Classification of fetal abnormalities based on CTG signal

The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was transformed using transform domains Discrete Wavelet Transform(DWT) in order to obtain the system features .At the last stage the approximation coefficients result from the Discrete Wavelet Transform were fed to the Artificial Neural Networks and to the Fuzzy Logic, then compared between two results to obtain the best for classifying fetal heart rate

Neural-Symbolic Recommendation with Graph-Enhanced Information

The recommendation system is not only a problem of inductive statistics from data but also a cognitive task that requires reasoning ability. The most advanced graph neural networks have been widely used in recommendation systems because they can capture implicit structured information from graph-structured data. However, like most neural network algorithms, they only learn matching patterns from a perception perspective. Some researchers use user behavior for logic reasoning to achieve recommendation prediction from the perspective of cognitive reasoning, but this kind of reasoning is a local one and ignores implicit information on a global scale. In this work, we combine the advantages of graph neural networks and propositional logic operations to construct a neuro-symbolic recommendation model with both global implicit reasoning ability and local explicit logic reasoning ability. We first build an item-item graph based on the principle of adjacent interaction and use graph neural networks to capture implicit information in global data. Then we transform user behavior into propositional logic expressions to achieve recommendations from the perspective of cognitive reasoning. Extensive experiments on five public datasets show that our proposed model outperforms several state-of-the-art methods, source code is avaliable at [https://github.com/hanzo2020/GNNLR].Comment: 12 pages, 2 figures, conferenc

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Markov Logic Networks with Complex Weights and Algorithms to Train Them

Tato práce se zabývá markovskými logickými sítěmi s komplexními vahami (komplexními markovskými logickými sítěmi, C–MLNs). Ty jsou rozšířením markovských logických sítí, které je plně expresivní v kontextu počtových distribucí. Text identifikuje několik problémů s původní C–MLN definicí a navrhuje její úpravu. Dále je odvozena procedura pro inferenci založená na Gibbsově vzorkování. Kromě toho jsou odvozeny dva algoritmy pro učení parametrů. První z nich je založen na metodě maximální věrohodnosti. Z důvodu nekonvexity problému nepodává maximalizace založená na gradietním sestupu dobré výsledky. Druhý algoritmus využívá diskrétní Fourierovu transformaci k vyjádření libovolné počtové distribuce jako C–MLN. Algoritmus nicméně vyžaduje velkou trénovací množinu a učí se váhy o zbytečně vysoké dimenzi.This thesis studies Markov logic networks with complex weights (complex Markov logic networks, C–MLNs). Those are an extension of Markov logic networks that achieves full expressivity in terms of count distributions. Slight modification of the C–MLN definition is proposed attempting to solve a few identified problems. An inference procedure based on Gibbs sampling is developed for the model. Two parameter learning algorithms are proposed as well. The first one utilizes the maximum likelihood estimation. Due to the non-convexity of the problem, gradient descent-based maximization does not perform well. The other learning procedure uses the discrete Fourier transform to encode an arbitrary count distribution as a C–MLN. However, it requires a huge data set, and it learns weights of unnecessarily large dimensions

Automated reasoning in metabolic networks with inhibition

International audienceThe use of artificial intelligence to represent and reason about metabolic networks has been widely investigated due to the complexity of their imbrication. Its main goal is to determine the catalytic role of genomes and their interference in the process. This paper presents a logical model for metabolic pathways capable of describing both positive and negative reactions (activations and inhibitions) based on a fragment of first order logic. We also present a translation procedure that aims to transform first order formulas into quantifier free formulas, creating an efficient automated deduction method allowing us to predict results by deduction and infer reactions and proteins states by abductive reasoning