Results 271 to 280 of about 131,766 (331)

Fault classification in the architecture of virtual machine using deep learning. [PDF]

Sci Rep
Rawat A, Mishra A, Alsharif H, Alharthi RM, Gupta BB.   +4 more
europepmc   +1 more source

Hybrid deep learning framework for real-time fault detection in squirrel-cage induction motors. [PDF]

PLoS One
Jakaria JM, Sabir J, Rahman MZ, Ali MF.
europepmc   +1 more source

Chiller sensor fault diagnosis based on circle mapping and adaptive t-distribution Runge-Kutta algorithm optimized LSTM. [PDF]

Sci Rep
Wang Y, Liu J, Liu F, Duan X.
europepmc   +1 more source

Integrating machine learning and geomorphic indices to assess tectonic geomorphology in the Karoun River Basin, Zagros, SW Iran. [PDF]

Sci Rep
Khodabakhshnezhad A, Arian M, Pourkermani M, Matkan AA, Charchi A, Kianoush P.   +5 more
europepmc   +1 more source

Enhanced FPGA-based smart power grid simulation using Heun and Piecewise analytic method. [PDF]

Sci Rep
Gul U, Raza Ur Rehman HM, Gul MJ, Mezquita GM, Barrera AEP, Ashraf I.   +5 more
europepmc   +1 more source

Rand-Index Target Projective Gradient Deep Belief Network for Software Fault Prediction

T.Shathish Kumar
openalex   +2 more sources

Multi release software reliability modelling incorporating fault generation in detection process and fault dependency with change point in correction process. [PDF]

Sci Rep
Pradhan SK, Kumar A, Kumar V.
europepmc   +1 more source

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Software fault prediction tool

Proceedings of the 19th international symposium on Software testing and analysis, 2010
We have developed an interactive tool that predicts fault likelihood for the individual files of successive releases of large, long-lived, multi-developer software systems. Predictions are the result of a two-stage process: first, the extraction of current and historical properties of the system, and second, application of a negative binomial ...
Thomas J. Ostrand, Elaine J. Weyuker
openaire   +1 more source

Predicting and Classifying Software Faults

Proceedings of the 2019 7th International Conference on Computer and Communications Management, 2019
In the field of software engineering, the detection of fault in the software has become a major topic to explore. With the help of data mining and machine learning approaches, this paper aims to denote whether a software is fault prone or not. In order to accomplish that this paper gives importance to compare between different machine learning ...
Shamse Tasnim Cynthia, Shamim H. Ripon
openaire   +1 more source