Articoli di riviste sul tema "Algorithm explainability"
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Nuobu, Gengpan. "Transformer model: Explainability and prospectiveness". Applied and Computational Engineering 20, n. 1 (23 ottobre 2023): 88–99. http://dx.doi.org/10.54254/2755-2721/20/20231079.
Hwang, Hyunseung, e Steven Euijong Whang. "XClusters: Explainability-First Clustering". Proceedings of the AAAI Conference on Artificial Intelligence 37, n. 7 (26 giugno 2023): 7962–70. http://dx.doi.org/10.1609/aaai.v37i7.25963.
Pendyala, Vishnu, e Hyungkyun Kim. "Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI". Electronics 13, n. 6 (8 marzo 2024): 1025. http://dx.doi.org/10.3390/electronics13061025.
Loreti, Daniela, e Giorgio Visani. "Parallel approaches for a decision tree-based explainability algorithm". Future Generation Computer Systems 158 (settembre 2024): 308–22. http://dx.doi.org/10.1016/j.future.2024.04.044.
Wang, 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. 19 (24 marzo 2024): 21690–98. http://dx.doi.org/10.1609/aaai.v38i19.30168.
Yiğit, 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. 3 (30 giugno 2022): 863–69. http://dx.doi.org/10.18280/ts.390311.
Powell, Alison B. "Explanations as governance? Investigating practices of explanation in algorithmic system design". European Journal of Communication 36, n. 4 (agosto 2021): 362–75. http://dx.doi.org/10.1177/02673231211028376.
Xie, 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. 3 (6 marzo 2021): 1241–52. http://dx.doi.org/10.1007/s40747-021-00315-y.
Kabir, Sami, Mohammad Shahadat Hossain e Karl Andersson. "An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings". Energies 17, n. 8 (9 aprile 2024): 1797. http://dx.doi.org/10.3390/en17081797.
Bulitko, 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. 1 (17 luglio 2022): 29–37. http://dx.doi.org/10.1609/socs.v15i1.21749.
Gräßer, Felix, Hagen Malberg e Sebastian Zaunseder. "Neighborhood Optimization for Therapy Decision Support". Current Directions in Biomedical Engineering 5, n. 1 (1 settembre 2019): 1–4. http://dx.doi.org/10.1515/cdbme-2019-0001.
Kottinger, 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 giugno 2022): 692–700. http://dx.doi.org/10.1609/icaps.v32i1.19859.
Monsarrat, 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. 2 (4 febbraio 2022): 217. http://dx.doi.org/10.3390/jpm12020217.
Lv, Ge, e Lei Chen. "On Data-Aware Global Explainability of Graph Neural Networks". Proceedings of the VLDB Endowment 16, n. 11 (luglio 2023): 3447–60. http://dx.doi.org/10.14778/3611479.3611538.
Li, 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. 7 (26 giugno 2023): 8640–47. http://dx.doi.org/10.1609/aaai.v37i7.26040.
Kong, Weihao, Jianping Chen e Pengfei Zhu. "Machine Learning-Based Uranium Prospectivity Mapping and Model Explainability Research". Minerals 14, n. 2 (24 gennaio 2024): 128. http://dx.doi.org/10.3390/min14020128.
Fauvel, Kevin, Tao Lin, Véronique Masson, Élisa Fromont e Alexandre Termier. "XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification". Mathematics 9, n. 23 (5 dicembre 2021): 3137. http://dx.doi.org/10.3390/math9233137.
Huang, 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. 4 (26 giugno 2023): 3996–4006. http://dx.doi.org/10.1609/aaai.v37i4.25514.
Patel, 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. 13 (24 marzo 2024): 14563–71. http://dx.doi.org/10.1609/aaai.v38i13.29372.
Arous, 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. 7 (18 maggio 2021): 5868–76. http://dx.doi.org/10.1609/aaai.v35i7.16734.
Tsiami, Lydia, e Christos Makropoulos. "Cyber—Physical Attack Detection in Water Distribution Systems with Temporal Graph Convolutional Neural Networks". Water 13, n. 9 (29 aprile 2021): 1247. http://dx.doi.org/10.3390/w13091247.
Botana, Iñigo López-Riobóo, Carlos Eiras-Franco e Amparo Alonso-Betanzos. "Regression Tree Based Explanation for Anomaly Detection Algorithm". Proceedings 54, n. 1 (18 agosto 2020): 7. http://dx.doi.org/10.3390/proceedings2020054007.
