We describe a simple Hadamard design for neural architecture with an equal number of input and output elements that is both error-tolerant and robust to missing information. The design provides a basis for calculation using a classification scheme based on the Chinese remainder theorem, producing an abstract representation of the physical world. The underlying co-prime arrays can be generated i...

This paper is concerned with the problem of how to make inferences about an agent’s beliefs based on an observation of how that agent responded to a sequence of revision inputs over time. We collect and review some earlier results for the case where the observation is complete in the sense that (i) the logical content of all formulae appearing in the observation is known, and (ii) all revision ...

background: hypodontia is the most common dental anomaly and might cause clinical complications. the aim of this study was to assess the prevalence and pattern of congenital missing in the permanent dentition dentition (excluding third molars), among iranian orthodontic patients. materials and methods: in this descriptive cross-sectional study, all approved panoramic radiographs of 3374 orthodo...

An ensemble based approach for dealing with missing data, without predicting or imputing the missing values is proposed. This technique is suitable for online operations of neural networks and as a result, is used for online condition monitoring. The proposed technique is tested in both classification and regression problems. An ensemble of Fuzzy-ARTMAPs is used for classification whereas an en...

There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson’s fuzzy min-max neural ne...

An approach to dealing with missing data, both during the design and normal operation of a neuro-fuzzy classifier is presented in this paper. Missing values are processed within a general fuzzy min–max neural network architecture utilising hyperbox fuzzy sets as input data cluster prototypes. An emphasis is put on ways of quantifying the uncertainty which missing data might have caused. This ta...

In the context of current global pandemic and limitations RT-PCR test, we propose a novel deep learning architecture, DFCN (Denoising Fully Connected Network). Since medical facilities around world differ enormously in what laboratory tests or chest imaging may be available, is designed to robust missing input data. An ablation study extensively evaluates performance benefits as well its robust...

We report on the current simulation studies regarding the reconstruction of Jets and Missing Transverse Energy (MET) with the CMS detector at the CERN proton-proton LHC accelerator. The performance of various jet algorithms is compared, when using calorimeter energy deposits as inputs to the algorithm. The plan for obtaining jet energy corrections is outlined and data-driven correction methods ...