Success cases

PRIMAGE

Medical Imaging Artificial Intelligence Childhood Cancer Research

Solution:

The PRIMAGE project is devoted to developing methods of computational analysis of medical images applied to childhood cancer.

PRIMAGE proposes a cloud-based platform to support decision making in the clinical management of malignant solid tumors, offering predictive tools to assist diagnosis, prognosis, therapies choice and treatment follow-up, based on the use of novel imaging biomarkers, in-silico tumor growth simulation, advanced visualization of predictions with weighted confidence scores and machine-learning based translation of this knowledge into predictors for the most relevant, disease-specific, Clinical End Points.

The final aim is to  integrate and validate a  functional prototype of the PRIMAGE cloud-based platform offering predictive tools to assist management of Neuroblastoma and DIPG paediatric cancers, from diagnosis to prognosis, therapies choice and treatment follow-up, based on the use of novel imaging biomarkers, tumor growth models and advanced visualisation of predictions.

QUIBIM

Quantitative Radiology Solution for Innovative Doctor, Researchers and Clinical Trial Companies

Solution:

QUIBIM Precision is the first imaging biomarkers analysis platform in the cloud presenting innovative whilst extremely useful characteristics for the sector: 1) Automated analysis of imaging biomarkers (results are ready just within minutes) with the best accuracy and reproducibility; 2) Medically certified: QUIBIM are medically valid to scientifically support decision making; 3) Open to any physicians: Optimized User Interface (UI), user experience (UX) and imaging analysis functionalities 4) Cost-effective: QUIBIM helps reduce costs of medical testing and misdiagnosis, especially from specialists as each report costs 45€.

Quick Urban Forestation

Quick Urban Forestation

Solution:

Cesefor coordinated the Life+ Quick Urban Forestation project, which was carried out in collaboration with IClaves SL and SDL Medio Ambiente, and with the Valladolid City Council. Its main objective was to create an experimental urban forest in the city of Valladolid. The aim of the experiment was to test different techniques for planting native species in an effort to grow an urban forest which would not require irrigation in the heat of the summer. Nearly 15,000 trees have been planted, a network of monitoring and information analysis has been deployed in order to obtain information about the temperature and humidity in the roots and on the surface, and to observe the behaviour of the plants' stems. The data obtained from sensors was processed and a survival analysis was conducted to study the results of the experiment.  Plant data was extracted every 30 minutes during 2 and a half years, the dataset that has been generated as a result of the project is available in open data formats on the project's website.

People analytics

People analytics

Solution:

People Analytics Expert System. Applies Artificial Intelligence and Big Data techniques to the information about people that is extracted from different public sources. Patterns are identified and profiles are elaborated to help gain greater knowledge about people and predict their behaviour. This enables the system users to make decisions on the basis of objective data.

Deep cyberbullying analytics

Deep cyberbullying analytics

Solution:

A cognitive engine for the detection and prevention of cyberbullying on the Internet. Applies Artificial Intelligence and Data Analytics technologies to build graphs and social interaction models on the basis of Big Data, automating and facilitating the tasks associated with the timely prevention, detection and suppression of cyberbullying in the workplace and at school, since information will be available from a variety of open sources such as Facebook, Twitter and LinkedIn.

Maintenance 4.0

Intelligent and predictive maintenance management in production systems

Solution:

This project aims to develop an integrated and intelligent solution for optimal industrial maintenance, that will improve, according to the principles of Industry 4.0, the performance of production processes. To this end, the following aspects are considered: (i) advanced online analysis of the collected data in order to monitor and detect premature failures in the machines that operate on the production plant and, consequently, the need for maintenance interventions, and (ii) support to technicians during maintenance interventions through an intelligent decision support system, contributing to a faster and more effective recovery from any failure that may have occurred.

PERFoRM

Production harmonizEd Reconfiguration of Flexible Robots and Machinery

Solution:

Conceptual transformation of existing Production Systems towards plug&produce production systems to achieve flexible manufacturing environments based on rapid and seamless reconfiguration of machinery and robots as response to operational or business events.

ARUM

Adaptive Production Management

Solution:

Development of mechanisms focusing the achievement of faster and more adaptive ramp-up processes using multi-agent systems.

GRACE

Integration of Process and Quality Control Using Multi-agent Technology

Solution:

Development of distributed production control systems integrating process and quality control levels using multi-agent systems principles and self-adaptation procedures to support variation and fluctuation in processes and products.

GO0D MAN

aGent Oriented Zero Defect Multi-stage mANufacturing

Solution:

The project aims the development of Zero-Defect Manufacturing (ZDM) strategies in multi-stage production systems, through the integration of quality control and process control using cyber-physical systems, intelligent inspection systems and advanced data analysis tools. As result, it will be possible the earlier and real-time deviations and patterns, and consequently to avoid the occurrence of defects in the stations and their propagation to posterior processes.

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