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62

Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data
Osotsi, Ame ; Oravecz, Zita ; Li, Qunhua ; et al.
Journal of Healthcare Informatics Research. 4(1):91-109

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63

Helmholtz Drum Machine: A Real-time Human-Computer Interactive Music System
Liu, Jingwei ; University of California [San Diego] (UC San Diego) ; University of California (UC) ; et al.

Human-computer Interacti... Music interface Music interaction AI music [INFO]Computer Science [... [INFO.INFO-AI]Computer S...
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65

Adaptive Machine Learning Algorithms with Python
Chanchal Chatterjee ; Chanchal Chatterjee ; Chanchal Chatterjee ; et al.
2022

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66

Extending DEAP with Active Sampling for Evolutionary Supervised Learning
Ben Hamida, Sana ; Benjelloun, Ghita ; Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE) ; et al.
Proceedings of the 16th International Conference on Software Technologies, ICSOFT 2021, Aug 2021, Online Streaming, France

Online Streaming, France Genetic Programming DEAP active learning random sampling weighted sampling
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67

Adaptive Machine Learning Algorithms with Python
Chanchal Chatterjee ; Chanchal Chatterjee ; Chanchal Chatterjee ; et al.
2022

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68

Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams.
Sassu, Alessandro ; Saenz-Cogollo, Jose Francisco ; Agelli, Maurizio
Sensors (14248220); Jun2021, Vol. 21 Issue 12, p4045, 1p

DEEP learning STREAMING video & televi... MACHINE learning EDGE computing DATA mining RESOURCE allocation
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69

Adaptive Machine Learning Algorithms with Python
Chanchal Chatterjee ; Chanchal Chatterjee ; Chanchal Chatterjee ; et al.
2022

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70

Large Scale Machine Learning with Spark
Karim, Md. Rezaul ; Kaysar, Md. Mahedi ; Karim, Md. Rezaul ; et al.

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71

Large Scale Machine Learning with Spark
Kaysar, Md. Mahedi ; Kaysar, Md. Mahedi ; Karim, Md. Rezaul ; et al.

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72

Detection and Classification of Levels of Consciousness Using Parietal EEG-fNIRS During Anesthesia
Catalina Saini Ferrón, BS
Detection and Classification of Levels of Consciousness Using Parietal EEG-fNIRS During Anesthesia
Aru J, Suzuki M, Larkum ME. Cellular Mechanisms of Conscious Processing. Trends Cogn Sci. 2020 Oct;24(10):814-825. doi: 10.1016/j.tics.2020.07.006. Epub 2020 Aug 24.
Campbell JM, Huang Z, Zhang J, Wu X, Qin P, Northoff G, Mashour GA, Hudetz AG. Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI. Neuroimage. 2020 Feb 1;206:116316. doi: 10.1016/j.neuroimage.2019.116316. Epub 2019 Oct 29.
Davidson AJ. Anesthesia and neurotoxicity to the developing brain: the clinical relevance. Paediatr Anaesth. 2011 Jul;21(7):716-21. doi: 10.1111/j.1460-9592.2010.03506.x. Epub 2011 Apr 6.
Hirota K. Special cases: ketamine, nitrous oxide and xenon. Best Pract Res Clin Anaesthesiol. 2006 Mar;20(1):69-79. doi: 10.1016/j.bpa.2005.08.014.
Saadeh W, Khan FH, Altaf MAB. Design and Implementation of a Machine Learning Based EEG Processor for Accurate Estimation of Depth of Anesthesia. IEEE Trans Biomed Circuits Syst. 2019 Aug;13(4):658-669. doi: 10.1109/TBCAS.2019.2921875. Epub 2019 Jun 10.
Kotsovolis G, Komninos G. Awareness during anesthesia: how sure can we be that the patient is sleeping indeed? Hippokratia. 2009 Apr;13(2):83-9.
Lee, M. H., Fazli, S., Mehnert, J., & Lee, S. W. (2015). Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI. Pattern Recognition, 48(8), 2725-2737. https://doi.org/10.1016/j.patcog.2015.03.010
Leon-Dominguez U, Izzetoglu M, Leon-Carrion J, Solis-Marcos I, Garcia-Torrado FJ, Forastero-Rodriguez A, Mellado-Miras P, Villegas-Duque D, Lopez-Romero JL, Onaral B, Izzetoglu K. Molecular concentration of deoxyHb in human prefrontal cortex predicts the emergence and suppression of consciousness. Neuroimage. 2014 Jan 15;85 Pt 1:616-25. doi: 10.1016/j.neuroimage.2013.07.023. Epub 2013 Jul 17.
Levitt DG, Schnider TW. Human physiologically based pharmacokinetic model for propofol. BMC Anesthesiol. 2005 Apr 22;5(1):4. doi: 10.1186/1471-2253-5-4.
Sheahan CG, Mathews DM. Monitoring and delivery of sedation. Br J Anaesth. 2014 Dec;113 Suppl 2:ii37-47. doi: 10.1093/bja/aeu378.
Sebel PS, Bowdle TA, Ghoneim MM, Rampil IJ, Padilla RE, Gan TJ, Domino KB. The incidence of awareness during anesthesia: a multicenter United States study. Anesth Analg. 2004 Sep;99(3):833-839. doi: 10.1213/01.ANE.0000130261.90896.6C.
Sepulveda P, Cortinez LI, Irani M, Egana JI, Contreras V, Sanchez Corzo A, Acosta I, Sitaram R. Differential frontal alpha oscillations and mechanisms underlying loss of consciousness: a comparison between slow and fast propofol infusion rates. Anaesthesia. 2020 Feb;75(2):196-201. doi: 10.1111/anae.14885. Epub 2019 Dec 1.
Sitaram R, Ros T, Stoeckel L, Haller S, Scharnowski F, Lewis-Peacock J, Weiskopf N, Blefari ML, Rana M, Oblak E, Birbaumer N, Sulzer J. Closed-loop brain training: the science of neurofeedback. Nat Rev Neurosci. 2017 Feb;18(2):86-100. doi: 10.1038/nrn.2016.164. Epub 2016 Dec 22.
Yeom SK, Won DO, Chi SI, Seo KS, Kim HJ, Muller KR, Lee SW. Spatio-temporal dynamics of multimodal EEG-fNIRS signals in the loss and recovery of consciousness under sedation using midazolam and propofol. PLoS One. 2017 Nov 9;12(11):e0187743. doi: 10.1371/journal.pone.0187743. eCollection 2017.
Zimeo Morais GA, Balardin JB, Sato JR. fNIRS Optodes' Location Decider (fOLD): a toolbox for probe arrangement guided by brain regions-of-interest. Sci Rep. 2018 Feb 20;8(1):3341. doi: 10.1038/s41598-018-21716-z.

