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Filters
Filtering search results using an optimal set of terms identified by an artificial neural network
2006
Information Processing & Management
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Information filtering (IF) systems usually filter data items by correlating a set of terms representing the user's interest (a user profile) with similar sets of terms representing the data items. ...
We describe a new terms selection technique including a dimensionality-reduction mechanism which is based on the analysis of a trained artificial neural network (ANN) model. ...
A BRIEF INTRODUCTION ON ARTIFICIAL NEURAL NETWORKS MODELING An efficient training algorithm set, developed by Guterman and Boger (Guterman 1994; Boger & Guterman 1997) , can easily train large scale ANN ...
doi:10.1016/j.ipm.2005.03.020
fatcat:c3ilzpqt4zd2xc6dm27x2gaw7y
Introduction to the Special Issue on Intelligence on Scalable computing for Recent Applications
2020
Scalable Computing : Practice and Experience
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Once the artefact signal is identified using the adaptive filter, the identified signal is subtracted from the primary signal that is composed of the ECG signal and the artefacts through an adaptive subtraction ...
The DCNN is optimally trained using Chicken-Moth search optimization (CMSO). ...
In "Artefacts removal from ECG Signal: Dragonfly optimization-based learning algorithm for neural network-enhanced adaptive filtering", proposed a method utilizes the adaptive filter termed as the (Dragonfly ...
doi:10.12694/scpe.v21i2.1581
fatcat:5ynzph7uyjektl2xv4ddkq5h2y
The rational design of amino acid sequences by artificial neural networks and simulated molecular evolution: de novo design of an idealized leader peptidase cleavage site
1994
Biophysical Journal
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It is based on a simple genetic algorithm that takes the quality values calculated by the artificial neural network as a heuristic for inductive sequence optimization. ...
A method for the rational design of locally encoded amino acid sequence features using artificial neural networks and a technique for simulating molecular evolution has been developed. ...
Gisbert Schneider receives a Ph.D. fellowship from the Fonds der Chemischen Industrie (FCI), and the project has been supported by the Deutsche Forschungsgemeinschaft (SfB 312) and the BMFT. ...
doi:10.1016/s0006-3495(94)80782-9
pmid:8161687
pmcid:PMC1275700
fatcat:7othmeripfb25numqwnhqri4ma
Guest Editorial Applications Of Artificial Neural Networks To Image Processing
1998
IEEE Transactions on Image Processing
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A simultaneous top-down-and-bottom-up search is implemented by using a multilayer Hopfield neural network to minimize this energy function. ...
Guest Editorial Applications of Artificial Neural Networks to Image Processing A RTIFICIAL neural network (NN) architectures have been recognized for a number of years as a powerful technology for solving ...
He is a Fellow of the American Association for Artificial Intelligence and of the American Academy of Arts and Sciences, and an Honorary Associate of the Neuroscience Research Program, Rockefeller University ...
doi:10.1109/tip.1998.704303
fatcat:rddf6sqzyzb3pdsroxobiix7ue
Towards an Effective Personalized Information Filter for P2P Based Focused Web Crawling
2006
Journal of Computer Science
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So we furthermore present an efficient personalized information filter in detail, which combines several component neural networks to accomplish the filtering task. ...
of support vector machine, naïve bayesian and individual neural network. ...
Fig. 2 : 2 Adaptive content filter based on three layer feedforward artificial neural network
Foundation of Neural Network Ensemble Suppose a data set D:= {(x 1 ,y 1 ),(x 2 ,y 2 ),... ...
doi:10.3844/jcssp.2006.97.103
fatcat:cm7yijx5mvgtjh2i2vnj4lhn2y
Construction of Optimal Artificial Neural Network Architectures for Application to Chemical Systems: Comparison of Generalized Pattern Search Method and Evolutionary Algorithm
[chapter]
2011
Artificial Neural Networks - Application
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The design of a specific network topology with optimal performance can be formulated as an optimization problem. ...
Artificial neural networks (ANNs) are computational models of their biological counterparts. They consists of densely interconnected computing units that work together to solve a specific problem. ...
(17) , is approximated by optimal ANNs. For this, GPS and ES are used to identify optimal network topologies that result in the lowest approximation error. ...
doi:10.5772/15191
fatcat:zoy3g2b4incklpierhjimc5vti
CORRELATION OF ARTIFICIAL NEURAL NETWORK CLASSIFICATION AND NFRS ATTRIBUTE FILTERING ALGORITHM FOR PCOS DATA
2015
International Journal of Research in Engineering and Technology
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In this paper, a new algorithm which is based on Fuzzy neural subset evaluation and artificial neural network is proposed which reduces the task of classification and feature selection separately. ...
