28 research outputs found
Extracting Temporal Relationships in EHR: Application to COVID-19 Patients
Association rules are one of the most used data mining techniques. The
first proposals have considered relations over time in different ways, resulting in the
so-called Temporal Association Rules (TAR). Although there are some proposals to
extract association rules in OLAP systems, to the best of our knowledge, there is no
method proposed to extract temporal association rules over multidimensional
models in these kinds of systems. In this paper we study the adaptation of TAR to
multidimensional structures, identifying the dimension that establishes the number
of transactions and how to find time relative correlations between the other
dimensions. A new method called COGtARE is presented as an extension of a
previous approach proposed to reduce the complexity of the resulting set of
association rules. The method is tested in application to COVID-19 patients data.B-TIC-744-UGR20 ADIM: Accesibilidad de
Datos para Investigación Médica of the Junta de Andalucí
Improving Fuzzy Knowledge Integration with Particle Swarm Optimization
[[abstract]]"This paper presents an approach to integrate multiple fuzzy
knowledge bases for increasing the accuracy and decreasing the
complexity of the integrated knowledge base. The proposed
approach consists of two phases: PSO -based fuzzy knowledge
encoding, and PSO-based fuzzy knowledge fusion. In the
encoding phase, the fuzzy rule sets and fuzzy sets with its
corresponding membership functions are encoded as a string and
are put together in the initial knowledge population. In the fusion
phase, the particle swarm algorithm is used to explore the fuzzy
rule sets, fuzzy sets and membership functions to its optimal or
the approximately optimal extent. Two application domains,
including diagnosis on student’s program learning style and
situational learning services composition, were used to
demonstrate the performance of the proposed knowledge
integration approach. Experiment results revealed that our
approach will effectively increase the accuracy and decrease the
complexity of integrated knowledge base. The results of this
study could extend the effectiveness of knowledge inference and
decision making.
Service robotics and machine learning for close-range remote sensing
L'abstract è presente nell'allegato / the abstract is in the attachmen
Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018
The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes
Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018
The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards:
• regional, continental and global sharing of ecological data,
• thorough integration of complementing monitoring technologies including DNA-barcoding,
• sophisticated pattern recognition by deep learning,
• advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling,
• decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes