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A Hybrid Evolutionary System for Automated Artificial Neural Networks Generation and Simplification in Biomedical Applications

Enrique Fernandez-Blanco, Daniel Rivero, Marcos Gestal, Carlos Fernandez-Lozano, Norberto Ezquerra, Cristian Robert Munteanu, Julian Dorado
2015 Current Bioinformatics  
Among the great diversity of techniques that can be used for this purpose, Artifical Neural Networks (ANNs) is one of the most suited.  ...  The work goes further, and the system described here allows to obtain simplified networks with a low number of neurons to resolve the problems.  ...  Medical Center, Long Beach and Cleveland Clinic Foundation.  ... 
doi:10.2174/1574893610666151008012923 fatcat:vvfdjm2kcrczpp7ul2xvp2corm

Artificial Intelligence in Nutrients Science Research: A Review

Jarosław Sak, Magdalena Suchodolska
2021 Nutrients  
It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients.  ...  In recent decades, there has been an expansion of AI applications in biomedical sciences.  ...  There is a possibility to create fuzzy neural networks and convert FLM-based models into neural networks.  ... 
doi:10.3390/nu13020322 pmid:33499405 pmcid:PMC7911928 fatcat:3qssvkrtpfernauajowizhxzp4

Progress on Artificial Neural Networks for Big Data Analytics: A Survey

Haruna Chiroma, Usman Ali Abdullahi, Shafi'i Muhammad Abdulhamid, Ala Abdulsalam Alarood, Lubna A. Gabralla, Nadim Rana, Liyana Shuib, Ibrahim Abaker Targio Hashem, Dada Emmanuel Gbenga, Adamu I. Abubakar, Akram M. Zeki, Tutut Herawan
2019 IEEE Access  
INDEX TERMS Big data analytics, artificial neural networks, evolutionary neural network, convolutional neural network, dataset.  ...  Artificial neural networks (ANNs) are known for their effectiveness and efficiency for small datasets, and this era of big data has posed a challenge to the big data analytics using ANN.  ...  Applications of deep RNN and spiking ANN are scarce in big data analytics despite their excellent performance in other domains [96] , [97] . 2) SWARM AND EVOLUTIONARY NEURAL NETWORKS Evolutionary algorithms  ... 
doi:10.1109/access.2018.2880694 fatcat:jnmfnyjaevczddtskwtf64ltti

Modular symbiotic adaptive neuro evolution for high dimensionality classificatory problems

Rahul Kala, Anupam Shukla, Ritu Tiwari
2011 International Journal of Intelligent Decision Technologies  
There has been a considerable effort in the design of evolutionary systems for the automatic generation of neural networks.  ...  Symbiotic Adaptive Neuro Evolution (SANE) is a novel approach that carries co-evolution of neural networks at two levels of neuron and network.  ...  power of the evolutionary algorithms for a more sophisticated hybrid system.  ... 
doi:10.3233/idt-2011-0114 fatcat:7yjd7yll6zhlzalldpvheoibgq

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

Pablo Mesejo, Rubén Martos, Óscar Ibáñez, Jorge Novo, Marcos Ortega
2020 Applied Sciences  
This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes.  ...  We first describe the importance and applicability of forensic anthropology in many identification scenarios.  ...  Neural Network 267 skull CT scans Darmawan et al. [73] used a Hybrid Particle Swarm Artificial Neural Network technique for sex estimation of individuals.  ... 
doi:10.3390/app10144703 fatcat:hyupdin4qfcsbc4t7fm4noofkm

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  
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.  ...  In this study, the use of SC methods is considered an important extension to the quantitative model-based approach for residual generation in FDI.  ...  A combined artificial neural network and expert system tool (ANNEPS) is developed (Wang, 1998) for transformer fault diagnosis using dissolved gas-in-oil analysis (DGA).  ... 
doi:10.3166/ejc.7.248-286 fatcat:v6hesxhjwbbtjfde6zci6qxv34


S. D. Leoshchenko, A. O. Oliinyk, S. A. Subbotin, V. A. Lytvyn, V. V. Shkarupylo
2019 Radìoelektronika, Ìnformatika, Upravlìnnâ  
The problem of automation synthesis of artificial neural networks for further use in diagnosing, forecasting and pattern recognition is solved.  ...  The experiments have confirmed the efficiency of the proposed method of synthesis of artificial neural networks and allow us to recommend it for use in practice in the processing of data sets for further  ...  non-destructive quality control of military and civilian applications" (state registration number 0119U100360).  ... 
doi:10.15588/1607-3274-2019-4-7 fatcat:kbk4h5barjflvji7hs32szytj4

Conference Main Schedule

2021 2021 International Conference on Intelligent Technologies (CONIT)  
PM 3.30 PM Parth Vaghela Multiresolution Features through Artificial Neural Network 8 766 Sheema D Comparative Study of Major Algorithms for Pest Detection in Maize Crop 3.31 PM 3.45  ...  Representation of Mechanical Systems for the Automated Design 9.31 AM 9.45 AM 5 148 Pinku Ranjan and Shubham Singh Dual Band Cylindrical Dielectric Resonator Antenna for WiMAX Application  ... 
doi:10.1109/conit51480.2021.9498269 fatcat:6kqyrpvszjcyjjfretndzzdpfy

