Search results for "Recurrent Neural Network"
showing 10 items of 40 documents
A NEURAL NETWORK PRIMER
1994
Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates independently (in paral lel) the information provided by its synapses in order to evaluate its state of activation. The unit response is then a linear or nonlinear function of its activation. Linear algebra concepts are used, in general, to analyze linear units, with eigenvectors and eigenvalues being the core concepts involved. This analysis makes clear the strong similarity between linear neural networks and the general linear model developed by statisticia…
Comparing Recurrent Neural Networks using Principal Component Analysis for Electrical Load Predictions
2021
Electrical demand forecasting is essential for power generation capacity planning and integrating environment-friendly energy sources. In addition, load predictions will help in developing demand-side management in coordination with renewable power generation. Meteorological conditions influence urban area load pattern; therefore, it is vital to include weather parameters for load predictions. Machine Learning algorithms can effectively be used for electrical load predictions considering impact of external parameters. This paper explores and compares the basic Recurrent Neural Networks (RNN); Simple Recurrent Neural Networks (Vanilla RNN), Gated Recurrent Units (GRU), and Long Short-Term Me…
No Reservations Required: Achieving Fairness between Wi-Fi and NR-U with Self-Deferral Only
2021
Wireless technologies coexisting in unlicensed bands should receive a fair share of the available channel resources, even when they use different access methods. We consider the problem of coexistence between Wi-Fi and New Radio Unlicensed (NR-U) nodes, which employ, respectively, a random and scheduled access scheme. The latter typically resorts to reservation signals (RSs), which allow keeping the control of the channel until the start of the next synchronized slot. This mechanism, although effective for increasing the channel access opportunities of scheduled-based nodes, is also a waste of channel resources. We investigate alternative solutions, based on self-deferral only. We built ana…
Learning Automata Based Q-learning for Content Placement in Cooperative Caching
2019
An optimization problem of content placement in cooperative caching is formulated, with the aim of maximizing sum mean opinion score (MOS) of mobile users. Firstly, a supervised feed-forward back-propagation connectionist model based neural network (SFBC-NN) is invoked for user mobility and content popularity prediction. More particularly, practical data collected from GPS-tracker app on smartphones is tackled to test the accuracy of mobility prediction. Then, a learning automata-based Q-learning (LAQL) algorithm for cooperative caching is proposed, in which learning automata (LA) is invoked for Q-learning to obtain an optimal action selection in a random and stationary environment. It is p…
Diffusive neural network
2002
Abstract A non-connectionist model of a neuronal network based on passive diffusion of neurotransmitters is presented as an alternative to hard-wired artificial neural networks. Classic thermodynamical approach shows that the diffusive network is capable of exhibiting asymptotic stability and a dynamics resembling that of a chaotic system. Basic computational capabilities of the net are discussed based on the equivalence with a Turing machine. The model offers a way to represent mass-sustained brain functions in terms of recurrent behaviors in the phase space.
Heart Failure Occurrence: Mining Significant Patterns and 10 Days Early Prediction
2021
Electronic health records containing patient’s medical history, drug prescription, vital signs measurements, and many more parameters, are being frequently extracted and stored as unused raw data. On the other hand, machine learning and data mining techniques are becoming popular in the medical field, providing the ability to extract knowledge and valuable information from electronic health records along with accurately predicting future disease occurrence. This chapter presents a study on medical data containing vital signs recorded over the course of some years, for real patients suffering from heart failure. The first significant patterns that come along with heart failure occurrence are…
Dynamics of Vertebral Column Observed by Stereovision and Recurrent Neural Network Model
2005
A new non-invasive method for investigation of movement of selected points on the vertebral column is presented. The registration of position of points marked on patient's body is performed by 4 infrared cameras. This experiment enables to reconstruct 3-dimensional trajectories of displacement of marked points. We introduce recurrent neural networks as formal nonlinear dynamical models of each point trajectory. These models are based only on experimental data and are set up of minimal number of parameters. Therefore they are suitable for pattern recognition problems.
Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks
2021
Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weath…
Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks.
2019
Erythropoiesis Stimulating Agents (ESAs) have become a standard anemia management tool for End Stage Renal Disease (ESRD) patients. However, dose optimization constitutes an extremely challenging task due to huge inter and intra-patient variability in the responses to ESA administration. Current data-based approaches to anemia control focus on learning accurate hemoglobin prediction models, which can be later utilized for testing competing treatment choices and choosing the optimal one. These methods, despite being proven effective in practice, present several shortcomings which this paper intends to tackle. Namely, they are limited to a small cohort of patients and, even then, they fail to…
Machine Learning Models for Measuring Syntax Complexity of English Text
2019
In this paper we propose a methodology to assess the syntax complexity of a sentence representing it as sequence of parts-of-speech and comparing Recurrent Neural Networks and Support Vector Machine. We have carried out experiments in English language which are compared with previous results obtained for the Italian one.