Zobrazeno 1 - 10
of 35
pro vyhledávání: '"H. M. Dipu Kabir"'
Autor:
Subrota Kumar Mondal, Chengwei Wang, Yijun Chen, Yuning Cheng, Yanbo Huang, Hong-Ning Dai, H. M. Dipu Kabir
Publikováno v:
Applied Sciences, Vol 14, Iss 15, p 6848 (2024)
An English-Bengali machine translation (MT) application can convert an English text into a corresponding Bengali translation. To build a better model for this task, we can optimize English-Bengali MT. MT for languages with rich resources, like Englis
Externí odkaz:
https://doaj.org/article/1e3c4b2d443f48d0814e02bedafc5164
Autor:
Roohallah Alizadehsani, Mohamad Roshanzamir, Navid Hoseini Izadi, Raffaele Gravina, H. M. Dipu Kabir, Darius Nahavandi, Hamid Alinejad-Rokny, Abbas Khosravi, U. Rajendra Acharya, Saeid Nahavandi, Giancarlo Fortino
Publikováno v:
Sensors, Vol 23, Iss 3, p 1466 (2023)
Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, sw
Externí odkaz:
https://doaj.org/article/72388fc47be543fe8476888138fbc7bb
Publikováno v:
IEEE Access, Vol 6, Pp 36218-36234 (2018)
Uncertainty quantification plays a critical role in the process of decision making and optimization in many fields of science and engineering. The field has gained an overwhelming attention among researchers in recent years resulting in an arsenal of
Externí odkaz:
https://doaj.org/article/29a5bd4cd75a4cc9bd22196a49b1336a
Synthetic Datasets for Numeric Uncertainty Quantification: Proposing Datasets for Future Researchers
Autor:
H M Dipu Kabir, Moloud Abdar, Abbas Khosravi, Darius Nahavandi, Subrota Kumar Mondal, Sadia Khanam, Shady Mohamed, Dipti Srinivasan, Saeid Nahavandi, Ponnuthurai Nagaratnam Suganthan
Publikováno v:
IEEE Systems, Man, and Cybernetics Magazine. 9:39-48
Publikováno v:
Artificial Intelligence Review.
Publikováno v:
IEEE Transactions on Emerging Topics in Computational Intelligence. 5:768-779
Traditional uncertainty quantification (UQ) algorithms are mostly developed for a fixed time (term), such as hourly or daily predictions. Although a few UQ techniques can compute UQ over time-range, their quantified uncertainty is usually ever-increa
Publikováno v:
IEEE Transactions on Emerging Topics in Computational Intelligence. 5:595-606
Currently available uncertainty quantification (UQ) neural networks (NNs) are trained through the statistical error minimization. Therefore, NNs perform poorly for critical input patterns. Some input patterns have lower coverage probabilities than ot
Kubernetes in IT administration and serverless computing: An empirical study and research challenges
Publikováno v:
The Journal of Supercomputing. 78:2937-2987
Today’s industry has gradually realized the importance of lifting efficiency and saving costs during the life-cycle of an application. In particular, we see that most of the cloud-based applications and services often consist of hundreds of micro-s
Autor:
Abbas Khosravi, H M Dipu Kabir, Saeid Nahavandi, Rajkumar Buyya, Mustaneer Rahman, Subrota K. Mondal
Publikováno v:
ACM Computing Surveys. 54:1-30
The rapid growth of the cloud industry has increased challenges in the proper governance of the cloud infrastructure. Many intelligent systems have been developing, considering uncertainties in the cloud. Intelligent approaches with the consideration
Autor:
Abbas Khosravi, David G. Carmichael, Farnad Nasirzadeh, Saeid Nahavandi, Mahmood Akbari, H M Dipu Kabir
Publikováno v:
Engineering, Construction and Architectural Management. 27:2335-2351
PurposeThis study aims to propose the adoption of artificial neural network (ANN)-based prediction intervals (PIs) to give more reliable prediction of labour productivity using historical data.Design/methodology/approachUsing the proposed PI method,