Open Access
Issue |
Int. J. Simul. Multidisci. Des. Optim.
Volume 16, 2025
|
|
---|---|---|
Article Number | 1 | |
Number of page(s) | 17 | |
DOI | https://doi.org/10.1051/smdo/2024021 | |
Published online | 07 January 2025 |
- G. Çınarer, B.G. Emiroğlu, Classification of brain tumors by machine learning algorithms, in 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), IEEE (2019) pp. 1–4 [Google Scholar]
- A. Omuro, L.M. DeAngelis, Glioblastoma and other malignant gliomas: a clinical review, JAMA 310, 1842–1850 (2013) [Google Scholar]
- M. Weller et al., Glioma, Nat. Rev. Dis. Primers 1, 1–18 (2015) [Google Scholar]
- M.L. Bondy et al., Brain tumor epidemiology: consensus from the brain tumor epidemiology consortium, Cancer 113, 1953–1968 (2008) [CrossRef] [Google Scholar]
- J. Amin, M. Sharif, M. Raza, M. Yasmin, Detection of brain tumor based on features fusion and machine learning, J. Ambient Intell. Humanized Comput. 15, 1–17 (2024) [Google Scholar]
- D.P.R. Chieffo, F. Lino, D. Ferrarese, D. Belella, G.M. Della Pepa, F. Doglietto, Brain tumor at diagnosis: from cognition and behavior to quality of life, Diagnostics 13, 541 (2023) [CrossRef] [Google Scholar]
- G. Katti, S.A. Ara, A. Shireen, Magnetic resonance imaging (MRI) − a review, Int. J. Dental Clin. 3, 65–70 (2011) [Google Scholar]
- T.M. Buzug, Computed tomography, in Springer handbook of medical technology, Springer (2011) pp. 311–342 [Google Scholar]
- P.Y. Wen et al., Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group, J. Clin. Oncol. 28, 1963–1972 (2010) [CrossRef] [Google Scholar]
- Y.P. Singh, D.K. Lobiyal, A comparative analysis and classification of cancerous brain tumors detection based on classical machine learning and deep transfer learning models, Multimedia Tools Appl. 83, 39537–39562 (2024) [Google Scholar]
- D.A. Reardon et al., Immunotherapy advances for glioblastoma, Neuro Oncol. 16, 1441–1458 (2014) [CrossRef] [Google Scholar]
- D. Black et al., Towards machine learning-based quantitative hyperspectral image guidance for brain tumor resection, Commun. Med. 4, 131 (2024) [CrossRef] [Google Scholar]
- S.U.R. Khan, M. Zhao, S. Asif, X. Chen, Hybrid‐NET: a fusion of DenseNet169 and advanced machine learning classifiers for enhanced brain tumor diagnosis, Int. J. Imaging Syst. Technol. 34, e22975 (2024) [CrossRef] [Google Scholar]
- M. Celik, O. Inik, Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification, Expert Syst. Appl. 238, 122159 (2024) [CrossRef] [Google Scholar]
- M. Rivera, S. Norman, R. Sehgal, R. Juthani, Updates on surgical management and advances for brain tumors, Curr. Oncol. Rep. 23, 1–9 (2021) [CrossRef] [Google Scholar]
- C. Turnquist, B.T. Harris, C.C. Harris, Radiation-induced brain injury: current concepts and therapeutic strategies targeting neuroinflammation, Neurooncol. Adv. 2, vdaa057 (2020) [Google Scholar]
- J.L. Martínez-Tlahuel et al., Chemotherapy for brain tumors BT, in Principles of Neuro-Oncology: Brain & Skull Base, edited by A. Monroy-Sosa, S.S. Chakravarthi, J.G. de la Garza-Salazar, A. Meneses Garcia, and A.B. Kassam (Springer International Publishing, Cham, 2021), pp. 357–383 [Google Scholar]
- B. Ganjeifar, F.S. Morshed, Targeted drug delivery in brain tumors-nanochemistry applications and advances, Curr. Topics Med. Chem. (2021) http://dx.doi.org/10.2174/1568026620666201113140258. [Google Scholar]
- J. Blakeley, Drug delivery to brain tumors, Curr. Neurol. Neurosci. Rep. 8, 235–241 (2008) [CrossRef] [Google Scholar]
- M. Aryal, T. Porter, Emerging therapeutic strategies for brain tumors, Neuromolecular Med. 24, 23–34 (2022) [CrossRef] [Google Scholar]