This algorithm combines the neural fuzzy rough subset evaluation and artificial neural network together for the better performance than doing the tasks separately. ...
combing neural fuzzy rough set evaluation and artificial neural network. ...
doi:10.15623/ijret.2015.0403087
fatcat:clrlldiolvfc3ha76p5n2rbvnu
SCNN: Swarm Characteristic Neural Network
[article]
2021
arXiv
pre-print
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In this paper, we propose and verify the effectiveness and efficiency of SCNN, an innovative neural network inspired by the swarm concept. ...
That could be an essential hint for problems where there is not much data. ...
In other words, the swarm filter is a set of weights in the neural network. By calculating the outer product with input, it produces swarm features. ...
arXiv:2103.15550v1
fatcat:wdqapff7zfgzvdguzt3uvqjlyi
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search
[article]
2018
arXiv
pre-print
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Deep Neural networks are efficient and flexible models that perform well for a variety of tasks such as image, speech recognition and natural language understanding. ...
CNN architecture and related hyperparameters are generally correlated to the nature of the processed task as the network extracts complex and relevant characteristics allowing the optimal convergence. ...
Then, iteratively, the model identifies a set of optimal hyperparameters for which the objective function returns corresponding results (e.g. loss values). ...
arXiv:1812.07995v1
fatcat:352eyqnvqffbbbvk4fci2k2g2q
Stenosis Detection with Deep Convolutional Neural Networks
2018
MATEC Web of Conferences
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Test results shows that DCNN trained on artificial data and fine-tuned using real samples can achieve up to 90% accuracy, exceeding results obtained by both traditional, feed-forward networks and networks ...
In this paper we explore the possibility of using Deep Convolutional Neural Networks (DCNN) for detection of stenoses in angiographic images. ...
Network configurations Choosing optimal network configuration was performed using grid-search algorithm with some additional heuristics. ...
doi:10.1051/matecconf/201821004001
fatcat:sob7dqagzbezjmuuuly2lix7tm
Artificial Intelligence and Its Applications 2014
2016
Mathematical Problems in Engineering
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The input-output maps of a real nonlinear system studied were identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. ...
Linhares et al. investigated the fuzzy wavelet neural networks (FWNNs) that are an efficient tool to identify nonlinear systems. ...
Linhares et al. investigated the fuzzy wavelet neural networks (FWNNs) that are an efficient tool to identify nonlinear systems. ...
doi:10.1155/2016/3871575
fatcat:irj62qjsdzfu7h4fdslkgy5hny
Email Spam Detection Using Combination of Particle Swarm Optimization and Artificial Neural Network and Support Vector Machine
2016
International Journal of Modern Education and Computer Science
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In this paper, the combined Particle Swarm Optimization algorithms and Artificial Neural Network for feature selection and Support Vector Machine to classify and separate spam used have and finally, we ...
The increasing use of e-mail in the world because of its simplicity and low cost, has led many Internet users are interested in developing their work in the context of the Internet. ...
Besides, in order to review the method of Particle Swarm Optimization, Artificial Neural Network and Support Vector Machine pay and the proposed methods to identify spam, the evaluation results will be ...
doi:10.5815/ijmecs.2016.07.08
fatcat:q3bdgdrmvnh3rezxppqqa5obqy
A Novel Approach to Classify Noises in Images Using Artificial Neural Network
2010
Journal of Computer Science
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Results: Neural networks provided a better solution in identifying the noise. Conclusion: By identifying the noise the precise filter can be used for enhancing the given image. ...
Approach: An Artificial Neural Network (ANN) based approach was proposed for noise identification. ...
MATERIALS AND METHODS
Artificial neural networks: An Artificial Neural Network (ANN), usually called "Neural Network" (NN), is a mathematical model or computational model that tries to simulate the structure ...
doi:10.3844/jcssp.2010.506.510
fatcat:upqcx3g4vbbulfqp7m3juq7htu
A Novel Wrapper-filter Hybrid Method for Candidate SNPs Selection
2016
IOSR Journal of Computer Engineering
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It trains a Neural Network (NN) to predict the accuracy in terms of the number of features, MFFC and MFTC.We ran experiments on artificial SNPs datasets, comparing our algorithm with well-known feature ...
Genomic studies provide massive amount of data including thousands of Single Nucleotide Polymorphisms (SNPs). The analysis of SNPs helps to identify genetic variants related to complex traits. ...
The terms used in Table I High recall means an algorithm returned most of the relevant results. Its value is between 0-1. ...
doi:10.9790/0661-1804063138
fatcat:ynnoevy7zfguvgriel4cvm2cyy
The Last State of Artificial Intelligence in Project Management
[article]
2020
arXiv
pre-print
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This systematic review identified relevant papers using Web of Science, Science Direct, and Google Scholar databases. ...
Artificial intelligence (AI) has been used to advance different fields, such as education, healthcare, and finance. ...
Two sets of keywords were defined and a combination of the first set and the second set was used to identify relevant papers. • First set: Artificial intelligence, pattern recognition, machine learning ...
arXiv:2012.12262v1
fatcat:q3qxxsb6rbailg7leuoputgphy
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