Mehmet Oguz MulayimSpanish National Research Council | CSIC · Artificial Intelligence Research Institute
Mehmet Oguz Mulayim
PhD in AI
enhancing Citizen Science with AI & ML in the crowd4sdg.eu project
About
21
Publications
1,072
Reads
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45
Citations
Citations since 2017
Introduction
Artificial Intelligence & Machine Learning for Healthcare & Citizen Science
Additional affiliations
September 2022 - February 2023
Position
- Lecturer
Description
- Co-teaching the Machine Learning Fundamentals course in the Artificial Intelligence degree.
October 2017 - September 2020
Position
- PhD Student
Description
- Research and development on fields including large-scale Case-Based Reasoning (CBR), Machine Learning for healthcare, applied Artificial Intelligence
September 2006 - January 2009
Position
- MSc Student
Description
- Investigating application of introspective reasoning techniques particularly to the maintenance of Case-Based Reasoning (CBR) systems
Education
December 2017 - November 2020
January 2006 - December 2008
October 1991 - August 1996
Publications
Publications (21)
Case-Based Reasoning (CBR) methodology's approach to problem-solving that "similar problems have similar solutions" has proved quite favorable for many industrial artificial intelligence applications. However, CBR's very advantages hinder its performance as case bases (CBs) grow larger than moderate sizes. Searching similar cases is expensive. This...
Over the last decade, hundreds of thousands of volunteers have contributed to science by collecting or analyzing data. This public participation in science, also known as citizen science, has contributed to significant discoveries and led to publications in major scientific journals. However, little attention has been paid to data quality issues. I...
Rapid impact assessment in the immediate aftermath of a natural disaster is essential to provide adequate information to international organisations, local authorities, and first responders. Social media can support emergency response with evidence-based content posted by citizens and organisations during ongoing events. In the paper, we propose Tr...
Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among the millions of posts being added every day can be difficult, and in current approaches developing an automatic data analysis project requires time and technical skills. This work presents a new app...
This work is about speeding up retrieval in Case-Based Reasoning (CBR) for large-scale case bases (CBs) comprised of temporally related cases in metric spaces. A typical example is a CB of electronic health records where consecutive sessions of a patient forms a sequence of related cases. k-Nearest Neighbors (kNN) search is a widely used algorithm...
We address the problem of estimating a photo's geographical location. Success in this estimation enables many impactful applications, like facilitating Disaster Management circumstances. However, this is also a very challenging task. Due to the complexity of the problem, we restrict the area of geolocation to a single city, treating geolocation as...
Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among millions of posts being posted every day can be difficult, and developing a data analysis project usually requires time and technical skills. This study presents an approach that provides flexible s...
Dataset used in the article "A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data".
Rapid impact assessment in the immediate aftermath of a natural disaster is essential to provide adequate information to international organisations, local authorities, and first responders. Social media can support emergency response with evidence-based content posted by citizens and organisations during ongoing events. In the paper, we propose Tr...
In the Play&Sing project, we are developing an AI platform to support home-based self-training interventions for chronic stroke patients. A large percentage of patients suffering from this disease show motor deficits that clearly hinder their daily activities and diminish their quality of life. In this project we are proposing and testing a new Mus...
A visual example to step by step execution of the Lazy KNN Search algorithm.
Case-Based Reasoning (CBR) is a lazy learning method and, being such, when a new query is made to a CBR system, the swiftness of its retrieval phase proves to be very important for the overall system performance. The availability of ubiquitous data today is an opportunity for CBR systems as it implies more cases to reason with. Nevertheless, this a...
The poster for the ICCBR 2018 conference paper "Perks of Being Lazy: Boosting Retrieval Performance". (https://www.researchgate.net/publication/326059275_Perks_of_Being_Lazy_Boosting_Retrieval_Performance)
Despite being listed as a principal range state for the Lesser White-fronted Goose (LWfG), still very little –if any– is known about the movement of the species in Turkey. Rather scarce observations since 1980 suggest that this rare winter visitor can use deltas at the Mediterranean, Aegean and Black Sea coasts of Turkey, together with the lakes in...
When AI technologies are applied to real-world problems, it is often difficult for developers to anticipate all the knowledge needed. Previous research has shown that introspective reasoning can be a useful tool for helping to address this problem in case-based reasoning systems, by enabling them to augment their routine learning of cases with lear...
To be able to guide the design and maintenance of Case-Based Reasoning (CBR) systems, we present a novel and domain inde-pendent method based on evolutionary techniques, for anticipating the performance of a system against a set of possible future problems by identifying low confidence regions in its case-base. Moreover, a simple experimentation is...
This work investigates applying introspective reasoning to improve the performance of Case-Based Reasoning (CBR) systems, in both reactive and proactive fashion, by guiding learning to improve how a CBR system applies its cases and by identifying possible future system deficiencies. First we present our reactive approach, a new introspective reason...
Being able to predict the performance of a Case-Based Reasoning system against a set of future problems would provide invaluable
information for design and maintenance of the system. Thus, we could carry out the needed design changes and maintenance tasks
to improve future performance in a proactive fashion. This paper proposes a novel method for i...
When AI technologies are applied to real-world problems, it is often difficult for developers to anticipate all the knowledge needed. Previous research has shown that introspective reasoning can be a useful tool for helping to address this problem in case-based reasoning systems, by enabling them to augment their routine learning of cases with lear...
In Case-Based Reasoning systems, the election of the appro-priate cases for the case base and the design of an accurate similarity measure are crucial issues. Case-Base Maintenance techniques provide a way to improve the case base mainly by identifying and deleting the cases that produce noise in a system. In this paper we present a novel method fo...
Projects
Project (1)
Through an innovation cycle called GEAR (Gather, Evaluate, Accelerate, Refine), the transdisciplinary Crowd4SDG consortium of six partners will promote the development of citizen science projects aimed at tackling the United Nations Sustainable Development Goals (SDGs), with a focus on climate action. Its goal is to assess the usefulness of practical innovations developed by the teams and to research how AI applications can enhance and provide effective monitoring of SDG targets and indicators by citizens. This project has received funding from European Union's Horizon 2020 Research and Innovation Programme (Grant Number: 872944). Project Website: https://crowd4sdg.eu
