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AI and Digital Transformation: Innovations in Supply Chain, Education, and Energy Systems : Proceedings of the 6th International Conference on Advanced Technologies for Humanity (ICATH'2024) / / edited by Brahim El Bhiri, Amir Hussain, Yassine Maleh
| AI and Digital Transformation: Innovations in Supply Chain, Education, and Energy Systems : Proceedings of the 6th International Conference on Advanced Technologies for Humanity (ICATH'2024) / / edited by Brahim El Bhiri, Amir Hussain, Yassine Maleh |
| Autore | El Bhiri Brahim |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (350 pages) |
| Disciplina | 658.50028563 |
| Collana | Sustainable Artificial Intelligence-Powered Applications, IEREK Interdisciplinary Series for Sustainable Development |
| Soggetto topico |
Sustainability
Business logistics Artificial intelligence Neural networks (Computer science) Supply Chain Management Artificial Intelligence Mathematical Models of Cognitive Processes and Neural Networks |
| ISBN | 3-031-86837-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Section I: AI in Supply Chain and Logistics -- 1. A Comparative Study of HBOS and Isolation Forest Anomaly Detection Models: An Experimental Analysis -- 2. Recurrent Neural Models for Retail Supply Chain Stock Prices Forecasting -- 3. The Trend of Integration of Artificial Intelligence in Supply Chain: A Bibliometric Review -- 4. Artificial Intelligence Approach for Enhancing Vehicle Routing Problem Solutions Integrating Smart Lockers for Dynamic Delivery and Pickup Operations -- 5. Understanding Customer Experiences in the Logistics Sector: Insights from Individual Cargo Companies -- Section II: Digital Transformation in Education and Industry -- 6. Towards a Pedagogical Revolution: Intelligent Learning Systems in the Service of Higher Education -- 7. Preparing for Industry 5.0: Inclusive Curricular Approaches and Digital Empowerment -- 8. Production Leveling Implementation Methodologies: Comparison and Review -- 9. Enhancing Candidate Evaluation in Recruitment Interviews Based on ResNeXt-101 for Facial Expression Recognition Section III: AI and Machine Learning Applications -- 10. Bayesian Deep Learning for Multi-Disease Detection in Retinal Imaging with Uncertainty Quantification. |
| Record Nr. | UNINA-9911039323103321 |
El Bhiri Brahim
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The AI product playbook : strategies, skills, and frameworks for the AI-driven product manager / / Marily Nika, Diego Granados
| The AI product playbook : strategies, skills, and frameworks for the AI-driven product manager / / Marily Nika, Diego Granados |
| Autore | Nika Marily |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , [2026] |
| Descrizione fisica | 1 online resource (339 pages) |
| Disciplina | 658.50028563 |
| Soggetto topico |
Product management
Artificial intelligence |
| ISBN |
1394352468
1394335660 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Introduction -- Part I Foundational AI/ML Concepts -- Chapter 1 Artificial Intelligence and Machine Learning: What Every Product Manager Needs to Know -- AI vs. ML -- Why This Matters to a PM -- Key Differences Between AI and ML -- Common Misconceptions for PMs: Myths vs. Reality -- Your Glossary as a PM -- Grounding the Concepts: Real-World AI in Action -- The AI PM's Guiding Principles -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Peeking Under the Hood -- Chapter 2 How Machine Learning Models Learn: A Peek Under the Hood -- The Learning Process: Training, Validation, and Testing -- How Models Learn: An Example with k-Nearest Neighbors (k-NN) -- Applying k-NN (with k=1): -- Another Example: Testing an Unknown Fruit -- Evaluating Model Performance -- The Confusion Matrix: A Foundation for Understanding -- Key Classification Metrics (and Their PM Implications) -- The Precision-Recall Trade-Off -- Choosing the Right Metric -- Overfitting and Underfitting: Striking the Right Balance for Real-World Performance -- Overfitting: Memorizing Instead of Learning -- Underfitting: Missing the Forest for the Trees -- Visual Analogy: Fitting a Curve -- Finding the Sweet Spot: Generalization -- The PM's Role -- Human-in-the-Loop: Blending AI Power with Human Expertise -- What Is Human-in-the-Loop? -- Why HITL Is Essential for Product Managers (and Their Products) -- How to Implement HITL (PM Considerations) -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Understanding the Broader Process -- Chapter 3 The Big Picture: AI, ML, and You -- Understanding the Relationship Between AI, ML, and Product Goals -- Types of Machine Learning: Understanding the Spectrum of Learning.
