Artigos de revistas sobre o tema "Algorithm explainability"
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Nuobu, Gengpan. "Transformer model: Explainability and prospectiveness". Applied and Computational Engineering 20, n.º 1 (23 de outubro de 2023): 88–99. http://dx.doi.org/10.54254/2755-2721/20/20231079.
Texto completo da fonteHwang, Hyunseung, e Steven Euijong Whang. "XClusters: Explainability-First Clustering". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junho de 2023): 7962–70. http://dx.doi.org/10.1609/aaai.v37i7.25963.
Texto completo da fontePendyala, Vishnu, e Hyungkyun Kim. "Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI". Electronics 13, n.º 6 (8 de março de 2024): 1025. http://dx.doi.org/10.3390/electronics13061025.
Texto completo da fonteLoreti, Daniela, e Giorgio Visani. "Parallel approaches for a decision tree-based explainability algorithm". Future Generation Computer Systems 158 (setembro de 2024): 308–22. http://dx.doi.org/10.1016/j.future.2024.04.044.
Texto completo da fonteWang, Zhenzhong, Qingyuan Zeng, Wanyu Lin, Min Jiang e Kay Chen Tan. "Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 19 (24 de março de 2024): 21690–98. http://dx.doi.org/10.1609/aaai.v38i19.30168.
Texto completo da fonteYiğit, Tuncay, Nilgün Şengöz, Özlem Özmen, Jude Hemanth e Ali Hakan Işık. "Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning". Traitement du Signal 39, n.º 3 (30 de junho de 2022): 863–69. http://dx.doi.org/10.18280/ts.390311.
Texto completo da fontePowell, Alison B. "Explanations as governance? Investigating practices of explanation in algorithmic system design". European Journal of Communication 36, n.º 4 (agosto de 2021): 362–75. http://dx.doi.org/10.1177/02673231211028376.
Texto completo da fonteXie, Lijie, Zhaoming Hu, Xingjuan Cai, Wensheng Zhang e Jinjun Chen. "Explainable recommendation based on knowledge graph and multi-objective optimization". Complex & Intelligent Systems 7, n.º 3 (6 de março de 2021): 1241–52. http://dx.doi.org/10.1007/s40747-021-00315-y.
Texto completo da fonteKabir, Sami, Mohammad Shahadat Hossain e Karl Andersson. "An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings". Energies 17, n.º 8 (9 de abril de 2024): 1797. http://dx.doi.org/10.3390/en17081797.
Texto completo da fonteBulitko, Vadim, Shuwei Wang, Justin Stevens e Levi H. S. Lelis. "Portability and Explainability of Synthesized Formula-based Heuristics". Proceedings of the International Symposium on Combinatorial Search 15, n.º 1 (17 de julho de 2022): 29–37. http://dx.doi.org/10.1609/socs.v15i1.21749.
Texto completo da fonteGräßer, Felix, Hagen Malberg e Sebastian Zaunseder. "Neighborhood Optimization for Therapy Decision Support". Current Directions in Biomedical Engineering 5, n.º 1 (1 de setembro de 2019): 1–4. http://dx.doi.org/10.1515/cdbme-2019-0001.
Texto completo da fonteKottinger, Justin, Shaull Almagor e Morteza Lahijanian. "Conflict-Based Search for Explainable Multi-Agent Path Finding". Proceedings of the International Conference on Automated Planning and Scheduling 32 (13 de junho de 2022): 692–700. http://dx.doi.org/10.1609/icaps.v32i1.19859.
Texto completo da fonteMonsarrat, Paul, David Bernard, Mathieu Marty, Chiara Cecchin-Albertoni, Emmanuel Doumard, Laure Gez, Julien Aligon, Jean-Noël Vergnes, Louis Casteilla e Philippe Kemoun. "Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study". Journal of Personalized Medicine 12, n.º 2 (4 de fevereiro de 2022): 217. http://dx.doi.org/10.3390/jpm12020217.
Texto completo da fonteLv, Ge, e Lei Chen. "On Data-Aware Global Explainability of Graph Neural Networks". Proceedings of the VLDB Endowment 16, n.º 11 (julho de 2023): 3447–60. http://dx.doi.org/10.14778/3611479.3611538.
