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Computational Intelligence-Based Biometric Technologies

D. Zhang, Wangmeng Zuo
2007 IEEE Computational Intelligence Magazine  
Varieties of evolutionary computation and neural networks techniques have been successfully applied to biometric data representation and dimensionality reduction.  ...  CI-based methods, including neural network and fuzzy technologies, have also been extensively investigated for biometric matching.  ...  Acknowledgments The work is partially supported by the UGC/CRC fund from the HKSAR Government, the central fund from the Hong Kong Polytechnic University and the National Natural Science Foundation of  ... 
doi:10.1109/mci.2007.353418 fatcat:aynahy3ttbesfl3qm3u25gcawq

Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs

Cristian Rotaru, Octav Brudaru
2012 2012 IEEE Congress on Evolutionary Computation  
A similarity based communication protocol between clusters of individuals from parallel grids is defined. The exchange of genetic material proves to considerably boost the quality of the solution.  ...  The population evolves on a bidimensional grid and is implicitly organized in geographical clusters that present a form of structural similarity between individuals.  ...  1, Jimmy Lauber 182, Maowen Nie and Woei Wan Tan, Modeling Capability of Type-1 Fuzzy Set and Interval Type-2 Fuzzy Set 265, Miguel A.  ... 
doi:10.1109/cec.2012.6256590 dblp:conf/cec/RotaruB12 fatcat:4ly3nrktw5habc6lf5err7d5py

Computational Intelligence in the Context of Industry 4.0 [chapter]

Alexander Hošovský, Ján Piteľ, Monika Trojanová, Kamil Židek
2021 Implementing Industry 4.0 in SMEs  
The ending part of the chapter is focused on connecting theory and practice in a case study, which lists industrial parts recognition by convolutional neural networks for assisted assembly.  ...  AbstractIndustry 4.0 is affecting almost every area of the industry, and as a result of its effects, systems, technologies, and the way information is processed are being transformed.  ...  In addition to that, DL techniques can be potentially hybridized with fuzzy logic to form so-called deep fuzzy neural networks that fuse the capabilities of neural networks with our way of reasoning.  ... 
doi:10.1007/978-3-030-70516-9_2 fatcat:3bgkvenwcvcqvnlfmhmjeoyury

Soft Computing Techniques for Various Image Processing Applications: A Survey

Rahul Kher, Heena Kher
2020 Journal Electrical and Electronic Engineering  
This paper represents a survey on various soft computing methods'-fuzzy logic, neural network, neuro-fuzzy systems, genetic algorithm, evolutionary computing, support vector machine etc.  ...  There are many hybridized approaches like neuro-fuzzy system (NFS), fuzzy-neural network (FNN), genetic-fuzzy systems, neuro-genetic systems, neuro-fuzzy-genetic system exist for various image processing  ...  The soft computing tools used for these applications include neural networks (NN), pixel neural networks (PNN), Fuzzy c-means clustering (FCM), hard c-means clustering (HCM), modular neural network (MNN  ... 
doi:10.11648/j.jeee.20200803.11 fatcat:ko47gevpuvavjnptb2j6xtltje

Soft Computing Approaches to Fault Diagnosis for Dynamic Systems

J.M.F. Calado, J. Korbicz, K. Patan, R.J. Patton, J.M.G. Sá da Costa
2001 European Journal of Control  
For example, fuzzy logic can be used together with state space models or neural networks to enhance FDI diagnostic reasoning capabilities.  ...  When quantitative models are not readily available, a correctly trained neural network (NN) can be used as a non-linear dynamic model of the system.  ...  The so-called fuzzy neural network (FNN) takes the advantages of neural networks in adaptation of knowledge learning, distributed parallel processing of data, associative memory and distributed storage  ... 
doi:10.3166/ejc.7.248-286 fatcat:v6hesxhjwbbtjfde6zci6qxv34

Neuro-fuzzy rule generation: survey in soft computing framework

S. Mitra, Y. Hayashi
2000 IEEE Transactions on Neural Networks  
Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network  ...  The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage.  ...  Neural networks with fuzzy neurons are also termed FNN as they are capable of processing fuzzy information.  ... 
doi:10.1109/72.846746 pmid:18249802 fatcat:3y2gnxmiorbbfocd2pp7ygrwiy

An Incremental Learning of Concept Drifts Using Evolving Type-2 Recurrent Fuzzy Neural Networks

Mahardhika Pratama, Jie Lu, Edwin Lughofer, Guangquan Zhang, Meng Joo Er
2017 IEEE transactions on fuzzy systems  
Fuzzy Neural Network (eT2RFNN). eT2RFNN is constructed in a new recurrent network architecture, featuring double recurrent layers.  ...  This paper aims to solve the issue of data uncertainty, temporal behaviour, and the absence of system order by developing a novel evolving recurrent fuzzy neural network, called Evolving Type-2 Recurrent  ...  Recurrent Neural Networks In contrast to its feedforward counterpart, the recurrent network architecture is fitted out by an internal memory component in the form of a feedback or recurrent layer [11]  ... 
doi:10.1109/tfuzz.2016.2599855 fatcat:6pvyhj22e5dsviht6aznjdqdzq

