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Recombination of Artificial Neural Networks [article]

Aaron Vose, Jacob Balma, Alex Heye, Alessandro Rigazzi, Charles Siegel, Diana Moise, Benjamin Robbins, Rangan Sukumar
2019 arXiv   pre-print
We propose a genetic algorithm (GA) for hyperparameter optimization of artificial neural networks which includes chromosomal crossover as well as a decoupling of parameters (i.e., weights and biases) from  ...  Our methods improve final accuracy as well as time to fixed accuracy on a wide range of deep neural network architectures including convolutional neural networks, recurrent neural networks, dense neural  ...  architectures, including convolutional neural networks, recurrent neural networks, dense neural networks, and capsule networks.  ... 
arXiv:1901.03900v1 fatcat:kcxcxrd5lraafeyymfu3pnw5om

Genome-Scale Classification of Recombinant and Non-Recombinant HIV-1 Sequences Using Artificial Neural Network Ensembles

Ashok Kumar Dwivedi, Usha Chouhan
2016 Current Science  
This article presents ensemble models of artificial neural network for the classification of recombinant and non-recombinant sequences of HIV-1 genome.  ...  Consequently, it is indispensable to devise an effective method for recognition of recombination in HIV-1 strains.  ...  We thank the Department of Biotechnology (DBT), New Delhi for providing support for this work under Bioinformatics Infrastructure Facility of DBT at Maulana Azad National Institute Technology, Bhopal.  ... 
doi:10.18520/cs/v111/i5/853-860 fatcat:yyv6kkg3kzgwdlu6lafrqpzcqe

Application of Artificial Neural Network in Predicting the Electrical Conductivity of Recombined Milk

Haidar Naseri, isa hazbavi, Feizollah Shahbazi, Graduate Master, Biosystem Engineering, Lorestan University, Khorramabad, Iran, Assistant Professor, Biosystem Engineering, Lorestan University, Khorramabad, Iran, Associate Professor, Biosystem Engineering, Lorestan University, Khorramabad, Iran
2020 Food Science and Technology  
artificial neural network have been used Network Learning Network test Network Arrangement Epoch Training Algorithm Actuator function MSE R 2 MSE R 2 4-2-1 33 Mom Tanh 0.019 0.936  ...  the recombined milk is influenced by independent factors Fat 6% Fat 3% Temperature (ºC) Temperature (ºC) 65 60 55 50 65 60 55 50 Lactose (%) Protein (%) 2.71±0.2 2.54±0.2 2.47±0.2 2.37  ... 
doi:10.29252/fsct.16.96.65 fatcat:ldv3qmgzlja3ro4rvnuergiroy

PRELIMINARY MODELING OF AN INDUSTRIAL RECOMBINANT HUMAN ERYTHROPOIETIN PURIFICATION PROCESS BY ARTIFICIAL NEURAL NETWORKS

R. H. R. Garcel1, O. G. León, E. O. Magaz
2015 Brazilian Journal of Chemical Engineering  
In the present study a preliminary neural network modelling to improve our understanding of Recombinant Human Erythropoietin purification process in a plant was explored.  ...  A three layer feed-forward back propagation neural network was constructed for predicting the efficiency of the purification section comprising four chromatographic steps as a function of eleven operational  ...  Neural Network An artificial neural network is a nonlinear statistical data modeling tool that simulates the structure and functional aspects of biological neural networks.  ... 
doi:10.1590/0104-6632.20150323s00003527 fatcat:yt6pcyhjdnc77fc62f4jzxbj6m

Rapid and quantitative analysis of recombinant protein expression using pyrolysis mass spectrometry and artificial neural networks: application to mammalian cytochrome b5 in Escherichia coli

Royston Goodacre, Amna Karim, Mustak A. Kaderbhai, Douglas B. Kell
1994 Journal of Biotechnology  
To deconvolute the pyrolysis mass spectra so as to obtain quantitative information on the amount of cytochrome b 5 produced fully-interconnected feedforward artificial neural networks (ANNs) were studied  ...  PyMS is a novel, convenient and rapid method for the screening and analysis of microbial and other cultures producing recombinant proteins.  ...  Acknowledgments RG and DBK are supported by the Biotechnology Directorate of the UK SERC, under the terms of the LINK scheme in Biochemical Engi-  ... 
doi:10.1016/0168-1656(94)90088-4 pmid:7764850 fatcat:2u646sr75ndm7etrhdsagljriu

The Use of Artificial Neural Network (ANN) for Modelling, Simulation and Prediction of Advanced Oxidation Process Performance in Recalcitrant Wastewater Treatment [chapter]

Emad S., Malay Chaudhuri
2011 Artificial Neural Networks - Application  
Training of Artificial Neural Network According to Artificial neural network tutorial (2008), the learning situation can be categorized as the following.  ...  The chapter presents artificial neural network and training of artificial neural network, advanced oxidation processes (AOPs), case studies, conclusions and references.  ...  Artificial Neural Networks -Application This book covers 27 articles in the applications of artificial neural networks (ANN) in various disciplines which includes business, chemical technology, computing  ... 
doi:10.5772/14920 fatcat:ishuxjxdpnfsbawu3xobsrt43i

Prediction of thermal conductivity of aqueous solution at high pressures by using artificial neural network

