Статті в журналах з теми "Model-agnostic Explainability"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Model-agnostic Explainability".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
Diprose, William K., Nicholas Buist, Ning Hua, Quentin Thurier, George Shand, and Reece Robinson. "Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator." Journal of the American Medical Informatics Association 27, no. 4 (February 27, 2020): 592–600. http://dx.doi.org/10.1093/jamia/ocz229.
Повний текст джерелаZafar, Muhammad Rehman, and Naimul Khan. "Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability." Machine Learning and Knowledge Extraction 3, no. 3 (June 30, 2021): 525–41. http://dx.doi.org/10.3390/make3030027.
Повний текст джерелаTOPCU, Deniz. "How to explain a machine learning model: HbA1c classification example." Journal of Medicine and Palliative Care 4, no. 2 (March 27, 2023): 117–25. http://dx.doi.org/10.47582/jompac.1259507.
Повний текст джерелаUllah, Ihsan, Andre Rios, Vaibhav Gala, and Susan Mckeever. "Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation." Applied Sciences 12, no. 1 (December 23, 2021): 136. http://dx.doi.org/10.3390/app12010136.
Повний текст джерелаSrinivasu, Parvathaneni Naga, N. Sandhya, Rutvij H. Jhaveri, and Roshani Raut. "From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies." Mobile Information Systems 2022 (June 13, 2022): 1–20. http://dx.doi.org/10.1155/2022/8167821.
Повний текст джерелаLv, Ge, Chen Jason Zhang, and Lei Chen. "HENCE-X: Toward Heterogeneity-Agnostic Multi-Level Explainability for Deep Graph Networks." Proceedings of the VLDB Endowment 16, no. 11 (July 2023): 2990–3003. http://dx.doi.org/10.14778/3611479.3611503.
Повний текст джерелаFauvel, Kevin, Tao Lin, Véronique Masson, Élisa Fromont, and Alexandre Termier. "XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification." Mathematics 9, no. 23 (December 5, 2021): 3137. http://dx.doi.org/10.3390/math9233137.
Повний текст джерелаHassan, Fayaz, Jianguo Yu, Zafi Sherhan Syed, Nadeem Ahmed, Mana Saleh Al Reshan, and Asadullah Shaikh. "Achieving model explainability for intrusion detection in VANETs with LIME." PeerJ Computer Science 9 (June 22, 2023): e1440. http://dx.doi.org/10.7717/peerj-cs.1440.
Повний текст джерелаVieira, Carla Piazzon Ramos, and Luciano Antonio Digiampietri. "A study about Explainable Articial Intelligence: using decision tree to explain SVM." Revista Brasileira de Computação Aplicada 12, no. 1 (January 8, 2020): 113–21. http://dx.doi.org/10.5335/rbca.v12i1.10247.
Повний текст джерелаNguyen, Hung Viet, and Haewon Byeon. "Prediction of Out-of-Hospital Cardiac Arrest Survival Outcomes Using a Hybrid Agnostic Explanation TabNet Model." Mathematics 11, no. 9 (April 25, 2023): 2030. http://dx.doi.org/10.3390/math11092030.
Повний текст джерелаSzepannaek, Gero, and Karsten Lübke. "How much do we see? On the explainability of partial dependence plots for credit risk scoring." Argumenta Oeconomica 2023, no. 2 (2023): 137–50. http://dx.doi.org/10.15611/aoe.2023.1.07.
Повний текст джерелаSovrano, Francesco, Salvatore Sapienza, Monica Palmirani, and Fabio Vitali. "Metrics, Explainability and the European AI Act Proposal." J 5, no. 1 (February 18, 2022): 126–38. http://dx.doi.org/10.3390/j5010010.
Повний текст джерелаKaplun, Dmitry, Alexander Krasichkov, Petr Chetyrbok, Nikolay Oleinikov, Anupam Garg, and Husanbir Singh Pannu. "Cancer Cell Profiling Using Image Moments and Neural Networks with Model Agnostic Explainability: A Case Study of Breast Cancer Histopathological (BreakHis) Database." Mathematics 9, no. 20 (October 17, 2021): 2616. http://dx.doi.org/10.3390/math9202616.
