Have a personal or library account? Click to login

Revolutionizing cancer care with machine learning: a comprehensive review

Open Access
|Jul 2025

References

  1. Holford NH, Kimko HC, Monteleone JP, Peck CC, “Simulation of clinical trials”, Annu Rev Pharmacol Toxicol, Vol. 40, No. 1, pp. 209–234, 2000. http://dx.doi.org/10.1146/annurev.pharmtox.40.1.209
  2. Kuo I-Y, Hsieh C-H, Kuo W-T, Chang C-P, Wang Y-C, “Recent advances in conventional and unconventional vesicular secretion pathways in the tumor microenvironment”, J Biomed Sci., Vol. 29, Article No. 56, 2022. http://dx.doi.org/10.1186/s12929-022-00837-8
  3. Neppelenbroek KH, Campanha NH, Spolidorio DMP, Spolidorio LC, Seó RS, Pavarina AC, “Molecular fingerprinting methods for the discrimination between C. albicans and C. dubliniensis”, Oral Dis., Vol. 12, No. 3, pp. 242–253, 2006. http://dx.doi.org/10.1111/j.1601-0825.2005.01189.x
  4. Miró O, Sánchez M, Espinosa G, Coll-Vinent B, Bragulat E, Millá J, “Analysis of patient flow in the emergency department and the effect of an extensive reorganisation”, Emerg Med J., Vol. 20, No. 2, pp. 143–148, 2003. http://dx.doi.org/10.1136/emj.20.2.143
  5. Gamberi T, Chiappetta G, Fiaschi T, Modesti A, Sorbi F, Magherini F, “Upgrade of an old drug: Auranofin in innovative cancer therapies to overcome drug resistance and to increase drug effectiveness”, Med Res Rev., Vol. 42, No. 3, pp. 1111–1146, 2022. http://dx.doi.org/10.1002/med.21872
  6. Luca AR, Ursuleanu TF, Gheorghe L, Grigorovici R, Iancu S, Hlusneac M, et al., “Impact of quality, type and volume of data used by deep learning models in the analysis of medical images”, Inform Med Unlocked, Vol. 29, Article No. 100911, 2022. http://dx.doi.org/10.1016/j.imu.2022.100911
  7. Nalini T, Revathi S, “An enhanced clustering algorithm implemented on biological data in data mining”, International Journal of Pharma and Bio Sciences, Vol. 4, No. 2, pp. 1281 – 1286, 2013.
  8. Kaur I, Doja MN, Ahmad T, “Data mining and machine learning in cancer survival research: An overview and future recommendations”, J Biomed Inform, Vol. 128, Article No. 104026, 2022. http://dx.doi.org/10.1016/j.jbi.2022.104026
  9. Napolitano G, Fox C, Middleton R, Connolly D, “Pattern-based information extraction from pathology reports for cancer registration”, Cancer Causes Control, Vol. 21, No. 11, pp. 1887–1894, 2010. http://dx.doi.org/10.1007/s10552-010-9616-4
  10. Peng C, Cheng J, Cheng Q, “A supervised learning model for high-dimensional and large-scale data”, ACM Transactions on Intelligent System Technology, Vol. 8, No. 2, pp. 1–23, 2017. http://dx.doi.org/10.1145/2972957
  11. Parikh RB, Manz CR, Nelson MN, Evans CN, Regli SH, Connor O, et al., “Clinician perspectives on machine learning prognostic algorithms in the routine care of patients with cancer: a qualitative study”, Supportive Care in Cancer, Vol. 30, pp. 4363–4372, 2022.
  12. Mak K-K, Pichika MR, “Artificial intelligence in drug development: present status and future prospects”, Drug Discovery Today, Vol. 24, No. 3, pp. 773–780, 2019. http://dx.doi.org/10.1016/j.drudis.2018.11.014
  13. Pun FW, Ozerov IV, Zhavoronkov A, “AI-powered therapeutic target discovery”, Trends Pharmacol Sci., Vol. 44, No. 9, pp. 561–572, 2023. http://dx.doi.org/10.1016/j.tips.2023.06.010
  14. Stenzinger A, Alber M, Allgäuer M, Jurmeister P, Bockmayr M, Budczies J, et al., “Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling”, Semin Cancer Biology, Vol. 84, pp. 129–143, 2022. http://dx.doi.org/10.1016/j.semcancer.2021.02.011
  15. Lee WW, Alkureishi ML, “The impact of EMRs on communication within the doctor-patient relationship”, In: Papadakos, P., Bertman, S. (eds) Distracted Doctoring. Springer, Cham., pp. 101–120, 2017. https://doi.org/10.1007/978-3-319-48707-6_9.
