The Applied Artificial Intelligence and Machine Learning Group of iCoSys in Fribourg Switzerland is active in domains such as :
- Artificial Intelligence,
- Machine learning,
- Big data analysis,
- Signal processing,
Machine Learning for enterprise solutions
Our main focus is to work with economic partners on applied machine learning projects. We have ongoing project realisations in different domains such as biomedical, video surveillance, electricity consumption analysis, financial time series, massive text processing, Industry 4.0, etc. Partnerships are typically done through research mandates, CTI funding or EU framework projects.
Our team includes seniors in AI and Machine Learning, PhD-level and master-level computer scientists to make the best out of your projects. Contact Jean Hennebert for more information.
The European project BIOSMART aims to develop active and smart bio-based and compostable packages that make possible: light weighting, reduced food residues, shelf life monitoring, longer shelf life and easier consumer waste handling.
ADVANCe is an ongoing Innosuisse project that focuses on the analysis of emotions and their congruence in video feeds.
In an international industrial research project we apply the latest technologies in machine learning to do handwriting recognition in tax forms.
TFC is a distributed application development framework, based on POP-Java. It allows developers to develop decentralized applications in which the users can share computing power and data in a secure manner.
The connection between the different users of the software is made in a way similar to social networks, allowing users to create communities around certain applications.
BBDATA stands for Big Building Data and aims at developing a scalable cloud platform and tools for storing and processing smart building data. The services are targeting data access, processing and analysis, using open, robust, standardized and secured big data technologies.
DAPLAB aims at facilitating the access for enterprises and universities to emerging technologies in the area of big data and intelligent data analysis by managing an infrastructure composed of cluster of servers dedicated to storage and computation, to which universities and local companies will have access.
PhD Thesis by Christophe Gisler: time series represent a large part of the data supply worldwide and many data mining tasks, such as prediction and classification, are concerned with them. This thesis focused on the analysis and development of generic machine learning approaches to multivariate time series classification.
Tactical Ad-hoc networK Emulation aims to conceive, develop and implement algorithms to deploy reliable tactical applications over ad-hoc networks for Armasuisse.
SENSIMED AG has developed Triggerfish, a wearable non-invasive solution to monitor continuously intra-ocular pressure (IOP). The purpose of the Sensimed Diagnosis project is to analyze, design and implement signal processing, feature extraction and pattern recognition tools to exploit the signals monitored by the Triggerfish.
PhD thesis of Antonio Ridi: In this thesis we describe our work on the use of generative modeling for the classication of time series in the context of in-home monitoring. More precisely, we use generative approaches as Gaussian Mixture models (GMMs) and Hidden Markov models (HMMs).
VideoProtector is an innovative solution to manage cameras streams in a central place. This centralization offers also the possibility to run incident detection algorithms in a Software-as-a-Service (SaaS) mode.
PhD Thesis of Gérôme Bovet - The thesis is about the conception and implementation of a framework to develop dedicated smart buildings applications. By relying on Web technologies, we demonstrate the development of seamless services while reusing the available resources within the network. We also demonstrated in this thesis the exposure of machine learning services made accessible through Web interfaces and hiding the complexity of the process.
GreenMod aims at providing a framework easing the development of building management systems on top of Web technologies.
The idea of the project is to leverage on large sets of linguistic data and on machine learning algorithms able to discover automatically pronunciation rules. Such rules are then used in “symbolic mappers” able to transform a grapheme representation into a phonetic representation and to propose probable alterations of the pronunciation. The mappers are then used to propose clients with new domain names that sound similar to their original search.
All projects here.