Supervised Learning: Guiding the Model with Labeled Examples -- Unsupervised Learning: Discovering Hidden Patterns in Your Data -- Reinforcement Learning: Learning Through Trial and Error -- Generative AI: Powering a New Era of Language-Based Applications -- The "Gotchas": A PM's Guide to LLM Limitations and Risks -- Types of Machine Learning: A Recap -- Introduction to Neural Networks and Deep Learning: The Engines of Complex Pattern Recognition -- Neural Networks: Mimicking the Brain's Connections (But Not Really) -- How Neural Networks Learn: Adjusting the Connections -- Technical Deep Dive: The Mechanics of Neural Networks and Deep Learning -- Challenges in Deep Learning -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Mapping the Process -- Chapter 4 The AI Lifecycle -- Problem Definition and Business Understanding: The "Why" -- Data Collection and Exploration: Understanding Your Ingredients -- Data Preprocessing: Preparing the Ingredients -- Feature Engineering: Crafting the Inputs for Success -- Model Selection and Training: Choosing the Right Algorithm -- Model Evaluation and Tuning: Ensuring Quality -- Model Deployment and Monitoring: Bringing AI to Life (and Keeping It Healthy) -- Retraining and Maintenance: Keeping Your Model Up-to-Date -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Exploring the AI PM Roles -- Part II AI PM Specializations -- Chapter 5 AI-Experiences PM: Shaping User Interaction with AI -- Key Responsibilities: Shaping the AI User Experience -- Day-to-Day Activities -- Required Skills and Knowledge: The AI-Experiences PM Toolkit -- Core Product Management Craft and Practices -- Engineering Foundations for PMs -- Essential Leadership and Collaboration Skills -- AI Lifecycle and Operational Awareness -- Illustrative Example: A Day in the Life of an AI-Experiences PM. Challenges and Complexities -- How the AI-Experiences PM Interacts with Other Roles -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Architecting the AI Foundation -- Chapter 6 AI-Builder PM: Architecting the Foundation of Intelligent Systems -- Key Responsibilities: Building and Managing the AI Foundation -- Day-to-Day Activities -- Required Skills and Knowledge: The AI-Builder PM's Technical and Strategic Toolkit -- Core Product Management Craft and Practices -- Engineering Foundations for PMs -- Essential Leadership and Collaboration Skills -- AI Lifecycle and Operational Awareness -- Illustrative Example: A Day in the Life of an AI-Builder PM -- Challenges and Complexities -- How the AI-Builder PM Interacts with Other Roles -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Supercharging the PM Workflow -- Chapter 7 AI-Enhanced PM: Supercharging Product Management with AI -- Key Responsibilities: Augmenting PM Workflows and Decision-Making with AI -- Day-to-Day Activities -- Required Skills and Knowledge: The AI-Enhanced PM's Toolkit -- Core Product Management Craft and Practices -- Engineering Foundations for PMs -- Essential Leadership and Collaboration Skills -- AI Lifecycle and Operational Awareness -- Illustrative Example: A Day in the Life of an AI-Enhanced PM -- Examples of AI Tools -- Challenges and Complexities -- How the AI-Enhanced PM Interacts with Other Roles -- Skill Comparison: AI-Experiences PM, AI-Builder PM, and AI-Enhanced PM -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: From Theory to Action -- Part III Connecting the Dots Between AI/ML Knowledge and PM Craft -- Chapter 8 Identifying and Evaluating AI Opportunities -- Uncovering Potential Use Cases-Mining Your Product for AI Gold -- Recognizing Data-Rich Problem Areas -- Analyzing Existing Data Sources. Asking the Right Questions -- AI/ML Capability Matching: Connecting Problems to Solutions -- Understanding Your AI/ML Toolkit: Key Capabilities -- Matching Capabilities to Problems: A Practical Approach -- Finding AI Opportunities in the User Journey -- Mapping the User Journey: Charting the Course -- Identifying Pain Points and Opportunities: The AI Detective Work -- Applying AI/ML to Enhance Touchpoints: The Transformation -- Feature Enhancement Through AI/ML-Transforming Existing Functionality -- Identifying Enhancement Opportunities: Finding