  16. Khorram-Manesh A, Dulebenets MA, Goniewicz K, “Implementing public health strategies-the need for educational initiatives: A systematic review”, Int J Environ Res Public Health, Vol. 18, No. 11, Article No. 5888, 2021. http://dx.doi.org/10.3390/ijerph18115888
  17. Drake CG, “Combination immunotherapy approaches”, Ann Oncol., Vol. 23, Suppl. 8, pp. 41–46, 2012. http://dx.doi.org/10.1093/annonc/mds262
  18. Watanabe N, Woo SL-Y, Papageorgiou C, Celechovsky C, Takai S, “Fate of donor bone marrow cells in medial collateral ligament after simulated autologous transplantation”, Microsc Res Tech, Vol. 58, No. 1, pp. 39–44, 2002. http://dx.doi.org/10.1002/jemt.10115
  19. Aldwin CM, Spiro A, Levenson MR, Cupertino AP, “Longitudinal findings from the Normative Aging Study: III. Personality, individual health trajectories, and mortality”, Psychol Aging, Vol. 16, No. 3, pp. 450–465, 2001. http://dx.doi.org/10.1037//0882-7974.16.3.450
  20. Ktistakis IP, Goodman G, Britzolaki A, “Advances in Assistive Technologies: Selected Papers in Honour of. Bourbakis”, Vol. 3, pp. 11–31, 2021. https://doi.org/10.1007/978-3-030-87132-1
  21. Lee KY, Kim J, “Artificial intelligence technology trends and IBM Watson references in the medical field”, Korean Med Educ Rev, Vol. 18, No. 2, pp. 51–57, 2016. http://dx.doi.org/10.17496/kmer.2016.18.2.51
  22. Ratti E, Graves M., “Explainable machine learning practices: opening another black box for reliable medical AI”, AI and Ethics, Vol. 2, pp. 801–814, 2022. https://doi.org/10.1007/s43681-022-00141-z
  23. Boyd LA, Earnhardt RC, Dunn JT, Frierson HF, Hanks JB, “Preoperative evaluation and predictive value of fine-needle aspiration and frozen section of thyroid nodules” J Am Coll Surg., Vol. 187, No. 5, pp. 494–502, 1998. http://dx.doi.org/10.1016/s1072-7515(98)00221-x
  24. Mahmood AA, Murgod R, Swarup Badapanda S, “Artificial Intelligence in Oncology: Present Potential, Prospective Prospects, and Ethical Reviews”, International Journal of Trends in OncoScience, pp. 37–45, 2024.