Texto completo da fonteLi, Tong, Jiale Deng, Yanyan Shen, Luyu Qiu, Huang Yongxiang e Caleb Chen Cao. "Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junho de 2023): 8640–47. http://dx.doi.org/10.1609/aaai.v37i7.26040.
Texto completo da fonteKong, Weihao, Jianping Chen e Pengfei Zhu. "Machine Learning-Based Uranium Prospectivity Mapping and Model Explainability Research". Minerals 14, n.º 2 (24 de janeiro de 2024): 128. http://dx.doi.org/10.3390/min14020128.
Texto completo da fonteFauvel, Kevin, Tao Lin, Véronique Masson, Élisa Fromont e Alexandre Termier. "XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification". Mathematics 9, n.º 23 (5 de dezembro de 2021): 3137. http://dx.doi.org/10.3390/math9233137.
Texto completo da fonteHuang, Xuanxiang, Yacine Izza e Joao Marques-Silva. "Solving Explainability Queries with Quantification: The Case of Feature Relevancy". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 4 (26 de junho de 2023): 3996–4006. http://dx.doi.org/10.1609/aaai.v37i4.25514.
Texto completo da fontePatel, Sagar, Sangeetha Abdu Jyothi e Nina Narodytska. "CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 13 (24 de março de 2024): 14563–71. http://dx.doi.org/10.1609/aaai.v38i13.29372.
Texto completo da fonteArous, Ines, Ljiljana Dolamic, Jie Yang, Akansha Bhardwaj, Giuseppe Cuccu e Philippe Cudré-Mauroux. "MARTA: Leveraging Human Rationales for Explainable Text Classification". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 7 (18 de maio de 2021): 5868–76. http://dx.doi.org/10.1609/aaai.v35i7.16734.
Texto completo da fonteTsiami, Lydia, e Christos Makropoulos. "Cyber—Physical Attack Detection in Water Distribution Systems with Temporal Graph Convolutional Neural Networks". Water 13, n.º 9 (29 de abril de 2021): 1247. http://dx.doi.org/10.3390/w13091247.
Texto completo da fonteBotana, Iñigo López-Riobóo, Carlos Eiras-Franco e Amparo Alonso-Betanzos. "Regression Tree Based Explanation for Anomaly Detection Algorithm". Proceedings 54, n.º 1 (18 de agosto de 2020): 7. http://dx.doi.org/10.3390/proceedings2020054007.
Texto completo da fonteGao, Jingyue, Xiting Wang, Yasha Wang e Xing Xie. "Explainable Recommendation through Attentive Multi-View Learning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 3622–29. http://dx.doi.org/10.1609/aaai.v33i01.33013622.
Texto completo da fonteLv, Ting, Zhenkuan Pan, Weibo Wei, Guangyu Yang, Jintao Song, Xuqing Wang, Lu Sun, Qian Li e Xiatao Sun. "Iterative deep neural networks based on proximal gradient descent for image restoration". PLOS ONE 17, n.º 11 (4 de novembro de 2022): e0276373. http://dx.doi.org/10.1371/journal.pone.0276373.
Texto completo da fonteChatterjee, Soumick, Arnab Das, Chirag Mandal, Budhaditya Mukhopadhyay, Manish Vipinraj, Aniruddh Shukla, Rajatha Nagaraja Rao, Chompunuch Sarasaen, Oliver Speck e Andreas Nürnberger. "TorchEsegeta: Framework for Interpretability and Explainability of Image-Based Deep Learning Models". Applied Sciences 12, n.º 4 (10 de fevereiro de 2022): 1834. http://dx.doi.org/10.3390/app12041834.
Texto completo da fonteBanditwattanawong, Thepparit, e Masawee Masdisornchote. "On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability". Applied Computational Intelligence and Soft Computing 2021 (18 de fevereiro de 2021): 1–14. http://dx.doi.org/10.1155/2021/8899649.
Texto completo da fonteRudzite, Liva. "Algorithmic Explainability and the Sufficient-Disclosure Requirement under the European Patent Convention". Juridica International 31 (25 de outubro de 2022): 125–35. http://dx.doi.org/10.12697/ji.2022.31.09.