- S. Anantharajan, S. Gunasekaran, T. Subramanian, R. Venkatesh, MRI brain tumor detection using deep learning and machine learning approaches, Measurement: Sens. 31, 101026 (2024) [CrossRef] [Google Scholar]
- M.L. Montoya, N. Kasahara, H. Okada, Introduction to immunotherapy for brain tumor patients: challenges and future perspectives, Neurooncol. Pract. 7, 465–476 (2020) [Google Scholar]
- T.J. Kaufmann et al., Consensus recommendations for a standardized brain tumor imaging protocol for clinical trials in brain metastases, Neuro Oncol. 22, 757–772 (2020) [CrossRef] [Google Scholar]
- P. Domingos, A few useful things to know about machine learning, Commun. ACM 55, 78–87 (2012) [CrossRef] [Google Scholar]
- T. Hastie, R. Tibshirani, J.H. Friedman, J.H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2 (Springer, 2009) [Google Scholar]
- D. Bassir, H. Lodge, H. Chang, J. Majak, G. Chen, Application of artificial intelligence and machine learning for BIM, Int. J. Simulat. Multidiscip. Des. Optim. 14, 5 (2023) [CrossRef] [EDP Sciences] [Google Scholar]
- A. Mosavi, Application of data mining in multiobjective optimization problems, Int. J. Simul. Multidiscip. Des. Optim. 5, A15 (2014) [CrossRef] [EDP Sciences] [Google Scholar]
- Y. Freund, Boosting a weak learning algorithm by majority, Inf. Comput. 121, 256–285 (1995) [CrossRef] [Google Scholar]
- P.S. Efraimidis, P.G. Spirakis, Weighted random sampling with a reservoir, Inf. Process Lett. 97, 181–185 (2006) [CrossRef] [Google Scholar]
- C.E. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning 1 (Springer, 2006) [Google Scholar]
- H. Nickisch, C.E. Rasmussen, Approximations for binary Gaussian process classification, J. Mach. Learn. Res. 9, 2035–2078 (2008) [MathSciNet] [Google Scholar]
- J. Quinonero-Candela, C.E. Rasmussen, A unifying view of sparse approximate Gaussian process regression, J. Mach. Learn. Res. 6, 1939–1959 (2005) [MathSciNet] [Google Scholar]
- T.P. Minka, A family of algorithms for approximate Bayesian inference (Massachusetts Institute of Technology, 2001) [Google Scholar]
- V. Vapnik, Statistical Learning Theory (John Willey & Sons, Inc., New York, 1998) [Google Scholar]
- S. Maldonado, J. Pérez, R. Weber, M. Labbé, Feature selection for support vector machines via mixed integer linear programming, Inf. Sci. (N Y) 279, 163–175 (2014) [CrossRef] [Google Scholar]
- C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines, ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011) [CrossRef] [Google Scholar]
- M. Aydogdu, M. Firat, Estimation of failure rate in water distribution network using fuzzy clustering and LS-SVM methods, Water Resour. Manag. 29, 1575–1590 (2015) [CrossRef] [Google Scholar]
- A. Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (O'Reilly Media, Inc., 2022) [Google Scholar]
- A. Hedenström, M. Rosén, Predator versus prey: on aerial hunting and escape strategies in birds, Behavior. Ecol. 12, 150–156 (2001) [CrossRef] [Google Scholar]
- D. Lentink et al., How swifts control their glide performance with morphing wings, Nature 446, 1082–1085 (2007) [CrossRef] [Google Scholar]
- H.C. Howland, Optimal strategies for predator avoidance: the relative importance of speed and manoeuvrability, J. Theor. Biol. 47, 333–350 (1974) [CrossRef] [Google Scholar]
- M. Shahrouzi, A. Salehi, Design of large-scale structures by an enhanced metaheuristic utilizing opposition-based learning, in 2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), IEEE (2020) pp. 27–31 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.