  25. Zhou H, Baloch ZW, Nayar R, Bizzarro T, Fadda G, Adhikari-Guragain D, et al., “Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP): Implications for the risk of malignancy (ROM) in the Bethesda System for Reporting Thyroid Cytopathology (TBSRTC)”, Cancer Cytopathol., Vol. 126, No. 1, pp. 20–26, 2018. http://dx.doi.org/10.1002/cncy.21926
  26. Elliott Range DD, Dov D, Kovalsky SZ, Henao R, Carin L, Cohen J, “Application of a machine learning algorithm to predict malignancy in thyroid cytopathology: Machine learning and thyroid cytopathology”, Cancer Cytopathology, Vol. 128, No. 4, pp. 287–295, 2020. http://dx.doi.org/10.1002/cncy.22238
  27. Balentine CJ, Domingo RP, Patel R, Laucirica R, Suliburk JW, “Thyroid lobectomy for indeterminate FNA: not without consequences”, J Surg Res., Vol. 184, No. 1, pp. 189–192, 2013. http://dx.doi.org/10.1016/j.jss.2013.05.076
  28. Park Y, Heider D, Hauschild A-C, “Integrative analysis of next-generation sequencing for next-generation cancer research toward artificial intelligence”, Cancers (Basel), Vol. 13, No. 13, Article No. 3148, 2021. http://dx.doi.org/10.3390/cancers13133148
  29. Tripathi R, Sharma P, Chakraborty P, Varadwaj PK, “Next-generation sequencing revolution through big data analytics”, Front Life Sci., Vol. 9, No. 2, pp. 119–49, 2016. http://dx.doi.org/10.1080/21553769.2016.1178180
  30. Biswas N, Chakrabarti S, “Artificial intelligence (AI)-based systems biology approaches in multi-omics data analysis of cancer”, Front Oncology, Vol. 10, Article No. 588221, 2020. http://dx.doi.org/10.3389/fonc.2020.588221
  31. Perelmuter VM, Tashireva LA, Manskikh VN, Denisov EV, Savelieva OE, Kaygorodova EV, et al., “Heterogeneity and plasticity of immune inflammatory responses in the tumor microenvironment: Their role in the antitumor effect and tumor aggressiveness”, Biology Bull Rev., Vol. 8, No. 5, pp. 431–448, 2018. http://dx.doi.org/10.1134/s2079086418050055
  32. Rodellar J, Alférez S, Acevedo A, Molina A, Merino A, “Image processing and machine learning in the morphological analysis of blood cells”, Int J. Lab Hematology, Vol. 40, Suppl. 1, pp. 46–53, 2018. http://dx.doi.org/10.1111/ijlh.12818
  33. Kini SR, Miller JM, Hamburger JI, Smith-Purslow MJ, “Cytopathology of follicular lesions of the thyroid gland”, Diagn Cytopathology, Vol. 1, No. 2, pp. 123–132, 1985. http://dx.doi.org/10.1002/dc.2840010208
  34. Saini A, Breen I, Pershad Y, Naidu S, Knuttinen MG, Alzubaidi S, et al., “Radiogenomics and radiomics in liver cancers”, Diagnostics (Basel), Vol. 9, No. 1, pp. 1–23, 2018. http://dx.doi.org/10.3390/diagnostics9010004
  35. Jussupow E, Spohrer K, Heinzl A, Gawlitza J, “Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence”, Inf Syst Res., Vol. 32, No. 3, pp. 713–735, 2021. http://dx.doi.org/10.1287/isre.2020.0980
  36. Liang M, Ting Y, Fu H, “Estimating individualized optimal combination therapies through outcome weighted deep learning algorithms”, Statistics in Medicine, Vol. 37, No. 27, pp. 3869–3886, 2018. http://arxiv.org/abs/1804.05378
  37. Kumari M, Benzeval M, “Collecting biomarker data in longitudinal surveys”, Advances in Longitudinal Survey Methodology, pp. 26–46, 2021.
  38. Mahmood AA, Jha AM, Manivannan K, “Precision Medicine: Personalizing The Fight Against Cancer”, International Journal of Trends in OncoScience., pp. 10–18, 2024. DOI: https://doi.org/10.22376/ijtos.2023.2.1.10-18
  39. Dia AK, Ebrahimpour L, Yolchuyeva S, Tonneau M, Lamaze FC, Orain M, et al., “The Cross-Scale Association between Pathomics and Radiomics Features in Immunotherapy-Treated NSCLC Patients: A Preliminary Study”, Cancers, Vol. 16, No. 2, pp. 1–24, 2024. https://doi.org/10.3390/cancers16020348
  40. Huang C, Clayton EA, Matyunina LV, McDonald LD, Benigno BB, Vannberg F, et al., “Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy”, Scientific Reports, Vol. 8, No. 1, Article No. 16444., 2018. http://dx.doi.org/10.1038/s41598-018-34753-5
  41. Hong R, Liu W, DeLair D, Razavian N, Fenyö D, “Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models”, Cell Rep Med., Vol. 2, No. 9, Article No. 100400, 2021. http://dx.doi.org/10.1016/j.xcrm.2021.100400
  42. Wolfgruber TK, Presting GG, “JunctionViewer: customizable annotation software for repeat-rich genomic regions”, BMC Bioinformatics, Vol. 11, No. 1, pp. 1–23, 2010. http://dx.doi.org/10.1186/1471-2105-11-23
  43. Strug J, Strug B, “Machine learning approach in mutation testing”, In: Testing Software and Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 200–214, 2012.
