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Filtering search results using an optimal set of terms identified by an artificial neural network

Tsvi Kuflik, Zvi Boger, Peretz Shoval
2006 Information Processing & Management  
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

Vijaya P, Binu D
2020 Scalable Computing : Practice and Experience  
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

G. Schneider, P. Wrede
1994 Biophysical Journal  
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

R. Chellappa, K. Fukushima, A.K. Katsaggelos, Sun-Yuan Kung, Y. LeCun, N.M. Nasrabadi, T.A. Poggio
1998 IEEE Transactions on Image Processing  
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

Fu Xiang-hua
2006 Journal of Computer Science  
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]

Matthias Ihme
2011 Artificial Neural Networks - Application  
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


K. Meena .
2015 International Journal of Research in Engineering and Technology  
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]

Ha-Thanh Nguyen, Le-Minh Nguyen
2021 arXiv   pre-print
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]

Yesmina Jaafra, Jean Luc Laurent, Aline Deruyver, Mohamed Saber Naceur
2018 arXiv   pre-print
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

Karol Antczak, Łukasz Liberadzki, N. Mastorakis, V. Mladenov, A. Bulucea
2018 MATEC Web of Conferences  
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

Yudong Zhang, Saeed Balochian, Praveen Agarwal, Vishal Bhatnagar, Orwa Jaber Housheya
2016 Mathematical Problems in Engineering  
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

Mohammad Zavvar, Meysam Rezaei, Shole Garavand
2016 International Journal of Modern Education and Computer Science  
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  
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

Farideh Halakou
2016 IOSR Journal of Computer Engineering  
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]

Mohammad Reza Davahli
2020 arXiv   pre-print
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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