Recommender and support decision Systems
Els sistemes de recomanació són sistemes intel·ligents que proporcionen als usuaris un seguit de suggeriments personalitzats (recomanacions) sobre un determinat tipus d'elements (ítems) que poden interessar a l'usuari basant-se en les preferències que ha expressat de forma implícita o explícita. Així doncs, dona suport a l'usuari a l'hora de prendre decisions mentre interactua amb una massa important d'informació, emprant diversos mètodes per fer aquesta tria automàtica. De manera que permeten que a l'usuari li arribi allò que li interessa de manera més ràpida i eficaç.
These are the services that our research groups can develop. For customized information on the services, please contact us.
| Areas of service | Applications of the service | Scientific infrastructure |
| Data-based models and systems for decision making, management and risk prediction | Industrial risk, risk at the urban-forest interface, risk at the industry-forest interface, risk associated with forest fires | FlamesLab |
| Decision-making tools in the design and management of the supply chain. | ||
| Improvement in the management of supply chain operations based on IoT. Diagnosis, strategic reflection and definition of actions | ||
| Process analysis and diagnosis, design of proposals to improve processes, definition of the implementation plan | ||
| Development of decision support systems applied to predictive maintenance | ||
| Optimisation of the state of operation of production processes using predictive models | ||
| End-to-end quality management of the service offered based on artificial intelligence policies | Quality management of the network connectivity service (end-to-end) and of the service perception (QoE) in data centre networks and optical metro/core networks based on policies and/or artificial intelligence. | |
| Routing systems of network traffic | Traffic management systems using dynamic routing in core and metropolitan networks through machine learning techniques. | |
| Matrix completion techniques, joint learning, active sampling | Recommender systems, crowdsourcing, design of classifiers with multiple learners, binary and multiple class classifiers, boosting. | |
| Planning, dimensioning, operation, automation and optimisation of 5G networks | ||
| Planning of robotic system movements and tasks | Management of mobile manipulators, anthropomorphic and industrial robots | |
| Design and implementation of systems for gripping and manipulating objects using robotic hands. | IOC Robotics Laboratory | |
| Development of software and hardware | Automation of industrial processes | Perception and Manipulation Laborator Water Cycle Control Systems Laboratory, Mobile Robotics Laboratory |
| Cyber-physical applications in the cloud | Diagnosis of machinery and industrial processes | |
| Design and optimisation of industrial energy hubs |
industrial risk, forest fires, risk at the urban-forest interface, urban and industrial fires optical networks, metro-core networks, 5G, 6G, optoelectronic devices, photonics deep reinforcement learning, optimisation of IoT protocols, 5G, 6G network selection, binary classifiers, multiple class classifiers, boosting, crowdsourcing, recommender system 5G Planning, 5G Dimensioning, 5G Operation, 5G Automation, 5G Optimisation Supply chain management, decision-making tools, movement planning, robot system tasks, mobile manipulators, industrials, object handling with robotic hands modelling of production processes, system optimisation, production planning, supply chain diagnosis, supply chain optimisation, supply chain management, supply chain design, process analysis, implementation plan, Industrial processes, cobots, AI, industry 4.0 electronic equipment, electrical equipment, power converters, sensor networks, IoT sensors, IoT communications, decision support systems, predictive maintenance, fault-tolerant control systems, predictive models for processes, industrial processes, machine diagnosis, energy management of industrial processes
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