Texto completo da fonteLizzi, Francesca, Camilla Scapicchio, Francesco Laruina, Alessandra Retico e Maria Evelina Fantacci. "Convolutional Neural Networks for Breast Density Classification: Performance and Explanation Insights". Applied Sciences 12, n.º 1 (24 de dezembro de 2021): 148. http://dx.doi.org/10.3390/app12010148.
Texto completo da fonteFang, Xue, Lin Li e Zheng Wei. "Design of Recommendation Algorithm Based on Knowledge Graph". Journal of Physics: Conference Series 2425, n.º 1 (1 de fevereiro de 2023): 012025. http://dx.doi.org/10.1088/1742-6596/2425/1/012025.
Texto completo da fonteKrishna Adithya, Venkatesh, Bryan M. Williams, Silvester Czanner, Srinivasan Kavitha, David S. Friedman, Colin E. Willoughby, Rengaraj Venkatesh e Gabriela Czanner. "EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection". Journal of Imaging 7, n.º 6 (30 de maio de 2021): 92. http://dx.doi.org/10.3390/jimaging7060092.
Texto completo da fonteAdithyaram, N. "Early Detection of Lung Disease Using Deep Learning Algorithms on Image Data". International Journal for Research in Applied Science and Engineering Technology 11, n.º 7 (31 de julho de 2023): 466–69. http://dx.doi.org/10.22214/ijraset.2023.53802.
Texto completo da fonteSatoni Kurniawansyah, Arius. "EXPLAINABLE ARTIFICIAL INTELLIGENCE THEORY IN DECISION MAKING TREATMENT OF ARITHMIA PATIENTS WITH USING DEEP LEARNING MODELS". Jurnal Rekayasa Sistem Informasi dan Teknologi 1, n.º 1 (29 de agosto de 2022): 26–41. http://dx.doi.org/10.59407/jrsit.v1i1.75.
Texto completo da fonteShalev, Yuval, e Irad Ben-Gal. "Context Based Predictive Information". Entropy 21, n.º 7 (29 de junho de 2019): 645. http://dx.doi.org/10.3390/e21070645.
Texto completo da fonteSamaras, Agorastos-Dimitrios, Serafeim Moustakidis, Ioannis D. Apostolopoulos, Elpiniki Papageorgiou e Nikolaos Papandrianos. "Uncovering the Black Box of Coronary Artery Disease Diagnosis: The Significance of Explainability in Predictive Models". Applied Sciences 13, n.º 14 (12 de julho de 2023): 8120. http://dx.doi.org/10.3390/app13148120.
Texto completo da fonteSilva-Aravena, Fabián, Hugo Núñez Delafuente, Jimmy H. Gutiérrez-Bahamondes e Jenny Morales. "A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making". Cancers 15, n.º 9 (25 de abril de 2023): 2443. http://dx.doi.org/10.3390/cancers15092443.
Texto completo da fonteBUITEN, Miriam C. "Towards Intelligent Regulation of Artificial Intelligence". European Journal of Risk Regulation 10, n.º 1 (março de 2019): 41–59. http://dx.doi.org/10.1017/err.2019.8.
Texto completo da fonteAgarwal, Piyush, Melih Tamer e Hector Budman. "Explainability: Relevance based dynamic deep learning algorithm for fault detection and diagnosis in chemical processes". Computers & Chemical Engineering 154 (novembro de 2021): 107467. http://dx.doi.org/10.1016/j.compchemeng.2021.107467.
Texto completo da fonteZhao, Yuying, Yu Wang e Tyler Derr. "Fairness and Explainability: Bridging the Gap towards Fair Model Explanations". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junho de 2023): 11363–71. http://dx.doi.org/10.1609/aaai.v37i9.26344.
Texto completo da fonteChoi, Insu, e Woo Chang Kim. "Enhancing Exchange-Traded Fund Price Predictions: Insights from Information-Theoretic Networks and Node Embeddings". Entropy 26, n.º 1 (12 de janeiro de 2024): 70. http://dx.doi.org/10.3390/e26010070.
