Results 51 to 60 of about 796 (174)

A Detailed Study of NHPP Software Reliability Models (Invited Paper)

Journal of Software, 2012
Software reliability deals with the probability that software will not cause the failure of a system for a specified time under a specified condition. The probability is a function of the inputs to and use of the system as well as a function of the existing faults in the software.
Richard Lai 0001, Mohit Garg 0001
openaire   +1 more source

Analysis of Failures, Downtimes and Reliability of Wind Turbines From SCADA Data

Wind Energy, Volume 28, Issue 12, December 2025.
ABSTRACT With the growing demand for safe, efficient and reliable sustainable energy sources, wind energy has witnessed rapid growth worldwide in recent years. Reliability is critical to the operational performance of the wind turbines. Reliability analysis allows for the critical subassemblies to be identified and better maintenance strategies to ...
Cuong D. Dao, Ibrahim Husayn, Phong B. Dao   +2 more
wiley   +1 more source

An Imperfect Debugging Littlewood Non-homogeneous Poisson Process Model [PDF]

The Egyptian Statistical Journal, 2006
The large literature on software reliability assessment and prediction essentially assumes that once a failure occurs the fault causing the failure is immediately removed with certainty. i.e., perfect debugging. This assumption may not be realistic.
Abdalla Abdel-Ghaly, May Barghout
doaj   +1 more source

Flexible movement kernel estimation in habitat selection analyses with generalized additive models

Methods in Ecology and Evolution, Volume 16, Issue 8, Page 1796-1807, August 2025.
Abstract Habitat selection analysis includes resource selection analysis (RSA) and step selection analysis (SSA). These frameworks are used in order to understand space use of animals. Particularly, the SSA approach specifies the area available to the animal through a movement kernel.
Rafael Arce Guillen, Jennifer Pohle, Florian Jeltsch, Manuel Roeleke, Björn Reineking, Natasha Klappstein, Ulrike Schlägel   +6 more
wiley   +1 more source

A finite failure software reliability model using extended log-logistic distribution

AIP Advances
Software reliability engineering has been developed to examine software systems over the years. Non-homogeneous Poisson process (NHPP) models are widely employed for analyzing software reliability data, as they have the ability to describe the expected ...
Haneen Ahmad Aseri, Lutfiah Ismail Al-Turk, Saman Hanif Shahbaz   +2 more
doaj   +1 more source

Prediction and Comparative Analysis of Software Reliability Model Based on NHPP and Deep Learning

Applied Sciences, 2023
Over time, software has become increasingly important in various fields. If the current software is more dependent than in the past and is broken owing to large and small issues, such as coding and system errors, it is expected to cause significant ...
Youn Su Kim, Kwang Yoon Song, In Hong Chang   +2 more
doaj   +1 more source

Next Generation Models for Subsequent Sports Injuries

Applied Stochastic Models in Business and Industry, Volume 41, Issue 4, July/August 2025.
ABSTRACT This paper contributes a modelling approach for prediction of subsequent injury risk using longitudinal wearable data in the sports industry. Understanding subsequent injury events in sport, namely injuries that follow an initial injury, will assist athletes, support staff, and coaches to quantify the risk of future injury and better inform ...
Paul Pao‐Yen Wu, Yu Yi Yu, Liam A. Toohey, Michael Drew, Scott A. Sisson, Clara Grazian, Kerrie Mengersen   +6 more
wiley   +1 more source

Performance Evaluation of a Cloud Datacenter Using CPU Utilization Data

Mathematics, 2023
Cloud computing and its associated virtualization have already been the most vital architectures in the current computer system design. Due to the popularity and progress of cloud computing in different organizations, performance evaluation of cloud ...
Chen Li, Junjun Zheng, Hiroyuki Okamura, Tadashi Dohi   +3 more
doaj   +1 more source

Is There a Future for Stochastic Modeling in Business and Industry in the Era of Machine Learning and Artificial Intelligence?

Applied Stochastic Models in Business and Industry, Volume 41, Issue 2, March/April 2025.
ABSTRACT The paper arises from the experience of Applied Stochastic Models in Business and Industry which has seen, over the years, more and more contributions related to Machine Learning rather than to what was intended as a stochastic model. The very notion of a stochastic model (e.g., a Gaussian process or a Dynamic Linear Model) can be subject to ...
Fabrizio Ruggeri, David Banks, William S. Cleveland, Nicholas I. Fisher, Marcos Escobar‐Anel, Paolo Giudici, Emanuela Raffinetti, Roger W. Hoerl, Dennis K. J. Lin, Ron S. Kenett, Wai Keung Li, Philip L. H. Yu, Jean‐Michel Poggi, Marco S. Reis, Gilbert Saporta, Piercesare Secchi, Rituparna Sen, Ansgar Steland, Zhanpan Zhang   +18 more
wiley   +1 more source

Hybrid Firefly–Bayesian Neural Network Estimation for Reliable Modeling of the Inverse Gompertz–Gumbel Family and NHPP Systems: Properties and Comparative Evaluation

Journal of Statistical Theory and Applications (JSTA)
The idea commonly accepted is that probability distributions play a major role in reliability modeling, whereas classical models are often not flexible enough to accommodate the complex behavior of life data.
Saad Abbas Abed, Ali Akgül, Murad Khan Hassani, Zaid Amer Mohammed, Adel S. Hussain, Mohammad A. Tashtoush   +5 more
doaj   +1 more source