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73

Big data analytics on Apache Spark
Salloum, Salman ; Dautov, Ruslan ; Chen, Xiaojun ; et al.
International Journal of Data Science and Analytics. 1(3-4):145-164

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74

Applied Techniques for Twitter Data Retrieval in an Urban Area: Insight for Trip Production Modeling
Rempu Sora Rayat ; Adenantera Dwicaksono ; Heru P. H. Putro ; et al.
Applied Sciences, Vol 13, Iss 14, p 8539 (2023)

API data acquisition feature extraction trip production Twitter Indonesia
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75

RFSoC Modulation Classification With Streaming CNN: Data Set Generation & Quantized-Aware Training
Andrew Maclellan ; Louise H. Crockett ; Robert W. Stewart
IEEE Open Journal of Circuits and Systems, Vol 6, Pp 38-49 (2025)

Deep learning (DL) wireless communications AMD FPGA RFSoC PYNQ
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76

Geometric properties of quantum entanglement and machine learning
S. V. Zuev
Российский технологический журнал, Vol 11, Iss 5, Pp 19-33 (2023)

online learning adaptive artificial inte... quantum machine learning quantum entanglement Information theory Q350-390
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77

Sentiment analysis in geo social streams by using machine learning technique
Twanabasu, Bikesh ; Ramos Romero, José Francisco ; Fernandez, Oscar Belmonte ; et al.

Geovisualization Machine Learning Opinion Mining Sentiment Analysis
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78

Coffee Bean Defects Automatic Classification Realtime Application Adopting Deep Learning
Hong-Danh Thai ; Han-Jong Ko ; Jun-Ho Huh
IEEE Access, Vol 12, Pp 126503-126517 (2024)

Coffee bean defects quality classification computer vision YOLOv8 OpenCV cloud-based application
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79

Multi-Modal Comparative Analysis on Audio Dub Detection Using Artificial Intelligence
Divya Jennifer Dsouza ; Anisha P. Rodrigues ; Roshan Fernandes
IEEE Access, Vol 13, Pp 128856-128878 (2025)

Audio dub detection fake audio incremental learning feature extraction batch processing machine learning
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80

OpenCV with Python Blueprints
Beyeler, Michael ; Beyeler, Michael ; Beyeler, Michael ; et al.

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