Science in Digital Age [[electronic resource] ] : ICIS 2019 / / edited by Tatiana Antipova |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XIV, 384 p. 70 illus., 38 illus. in color.) |
| Disciplina | 620.00285 |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Engineering—Data processing
Computational intelligence Artificial intelligence Data Engineering Computational Intelligence Artificial Intelligence |
| ISBN | 3-030-22493-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484286403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Integration of mechanical and manufacturing engineering with IoT : a digital transformation / / edited by R. Rajasekar, C. Moganapriya, M.Harikrishna Kumar, P. Sathish Kumar
| Integration of mechanical and manufacturing engineering with IoT : a digital transformation / / edited by R. Rajasekar, C. Moganapriya, M.Harikrishna Kumar, P. Sathish Kumar |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , [2023] |
| Descrizione fisica | 1 online resource (342 pages) |
| Disciplina | 620.00285 |
| Soggetto topico |
Internet of things - Industrial applications
Engineering - Data processing Manufacturing processes Mechanical engineering Production engineering |
| ISBN |
9781119865001
1-119-86539-5 1-119-86538-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Evolution of Internet of Things (IoT): Past, Present and Future for Manufacturing Systems -- 1.1 Introduction -- 1.2 IoT Revolution -- 1.3 IoT -- 1.4 Fundamental Technologies -- 1.4.1 RFID and NFC -- 1.4.2 WSN -- 1.4.3 Data Storage and Analytics (DSA) -- 1.5 IoT Architecture -- 1.6 Cloud Computing (CC) and IoT -- 1.6.1 Service of CC -- 1.6.2 Integration of IoT With CC -- 1.7 Edge Computing (EC) and IoT -- 1.7.1 EC with IoT Architecture -- 1.8 Applications of IoT -- 1.8.1 Smart Mobility -- 1.8.2 Smart Grid -- 1.8.3 Smart Home System -- 1.8.4 Public Safety and Environment Monitoring -- 1.8.5 Smart Healthcare Systems -- 1.8.6 Smart Agriculture System -- 1.9 Industry 4.0 Integrated With IoT Architecture for Incorporation of Designing and Enhanced Production Systems -- 1.9.1 Five-Stage Process of IoT for Design and Manufacturing System -- 1.9.2 IoT Architecture for Advanced Manufacturing Technologies -- 1.9.3 Architecture Development -- 1.10 Current Issues and Challenges in IoT -- 1.10.1 Scalability -- 1.10.2 Issue of Trust -- 1.10.3 Service Availability -- 1.10.4 Security Challenges -- 1.10.5 Mobility Issues -- 1.10.6 Architecture for IoT -- 1.11 Conclusion -- References -- Chapter 2 Fourth Industrial Revolution: Industry 4.0 -- 2.1 Introduction -- 2.1.1 Global Level Adaption -- 2.2 Evolution of Industry -- 2.2.1 Industry 1.0 -- 2.2.2 Industry 2.0 -- 2.2.3 Industry 3.0 -- 2.2.4 Industry 4.0 (or) I4.0 -- 2.3 Basic IoT Concepts and the Term Glossary -- 2.4 Industrial Revolution -- 2.4.1 I4.0 Core Idea -- 2.4.2 Origin of I4.0 Concept -- 2.5 Industry -- 2.5.1 Manufacturing Phases -- 2.5.2 Existing Process Planning vs. I4.0 -- 2.5.3 Software for Product Planning-A Link Between Smart Products and the Main System ERP -- 2.6 Industry Production System 4.0 (Smart Factory).