Повний текст джерелаIbrahim, Muhammad Amien, Samsul Arifin, I. Gusti Agung Anom Yudistira, Rinda Nariswari, Abdul Azis Abdillah, Nerru Pranuta Murnaka, and Puguh Wahyu Prasetyo. "An Explainable AI Model for Hate Speech Detection on Indonesian Twitter." CommIT (Communication and Information Technology) Journal 16, no. 2 (June 8, 2022): 175–82. http://dx.doi.org/10.21512/commit.v16i2.8343.
Повний текст джерелаManikis, Georgios C., Georgios S. Ioannidis, Loizos Siakallis, Katerina Nikiforaki, Michael Iv, Diana Vozlic, Katarina Surlan-Popovic, Max Wintermark, Sotirios Bisdas, and Kostas Marias. "Multicenter DSC–MRI-Based Radiomics Predict IDH Mutation in Gliomas." Cancers 13, no. 16 (August 5, 2021): 3965. http://dx.doi.org/10.3390/cancers13163965.
Повний текст джерелаOubelaid, Adel, Abdelhameed Ibrahim, and Ahmed M. Elshewey. "Bridging the Gap: An Explainable Methodology for Customer Churn Prediction in Supply Chain Management." Journal of Artificial Intelligence and Metaheuristics 4, no. 1 (2023): 16–23. http://dx.doi.org/10.54216/jaim.040102.
Повний текст джерелаSathyan, Anoop, Abraham Itzhak Weinberg, and Kelly Cohen. "Interpretable AI for bio-medical applications." Complex Engineering Systems 2, no. 4 (2022): 18. http://dx.doi.org/10.20517/ces.2022.41.
Повний текст джерелаAhmed, Md Sabbir, Md Tasin Tazwar, Haseen Khan, Swadhin Roy, Junaed Iqbal, Md Golam Rabiul Alam, Md Rafiul Hassan, and Mohammad Mehedi Hassan. "Yield Response of Different Rice Ecotypes to Meteorological, Agro-Chemical, and Soil Physiographic Factors for Interpretable Precision Agriculture Using Extreme Gradient Boosting and Support Vector Regression." Complexity 2022 (September 19, 2022): 1–20. http://dx.doi.org/10.1155/2022/5305353.
Повний текст джерелаBaşağaoğlu, Hakan, Debaditya Chakraborty, Cesar Do Lago, Lilianna Gutierrez, Mehmet Arif Şahinli, Marcio Giacomoni, Chad Furl, Ali Mirchi, Daniel Moriasi, and Sema Sevinç Şengör. "A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications." Water 14, no. 8 (April 11, 2022): 1230. http://dx.doi.org/10.3390/w14081230.
Повний текст джерелаMehta, Harshkumar, and Kalpdrum Passi. "Social Media Hate Speech Detection Using Explainable Artificial Intelligence (XAI)." Algorithms 15, no. 8 (August 17, 2022): 291. http://dx.doi.org/10.3390/a15080291.
Повний текст джерелаLu, Haohui, and Shahadat Uddin. "Explainable Stacking-Based Model for Predicting Hospital Readmission for Diabetic Patients." Information 13, no. 9 (September 15, 2022): 436. http://dx.doi.org/10.3390/info13090436.
Повний текст джерелаAbdullah, Talal A. A., Mohd Soperi Mohd Zahid, Waleed Ali, and Shahab Ul Hassan. "B-LIME: An Improvement of LIME for Interpretable Deep Learning Classification of Cardiac Arrhythmia from ECG Signals." Processes 11, no. 2 (February 16, 2023): 595. http://dx.doi.org/10.3390/pr11020595.
Повний текст джерелаMerone, Mario, Alessandro Graziosi, Valerio Lapadula, Lorenzo Petrosino, Onorato d’Angelis, and Luca Vollero. "A Practical Approach to the Analysis and Optimization of Neural Networks on Embedded Systems." Sensors 22, no. 20 (October 14, 2022): 7807. http://dx.doi.org/10.3390/s22207807.
Повний текст джерелаKim, Jaehun. "Increasing trust in complex machine learning systems." ACM SIGIR Forum 55, no. 1 (June 2021): 1–3. http://dx.doi.org/10.1145/3476415.3476435.