  44. Zhang H, Ji J, Liu Z, Lu H, Qian C, Wei C, et al., “Artificial intelligence for the diagnosis of clinically significant prostate cancer based on multimodal data: a multicenter study”, BMC Med., Vol. 21, No. 1, Article No. 270, 2023. http://dx.doi.org/10.1186/s12916-023-02964-x
  45. Corrales-Rodriguez L, Soulières D, Weng X, Tehfe M, Florescu M, Blais N, “Mutations in NSCLC and their link with lung cancer-associated thrombosis: a case-control study”, Thromb Res., Vol. 133, No. 1, pp. 48–51, 2014. http://dx.doi.org/10.1016/j.thromres.2013.10.042
  46. Schweitzer N, Vogel A, “Systemic therapy of cholangiocarcinoma: From chemotherapy to targeted therapies”, Best Pract Res Clin Gastroenterol, Vol. 29, No. 2, pp. 345–353, 2015. http://dx.doi.org/10.1016/j.bpg.2015.01.002
  47. Yardley-Jones A, Anderson D, Parke DV, “The toxicity of benzene and its metabolism and molecular pathology in human risk assessment”, Br J Ind Med., Vol. 48, No. 7, pp. 437–444, 1991. http://dx.doi.org/10.1136/oem.48.7.437
  48. Fredriksson NJ, Ny L, Nilsson JA, Larsson E, “Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types”, Nat Genet., Vol. 46, No. 12, pp. 1258–1263, 2014. http://dx.doi.org/10.1038/ng.3141
  49. Gruver AM, Lu H, Zhao X, Fulford AD, Soper MD, Ballard D, et al., “Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified Marsh score and dietary intervention response”, Diagn Pathology, Vol. 18, No. 1, Article No. 122, 2023. doi: 10.1186/s13000-023-01412-x
  50. Atkins D, Makridis CA, Alterovitz G, Ramoni R, Clancy C, “Developing and implementing predictive models in a learning healthcare system: Traditional and artificial intelligence approaches in the Veterans Health Administration”, Annu Rev Biomed Data Sci., Vol. 5, No. 1, pp. 393–413, 2022. http://dx.doi.org/10.1146/annurev-biodatasci-122220-110053
  51. Zhang S, Han P, Wu C, “Calibration techniques encompassing survey sampling, missing data analysis and causal inference”, Int Stat Rev., Vol. 91, No. 2, pp. 165–92, 2023. http://dx.doi.org/10.1111/insr.12518
  52. Ali WM, Alhumaidi MS, “Artificial Intelligence for Cancer Diagnosis & Radiology”, International Journal of Trends in OncoScience, Vol. 13–18, 2023.
  53. Pei Q, Luo Y, Chen Y, Li J, Xie D, Ye T, “Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis”, Clinical Chemistry and Laboratory Medicine (CCLM), Vol. 60, pp. 1974–1983, 2022.
  54. Ye W, Luo C, Liu F, Liu Z, Chen F, “CD96 correlates with immune infiltration and impacts patient prognosis: A pan-cancer analysis”, Front Oncol., Vol. 11, Article No. 634617, 2021. http://dx.doi.org/10.3389/fonc.2021.634617
  55. Wulfkuhle JD, Paweletz CP, Steeg PS, Petricoin EF, Liotta L., “Proteomic approaches to the diagnosis, treatment, and monitoring of cancer. InNew Trends in Cancer for the 21st Century”, In: Proceedings of the International Symposium on Cancer: New Trends in Cancer for the 21st Century. Valencia, Spain; US: Springer, pp. 59–68, 2002.