the Weak Spots -- Applying AI/ML to Enhance Features: The Transformation Process -- Proactive Product Management-Anticipating User Needs with AI -- Understanding the Power of Prediction and Automation -- Key Areas for Predictive and Automation Opportunities -- Identifying Opportunities: A Practical Approach -- Responsible AI Foundations-Ethical and Feasibility Considerations -- Ethical Considerations: The "Do No Harm" Principle -- Feasibility Considerations: Can We Actually Build This? -- Practical Ideation Techniques for AI/ML Use Cases-Thinking Like an AI-First Product Manager -- Ideation Techniques: Unleashing Your AI Creativity -- Cultivating an AI-First Mindset -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Measuring the Value of Your Ideas -- Chapter 9 ROI Calculation for AI Projects: Measuring the Impact and Demonstrating Value -- From Model Performance to Business Impact: A PM's Guide to AI Metrics -- Defining AI/ML-Specific Metrics: The Foundation for Measuring ROI -- The Importance of Baselines: Knowing Where You Started -- Understanding the Confusion Matrix: Decoding Classification Performance -- Key Performance Metrics for AI/ML Models: Beyond the Confusion Matrix -- Context Matters: Selecting the Right Metrics for Your AI/ML Application -- Important Considerations. End-to-End Example-Predicting Churn in a Subscription Service -- 1. Identify the Business Goal: Defining the "Why" -- 2. Define the AI/ML Application and Solution -- 3. Identify Data Sources and Engineer Features: The Raw Materials -- 4. Select the Metrics: Defining Success -- 5. Establish Baseline Metrics: Setting the Starting Point -- 6. Conduct Model Training and Evaluation: Building and Testing the AI -- 7. Conduct A/B Testing: Measuring Real-World Impact -- 8. Calculate the Results and ROI: Quantifying the Value -- 9. Monitor and Maintain the Model for Long-Term Success -- A/B Testing for AI and ML Projects: Validating Impact and Optimizing Performance -- What Is A/B Testing (in a Nutshell)? -- Why Is A/B Testing Especially Important for AI/ML? -- How to Conduct A/B Testing for AI and ML: A Step-by-Step Guide -- Key Considerations for AI/ML A/B Testing -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: From the Lab to a Live Product -- Chapter 10 Building and Deploying AI Solutions: From Lab to Live -- MLOps: The Key to Reliable and Scalable AI -- Key Components of MLOps-The AI Production Line -- CI/CD, IaC, and Collaboration: The Foundational Pillars of MLOps -- Glossary of Key MLOps Terms -- MLOps End-to-End Example: Churn Prediction in a Subscription Service (Product Manager's Perspective) -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Building with Integrity -- Chapter 11 Responsible AI and Ethical Considerations: Building AI with Integrity -- Understanding AI Bias and Fairness: The Foundation of Responsible AI -- Identifying Potential Biases: Where Bias Can Creep In -- Mitigating Potential Biases: A Proactive Approach -- Protected Classes and AI Fairness-Designing for Inclusion -- What are Protected Classes? -- Why Focus on Protected Classes? (The Legal and Ethical Imperative). How Protected Classes Relate to AI Bias: The Mechanisms of Discrimination. |
| Record Nr. | UNINA-9911031643503321 |
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Nika Marily
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| Newark : , : John Wiley & Sons, Incorporated, , [2026] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19) / / edited by Sergey Kovalev, Valery Tarassov, Vaclav Snasel, Andrey Sukhanov
| Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19) / / edited by Sergey Kovalev, Valery Tarassov, Vaclav Snasel, Andrey Sukhanov |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (713 pages) |
| Disciplina |
670.28563
658.50028563 |
| Collana | Advances in Intelligent Systems and Computing |
| Soggetto topico |
Computational intelligence
Artificial intelligence Computational Intelligence Artificial Intelligence |