2.6.1 IT Support -- 2.7 I4.0 in Functional Field -- 2.7.1 I4.0 Logistics -- 2.7.2 Resource Planning -- 2.7.3 Systems for Warehouse Management -- 2.7.4 Transportation Management Systems -- 2.7.5 Transportation Systems with Intelligence -- 2.7.6 Information Security -- 2.8 Existing Technology in I4.0 -- 2.8.1 Applications of I4.0 in Existing Industries -- 2.8.2 Additive Manufacturing (AM) -- 2.8.3 Intelligent Machines -- 2.8.4 Robots that are Self-Aware -- 2.8.5 Materials that are Smart -- 2.8.6 IoT -- 2.8.7 The Internet of Things in Industry (IIoT) -- 2.8.8 Sensors that are Smart -- 2.8.9 System Using a Smart Programmable Logic Controller (PLC) -- 2.8.10 Software -- 2.8.11 Augmented Reality (AR)/Virtual Reality (VR) -- 2.8.12 Gateway for the Internet of Things -- 2.8.13 Cloud -- 2.8.14 Applications of Additive Manufacturing in I4.0 -- 2.8.15 Artificial Intelligence (AI) -- 2.9 Applications in Current Industries -- 2.9.1 I4.0 in Logistics -- 2.9.2 I4.0 in Manufacturing Operation -- 2.10 Future Scope of Research -- 2.10.1 Theoretical Framework of I4.0 -- 2.11 Discussion and Implications -- 2.11.1 Hosting: Microsoft -- 2.11.2 Platform for the Internet of Things (IoT): Microsoft, GE, PTC, and Siemens -- 2.11.3 A Systematic Computational Analysis -- 2.11.4 Festo Proximity Sensor -- 2.11.5 Connectivity Hardware: HMS -- 2.11.6 IT Security: Claroty -- 2.11.7 Accenture Is a Systems Integrator -- 2.11.8 Additive Manufacturing: General Electric -- 2.11.9 Augmented and Virtual Reality: Upskill -- 2.11.10 ABB Collaborative Robots -- 2.11.11 Connected Vision System: Cognex -- 2.11.12 Drones/UAVs: PINC -- 2.11.13 Self-Driving in Vehicles: Clear Path Robotics -- 2.12 Conclusion -- References -- Chapter 3 Interaction of Internet of Things and Sensors for Machining -- 3.1 Introduction -- 3.2 Various Sensors Involved in Machining Process -- 3.2.1 Direct Method Sensors. 3.2.2 Indirect Method Sensors -- 3.2.3 Dynamometer -- 3.2.4 Accelerometer -- 3.2.5 Acoustic Emission Sensor -- 3.2.6 Current Sensors -- 3.3 Other Sensors -- 3.3.1 Temperature Sensors -- 3.3.2 Optical Sensors -- 3.4 Interaction of Sensors During Machining Operation -- 3.4.1 Milling Machining -- 3.4.2 Turning Machining -- 3.4.3 Drilling Machining Operation -- 3.5 Sensor Fusion Technique -- 3.6 Interaction of Internet of Things -- 3.6.1 Identification -- 3.6.2 Sensing -- 3.6.3 Communication -- 3.6.4 Computation -- 3.6.5 Services -- 3.6.6 Semantics -- 3.7 IoT Technologies in Manufacturing Process -- 3.7.1 IoT Challenges -- 3.7.2 IoT-Based Energy Monitoring System -- 3.8 Industrial Application -- 3.8.1 Integrated Structure -- 3.8.2 Monitoring the System Related to Service Based on Internet of Things -- 3.9 Decision Making Methods -- 3.9.1 Artificial Neural Network -- 3.9.2 Fuzzy Inference System -- 3.9.3 Support Vector Mechanism -- 3.9.4 Decision Trees and Random Forest -- 3.9.5 Convolutional Neural Network -- 3.10 Conclusion -- References -- Chapter 4 Application of Internet of Things (IoT) in the Automotive Industry -- 4.1 Introduction -- 4.2 Need For IoT in Automobile Field -- 4.3 Fault Diagnosis in Automobile -- 4.4 Automobile Security and Surveillance System in IoT-Based -- 4.5 A Vehicle Communications -- 4.6 The Smart Vehicle -- 4.7 Connected Vehicles -- 4.7.1 Vehicle-to-Vehicle (V2V) Communications -- 4.7.2 Vehicle-to-Infrastructure (V2I) Communications -- 4.7.3 Vehicle-to-Pedestrian (V2P) Communications -- 4.7.4 Vehicle to Network (V2N) Communication -- 4.7.5 Vehicle to Cloud (V2C) Communication -- 4.7.6 Vehicle to Device (V2D) Communication -- 4.7.7 Vehicle to Grid (V2G) Communications -- 4.8 Conclusion -- References -- Chapter 5 IoT for Food and Beverage Manufacturing -- 5.1 Introduction -- 5.2 The Influence of IoT in a Food Industry. 