2020 Index IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems Vol. 39

2020 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems  
., +, TCAD Jan. 2020 157-170 Drugs Microfluidic Design for Concentration Gradient Generation Using Artificial Neural Network.  ...  Shao, L., +, TCAD Oct. 2020 2708-2721 Microfluidic Design for Concentration Gradient Generation Using Artificial Neural Network.  ...  Entropy-Directed Scheduling for FPGA High-Level Synthesis. Shen, M., +, TCAD Oct. 2020 2588 -2601 FLASH: Fast, Parallel, and Accurate Simulator for HLS.  ... 
doi:10.1109/tcad.2021.3054536 fatcat:wsw3olpxzbeclenhex3f73qlw4

The Era of Big Data: Genome-scale Modelling meets Machine Learning

Athanasios Antonakoudis, Rodrigo Barbosa, Pavlos Kotidis, Cleo Kontoravdi
2020 Computational and Structural Biotechnology Journal  
We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design.  ...  With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis.  ...  RB thanks the UK Biotechnology and Biological Sciences Research Council and GlaxoSmithKline for his studentship.  ... 
doi:10.1016/j.csbj.2020.10.011 pmid:33240470 pmcid:PMC7663219 fatcat:nvzko7mayzc67eqkfnf25c7fni

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation [article]

Seungmoon Song, Łukasz Kidziński, Xue Bin Peng, Carmichael Ong, Jennifer L. Hicks, Serge Levine, Christopher Atkeson, Scot Delp
2020 bioRxiv   pre-print
Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has  ...  interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research.  ...  For instance, one could train a controller (i. e., a policy in RL 24 terminology) implemented on an artificial neural network using deep RL in a physiologically plausible 25 simulation environment, and  ... 
doi:10.1101/2020.08.11.246801 fatcat:qjkr2kn3hnehppsp7fnzsqz34m

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety [article]

Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser (+29 others)
2021 arXiv   pre-print
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings.  ...  These shortcomings are diverse and range from a lack of generalization over insufficient interpretability to problems with malicious inputs.  ...  Acknowledgment The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Energy within the project "KI Absicherung -Safe AI for Automated Driving".  ... 
arXiv:2104.14235v1 fatcat:f6sj3v2brza7thyzw7b7fkpo2m

Effective Brain Connectivity for fNIRS with Fuzzy Cognitive Maps in Neuroergonomics

Mehrin Kiani, Javier Andreu-Perez, Hani Hagras, Elpiniki I. Papageorgiou, Mukesh Prasad, Chin-Teng Lin
2020 IEEE Transactions on Cognitive and Developmental Systems  
A comparison of EC in fNIRS signals obtained from E-FCM with that obtained from standard FCM, general linear model (GLM) parameters that power Dynamic Causal Modelling (DCM), and Granger Causality (GC)  ...  The proposed method presents a regularized methodology of FCMs, called effective FCMs (E-FCMs), with improved accuracy for predicting EC between real, and synthetic fNIRS signals.  ...  His major research interests are in Explainable Artificial Intelligence (XAI ) and computational intelligence, notably type-2 fuzzy systems, fuzzy logic, neural networks, genetic algorithms, and evolutionary  ... 
doi:10.1109/tcds.2019.2958423 fatcat:wxqss6ghcfbazcpi3gnnv7j574

Vision Paper: Grand Challenges in Resilience: Autonomous System Resilience through Design and Runtime Measures

Saurabh Bagchi, Vaneet Aggarwal, Somali Chaterji, Fred Douglis, Aly El Gamal, Jiawei Han, Brian Henz, Henry Hoffmann, Suman Jana, Milind Kulkarni, Felix Xiaozhu Lin, Karen Marais (+3 others)
2020 IEEE Open Journal of the Computer Society  
We use several application drivers from autonomous systems to motivate the challenges in cyber resilience and to demonstrate the benefit of the solutions.  ...  We focus on some autonomous systems in the near horizon (autonomous ground and aerial vehicles) and also a little more distant (autonomous rescue and relief).  ...  These success stories suggest the applicability of deep neural networks in a ubiquitous fashion in the near future.  ... 
doi:10.1109/ojcs.2020.3006807 fatcat:sngt6fii3rhi7hiovtbjj5ptuy

2019 Index IEEE Robotics and Automation Letters Vol. 4

2019 IEEE Robotics and Automation Letters  
., +, LRA April 2019 808-815 A Generative Neural Network for Learning Coordinated Reach-Grasp Motions.  ...  , and Wimmer, M., Flexible Production Systems: Automated Generation of Operations Plans Based on ISA-95 and PDDL; LRA Oct. 2019 4062-4069 Walter, V., Staub, N., Franchi, A., and Saska, M., UVDAR System  ...  Permanent magnets Adaptive Dynamic Control for Magnetically Actuated Medical Robots.  ... 
doi:10.1109/lra.2019.2955867 fatcat:ckastwefh5chhamsravandtnx4
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