Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Ali Ouni"'
Publikováno v:
Energies, Vol 15, Iss 5, p 1741 (2022)
There are two primary ways to save energy within a building: (1) through improving building engineering structures and adopting efficient appliance ownership, and (2) through changing occupants’ energy-consuming behaviors. Unfortunately the second
Externí odkaz:
https://doaj.org/article/64163cc450964a6b9292ae76e2bf9e99
Autor:
Priyadarshni Suresh Sagar, Eman Abdulah AlOmar, Mohamed Wiem Mkaouer, Ali Ouni, Christian D. Newman
Publikováno v:
Algorithms, Vol 14, Iss 10, p 289 (2021)
Understanding how developers refactor their code is critical to support the design improvement process of software. This paper investigates to what extent code metrics are good indicators for predicting refactoring activity in the source code. In ord
Externí odkaz:
https://doaj.org/article/5dc933a72ecd424f8d1bb0abe2db482d
Publikováno v:
Sensors, Vol 21, Iss 7, p 2302 (2021)
Recent research works on intelligent traffic signal control (TSC) have been mainly focused on leveraging deep reinforcement learning (DRL) due to its proven capability and performance. DRL-based traffic signal control frameworks belong to either disc
Externí odkaz:
https://doaj.org/article/59001efee2474721b816b4040517fbf1
Publikováno v:
Energies, Vol 13, Iss 2, p 391 (2020)
Short term electric load forecasting plays a crucial role for utility companies, as it allows for the efficient operation and management of power grid networks, optimal balancing between production and demand, as well as reduced production costs. As
Externí odkaz:
https://doaj.org/article/1469d492a45c415c8614de55b90b85f7
Publikováno v:
Energies, Vol 12, Iss 1, p 149 (2019)
Time series analysis using long short term memory (LSTM) deep learning is a very attractive strategy to achieve accurate electric load forecasting. Although it outperforms most machine learning approaches, the LSTM forecasting model still reveals a l
Externí odkaz:
https://doaj.org/article/713d4388c99b4285973b0e37af7c44f9
Publikováno v:
Energies, Vol 11, Iss 7, p 1636 (2018)
Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and select
Externí odkaz:
https://doaj.org/article/ad514c7fb4344cfc9bf90f8ddf302d07
Publikováno v:
IEEE Transactions on Software Engineering. 48:4873-4891
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8e076afd5e652caecd050a0b68ea2514
https://doi.org/10.1109/tse.2021.3129165
https://doi.org/10.1109/tse.2021.3129165
Publikováno v:
Empirical Software Engineering. 28
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4667f9bb6845a292f28b69a45fec1e44
https://doi.org/10.1007/s10664-023-10300-3
https://doi.org/10.1007/s10664-023-10300-3
Autor:
Eman Abdullah Alomar, Tianjia Wang, Vaibhavi Raut, Mohamed Wiem Mkaouer, Christian Newman, Ali Ouni
Publikováno v:
Innovations in Systems and Software Engineering. 18:105-135
Refactoring is the de-facto practice to optimize software health. While several studies propose refactoring strategies to optimize software design through applying design patterns and removing design defects, little is known about how developers actu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6f377e343f5d41fb434d4f970cf00d6
https://doi.org/10.1007/s11334-021-00422-6
https://doi.org/10.1007/s11334-021-00422-6
Autor:
Eman Abdullah AlOmar, Anton Ivanov, Zarina Kurbatova, Yaroslav Golubev, Mohamed Wiem Mkaouer, Ali Ouni, Timofey Bryksin, Le Nguyen, Amit Kini, Aditya Thakur
Refactoring is a critical task in software maintenance, and is usually performed to enforce better design and coding practices, while coping with design defects. The Extract Method refactoring is widely used for merging duplicate code fragments into
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4f35600e504a2a0156b600d01e935d60