| ISBN | 3-030-50097-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Neural networks -- Adaptive Diagnosis Model of Dempster-Shafer Based on Recurrent Neural-Fuzzy Network -- The method of clearing printed and handwritten texts from noise -- Interval Signs Enlargement Algorithm in the Classication Problem of Biomedical Signals -- Age and Gender Recognition on Imbalanced Dataset of Face Images with Deep Learning -- A Complex Approach to the Data Labeling E‑ciency Improvement -- Automation of Musical Compositions Synthesis Process Based on Neural Networks -- Convolutional Neural Network Application for Analysis of Fundus Images -- Approximation Methods for Monte Carlo Tree Search -- Labor intensity evaluation technique in software development process based on neural networks -- An Analysis of Convolutional Neural Network for Fashion Images Classication (Fashion-MNIST) -- Multiagent Systems -- Implementation of the real-time intelligent system based on theintegration approach -- Agent-based situational modeling and identication technological systems in conditions of uncertainty -- Features of Data Warehouse Support Based on a Search Agent and an Evolutionary Model for Innovation Information Selection -- Multi-Agent System of Knowledge Representation and Processing -- The Technique of Data Analysis Tasks Distribution in the Fog-Computing Environment -- Non-Classical Logic -- Model of the Operating Device with a Tunable Structure for the Implementation of the Accelerated Deductive Inference Method -- A Model Checking Based Approach for Verication of Attribute-Based Access Control Policies in Cloud Infrastructures. -- Detection of anomalous situations in an unforeseen increase in the duration of inference step of the agent in hard real time -- Bayesian Networks and Trust Networks, Fuzzy-Stocastical Modelling -- Protection System for a Group of Robots Based on the Detection of Anomalous Behavior -- Employees' social graph analysis: a model of detection the most criticality trajectories of the social engineering attack's spread -- An approach to quantication of relationship types between users based on the frequency of combinations of non-numeric evaluations -- Algebraic Bayesian networks: parallel algorithms for maintaining local consistency. |
| Record Nr. | UNINA-9910483050503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Service Orientation in Holonic and Multi-Agent Manufacturing / / edited by Theodor Borangiu, Damien Trentesaux, André Thomas, Duncan McFarlane
| Service Orientation in Holonic and Multi-Agent Manufacturing / / edited by Theodor Borangiu, Damien Trentesaux, André Thomas, Duncan McFarlane |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XXII, 340 p. 111 illus., 87 illus. in color.) |
| Disciplina | 658.50028563 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Artificial intelligence Industrial engineering Production engineering Robotics Automation Computational Intelligence Artificial Intelligence Industrial and Production Engineering Robotics and Automation |
| ISBN | 3-319-30337-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Applications of Intelligent Products -- Recent Advances in Control for Physical Internet and Interconnected Logistics -- Sustainability Issues in Intelligent Manufacturing Systems -- Holonic and Multi-Agent System Design for Industry and Services -- Service Oriented Enterprise Management and Control -- Cloud and Computing-oriented Manufacturing -- Smart Grids and Wireless Sensor Networks. |
| Record Nr. | UNINA-9910253960903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Supply chain management e intelligenza artificiale : migliorare i processi e la competitività aziendale / a cura di Raffaele Secchi
| Supply chain management e intelligenza artificiale : migliorare i processi e la competitività aziendale / a cura di Raffaele Secchi |
| Pubbl/distr/stampa | Milano : Guerini Next, 2022 |
| Descrizione fisica | 196 p. : tab., diagr. ; 23 cm. |
| Disciplina | 658.50028563 |
| Altri autori (Persone) | Secchi, Raffaele |
| Collana | Università Cattaneo libri ; 21 |
| Soggetto topico | Logistica aziendale - Impiego [dell'] Intelligenza artificiale |
| ISBN | 9788868964610 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Record Nr. | UNISALENTO-991004368138407536 |
| Milano : Guerini Next, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
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