Results 81 to 90 of about 336,769 (240)

LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing [PDF]

PeerJ Computer Science
This study aims to investigate the problem of concept drift in cloud computing and emphasizes the importance of early detection for enabling optimum resource utilization and offering an effective solution.
Tajwar Mehmood, Seemab Latif, Nor Shahida Mohd Jamail, Asad Malik, Rabia Latif   +4 more
doaj   +2 more sources

Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems

Sensors, 2020
With the increasing popularity of the Internet-of-Medical-Things (IoMT) and smart devices, huge volumes of data streams have been generated. This study aims to address the concept drift, which is a major challenge in the processing of voluminous data ...
Affan Ahmed Toor, Muhammad Usman, Farah Younas, Alvis Cheuk M. Fong, Sajid Ali Khan, Simon Fong   +5 more
doaj   +1 more source

Further progress in ion back-flow reduction with patterned gaseous hole-multipliers

, 2007
A new idea on electrostatic deviation and capture of back-drifting avalanche-ions in cascaded gaseous hole-multipliers is presented. It involves a flipped reversed-bias Micro-Hole & Strip Plate (F-R-MHSP) element, the strips of which are facing the drift
, A Breskin, A V Lyashenko, A. Buzulutskov ., A. Lyashenko ., D. Mörmann, F D Amaro, J F C A Veloso, J M F Dos Santos, J.F.C.A. Veloso ., R Chechik   +10 more
core   +1 more source

Sentiment Drift Detection and Analysis in Real Time Twitter Data Streams [PDF]

, 2022
E. Susi, A. P. Shanthi
openalex   +1 more source

A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing

Applied Sciences
The semiconductor industry currently lacks a robust, holistic method for detecting parameter drifts in wide-bandgap (WBG) manufacturing, where conventional fault detection and classification (FDC) practices often rely on static thresholds or isolated ...
Chin-Yi Lin, Tzu-Liang (Bill) Tseng, Tsung-Han Tsai   +2 more
doaj   +1 more source

An Integrated Preprocessing and Drift Detection Approach With Adaptive Windowing for Fraud Detection in Payment Systems

IEEE Access
As fraudulent transaction methods evolve rapidly; it becomes progressively more challenging to detect them in payment systems. Static machine learning and rule-based traditional detection methods cannot capture all the dynamic and evolving nature of ...
Hadi M. R. Al Lawati, Anazida Zainal, Bander Ali Saleh Al-Rimy, Mohammad Al-Azawi, Mohamad Nizam Kassim, Sultan Ahmed Almalki, Tami Abdulrahman Alghamdi   +6 more
doaj   +1 more source

Frouros: An open-source Python library for drift detection in machine learning systems

SoftwareX
Frouros is an open-source Python library capable of detecting drift in machine learning systems. It provides a combination of classical and more recent algorithms for drift detection, covering both concept and data drift.
Jaime Céspedes Sisniega, Álvaro López García   +1 more
doaj   +1 more source

PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes. [PDF]

Entropy (Basel), 2022
Hanga KM, Kovalchuk Y, Gaber MM.
europepmc   +1 more source

Adversarial concept drift detection under poisoning attacks for robust data stream mining. [PDF]

Mach Learn, 2022
Korycki Ł, Krawczyk B.
europepmc   +1 more source

The DRIFT Dark Matter Experiments

, 2011
The current status of the DRIFT (Directional Recoil Identification From Tracks) experiment at Boulby Mine is presented, including the latest limits on the WIMP spin-dependent cross-section from 1.5 kg days of running with a mixture of CS2 and CF4 ...
Daw, E., Dorofeev, A., Fox, J. R., Gauvreau, J. -L., Ghag, C., Gold, M., Harmon, L. J., Harton, J. L., Landers, J. M., Lee, E. R., Loomba, D., Miller, E. H., Murphy, A. St. J., Paling, S. M., Phan, N., Pipe, M., Pushkin, K., Robinson, M., Sadler, S. W., Snowden-Ifft, D. P., Spooner, N. J. C., Walker, D., Warner, D.   +22 more
core