Повний текст джерелаDu, Yuhan, Anthony R. Rafferty, Fionnuala M. McAuliffe, John Mehegan, and Catherine Mooney. "Towards an explainable clinical decision support system for large-for-gestational-age births." PLOS ONE 18, no. 2 (February 21, 2023): e0281821. http://dx.doi.org/10.1371/journal.pone.0281821.
Повний текст джерелаAntoniadi, Anna Markella, Yuhan Du, Yasmine Guendouz, Lan Wei, Claudia Mazo, Brett A. Becker, and Catherine Mooney. "Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review." Applied Sciences 11, no. 11 (May 31, 2021): 5088. http://dx.doi.org/10.3390/app11115088.
Повний текст джерелаKim, Kipyo, Hyeonsik Yang, Jinyeong Yi, Hyung-Eun Son, Ji-Young Ryu, Yong Chul Kim, Jong Cheol Jeong, et al. "Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation." Journal of Medical Internet Research 23, no. 4 (April 16, 2021): e24120. http://dx.doi.org/10.2196/24120.
Повний текст джерелаAbir, Wahidul Hasan, Md Fahim Uddin, Faria Rahman Khanam, Tahia Tazin, Mohammad Monirujjaman Khan, Mehedi Masud, and Sultan Aljahdali. "Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method." Computational Intelligence and Neuroscience 2022 (April 27, 2022): 1–14. http://dx.doi.org/10.1155/2022/5140148.
Повний текст джерелаWikle, Christopher K., Abhirup Datta, Bhava Vyasa Hari, Edward L. Boone, Indranil Sahoo, Indulekha Kavila, Stefano Castruccio, Susan J. Simmons, Wesley S. Burr, and Won Chang. "An illustration of model agnostic explainability methods applied to environmental data." Environmetrics, October 25, 2022. http://dx.doi.org/10.1002/env.2772.
Повний текст джерелаXu, Zhichao, Hansi Zeng, Juntao Tan, Zuohui Fu, Yongfeng Zhang, and Qingyao Ai. "A Reusable Model-agnostic Framework for Faithfully Explainable Recommendation and System Scrutability." ACM Transactions on Information Systems, June 18, 2023. http://dx.doi.org/10.1145/3605357.
Повний текст джерелаJoyce, Dan W., Andrey Kormilitzin, Katharine A. Smith, and Andrea Cipriani. "Explainable artificial intelligence for mental health through transparency and interpretability for understandability." npj Digital Medicine 6, no. 1 (January 18, 2023). http://dx.doi.org/10.1038/s41746-023-00751-9.
Повний текст джерелаNakashima, Heitor Hoffman, Daielly Mantovani, and Celso Machado Junior. "Users’ trust in black-box machine learning algorithms." Revista de Gestão, October 25, 2022. http://dx.doi.org/10.1108/rege-06-2022-0100.
Повний текст джерелаSzepannek, Gero, and Karsten Lübke. "Explaining Artificial Intelligence with Care." KI - Künstliche Intelligenz, May 16, 2022. http://dx.doi.org/10.1007/s13218-022-00764-8.
Повний текст джерелаSharma, Jeetesh, Murari Lal Mittal, Gunjan Soni, and Arvind Keprate. "Explainable Artificial Intelligence (XAI) Approaches in Predictive Maintenance: A Review." Recent Patents on Engineering 18 (April 17, 2023). http://dx.doi.org/10.2174/1872212118666230417084231.
Повний текст джерелаSzczepański, Mateusz, Marek Pawlicki, Rafał Kozik, and Michał Choraś. "New explainability method for BERT-based model in fake news detection." Scientific Reports 11, no. 1 (December 2021). http://dx.doi.org/10.1038/s41598-021-03100-6.
Повний текст джерелаÖZTOPRAK, Samet, and Zeynep ORMAN. "A New Model-Agnostic Method and Implementation for Explaining the Prediction on Finance Data." European Journal of Science and Technology, June 29, 2022. http://dx.doi.org/10.31590/ejosat.1079145.
Повний текст джерелаBachoc, François, Fabrice Gamboa, Max Halford, Jean-Michel Loubes, and Laurent Risser. "Explaining machine learning models using entropic variable projection." Information and Inference: A Journal of the IMA 12, no. 3 (April 27, 2023). http://dx.doi.org/10.1093/imaiai/iaad010.