Ali Amooey, Maryam Ahangarian, Farshad Rezazadeh
2014 Chemical Industry and Chemical Engineering Quarterly  
The objective of this study is to predict thermal conductivity of aqueous solution with artificial neural network (ANN) model with three inputs (pressure, temperature and concentration).  ...  A feed forward artificial neural network with three neurons in its hidden layer is recommended to predict thermal conductivity.  ...  Zhou et al. calculated the electrical conductivity of recombined milk by artificial neural network, aiming to establish a nonlinear relationship that accounts for the effect of milk constituents and temperature  ... 
doi:10.2298/ciceq130729038a fatcat:blj4uz6vkbddto3oxmbugyk5pu

Conditions for Outperformance of Recombination in Online Evolution of Swarm Robots

Christopher Schwarzer, Nico Michiels
2013 Advances in Artificial Life, ECAL 2013  
Using neural networks as robot controllers, we reduce this disruptiveness with an adaptive mate choice that evolves the probability of recombination and the genetic similarity of mates.  ...  We also found that treatments with recombination evolved smaller neural networks with fewer links.  ...  Acknowledgments This work is part of project "SYMBRION" and is funded by the European Commission within the work programme "Future and Emergent Technologies Proactive" under the grant agreement no. 216342  ... 
doi:10.7551/978-0-262-31709-2-ch154 dblp:conf/ecal/SchwarzerM13 fatcat:j6fqtzkuczbqrluqpcfkig6xfa

Futuristic methods in virus genome evolution using the Third-Generation DNA sequencing and artificial neural networks [article]

Hyunjin Shim
2019 arXiv   pre-print
In particular, deep learning is a field of machine learning that is used to solve complex problems through artificial neural networks.  ...  Furthermore, we discuss how futuristic methods using artificial neural networks combined with long-read sequences can revolutionize virus studies, using specific examples in supervised and unsupervised  ...  Artificial Neural Networks Artificial Neural Networks (ANN) is a model-free approach that has a great potential for applications to big genetic data.  ... 
arXiv:1902.09148v1 fatcat:p74hb5tvkfdcbkz4nba6gvyvca

Development of Soft Sensors for Online Biomass Prediction in Production of Hepatitis B Vaccine

2022 Biointerface Research in Applied Chemistry  
This study attempted to obtain the best neural network structure for online estimation of P. pastoris yeast biomass, which is used to express the hepatitis B surface antigen (HBsAg).  ...  Neural networks can provide highly accurate and robust solutions for complex non-linear kinetic like bioprocess reactions.  ...  Artificial neural network architecture.  ... 
doi:10.33263/briac132.194 fatcat:3cljkzoh4ra7nhoxuldklqfwae

Towards efficient evolution of morphology and control

Peter Krah
2008 Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08  
The proposed algorithm is inspired by NeuroEvolution of Augmenting Topologies (NEAT) which efficiently evolves artificial neural networks.  ...  We propose a novel algorithm for the evolution of body and control of three-dimensional, physically simulated virtual creatures controlled by artificial neural networks.  ...  Each body part of the creature contains a local neural network.  ... 
doi:10.1145/1389095.1389142 dblp:conf/gecco/Krah08 fatcat:njl2sg4ncnhqblkb76kimo26yu

POLSAR image classification using BP neural network based on Quantum Clonal Evolutionary Algorithm

Bin Zou, Huijun Li, Lamei Zhang
2010 2010 IEEE International Geoscience and Remote Sensing Symposium  
capability of BP neural network.  ...  In 2007, Kamran Ullah Khan and Yang Jian classified the POLSAR image using artificial neural networks based on SOM [2].  ... 
doi:10.1109/igarss.2010.5653650 dblp:conf/igarss/ZouLZ10 fatcat:32xg2pkv5jh2bknpnlc4vlrquq

PM2.5 Concentration Forecasting Based on Data Preprocessing Strategy and LSTM Neural Network

Tao Liang, Hebei University of Technology, college of Artificial Intelligence, Tianjin, 300130, China, Gaofeng Xie, Dabin Mi, Wen Jiang, Guilin Xu
2020 International Journal of Machine Learning and Computing  
In order to better grasp the change rule of PM2.5 concentration, this paper presents a prediction model of PM2.5 concentration based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise  ...  The data of four monitoring stations in Tangshan City, Hebei Province is used to implement simulation experiment.  ...  CONFLICT OF INTEREST The authors declare no conflict of interest.  ... 
doi:10.18178/ijmlc.2020.10.6.997 fatcat:vlvifzyadnaqncg2dr7w2g2vk4

Airline Passenger Forecasting in EGYPT (Domestic and International)

M. M., M. S., A. A.
2017 International Journal of Computer Applications  
This study employed the back-propagation neural network and genetic algorithm to forecast the air passenger demand in Egypt(International and Domestic).  ...  The factors that influence air passenger are identified, evaluated and analyzed by applying the back-propagation neural network on the monthly data from 1970 to 2013 by using Matlab R2013b.  ...  Backpropagation network (BPN) is one of the most commonly used supervised Artificial neural network(ANN) models.  ... 
doi:10.5120/ijca2017913113 fatcat:32h66ba4ffdppfv5ipll3im2gm

Cellular Automata in an Artificial Life perspective [chapter]

Raffaele Calabretta
1998 Cellular Automata: Research Towards Industry  
in genetic algorithms applied to populations of neural networks (e.g., [9] , [10] , [11] , [12] ).  ...  We compared the performance of haploid and diploid populations of ecological neural networks living in both fixed and changing environments.  ... 
doi:10.1007/978-1-4471-1281-5_22 fatcat:dhdtlbkhwrf5bny6ses4g3oewe
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