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PubMed Publications| Keyword search (4,163 papers available) |  |
"Neural networks" Keyword-tagged Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Tuning Deep Learning for Predicting Aluminum Prices Under Different Sampling: Bayesian Optimization Versus Random Search | Alicia Estefania Antonio Figueroa | 41751647 CONCORDIA |
| 2 | Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm | Lahmiri S; Tadj C; Gargour C; | 41294952 ENCS |
| 3 | Efficient neural encoding as revealed by bilingualism | Moore C; Donhauser PW; Klein D; Byers-Heinlein K; | 40828024 PSYCHOLOGY |
| 4 | Personalizing brain stimulation: continual learning for sleep spindle detection | Sobral M; Jourde HR; Marjani Bajestani SE; Coffey EBJ; Beltrame G; | 40609549 PSYCHOLOGY |
| 5 | Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting | Ahmed U; Mahmood A; Khan AR; Kuhlmann L; Alimgeer KS; Razzaq S; Aziz I; Hammad A; | 40185800 PHYSICS |
| 6 | Large language models deconstruct the clinical intuition behind diagnosing autism | Stanley J; Rabot E; Reddy S; Belilovsky E; Mottron L; Bzdok D; | 40147442 ENCS |
| 7 | MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle | McKay MJ; Weber KA; Wesselink EO; Smith ZA; Abbott R; Anderson DB; Ashton-James CE; Atyeo J; Beach AJ; Burns J; Clarke S; Collins NJ; Coppieters MW; Cornwall J; Crawford RJ; De Martino E; Dunn AG; Eyles JP; Feng HJ; Fortin M; Franettovich Smith MM; Galloway G; Gandomkar Z; Glastras S; Henderson LA; Hides JA; Hiller CE; Hilmer SN; Hoggarth MA; Kim B; Lal N; LaPorta L; Magnussen JS; Maloney S; March L; Nackley AG; O' Leary SP; Peolsson A; Perraton Z; Pool-Goudzwaard AL; Schnitzler M; Seitz AL; Semciw AI; Sheard PW; Smith AC; Snodgrass SJ; Sullivan J; Tran V; Valentin S; Walton DM; Wishart LR; Elliott JM; | 39590726 HKAP |
| 8 | A protocol for trustworthy EEG decoding with neural networks | Borra D; Magosso E; Ravanelli M; | 39549492 ENCS |
| 9 | Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks | Adcock B; Brugiapaglia S; Dexter N; Moraga S; | 39454372 MATHSTATS |
| 10 | Deep neural network-based robotic visual servoing for satellite target tracking | Ghiasvand S; Xie WF; Mohebbi A; | 39440297 ENCS |
| 11 | Generalization limits of Graph Neural Networks in identity effects learning | D' Inverno GA; Brugiapaglia S; Ravanelli M; | 39426036 ENCS |
| 12 | The immunomodulatory effect of oral NaHCO3 is mediated by the splenic nerve: multivariate impact revealed by artificial neural networks | Alvarez MR; Alkaissi H; Rieger AM; Esber GR; Acosta ME; Stephenson SI; Maurice AV; Valencia LMR; Roman CA; Alarcon JM; | 38549144 CSBN |
| 13 | Reinforcement learning for automatic quadrilateral mesh generation: A soft actor-critic approach | Pan J; Huang J; Cheng G; Zeng Y; | 36375347 ENCS |
| 14 | Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer | Ocampo I; López RR; Camacho-León S; Nerguizian V; Stiharu I; | 34683215 ENCS |
| 15 | X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech | Jeancolas L; Petrovska-Delacrétaz D; Mangone G; Benkelfat BE; Corvol JC; Vidailhet M; Lehéricy S; Benali H; | 33679361 PERFORM |
| Title: | Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer | ||||
| Authors: | Ocampo I, López RR, Camacho-León S, Nerguizian V, Stiharu I | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/34683215/ | ||||
| DOI: | 10.3390/mi12101164 | ||||
| Publication: | Micromachines | ||||
| Keywords: | artificial neural networks; data analysis; liposome; micromixer; | ||||
| PMID: | 34683215 | Category: | Date Added: | 2021-10-25 | |
| Dept Affiliation: |
ENCS
1 Tecnologico de Monterrey, School of Engineering and Sciences, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico. 2 Departments of Oncology & Pathology, Research Institute of the McGill University Health Centre, 1001 Décarie, Montreal, QC H4A 3J1, Canada. 3 Department of Electrical Engineering, École de Technologie Supérieure, 1100 Notre Dame West, Montreal, QC H3C 1K3, Canada. 4 Department of Mechanical and Industrial Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, QC H3G 1M8, Canada. |
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Description: |
Artificial neural networks (ANN) and data analysis (DA) are powerful tools for supporting decision-making. They are employed in diverse fields, and one of them is nanotechnology; for example, in predicting silver nanoparticles size. To our knowledge, we are the first to use ANN to predict liposome size (LZ). Liposomes are lipid nanoparticles used in different biomedical applications that can be produced in Dean-Forces-based microdevices such as the Periodic Disturbance Micromixer (PDM). In this work, ANN and DA techniques are used to build a LZ prediction model by using the most relevant variables in a PDM, the Flow Rate Radio (FRR), and the Total Flow Rate (TFR), and the temperature, solvents, and concentrations were kept constant. The ANN was designed in MATLAB and fed data from 60 experiments with 70% training, 15% validation, and 15% testing. For DA, a regression analysis was used. The model was evaluated; it showed a 0.98147 correlation coefficient for training and 0.97247 in total data compared with 0.882 obtained by DA. |