Повний текст джерелаLoveleen, Gaur, Bhandari Mohan, Bhadwal Singh Shikhar, Jhanjhi Nz, Mohammad Shorfuzzaman, and Mehedi Masud. "Explanation-driven HCI Model to Examine the Mini-Mental State for Alzheimer’s Disease." ACM Transactions on Multimedia Computing, Communications, and Applications, April 2022. http://dx.doi.org/10.1145/3527174.
Повний текст джерелаVilone, Giulia, and Luca Longo. "A Quantitative Evaluation of Global, Rule-Based Explanations of Post-Hoc, Model Agnostic Methods." Frontiers in Artificial Intelligence 4 (November 3, 2021). http://dx.doi.org/10.3389/frai.2021.717899.
Повний текст джерелаAlabi, Rasheed Omobolaji, Mohammed Elmusrati, Ilmo Leivo, Alhadi Almangush, and Antti A. Mäkitie. "Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP." Scientific Reports 13, no. 1 (June 2, 2023). http://dx.doi.org/10.1038/s41598-023-35795-0.
Повний текст джерелаBogdanova, Anna, Akira Imakura, and Tetsuya Sakurai. "DC-SHAP Method for Consistent Explainability in Privacy-Preserving Distributed Machine Learning." Human-Centric Intelligent Systems, July 6, 2023. http://dx.doi.org/10.1007/s44230-023-00032-4.
Повний текст джерелаZini, Julia El, and Mariette Awad. "On the Explainability of Natural Language Processing Deep Models." ACM Computing Surveys, July 19, 2022. http://dx.doi.org/10.1145/3529755.
Повний текст джерелаEsam Noori, Worood, and A. S. Albahri. "Towards Trustworthy Myopia Detection: Integration Methodology of Deep Learning Approach, XAI Visualization, and User Interface System." Applied Data Science and Analysis, February 23, 2023, 1–15. http://dx.doi.org/10.58496/adsa/2023/001.
Повний текст джерелаFilho, Renato Miranda, Anísio M. Lacerda, and Gisele L. Pappa. "Explainable regression via prototypes." ACM Transactions on Evolutionary Learning and Optimization, December 15, 2022. http://dx.doi.org/10.1145/3576903.
Повний текст джерелаAhmed, Zia U., Kang Sun, Michael Shelly, and Lina Mu. "Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA." Scientific Reports 11, no. 1 (December 2021). http://dx.doi.org/10.1038/s41598-021-03198-8.
Повний текст джерелаChen, Tao, Meng Song, Hongxun Hui, and Huan Long. "Battery Electrode Mass Loading Prognostics and Analysis for Lithium-Ion Battery–Based Energy Storage Systems." Frontiers in Energy Research 9 (October 5, 2021). http://dx.doi.org/10.3389/fenrg.2021.754317.
Повний текст джерелаChen, Tao, Meng Song, Hongxun Hui, and Huan Long. "Battery Electrode Mass Loading Prognostics and Analysis for Lithium-Ion Battery–Based Energy Storage Systems." Frontiers in Energy Research 9 (October 5, 2021). http://dx.doi.org/10.3389/fenrg.2021.754317.
Повний текст джерелаJaved, Abdul Rehman, Habib Ullah Khan, Mohammad Kamel Bader Alomari, Muhammad Usman Sarwar, Muhammad Asim, Ahmad S. Almadhor, and Muhammad Zahid Khan. "Toward explainable AI-empowered cognitive health assessment." Frontiers in Public Health 11 (March 9, 2023). http://dx.doi.org/10.3389/fpubh.2023.1024195.
Повний текст джерелаMustafa, Ahmad, Klaas Koster, and Ghassan AlRegib. "Explainable Machine Learning for Hydrocarbon Risk Assessment." GEOPHYSICS, July 13, 2023, 1–52. http://dx.doi.org/10.1190/geo2022-0594.1.
Повний текст джерелаYang, Darrion Bo-Yun, Alexander Smith, Emily J. Smith, Anant Naik, Mika Janbahan, Charee M. Thompson, Lav R. Varshney, and Wael Hassaneen. "The State of Machine Learning in Outcomes Prediction of Transsphenoidal Surgery: A Systematic Review." Journal of Neurological Surgery Part B: Skull Base, September 12, 2022. http://dx.doi.org/10.1055/a-1941-3618.
Повний текст джерела