Interweave Neural Networks with Evolutionary Algorithms, Cellular Computing, Bayesian Learning and Ensemble Learning

2009 International Conference on Business Intelligence and Financial Engineering ◽

10.1109/bife.2009.25 ◽

2009 ◽

Author(s):

Jing Zhang

Keyword(s):

Neural Networks ◽

Evolutionary Algorithms ◽

Ensemble Learning ◽

Bayesian Learning ◽

Cellular Computing

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Automated Test Input Generation for Convolutional Neural Networks by Implementing Multi-objective Evolutionary Algorithms

2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW) ◽

10.1109/candarw51189.2020.00040 ◽

2020 ◽

Author(s):

Lingfeng ZHANG ◽

Hiroyuki SATO

Keyword(s):

Neural Networks ◽

Evolutionary Algorithms ◽

Convolutional Neural Networks ◽

Multi Objective ◽

Test Input ◽

Automated Test

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Predicting Acute Kidney Injury via Interpretable Ensemble Learning and Attention Weighted Convoutional-Recurrent Neural Networks

2021 55th Annual Conference on Information Sciences and Systems (CISS) ◽

10.1109/ciss50987.2021.9400242 ◽

2021 ◽

Author(s):

Yu-Chung Peng ◽

Niharika Shimona D'Souza ◽

Brian Bush ◽

Charles Brown ◽

Archana Venkataraman

Keyword(s):

Neural Networks ◽

Acute Kidney Injury ◽

Ensemble Learning ◽

Recurrent Neural Networks ◽

Kidney Injury

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Analysis of Driver Performance Using Hybrid of Weighted Ensemble Learning Technique and Evolutionary Algorithms

Arabian Journal for Science and Engineering ◽

10.1007/s13369-020-05115-z ◽

2021 ◽

Author(s):

Afsaneh Koohestani ◽

Moloud Abdar ◽

Sadiq Hussain ◽

Abbas Khosravi ◽

Darius Nahavandi ◽

...

Keyword(s):

Evolutionary Algorithms ◽

Ensemble Learning ◽

Driver Performance ◽

Learning Technique

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A novel multitask ensemble learning based on neural networks

Control Engineering and Information Systems ◽

10.1201/b17732-196 ◽

2015 ◽

pp. 985-988

Keyword(s):

Neural Networks ◽

Ensemble Learning

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Bayesian Learning of Neural Networks by Means of Artificial Immune Systems

The 2006 IEEE International Joint Conference on Neural Network Proceedings ◽

10.1109/ijcnn.2006.247161 ◽

2006 ◽

Cited By ~ 3

Author(s):

P.A.D. Castro ◽

F.J. Von Zuben

Keyword(s):

Neural Networks ◽

Bayesian Learning ◽

Artificial Immune Systems ◽

Artificial Immune ◽

Immune Systems

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Case studies in applying fitness distributions in evolutionary algorithms. II. Comparing the improvements from crossover and Gaussian mutation on simple neural networks

2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (Cat. No.00EX448) ◽

10.1109/ecnn.2000.886224 ◽

2002 ◽

Cited By ~ 2

Author(s):

A. Jain ◽

D.B. Fogel

Keyword(s):

Neural Networks ◽

Evolutionary Algorithms ◽

Case Studies ◽

Gaussian Mutation

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Designing Radial Basis Function Neural Networks with Meta-Evolutionary Algorithms: The Effect of Chromosome Codification

Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living - Lecture Notes in Computer Science ◽

10.1007/978-3-642-02481-8_6 ◽

2009 ◽

pp. 37-40

Author(s):

Elisabet Parras-Gutierrez ◽

Victor M. Rivas ◽

M. Jose del Jesus ◽

Juan J. Merelo

Keyword(s):

Neural Networks ◽

Evolutionary Algorithms ◽

Radial Basis Function ◽

Basis Function ◽

Radial Basis

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Ensemble Learning of Diffractive Optical Neural Networks

10.1364/cosi.2021.cm6b.2 ◽

2021 ◽

Author(s):

Md Sadman Sakib Rahman ◽

Jingxi Li ◽

Deniz Mengu ◽

Yair Rivenson ◽

Aydogan Ozcan

Keyword(s):

Neural Networks ◽

Ensemble Learning ◽

Optical Neural Networks

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Forest Cover Classification Using Stacking of Ensemble Learning and Neural Networks

Advances in Intelligent Systems and Computing - Artificial Intelligence and Evolutionary Computations in Engineering Systems ◽

10.1007/978-981-15-0199-9_8 ◽

2020 ◽

pp. 89-102

Author(s):

Pruthviraj R. Patil ◽

M. Sivagami

Keyword(s):

Neural Networks ◽

Ensemble Learning ◽

Forest Cover

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Using Particle Swarm Optimization Algorithm as an Optimization Tool Within Developed Neural Networks

Critical Developments and Applications of Swarm Intelligence - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-5134-8.ch009 ◽

2018 ◽

pp. 215-244

Author(s):

Goran Klepac

Keyword(s):

Neural Network ◽

Neural Networks ◽

Particle Swarm Optimization ◽

Evolutionary Algorithms ◽

Optimization Algorithm ◽

Recommendation System ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Optimal Combination ◽

Swarm Optimization

Developed neural networks as an output could have numerous potential outputs caused by numerous combinations of input values. When we are in position to find optimal combination of input values for achieving specific output value within neural network model it is not a trivial task. This request comes from profiling purposes if, for example, neural network gives information of specific profile regarding input or recommendation system realized by neural networks, etc. Utilizing evolutionary algorithms like particle swarm optimization algorithm, which will be illustrated in this chapter, can solve these problems.

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