Named Entity Recognition using Neural Networks for Clinical Notes

International audienceCurrently, the best performance for Named Entity Recognition in medical notes is obtained by systems based on neural networks. These supervised systems require precise features in order to learn well fitted models from training data, for the purpose of recognizing medical entities like medication and Adverse Drug Events (ADE). Because it is an important issue before training the neural network, we focus our work on building comprehensive word representations (the input of the neural network), using character-based word representations and word representations. The proposed representation improves the performance of the baseline LSTM. However, it does not reach the performances of the top performing contenders in the challenge for detecting medical entities from clinical notes.Actuellement, la meilleure performance pour la reconnaissance de l'entité nommée dans les notes médicales est obtenue par des systèmes basés sur des réseaux de neurones. Ces systèmes supervisés nécessitent des caractéristiques précises afin d'apprendre des modèles bien ajustés à partir des données de formation, dans le but de reconnaître les entités médicales comme les médicaments et les événements indésirables liés aux médicaments (EIM). Parce qu'il s'agit d'une question importante avant la formation du réseau neuronal, nous concentrons notre travail sur la construction de représentations complètes de mots (l'entrée du réseau neuronal), en utilisant des représentations de mots basés sur des caractères et des représentations de mots. La représentation proposée améliore la performance de la LSTM de référence. Cependant, il n'atteint pas les performances des concurrents les plus performants dans le challenge de détection d'entités médicales à partir de notes cliniques

Attention Based Spatial-Temporal GCN with Kalman filter for Traffic Flow Prediction

Intelligent Transportation Systems (ITS) are becoming increasingly important as traditional traffic management systems struggle to handle the rapid growth of vehicles on the road. Accurate traffic prediction is a critical component of ITS, as it can help improve traffic management, avoid congested roads, and allocate resources more efficiently for connected vehicles. However, modeling traffic in a large and interconnected road network is challenging because of its complex spatio-temporal data. While classical statistics and machine learning methods have been used for traffic prediction, they have limited ability to handle complex traffic data, leading to unsatisfactory accuracy. In recent years, deep learning methods, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), have shown superior capabilities for traffic prediction. However, most CNN-based models are built for Euclidean grid-structured data, while traffic road network data are irregular and better formatted as graph-structured data. Graph Convolutional Neural Networks (GCNs) have emerged to extend convolution operations to more general graph-structured data. This paper reviews recent developments in traffic prediction using deep learning, focusing on GCNs as a promising technique for handling irregular, graph-structured traffic data. We also propose a novel GCN-based method that leverages attention mechanisms to capture both local and long-range dependencies in traffic data with Kalman Filter, and we demonstrate its effectiveness through experiments on real-world datasets where the model achieved around 5% higher accuracy compared to the original model

An evolutionary approach to optimising neural network predictors for passive sonar target tracking

Object tracking is important in autonomous robotics, military applications, financial time-series forecasting, and mobile systems. In order to correctly track through clutter, algorithms which predict the next value in a time series are essential. The competence of standard machine learning techniques to create bearing prediction estimates was examined. The results show that the classification based algorithms produce more accurate estimates than the state-of-the-art statistical models. Artificial Neural Networks (ANNs) and K-Nearest Neighbour were used, demonstrating that this technique is not specific to a single classifier. [Continues.

A Genetic Algorithm for a Minimax Network Design Problem

This paper considers the problem of designing a network to transport material from sources of supply to sites where demand occurs. However, the demand at each site is uncertain. We formulate the problem as a robust discrete optimization problem. The minimax objective is to find a robust solution that has the best worst-case performance over a set of possible scenarios. However, this is a difficult optimization problem. This paper describes a two-space genetic algorithm that is a general technique to solve such minimax optimization problems. This algorithm maintains two populations. The first population represents solutions. The second population represents scenarios. An individual in one population is evaluated with respect to the individuals in the other population. The populations evolve simultaneously, and they converge to a robust solution and a worst-case scenario. Experimental results show that the two-space genetic algorithm can find robust solutions to the minimax network design problem. Since robust discrete optimization problems occur in many areas, the algorithm will have a wide variety of applications

Transmission loss allocation using artificial neural networks

The introduction of deregulation and subsequent open access policy in electricity sector has brought competition in energy market. Allocation of transmission loss has become a contentious issue among the electricity producers and consumers. A closed form solution for transmission loss allocation does not exist due to the fact that transmission loss is a highly non-linear function of system states and it is a non-separable quantity. In absence of a closed form solution different utilities use different methods for transmission loss allocation. Most of these techniques involve complex mathematical operations and time consuming computations. A new transmission loss allocation tool based on artificial neural network has been developed and presented in this thesis. The proposed artificial neural network computes loss allocation much faster than other methods. A relatively short execution time of the proposed method makes it a suitable candidate for being a part of a real time decision making process. Most independent system variables can be used as inputs to this neural network which in turn makes the loss allocation procedure responsive to practical situations. Moreover, transmission line status (available or failed) was included in neural network inputs to make the proposed network capable of allocating loss even during the failure of a transmission line. The proposed neural networks were utilized to allocate losses in two types of energy transactions: bilateral contracts and power pool operation. Two loss allocation methods were utilized to develop training and testing patterns; the Incremental Load Flow Approach was utilized for loss allocation in the context of bilateral transaction and the Z-bus allocation was utilized in the context of pool operation. The IEEE 24-bus reliability network was utilized to conduct studies and illustrate numerical examples for bilateral transactions and the IEEE 14-bus network was utilized for pool operation. Techniques were developed to expedite the training of the neural networks and to improve the accuracy of results