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Found 20 papers, 0 papers with code
Gradient-based Fuzzy System Optimisation via Automatic Differentiation -- FuzzyR as a Use Case
Since their introduction, fuzzy sets and systems have become an important area of research known for its versatility in modelling, knowledge representation and reasoning, and increasingly its potential within the context explainable AI.
A Novel Meta Learning Framework for Feature Selection using Data Synthesis and Fuzzy Similarity
This paper presents a novel meta learning framework for feature selection (FS) based on fuzzy similarity.
A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets
In this paper, we propose a novel weighted combination feature selection method using bootstrap and fuzzy sets.
Performance Optimization of a Fuzzy Entropy based Feature Selection and Classification Framework
The optimized framework was also compared with other six classical filter-based feature selection methods.
Relative Geometry-Aware Siamese Neural Network for 6DOF Camera Relocalization
6DOF camera relocalization is an important component of autonomous driving and navigation.
Modelling Cyber-Security Experts' Decision Making Processes using Aggregation Operators
An important role carried out by cyber-security experts is the assessment of proposed computer systems, during their design stage.
Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill's Causality Considerations
Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality.
Juxtaposition of System Dynamics and Agent-based Simulation for a Case Study in Immunosenescence
Immunosenescence, the ageing of the immune system, is highly correlated to the negative effects of ageing, such as the increase of auto-inflammatory diseases and decrease in responsiveness to new diseases.
Augmented Neural Networks for Modelling Consumer Indebtness
Consumer Debt has risen to be an important problem of modern societies, generating a lot of research in order to understand the nature of consumer indebtness, which so far its modelling has been carried out by statistical models.
Tuning a Multiple Classifier System for Side Effect Discovery using Genetic Algorithms
The results of this research show that the novel framework implementing a multiple classifying system trained using genetic algorithms can obtain a higher partial area under the receiver operating characteristic curve than implementing a single classifier.
Attributes for Causal Inference in Longitudinal Observational Databases
In this paper we investigate potential attributes that can be used in causal inference to identify side effects based on the Bradford-Hill causality criteria.
A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery
Drugs are frequently prescribed to patients with the aim of improving each patient's medical state, but an unfortunate consequence of most prescription drugs is the occurrence of undesirable side effects.
Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs
Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions effectively.
Comparison of algorithms that detect drug side effects using electronic healthcare databases
In this paper we apply four existing electronic healthcare database signal detecting algorithms (MUTARA, HUNT, Temporal Pattern Discovery and modified ROR) on the THIN database for a selection of drugs from six chosen drug families.
Comparing Data-mining Algorithms Developed for Longitudinal Observational Databases
Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects.
An investigation into the relationship between type-2 FOU size and environmental uncertainty in robotic control
It has been suggested that, when faced with large amounts of uncertainty in situations of automated control, type-2 fuzzy logic based controllers will out-perform the simpler type-1 varieties due to the latter lacking the flexibility to adapt accordingly.
Discovering Sequential Patterns in a UK General Practice Database
The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses.
Investigating the Detection of Adverse Drug Events in a UK General Practice Electronic Health-Care Database
In this paper, existing methods developed for spontaneous reporting databases are implemented on both a spontaneous reporting database and a general practice electronic health-care database and compared.
A Comparison of Non-stationary, Type-2 and Dual Surface Fuzzy Control
Type-1 fuzzy logic has frequently been used in control systems.
Real-world Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms
In mobile robotics, a solid test for adaptation is the ability of a control system to function not only in a diverse number of physical environments, but also on a number of different robotic platforms.
