Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Vikas Pal"'
Publikováno v:
Cureus.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e0c6e6c4881bda1950c3a187b5ff443
https://doi.org/10.7759/cureus.38045
https://doi.org/10.7759/cureus.38045
Autor:
Sumit Gupta, Dheerendra Kumar sachan, Akshay Kumar Nigam, Vikas Pal, Chinki Bansal, Ruchita Sachan
Publikováno v:
INDIAN JOURNAL OF APPLIED RESEARCH. :26-28
BACKGROUND: Breast, cervical and oral cancers are the leading causes of cancers in India. High mortality with these cancers is due to presentation in the advanced stages. In India, doctors practically cannot treat each and every patient due to a huge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61ef4e78cd7247b005acd96b64c19c1d
https://doi.org/10.36106/ijar/9601067
https://doi.org/10.36106/ijar/9601067
Publikováno v:
INDIAN JOURNAL OF APPLIED RESEARCH. :6-8
Background: Complications due to cancer arise at any stage of treatment. May it be prior, during or after the treatment. Cachexia is one such complication, which is multifactorial and has a debilitating effect. The initial presentation is anorexia, f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a4f661213ca4eb71f89f02c7fa4a5129
https://doi.org/10.36106/ijar/7002901
https://doi.org/10.36106/ijar/7002901
Publikováno v:
IEEE Access, Vol 12, Pp 120612-120623 (2024)
Multiobjective evolutionary algorithms are highly effective in solving multiobjective optimization problems (MOPs). The selection strategy, involving mating and environmental selection, is crucial in shaping these algorithms. However, when applied to
Externí odkaz:
https://doaj.org/article/645e5a042a2a467aa792125aaafb01f0
Publikováno v:
IEEE Access, Vol 12, Pp 49275-49290 (2024)
In recent decades, multi-objective evolutionary algorithms (MOEAs) have been evaluated on artificial test problems with unrealistic characteristics, leading to uncertain conclusions about their efficacy in real-world applications. To address this iss
Externí odkaz:
https://doaj.org/article/0ae8b832d1a243bbb72f6307cbee8a22
Autor:
Yosoeb Shin, Vikas Palakonda, Sangseok Yun, Il-Min Kim, Seon-Gon Kim, Sang-Mi Park, Jae-Mo Kang
Publikováno v:
IEEE Access, Vol 12, Pp 8187-8197 (2024)
Deep learning models learn powerful representational spaces required for handling complex tasks. Recently, data augmentation techniques, region-level, and image-level augmentation have proved effective in significantly improving deep learning models
Externí odkaz:
https://doaj.org/article/58bf71ee5c204a418053071cfbe4ead6
Autor:
Vikas Palakonda, Jae-Mo Kang
Publikováno v:
IEEE Access, Vol 11, Pp 111636-111654 (2023)
Many-objective optimization has recently gained popularity as it poses significant challenges for the existing algorithms. Therefore, numerous optimization algorithms have been developed to handle many-objective optimization in the literature. In add
Externí odkaz:
https://doaj.org/article/24c6f5629b634c5288a25f19e2ae2f2a
Publikováno v:
Mathematics, Vol 11, Iss 18, p 3898 (2023)
Quality control in manufacturing equipment relies heavily on the detection of steel surface defects. Recently, there have been an increasing number of efforts in which object detection techniques have been utilized to achieve promising results in the
Externí odkaz:
https://doaj.org/article/802db54b0f414f729a9a0a48394d2e11
Autor:
Vikas Palakonda, Rammohan Mallipeddi
Publikováno v:
IEEE Access, Vol 8, Pp 82781-82796 (2020)
Multi-objective evolutionary algorithms (MOEAs) have received immense recognition due to their effectiveness and efficiency in tackling multi-objective optimization problems (MOPs). Recently, numerous studies on MOEAs revealed that when handling many
Externí odkaz:
https://doaj.org/article/c27b80d1ac62499ea96ea9dd63306d84
Autor:
Vikas Palakonda, Rammohan Mallipeddi
Publikováno v:
IEEE Access, Vol 5, Pp 11043-11053 (2017)
In Pareto dominance-based multi-objective evolutionary algorithms (PDMOEAs), Pareto dominance fails to provide the essential selection pressure required to drive the search toward convergence in many-objective optimization problems (MaOPs). Recently,
Externí odkaz:
https://doaj.org/article/1a43ca86041649f4a0ffbf1e344e5d07