Artificial Neural Networks

Silva, Ivan Nunes da; Spatti, Danilo Hernane; Flauzino, Rogério Andrade; Liboni, Luisa Helena Bartocci; Alves, Silas Franco dos Reis

doi:10.1007/978-3-319-43162-8

Cited by 361 publications

(249 citation statements)

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“…Training neural networks means that we minimize the loss functions so that our predicted values are as close as possible to the true values [85]. To minimize the loss functions, the networks adjust learnable parameters, i.e., the values of the weights and biases of the model.…”

Section: • Convolutional Neural Networkmentioning

confidence: 99%

Regression methods in waveform modeling: a comparative study

Setyawati

Pürrer

Ohme

2020

Class. Quantum Grav.

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Gravitational-wave astronomy of compact binaries relies on theoretical models of the gravitational-wave signal that is emitted as binaries coalesce. These models do not only need to be accurate, they also have to be fast to evaluate in order to be able to compare millions of signals in near real time with the data of gravitational-wave instruments. A variety of regression and interpolation techniques have been employed to build efficient waveform models, but no study has systematically compared the performance of these regression methods yet. Here we provide such a comparison of various techniques, including polynomial fits, radial basis functions, Gaussian process regression and artificial neural networks, specifically for the case of gravitational waveform modeling. We use all these techniques to regress analytical models of non-precessing and precessing binary black hole waveforms, and compare the accuracy as well as computational speed. We find that most regression methods are reasonably accurate, but efficiency considerations favour in many cases the most simple approach. We conclude that sophisticated regression methods are not necessarily needed in standard gravitational-wave modeling applications, although problems with higher complexity than what is tested here might be more suitable for machine-learning techniques and more sophisticated methods may have side benefits. ‡

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Section: • Convolutional Neural Networkmentioning

confidence: 99%

Regression methods in waveform modeling: a comparative study

Setyawati

Pürrer

Ohme

2020

Class. Quantum Grav.

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“…In the first step, the NARX ( Nonlinear Auto Regressive Exogenous ) Neural Network was used for intelligent prediction; this network was selected because it is a recurrent network by which the feedback of the current command of a thread on: (1) its next command and (2) command of other threads is considered. In the second step, we used the MLP (Multi‐Layer Perceptron) Neural Network for selection of an appropriate action to resolve deadlocks. Figure shows structure of the proposed framework consisting of six activities as following; these activities are done in two steps.…”

Section: The Proposed Frameworkmentioning

confidence: 99%

Runtime deadlock tracking and prevention of concurrent multithreaded programs: A learning‐based approach

Ghorbani

Babamir

2019

Concurrency and Computation

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Allocation of shared resources by multithreaded programs faces problem of deadlock. Many solutions have been presented to resolve this problem. Among others, the deadlock prevention is stressed when the deadlock detection and removal are costly. Removing deadlocks, which is carried out by aborting/recovering deadlocked threads, causes the waste of the resources used by the deadlocked threads. In such case, deadlock prevention can lead to avoiding the waste of resources. To this end, the runtime behavior of threads should be monitored in order to predict possible future deadlocks. In such case, the prediction mechanism becomes significant because a proper prediction helps us deny the allocation request of a resource by a thread if the allocation leads to a potential deadlock. A method to attain to proper prediction is learning the behavior of threads based on runtime monitoring their past behavior. In fact, based on past behavior of threads, potential deadlocks in their future behavior are verified, and current allocation request of a resource is denied if a future deadlock is predicted. The current study is an extension of our previous work where just deadlock tracking was predicted and no adaptation was suggested. In this study, a composite structure of a recurrent Neural Network (NN) called NARX (to track a potential deadlock) and a Multi-perceptron NN called MLP (to select a suitable action to resolve the potential deadlock) is proposed. Based on the experimental results, the accuracy of the first NN was about 80%, leading to high performance of the second NN, and more than 82% of the real deadlocks were prevented by selecting suitable actions.

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“…Artificial Neural Network (ANN) [36,37] is a branch of artificial intelligence inspired by the biological brain. It is a structure of connected nodes (i.e., artificial neurons) that are arranged in layers.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…These problems lead us to proposing a hybrid method that combines an artificial neural network (ANN) [36,37] with CBR. Our research develops a cold start context-aware recommender system applied to the tourism domain that offers personalized tours to the user based on his preferences and some contextual information.…”

Section: Introductionmentioning

confidence: 99%

A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning

Bahramian

Abbaspour

Claramunt

2017

Mobile Information Systems

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Nowadays, large amounts of tourism information and services are available over the Web. This makes it difficult for the user to search for some specific information such as selecting a tour in a given city as an ordered set of points of interest. Moreover, the user rarely knows all his needs upfront and his preferences may change during a recommendation process. The user may also have a limited number of initial ratings and most often the recommender system is likely to face the well-known cold start problem. The objective of the research presented in this paper is to introduce a hybrid interactive context-aware tourism recommender system that takes into account user's feedbacks and additional contextual information. It offers personalized tours to the user based on his preferences thanks to the combination of a case based reasoning framework and an artificial neural network. The proposed method has been tried in the city of Tehran in Iran. The results show that the proposed method outperforms current artificial neural network methods and combinations of case based reasoning with -nearest neighbor methods in terms of user effort, accuracy, and user satisfaction.

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Artificial Neural Networks

Cited by 361 publications

References 0 publications

Regression methods in waveform modeling: a comparative study

Regression methods in waveform modeling: a comparative study

Runtime deadlock tracking and prevention of concurrent multithreaded programs: A learning‐based approach

A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning

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