Hybrid soft computing systems for electromyographic signals analysis: a review

Hong-Bo Xie, Tianruo Guo, Siwei Bai, Socrates Dokos
2014 BioMedical Engineering OnLine  
This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis.  ...  With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose.  ...  activities based on a four-layer recurrent fuzzy neural network (RFNN) [52] .  ... 
doi:10.1186/1475-925x-13-8 pmid:24490979 pmcid:PMC3922626 fatcat:uifnqy6tmfe4nbnkif2fwmun44

Artificial intelligence in recommender systems

Qian Zhang, Jie Lu, Yaochu Jin
2020 Complex & Intelligent Systems  
learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning.  ...  It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer  ...  Recurrent neural networks (RNN) [36] are designed to deal with sequence data since its node connections form a directed graph.  ... 
doi:10.1007/s40747-020-00212-w fatcat:ev3cyoy2mjeuhmq3rymkx2shsy

Computational Intelligence in Multimedia Processing: Foundation and Trends [chapter]

Aboul-Ella Hassanien, Ajith Abraham, Janusz Kacprzyk, James F. Peters
2008 Studies in Computational Intelligence  
This chapter presents a broad overview of Computational Intelligence (CI) techniques including Neural Network (NN), Particle Swarm Optimization (PSO), Evolutionary Algorithm (GA), Fuzzy Set (FS), and Rough  ...  In addition, a very brief introduction to near sets and near images which offer a generalization of traditional rough set theory and a new approach to classifying perceptual objects by means of features  ...  Time-Lag Recurrent Networks (TLRN) are multi-layered perceptrons extended with short-term memory structures that have local recurrent connections.  ... 
doi:10.1007/978-3-540-76827-2_1 fatcat:7jl4ir66vvcl7g436ektwkqwpy

Short-Term Traffic Flow Prediction Using the Modified Elman Recurrent Neural Network Optimized Through a Genetic Algorithm

Abolghasem Sadeghi-Niaraki, Parima Mirshafiei, Maryam Shakeri, Soo-Mi Choi
2020 IEEE Access  
This model makes use of the modified Elman recurrent neural network with delay effects incorporated into the formulation to achieve higher degrees of realism.  ...  This paper presents a short-term traffic flow prediction model based on the Modified Elman Recurrent Neural Network model (GA-MENN) to deal with this practical problem.  ...  In [38], they suggest a recurrent neural network based on Attention-based long-term memory (LSTM).  ... 
doi:10.1109/access.2020.3039410 fatcat:253togtcmre77guxmnujvf7tg4

Computational intelligence in management of ATM networks: a survey of the current state of research

Y. A. Sekercioglu, Andreas Pitsillides, Athanasios V. Vasilakos, Bruno Bosacchi, David B. Fogel, James C. Bezdek
1999 Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II  
ATM network management schemes in which artificial neural networks, fuzzy systems and design methods based on evolutionary computation.  ...  Additionally, the uncertainties involved in identification of the network parameters cause analytical modeling of ATM networks to be almost impossible.  ...  The major building blocks of CI are artificial neural networks, fuzzy logic, and evolutionary computation.  ... 
doi:10.1117/12.367704 fatcat:yszfhkbskff3xnvdokkum7isja

Editorial Biologically Learned/Inspired Methods for Sensing, Control, and Decision

Yongduan Song, Jennie Si, Sonya Coleman, Dermot Kerr
2022 IEEE Transactions on Neural Networks and Learning Systems  
In [A14] , using a "divide and conquer" strategy, Wu et al. propose a chain-structure echo state network (CESN) with stacked subnetwork modules as a new deep recurrent neural network.  ...  Inspired by memory replay and synaptic consolidation mechanism in the brain, a novel and simple framework termed memory recall (MeRec) for continual learning with deep neural network is presented by Zhang  ... 
doi:10.1109/tnnls.2022.3161003 fatcat:4e6v2kclcbb5pgkqqsyyaiwzjy

Artificial Intelligence and Its Applications 2014

Yudong Zhang, Saeed Balochian, Praveen Agarwal, Vishal Bhatnagar, Orwa Jaber Housheya
2016 Mathematical Problems in Engineering  
In the paper entitled "Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification," L. L. S.  ...  Nawi et al. investigated recurrent neural network (RNN). This network can be educated with gradient descent backpropagation.  ...  In the paper entitled "Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification," L. L. S.  ... 
doi:10.1155/2016/3871575 fatcat:irj62qjsdzfu7h4fdslkgy5hny

Artificial Intelligence in Dentistry: The Current Concepts and a Peek into the Future

Shilpi Sharma
2019 International Journal of Contemporary Medical Research [IJCMR]  
The field of artificial intelligence is relatively young but has still come a long way in the fields of medicine and dentistry.  ...  Hence, there is a need for the dentists to be aware about its potential implications for a lucrative clinical practice in the future.  ...  prediction in esophageal cancer and for the prediction of oral cancer susceptibility. 24 -The neural network may be of value for the identification of individuals with a high risk of oral cancer or precancer  ... 
doi:10.21276/ijcmr.2019.6.12.7 fatcat:f6ovmqxcq5aqhatnfbqduclnii
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