• Suche: "Real-time Machine Learning"

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1

Wearable network for multilevel physical fatigue prediction in manufacturing workers.
Mohapatra, Payal ; Aravind, Vasudev ; Bisram, Marisa ; et al.
PNAS Nexus. 3(10)

continuous fatigue monit... manufacturing quantifying physical fat... real-time machine learni... wearable sensors
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2

SwinIoT: A Hierarchical Transformer-Based Framework for Behavioral Anomaly Detection in IoT-Driven Smart Cities
H. Mancy ; Qamar H. Naith
IEEE Access, Vol 13, Pp 48758-48774 (2025)

edge computing smart city surveillance Behavioral detection Electrical engineering.... IoT anomaly detection real-time machine learni... …
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3

An Experimental Study with Imbalanced Classification Approaches for Credit Card Fraud Detection
Christy, S. Nancy Lima ; Rathore, Prince ; Balanagu, Mohith ; et al.

International IST Credit...
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4

Operational Benchmarking of ML Models for Fraud Detection: A Comparative Study on AWS EC2 and ECS
Lucas Benevides E Braga ; Rodrigo Marins Piaba
IEEE Access, Vol 13, Pp 179010-179025 (2025)

AWS EC2 AWS ECS Fargate real-time machine learni... latency and throughput m... machine learning deploym... containerized inference …
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5

Closed-loop network of skin-interfaced wireless devices for quantifying vocal fatigue and providing user feedback.
Yoo, Jae-Young ; Ouyang, Wei ; Greane, Aurora ; et al.
Proceedings of the National Academy of Sciences of USA. 120(9)

closed-loop network haptic feedback quantifying vocal fatigu... real-time machine learni... wearable electronics Humans …
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6

A Novel End-to-End Provenance System for Predictive Maintenance: A Case Study for Industrial Machinery Predictive Maintenance
Emrullah Gultekin ; Mehmet S. Aktas
Computers ; Volume 13 ; Issue 12 ; Pages: 325

provenance PROV-O predictive maintenance anomaly detection user study remaining useful life as... …
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7

Edge2Train: a framework to train machine learning models (SVMs) on resource-constrained IoT edge devices
Sudharsan, Bharath ; Breslin, John G. ; Ali, Muhammad Intizar ; et al.
Proceedings of the 10th International Conference on the Internet of Things. :1-8

Real-time Machine Learni... Intelligent Microcontrol... 0202 electrical engineer... 02 engineering and techn... Self-learning IoT Edge Embedded C
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8

HeMA: A hierarchically enriched machine learning approach for managing false alarms in real time: A sepsis prediction case study
Liu, Zeyu ; Khojandi, Anahita ; Mohammed, Akram ; et al.
In Computers in Biology and Medicine April 2021 131

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9

Application of Real Time Machine Learning Models in Financial Fraud Identification
Zhang, Xuanrui
European Journal of Business, Economics & Management. 1(2):1-7

real-time machine learni...
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10

A Novel Three-Layer IoT Architecture for Shared, Private, Scalable, and Real-time Machine Learning from Ubiquitous Cyber-Physical Systems
Parto, Mahmoud ; Saldana, Christopher ; Kurfess, Thomas
In Procedia Manufacturing 2020 48:959-967

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11

Edge2Train: A framework to train machine learning models (SVMs) on resource-constrained IoT edge devices
Sudharsan, Bharath ; Breslin, John G. ; Ali, Muhammad Intizar ; et al.

Intelligent Microcontrol... Embedded C Real-time Machine Learni... Self-learning IoT Edge
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12

Product Attribute Forecast: Adaptive Model Selection Using Real-Time Machine Learning
Bayrak, Elif Seyma ; Wang, Tony ; Tulsyan, Aditya ; et al.
In IFAC PapersOnLine 2018 51(18):121-125

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13

TransPod Ultra-High-Speed Tube Transportation: Dynamics of Vehicles and Infrastructure
Janzen, Ryan
In Procedia Engineering 2017 199:8-17

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14

Automatic image annotation for small and ad hoc intelligent applications using Raspberry Pi
Jameel, S.M. ; Hashmani, M.A. ; Rizvi, S.S.H. ; et al.

Automatic Image Annotati... Real-Time Machine Learni... Big Data Annotation Small and Ad-hoc Intelli...
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15

Improving Robot Performance in Partially Observable and Adversarial Environments through Learning
Lu, Wentao

Real-time Machine Learni... Spatial-Temporal Learnin... 4611 Machine learning anzsrc-for: 4611 Machine... 460205 Intelligent robot... Robot Learning …
Dissertation
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16

Reactive Reinforcement Learning in Asynchronous Environments
Jaden B. Travnik ; Kory W. Mathewson ; Richard S. Sutton ; et al.
Frontiers in Robotics and AI, Vol 5 (2018)

reinforcement learning asynchronous environment... resource-limited systems reaction time real-time machine learni... Mechanical engineering a... …
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17

Real-Time Adaptive Event Detection in Astronomical Data Streams
Thompson, David R. ; Burke-Spolaor, Sarah ; Deller, Adam T. ; et al.
IEEE Intelligent Systems. 29:48-55

2. Zero hunger Fast Radio Transients Radio Astronomy Pattern Recognition Real Time Machine Learni... Time Series Analysis
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18

Closed-loop network of skin-interfaced wireless devices forquantifying vocal fatigue and providing user feedback.
Yoo, Jae-Young ; Ouyang, Wei ; Greane, Aurora ; et al.
Proceedings of the National Academy of Sciences of USA, vol 120, iss 9

closed-loop network haptic feedback quantifying vocal fatigu... real-time machine learni... wearable electronics Humans …
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19

Chapter 10 - Mixed reality-assisted smart bridge inspection for future smart cities
Karaaslan, Enes ; Zakaria, Mahta ; Catbas, F. Necati
In The Rise of Smart Cities 2022:261-280

Buch
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20

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