Texto completo da fonteBlomerus, Nicholas, Jacques Cilliers, Willie Nel, Erik Blasch e Pieter de Villiers. "Feedback-Assisted Automatic Target and Clutter Discrimination Using a Bayesian Convolutional Neural Network for Improved Explainability in SAR Applications". Remote Sensing 14, n.º 23 (1 de dezembro de 2022): 6096. http://dx.doi.org/10.3390/rs14236096.
Texto completo da fonteChin, Marshall H., Nasim Afsar-Manesh, Arlene S. Bierman, Christine Chang, Caleb J. Colón-Rodríguez, Prashila Dullabh, Deborah Guadalupe Duran et al. "Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care". JAMA Network Open 6, n.º 12 (15 de dezembro de 2023): e2345050. http://dx.doi.org/10.1001/jamanetworkopen.2023.45050.
Texto completo da fonteKlettke, Meike, Adrian Lutsch e Uta Störl. "Kurz erklärt: Measuring Data Changes in Data Engineering and their Impact on Explainability and Algorithm Fairness". Datenbank-Spektrum 21, n.º 3 (27 de outubro de 2021): 245–49. http://dx.doi.org/10.1007/s13222-021-00392-w.
Texto completo da fonteChetoui, Mohamed, Moulay A. Akhloufi, Bardia Yousefi e El Mostafa Bouattane. "Explainable COVID-19 Detection on Chest X-rays Using an End-to-End Deep Convolutional Neural Network Architecture". Big Data and Cognitive Computing 5, n.º 4 (7 de dezembro de 2021): 73. http://dx.doi.org/10.3390/bdcc5040073.
Texto completo da fonteSchober, Sebastian A., Yosra Bahri, Cecilia Carbonelli e Robert Wille. "Neural Network Robustness Analysis Using Sensor Simulations for a Graphene-Based Semiconductor Gas Sensor". Chemosensors 10, n.º 5 (21 de abril de 2022): 152. http://dx.doi.org/10.3390/chemosensors10050152.
Texto completo da fonteHÖLLER, Sonja, Thomas DILGER, Teresa SPIESS, Christian PLODER e Reinhard BERNSTEINER. "Awareness of Unethical Artificial Intelligence and its Mitigation Measures". European Journal of Interdisciplinary Studies 15, n.º 2 (22 de dezembro de 2023): 67–89. http://dx.doi.org/10.24818/ejis.2023.17.
Texto completo da fonteZeng, Wenhuan, e Daniel H. Huson. "Leverage the Explainability of Transformer Models to Improve the DNA 5-Methylcytosine Identification (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 21 (24 de março de 2024): 23703–4. http://dx.doi.org/10.1609/aaai.v38i21.30533.
Texto completo da fonteMollaei, Nafiseh, Carlos Fujao, Luis Silva, Joao Rodrigues, Catia Cepeda e Hugo Gamboa. "Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms". International Journal of Environmental Research and Public Health 19, n.º 15 (3 de agosto de 2022): 9552. http://dx.doi.org/10.3390/ijerph19159552.
Texto completo da fontePatil, Shruti, Vijayakumar Varadarajan, Siddiqui Mohd Mazhar, Abdulwodood Sahibzada, Nihal Ahmed, Onkar Sinha, Satish Kumar, Kailash Shaw e Ketan Kotecha. "Explainable Artificial Intelligence for Intrusion Detection System". Electronics 11, n.º 19 (27 de setembro de 2022): 3079. http://dx.doi.org/10.3390/electronics11193079.
Texto completo da fonteTrabassi, Dante, Mariano Serrao, Tiwana Varrecchia, Alberto Ranavolo, Gianluca Coppola, Roberto De Icco, Cristina Tassorelli e Stefano Filippo Castiglia. "Machine Learning Approach to Support the Detection of Parkinson’s Disease in IMU-Based Gait Analysis". Sensors 22, n.º 10 (12 de maio de 2022): 3700. http://dx.doi.org/10.3390/s22103700.
Texto completo da fonteGutierrez-Rojas, Daniel, Ioannis T. Christou, Daniel Dantas, Arun Narayanan, Pedro H. J. Nardelli e Yongheng Yang. "Performance evaluation of machine learning for fault selection in power transmission lines". Knowledge and Information Systems 64, n.º 3 (19 de fevereiro de 2022): 859–83. http://dx.doi.org/10.1007/s10115-022-01657-w.
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