5.2.1 Management -- 5.2.2 Workers -- 5.2.3 Data -- 5.2.4 IT -- 5.3 A Brief Review of IoT's Involvement in the Food Industry -- 5.4 Challenges to the Food Industry and Role of IoT -- 5.4.1 Handling and Sorting Complex Data -- 5.4.2 A Retiring Skilled Workforce -- 5.4.3 Alternatives for Supply Chain Management -- 5.4.4 Implementation of IoT in Food and Beverage Manufacturing -- 5.4.5 Pilot -- 5.4.6 Plan -- 5.4.7 Proliferate -- 5.5 Applications of IoT in a Food Industry -- 5.5.1 IoT for Handling of Raw Material and Inventory Control -- 5.5.2 Factory Operations and Machine Conditions Using IoT -- 5.5.3 Quality Control With the IoT -- 5.5.4 IoT for Safety -- 5.5.5 The Internet of Things and Sustainability -- 5.5.6 IoT for Product Delivery and Packaging -- 5.5.7 IoT for Vehicle Optimization -- 5.5.8 IoT-Based Water Monitoring Architecture in the Food and Beverage Industry -- 5.6 A FW Tracking System Methodology Based on IoT -- 5.7 Designing an IoT-Based Digital FW Monitoring and Tracking System -- 5.8 The Internet of Things (IoT) Architecture for a Digitized Food Waste System -- 5.9 Hardware Design: Intelligent Scale -- 5.10 Software Design -- References -- Chapter 6 Opportunities: Machine Learning for Industrial IoT Applications -- 6.1 Introduction -- 6.2 I-IoT Applications -- 6.3 Machine Learning Algorithms for Industrial IoT -- 6.3.1 Supervised Learning -- 6.3.2 Semisupervised Learning -- 6.3.3 Unsupervised Learning -- 6.3.4 Reinforcement Learning -- 6.3.5 The Most Common and Popular Machine Learning Algorithms -- 6.4 I-IoT Data Analytics -- 6.4.1 Tools for IoT Analytics -- 6.4.2 Choosing the Right IoT Data Analytics Platforms -- 6.5 Conclusion -- References -- Chapter 7 Role of IoT in Industry Predictive Maintenance -- 7.1 Introduction -- 7.2 Predictive Maintenance -- 7.3 IPdM Systems Framework and Few Key Methodologies. 7.3.1 Detection and Collection of Data -- 7.3.2 Initial Processing of Collected Data -- 7.3.3 Modeling as Per Requirement -- 7.3.4 Influential Parameters -- 7.3.5 Identification of Best Working Path -- 7.3.6 Modifying Output With Respect Sensed Input -- 7.4 Economics of PdM -- 7.5 PdM for Production and Product -- 7.6 Implementation of IPdM -- 7.6.1 Manufacturing with Zero Defects -- 7.6.2 Sense of the Windsene INDSENSE -- 7.7 Case Studies -- 7.7.1 Area 1-Heavy Ash Evacuation -- 7.7.2 Area 2-Seawater Pumps -- 7.7.3 Evaporators -- 7.7.4 System Deployment Considerations in General -- 7.8 Automotive Industry-Integrated IoT -- 7.8.1 Navigation Aspect -- 7.8.2 Continual Working of Toll Booth -- 7.8.3 Theft Security System -- 7.8.4 Black Box-Enabled IoT -- 7.8.5 Regularizing Motion of Emergency Vehicle -- 7.8.6 Pollution Monitoring System -- 7.8.7 Timely Assessment of Driver's Condition -- 7.8.8 Vehicle Performance Monitoring -- 7.9 Conclusion -- References -- Chapter 8 Role of IoT in Product Development -- 8.1 Introduction -- 8.1.1 Industry 4.0 -- 8.2 Need to Understand the Product Architecture -- 8.3 Product Development Process -- 8.3.1 Criteria to Classify the New Products -- 8.3.2 Product Configuration -- 8.3.3 Challenges in Product Development while Developing IoT Products (Data-Driven Product Development) -- 8.3.4 Role of IoT in Product Development for Industrial Applications -- 8.3.5 Impacts and Future Perspectives of IoT in Product Development -- 8.4 Conclusion -- References -- Chapter 9 Benefits of IoT in Automated Systems -- 9.1 Introduction -- 9.2 Benefits of Automation -- 9.2.1 Improved Productivity -- 9.2.2 Efficient Operation Management -- 9.2.3 Better Use of Resources -- 9.2.4 Cost-Effective Operation -- 9.2.5 Improved Work Safety -- 9.2.6 Software Bots -- 9.2.7 Enhanced Public Sector Operations -- 9.2.8 Healthcare Benefits. 9.3 Smart City Automation. |
| Record Nr. | UNINA-9910830752203321 |
| Hoboken, New Jersey : , : John Wiley & Sons, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Intelligent optimisation techniques : genetic algorithms, tabu search, simulated annealing and neural networks / D.T.Pham and D.Karaboga
| Intelligent optimisation techniques : genetic algorithms, tabu search, simulated annealing and neural networks / D.T.Pham and D.Karaboga |
| Autore | PHAM, Duc Truong |
| Pubbl/distr/stampa | London [etc.] : Springer, copyr.2000 |
| Descrizione fisica | X, 302 p. ; 24 cm |
| Disciplina | 620.00285 |
| Altri autori (Persone) | KARABOGA, D. |
| Soggetto topico |
Elaboratori elettronici - Impiego in ingegneria
Reti neurali |
| ISBN | 1-85233-028-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-990000344970203316 |
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PHAM, Duc Truong
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| London [etc.] : Springer, copyr.2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Introduction to Bayesian Tracking and Particle Filters [[electronic resource] /] / by Lawrence D. Stone, Roy L. Streit, Stephen L. Anderson
| Introduction to Bayesian Tracking and Particle Filters [[electronic resource] /] / by Lawrence D. Stone, Roy L. Streit, Stephen L. Anderson |
