Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Faramarz Safi-Esfahani"'
Autor:
Sepide Masoudi, Faramarz Safi-Esfahani
Publikováno v:
The Journal of Supercomputing. 78:19188-19227
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2858196ed2a19c5e050eb87735368270
https://doi.org/10.1007/s11227-022-04592-3
https://doi.org/10.1007/s11227-022-04592-3
Publikováno v:
Cybernetics and Systems. 53:679-701
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::203c61a2dac72816ad1f79e783aa5af3
https://doi.org/10.1080/01969722.2022.2042917
https://doi.org/10.1080/01969722.2022.2042917
Many engineering optimization problems can be solved using meta-heuristics. Despite their merits, such algorithms face common challenges of early convergence rate and the imbalance between the exploitation and exploration phases. These algorithms hav
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eab48f4d599dab7e737e6b0ed4502e70
https://doi.org/10.21203/rs.3.rs-2148347/v2
https://doi.org/10.21203/rs.3.rs-2148347/v2
Publikováno v:
The Journal of Supercomputing. 78:1182-1243
The cloud runtime environment is dynamic; therefore, allocating tasks to computing resources might include various scenarios. Metaheuristic algorithms are usually used to choose appropriate scheduling scenarios; however, they suffer from premature co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ecf8c6a354d29a84742e12829ac7fd3
https://doi.org/10.1007/s11227-021-03814-4
https://doi.org/10.1007/s11227-021-03814-4
Publikováno v:
Soft Computing. 24:15979-16004
One of the methods for solving optimization problems is applying metaheuristic algorithms that find near to optimal solutions. Dragonfly algorithm is one of the metaheuristic algorithms which search problem space by the inspiration of hunting and emi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c4bb3acbc13b64d9e8b0d79f52d13102
https://doi.org/10.1007/s00500-020-05340-6
https://doi.org/10.1007/s00500-020-05340-6
Publikováno v:
Neurocomputing. 412:514-542
One recent study in the field of deep learning is extending Artificial Neural Networks (ANNs) by coupling them to external memory resources. Neural Turing Machine (NTM) and Differentiable Neural Computer (DNC) are two counterparts in this field. Rese
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba38a6b779a33ec13a50e7d9d09d4c90
https://doi.org/10.1016/j.neucom.2020.06.018
https://doi.org/10.1016/j.neucom.2020.06.018
Publikováno v:
Neurocomputing. 396:133-152
Deep learning in artificial intelligence looks for a general-purpose computational machine to execute complex algorithms similar to humans’ brain. Neural Turing Machine (NTM) as a tool to realize deep learning approach brings together Turing machin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d71cc8f49e69c9fa070c76a74eda42e2
https://doi.org/10.1016/j.neucom.2018.07.097
https://doi.org/10.1016/j.neucom.2018.07.097
Autor:
Narges Jalaei, Faramarz Safi-Esfahani
Publikováno v:
International Journal of Information Technology. 13:239-250
The live migration of virtual machines among physical machines aims at efficient utilization of resources, load balancing, maintenance, energy management, fault tolerance, sharing resources and mobile computing. There are several methods for the live
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56795d7095b7e4228f7c7db2668c6b5c
https://doi.org/10.1007/s41870-020-00483-z
https://doi.org/10.1007/s41870-020-00483-z
Publikováno v:
The Journal of Supercomputing. 77:1214-1272
Cloud computing maps tasks to resources in a scalable fashion. The scheduling is an NP-hard problem; thus, the scheduler chooses one solution from among many. This is the reason why finding the best optimal solution, especially at a high scale of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aef516cb2f7584ee10a3ef60528c2f4b
https://doi.org/10.1007/s11227-020-03317-8
https://doi.org/10.1007/s11227-020-03317-8
Publikováno v:
Journal of Grid Computing. 18:879-901
MapReduce framework is used for the distribution and parallelization of large-scale data processing. This framework breaks a job into several MapReduce tasks and assigns them to different nodes. A weak performance of a node in executing a task may re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fedc7979457d91755da2b48ee174df94
https://doi.org/10.1007/s10723-020-09514-2
https://doi.org/10.1007/s10723-020-09514-2