  56. Picard M, Scott-Boyer M-P, Bodein A, Périn O, Droit A, “Integration strategies of multi-omics data for machine learning analysis”, Comput Struct Biotechnol J., Vol. 19, pp. 3735–3746, 2021. http://dx.doi.org/10.1016/j.csbj.2021.06.030
  57. Al-Tashi Q, Saad MB, Muneer A, Qureshi R, Mirjalili S, Sheshadri A, et al., “Machine learning models for the identification of prognostic and predictive cancer biomarkers: A systematic review”, Int J Mol Sci., Vol. 24, No. 9, Article No. 7781, pp. 1–42, 2023. http://dx.doi.org/10.3390/ijms24097781
  58. Dewey FE, Pan S, Wheeler MT, Quake SR, Ashley EA, “DNA sequencing: clinical applications of new DNA sequencing technologies”, Circulation, Vol. 125, No. 7, pp. 931–944, 2012. http://dx.doi.org/10.1161/CIRCULATIONAHA.110.972828
  59. Klein CA, Hölzel D, “Systemic cancer progression and tumor dormancy: mathematical models meet single cell genomics”, Cell Cycle., Vol. 5, No. 16, pp. 1788–1798, 2006. http://dx.doi.org/10.4161/cc.5.16.3097
  60. Kasula BY, “Harnessing Machine Learning for Personalized Patient Care”, Transactions on Latest Trends in Artificial Intelligence, Vol. 4, No. 4, pp. 1–9, 2023.
  61. Thiyagarajan S, Chakravarthy T, Arivoli PV, “Diagnosing Breast Cancer with Machine Learning Algorithms”, International Journal of Pharmaceutical Research, Vol. 23, pp. 1–13, 2020.
  62. Quinlan AR, “BEDTools: the Swiss-army tool for genome feature analysis”, Current protocols in bioinformatics, Vol. 47, pp. 11–22, 2014. https://doi.org/10.1002/0471250953.bi1112s47
  63. Singh Y, Bhatia PK, Sangwan O, “A review of studies on machine learning techniques”, International Journal of Computer Science and Security, Vol. 1, No. 1, pp. 70–84, 2007. https://www.cscjournals.org/manuscript/Journals/IJCSS/Volume1/Issue1/IJCSS-7.pdf
  64. Almomani F., “Prediction the performance of multistage moving bed biological process using artificial neural network (ANN)”, Sci Total Environ., Vol. 744, Article No. 140854, 2020. http://dx.doi.org/10.1016/j.scitotenv.2020.140854
  65. Mahmoud AS, Alkhenizan A, Shafiq M, Alsoghayer S, “The impact of the implementation of a clinical decision support system on the quality of healthcare services in a primary care setting”, J Family Med Prim Care, Vol. 9, No. 12, pp. 6078–6084, 2020. http://dx.doi.org/10.4103/jfmpc.jfmpc_1728_20
  66. Anderson G, Horvath J, “The growing burden of chronic disease in America”, Public Health Rep., Vol. 119, No. 3, pp. 263–270, 2004. http://dx.doi.org/10.1016/j.phr.2004.04.005
  67. Lenouvel E, Novak L, Biedermann A, Kressig RW, Klöppel S, “Preventive treatment options for fear of falling within the Swiss healthcare system: A position paper”, Z Gerontol Geriat., Vol. 55, pp. 597–602, 2022. https://doi.org/10.1007/s00391-021-01957-w
  68. Sim I, Gorman P, Greenes RA, Haynes RB, Kaplan B, Lehmann H, et al., “Clinical decision support systems for the practice of evidence-based medicine”, J Am Med Inform Assoc., Vol. 8, No. 6, pp. 527–534, 2001. http://dx.doi.org/10.1136/jamia.2001.0080527
  69. Almkvist O., “Neuropsychological features of early Alzheimer’s disease: preclinical and clinical stages”, Acta Neurol. Scand. Suppl., Vol. 165, pp. 63–71, 1996. http://dx.doi.org/10.1111/j.1600-0404.1996.tb05874.x
  70. Roncaglioni A, Toropov AA, Toropova AP, Benfenati E., “In silico methods to predict drug toxicity”, Current Opinion Pharmacology, Vol. 13, No. 5, pp. 802–806, 2013. http://dx.doi.org/10.1016/j.coph.2013.06.001
  71. Judson R, Elloumi F, Setzer RW, Li Z, Shah I., “A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model”, BMC Bioinformatics, Vol. 9, No. 1, Article No. 241, 2008. http://dx.doi.org/10.1186/1471-2105-9-241
  72. Raies AB, Bajic VB., “In silico toxicology: computational methods for the prediction of chemical toxicity: Computational methods for the prediction of chemical toxicity”, Wiley Interdisciplinary Rev Comput. Mol Sci., Vol. 6, No. 2, pp. 147–172, 2016. http://dx.doi.org/10.1002/wcms.1240
  73. Paster I, Shacham M, Brauner N, “Investigation of the relationships between molecular structure, molecular descriptors, and physical properties”, Ind Eng Chem Res., Vol. 48, No. 21, pp. 9723–9734, 2009. http://dx.doi.org/10.1021/ie801318y
  74. Tran TT, Wibowo S, Tayara A, Chong H, “Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives”, Journal of Chemical Information and Modeling, Vol. 63, No. 9, pp. 2628–2643, 2023.
