A Knowledge Representation and Reasoning System for Multimodal Neuroimaging Studies

Multimodal neuroimaging analyses are of major interest for both research and clinical practice, enabling the combined evaluation of the structure and function of the human brain. These analyses generate large volumes of data and consequently increase the amount of possibly useful information. Indeed, BrainArchive was developed in order to organize, maintain and share this complex array of neuroimaging data. It stores all the information available for each participant/patient, being dynamic by nature. Notably, the application of reasoning systems to this multimodal data has the potential to provide tools for the identification of undiagnosed diseases. As a matter of fact, in this work we explore how Artificial Intelligence techniques for decision support work, namely Case-Based Reasoning (CBR) that may be used to achieve such endeavour. Particularly, it is proposed a reasoning system that uses the information stored in BrainArchive as past knowledge for the identification of individuals that are at risk of contracting some brain disease.

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A Case Based Reasoning System For Recommendation Of Restaurant In Jimbaran Using K-Nearest Neighbor

JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) ◽

10.24843/jlk.2020.v09.i02.p14 ◽

2020 ◽

Vol 9 (2) ◽

pp. 267

Author(s):

I Gede Teguh Mahardika ◽

I Wayan Supriana

Keyword(s):

Recommendation System ◽

Nearest Neighbor ◽

Case Based Reasoning ◽

Retrieval Process ◽

K Nearest Neighbor ◽

Reasoning System ◽

Human Ability ◽

And Function ◽

Case Based ◽

Selection Of

Culinary is one of the favorite businesses today. The number of considerations to choose a restaurant or place to visit becomes one of the factors that is difficult to determine the restaurant or place to eat. To get the desired place to eat advice, one needs a recommendation system. Decisions made by the recommendation system can be used as a reference to determine the choice of restaurants. One method that can be used to build a recommendation system is Case Based Reasoning. The Case Based Reasoning (CBR) method mimics human ability to solve a problem or cases. The retrieval process is the most important stage, because at this stage the search for a solution for a new case is carried out. The study used the K-Nearest Neighbor method to find closeness between new cases and case bases. With the selection of features used as domains in the system, the results of recommendations presented can be more suggestive and accurate. The system successfully provides complex recommendations based on the type and type of food entered by the user. Based on blackbox testing, the system has features that can be used and function properly according to the purpose of creating the system.

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Modeling Affordances and Functioning for Personalized Robotic Assistance

Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning ◽

10.24963/kr.2020/94 ◽

2020 ◽

Author(s):

Alessandro Umbrico ◽

Gabriella Cortellessa ◽

Andrea Orlandini ◽

Amedeo Cesta

Keyword(s):

Domain Knowledge ◽

Experimental Tests ◽

International Classification Of Functioning ◽

Knowledge Representation And Reasoning ◽

Health State ◽

Knowledge Reasoning ◽

Cognitive Assistance ◽

Ontological Model ◽

And Function

A key aspect of robotic assistants is their ability to contextualize their behavior according to different needs of assistive scenarios. This work presents an ontology-based knowledge representation and reasoning approach supporting the synthesis of personalized behavior of robotic assistants. It introduces an ontological model of health state and functioning of persons based on the International Classification of Functioning, Disability and Health. Moreover, it borrows the concepts of affordance and function from the literature of robotics and manufacturing and adapts them to robotic (physical and cognitive) assistance domain. Knowledge reasoning mechanisms are developed on top of the resulting ontological model to reason about stimulation capabilities of a robot and health state of a person in order to identify action opportunities and achieve personalized assistance. Experimental tests assess the performance of the proposed approach and its capability of dealing with different profiles and stimuli.

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Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases

Genetics in Medicine ◽

10.1038/s41436-020-01084-8 ◽

2021 ◽

Author(s):

Shilpa Nadimpalli Kobren ◽

◽

Dustin Baldridge ◽

Matt Velinder ◽

Joel B. Krier ◽

...

Keyword(s):

Online Survey ◽

Variant Calling ◽

Theoretical Method ◽

The United States ◽

Genomic Sequencing ◽

Biomedical Data ◽

Sequencing Data ◽

Multimodal Data ◽

Undiagnosed Diseases ◽

Undiagnosed Diseases Network

Abstract Purpose Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful. Methods We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols. Results We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases. Conclusion The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.

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Using extended siamese networks to provide decision support in aquaculture operations

Applied Intelligence ◽

10.1007/s10489-021-02251-3 ◽

2021 ◽

Author(s):

Bjørn Magnus Mathisen ◽

Kerstin Bach ◽

Agnar Aamodt

Keyword(s):

Neural Networks ◽

Decision Support ◽

Similar Case ◽

Decision Makers ◽

Similarity Function ◽

Case Based Reasoning ◽

Similarity Learning ◽

Reasoning System ◽

Siamese Networks ◽

Case Based

AbstractAquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.

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