Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Mohammad H. Nadimi"'
Publikováno v:
Remote Sensing, Vol 15, Iss 13, p 3374 (2023)
In intelligent traffic control systems, the features extracted by loop detectors are insufficient to accurately impute missing data. Most of the existing imputation methods use only these extracted features, which leads to the construction of data mo
Externí odkaz:
https://doaj.org/article/453689ac07a0474b955ed698af57ded2
Publikováno v:
Mathematics, Vol 11, Iss 4, p 862 (2023)
Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is widely used to solve different optimization problems. However, MFO and its variants inherently suffer from poor population diversity, leading to premature con
Externí odkaz:
https://doaj.org/article/99f0496e4bc04bc79e6e0b9d4a5d0701
Publikováno v:
Applied Sciences, Vol 13, Iss 1, p 564 (2022)
Feature selection is an NP-hard problem to remove irrelevant and redundant features with no predictive information to increase the performance of machine learning algorithms. Many wrapper-based methods using metaheuristic algorithms have been propose
Externí odkaz:
https://doaj.org/article/bf249f810ad149b3978449fb3b4cb10b
Publikováno v:
Mathematics, Vol 10, Iss 15, p 2770 (2022)
Many metaheuristic approaches have been developed to select effective features from different medical datasets in a feasible time. However, most of them cannot scale well to large medical datasets, where they fail to maximize the classification accur
Externí odkaz:
https://doaj.org/article/1b9a23ec6f054bf5bb7089615c25b621
Publikováno v:
Mathematics, Vol 10, Iss 11, p 1929 (2022)
Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance o
Externí odkaz:
https://doaj.org/article/53ee86450ce548dab94a629275452e05
Publikováno v:
Entropy, Vol 23, Iss 12, p 1637 (2021)
Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergen
Externí odkaz:
https://doaj.org/article/8439cb372f6246289f872703d44c64ef
Autor:
Mohammad H. Nadimi-Shahraki, Shokooh Taghian, Seyedali Mirjalili, Ahmed A. Ewees, Laith Abualigah, Mohamed Abd Elaziz
Publikováno v:
Symmetry, Vol 13, Iss 12, p 2388 (2021)
The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its moveme
Externí odkaz:
https://doaj.org/article/6471229443d04357b301ead0534e118a
Autor:
Mohamed Abd Elaziz, Laith Abualigah, Dalia Yousri, Diego Oliva, Mohammed A. A. Al-Qaness, Mohammad H. Nadimi-Shahraki, Ahmed A. Ewees, Songfeng Lu, Rehab Ali Ibrahim
Publikováno v:
Mathematics, Vol 9, Iss 21, p 2786 (2021)
Feature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays a critical role in different real-world applications since it aims to determine the relevant features and remove other ones. This proce
Externí odkaz:
https://doaj.org/article/4ce1691c716d49c5b5c383a07d1c4110
Publikováno v:
Algorithms, Vol 14, Iss 11, p 314 (2021)
In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapt
Externí odkaz:
https://doaj.org/article/a59b7ef55a4b4be3b12f7767e62c2959
Autor:
Mohammad H. Nadimi-Shahraki, Mahdis Banaie-Dezfouli, Hoda Zamani, Shokooh Taghian, Seyedali Mirjalili
Publikováno v:
Computers, Vol 10, Iss 11, p 136 (2021)
Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle
Externí odkaz:
https://doaj.org/article/b114659a5c654ecdb3646531bfc5b459