  75. Huang B, von Lilienfeld OA. Ab initio, “Machine Learning in chemical compound space”, Chem. Rev., Vol. 121, No. 16, pp. 10001–10036, 2021. http://dx.doi.org/10.1021/acs.chemrev.0c01303
  76. Hildebrandt MAT, Komaki R, Liao Z, Gu J, Chang JY, Ye Y, et al., “Genetic variants in inflammation-related genes are associated with radiation-induced toxicity following treatment for non-small cell lung cancer”, PLoS One, Vol. 5, No. 8, Article No. e12402, 2010. http://dx.doi.org/10.1371/journal.pone.0012402
  77. Yuan Y, Failmezger H, Rueda OM, Ali HR, Gräf S, Chin S-F, et al, “Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling”, Sci Transl. Med., Vol. 4, Article No. 157, 2012. http://dx.doi.org/10.1126/scitranslmed.3004330
  78. Banning M, “A review of clinical decision making: models and current research”, J Clin Nurs, Vol. 17, No. 2, pp. 187–95, 2008. http://dx.doi.org/10.1111/j.1365-2702.2006.01791.x
  79. Giraud P, Giraud P, Gasnier A, El Ayachy R, Kreps S, Foy J-P, et al., “Radiomics and machine learning for radiotherapy in head and neck cancers”, Front Oncol., Vol. 9, No. 174, 2019. http://dx.doi.org/10.3389/fonc.2019.00174
  80. Palma G, Monti S, Conson M, Pacelli R, Cella L, “Normal tissue complication probability (NTCP) models for modern radiation therapy”, Semin Oncol., Vol. 46, No. 3, pp. 210–218, 2019. http://dx.doi.org/10.1053/j.seminoncol.2019.07.006
  81. Toropov AA, Toropova AP, Raska I Jr, Leszczynska D, Leszczynski J, “Comprehension of drug toxicity: software and databases”, Comput. Biol. Med., Vol. 45, pp. 20–25, 2014. http://dx.doi.org/10.1016/j.compbiomed.2013.11.013
  82. Watkins PB, Seligman PJ, Pears JS, Avigan MI, Senior JR, “Using controlled clinical trials to learn more about acute drug-induced liver injury”, Hepatology, Vol. 48, No. 5, pp. 1680–1689, 2008. http://dx.doi.org/10.1002/hep.22633
  83. Smusz S, Kurczab R, Bojarski AJ, “A multidimensional analysis of machine learning methods performance in the classification of bioactive compounds”, Chemometr Intell Lab Syst, Vol. 128, pp. 89–100, 2013. http://dx.doi.org/10.1016/j.chemolab.2013.08.003
  84. Lu H, Ma X, Huang K, Azimi M., “Carbon trading volume and price forecasting in China using multiple machine learning models”, J Clean Prod., Vol. 249, Article No. 119386, 2020. http://dx.doi.org/10.1016/j.jclepro.2019.119386
  85. Peng S, Wang W, Chen Y, Zhong X, Hu Q, “Regression-based hyperparameter learning for support vector machines”, IEEE Trans Neural Netw Learn Syst., Vol. 35, No. 12, pp. 18799–18813, 2024. http://dx.doi.org/10.1109/TNNLS.2023.3321685
Language: English
Submitted on: Feb 7, 2025
Published on: Jul 19, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2025 P. Leena Pavitha, Ganapathy Sannasi, Mark P. Allan, Devi Nithisha, A. Jerad Suresh, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.