| Autore | Stone Lawrence D |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (124 pages) |
| Disciplina | 620.00285 |
| Altri autori (Persone) |
StreitRoy L
AndersonStephen L |
| Collana | Studies in Big Data |
| Soggetto topico |
Engineering—Data processing
Statistics Big data Data Engineering Bayesian Inference Big Data |
| Soggetto non controllato | Mathematics |
| ISBN | 3-031-32242-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Bayesian Single Target Tracking -- Bayesian Particle Filtering -- Simple Multiple Target Tracking -- Intensity Filters. |
| Record Nr. | UNINA-9910728944303321 |
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Stone Lawrence D
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Introduction to C++ Excel Matlab & basic engineering numerical methods [Risorsa elettronica] / Harvey G. Stenger, Charles R. Smith
| Introduction to C++ Excel Matlab & basic engineering numerical methods [Risorsa elettronica] / Harvey G. Stenger, Charles R. Smith |
| Autore | Stenger, Harvey G. |
| Pubbl/distr/stampa | Upper Saddle River, NJ : Pearson Education, ©2009 |
| Descrizione fisica | 1 DVD-ROM : col. |
| Disciplina |
005.133
620.00285 |
| Altri autori (Persone) | Smith, Charles R. |
| Soggetto non controllato |
Matlab
Analisi numerica |
| Formato | Risorse elettroniche  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-990008811270403321 |
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Stenger, Harvey G.
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| Upper Saddle River, NJ : Pearson Education, ©2009 | ||
| Risorse elettroniche | ||
| Lo trovi qui: Univ. Federico II | ||
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The material point method : theory, implementations and applications / / Vinh Phu Nguyen, Alban de Vaucorbeil, and Stephane Bordas
| The material point method : theory, implementations and applications / / Vinh Phu Nguyen, Alban de Vaucorbeil, and Stephane Bordas |
| Autore | Nguyen Vinh Phu |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] |
| Descrizione fisica | 1 online resource (XV, 467 p. 278 illus., 238 illus. in color.) |
| Disciplina | 620.00285 |
| Collana | Scientific Computation |
| Soggetto topico |
Engineering - Data processing
Engineering mathematics Mathematical physics Mathematics - Data processing Mechanics, Applied Matemàtica per a enginyers Física matemàtica Mecànica aplicada Sòlids |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-24070-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- A general MPM for solid mechanics -- Various MPM formulations -- Constitutive models -- Implementation -- MPMat: a MPM Matlab code -- Karamelo: a multi-CPU/GPU C++ parallel MPM code -- Contact and fracture -- Stability, accuracy and recent improvements -- Other topics: modeling of fluids, membranes and temperature effects. |
| Record Nr. | UNINA-9910686468103321 |
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Nguyen Vinh Phu
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| Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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MATLAB and Simulink crash course for engineers / / Eklas Hossain
| MATLAB and Simulink crash course for engineers / / Eklas Hossain |
| Autore | Hossain Eklas |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (667 pages) |
| Disciplina | 620.00285 |
| Soggetto topico |
Computer-aided engineering
Engineering mathematics |
| ISBN |
9783030897628
9783030897611 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910768438203321 |
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Hossain Eklas
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Modeling and Simulation in Engineering : Selected Problems / / edited by Jan Valdman, Leszek Marcinkowski
| Modeling and Simulation in Engineering : Selected Problems / / edited by Jan Valdman, Leszek Marcinkowski |
| Pubbl/distr/stampa | London, England : , : IntechOpen, , 2020 |
| Descrizione fisica | 1 online resource (240 pages) |
| Disciplina | 620.00285 |
| Soggetto topico | Engineering - Data processing |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Modeling and Simulation in Engineering |
| Record Nr. | UNINA-9910688435003321 |
| London, England : , : IntechOpen, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