Gao, 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 luglio 2019): 3622–29. http://dx.doi.org/10.1609/aaai.v33i01.33013622.
Lv, 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. 11 (4 novembre 2022): e0276373. http://dx.doi.org/10.1371/journal.pone.0276373.
Chatterjee, 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. 4 (10 febbraio 2022): 1834. http://dx.doi.org/10.3390/app12041834.
Banditwattanawong, Thepparit, e Masawee Masdisornchote. "On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability". Applied Computational Intelligence and Soft Computing 2021 (18 febbraio 2021): 1–14. http://dx.doi.org/10.1155/2021/8899649.
Rudzite, Liva. "Algorithmic Explainability and the Sufficient-Disclosure Requirement under the European Patent Convention". Juridica International 31 (25 ottobre 2022): 125–35. http://dx.doi.org/10.12697/ji.2022.31.09.
Lizzi, 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. 1 (24 dicembre 2021): 148. http://dx.doi.org/10.3390/app12010148.
Fang, Xue, Lin Li e Zheng Wei. "Design of Recommendation Algorithm Based on Knowledge Graph". Journal of Physics: Conference Series 2425, n. 1 (1 febbraio 2023): 012025. http://dx.doi.org/10.1088/1742-6596/2425/1/012025.
Krishna 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. 6 (30 maggio 2021): 92. http://dx.doi.org/10.3390/jimaging7060092.
Adithyaram, 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. 7 (31 luglio 2023): 466–69. http://dx.doi.org/10.22214/ijraset.2023.53802.
Satoni 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. 1 (29 agosto 2022): 26–41. http://dx.doi.org/10.59407/jrsit.v1i1.75.
Shalev, Yuval, e Irad Ben-Gal. "Context Based Predictive Information". Entropy 21, n. 7 (29 giugno 2019): 645. http://dx.doi.org/10.3390/e21070645.
Samaras, 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. 14 (12 luglio 2023): 8120. http://dx.doi.org/10.3390/app13148120.
Silva-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. 9 (25 aprile 2023): 2443. http://dx.doi.org/10.3390/cancers15092443.
BUITEN, Miriam C. "Towards Intelligent Regulation of Artificial Intelligence". European Journal of Risk Regulation 10, n. 1 (marzo 2019): 41–59. http://dx.doi.org/10.1017/err.2019.8.
Agarwal, 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 (novembre 2021): 107467. http://dx.doi.org/10.1016/j.compchemeng.2021.107467.
Zhao, 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. 9 (26 giugno 2023): 11363–71. http://dx.doi.org/10.1609/aaai.v37i9.26344.
Choi, Insu, e Woo Chang Kim. "Enhancing Exchange-Traded Fund Price Predictions: Insights from Information-Theoretic Networks and Node Embeddings". Entropy 26, n. 1 (12 gennaio 2024): 70. http://dx.doi.org/10.3390/e26010070.
Blomerus, 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. 23 (1 dicembre 2022): 6096. http://dx.doi.org/10.3390/rs14236096.
Chin, 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. 12 (15 dicembre 2023): e2345050. http://dx.doi.org/10.1001/jamanetworkopen.2023.45050.
Klettke, 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. 3 (27 ottobre 2021): 245–49. http://dx.doi.org/10.1007/s13222-021-00392-w.
Chetoui, 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. 4 (7 dicembre 2021): 73. http://dx.doi.org/10.3390/bdcc5040073.
Schober, 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. 5 (21 aprile 2022): 152. http://dx.doi.org/10.3390/chemosensors10050152.
HÖLLER, 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. 2 (22 dicembre 2023): 67–89. http://dx.doi.org/10.24818/ejis.2023.17.
Zeng, 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. 21 (24 marzo 2024): 23703–4. http://dx.doi.org/10.1609/aaai.v38i21.30533.
Mollaei, 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. 15 (3 agosto 2022): 9552. http://dx.doi.org/10.3390/ijerph19159552.
Patil, 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. 19 (27 settembre 2022): 3079. http://dx.doi.org/10.3390/electronics11193079.
Trabassi, 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. 10 (12 maggio 2022): 3700. http://dx.doi.org/10.3390/s22103700.
Gutierrez-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. 3 (19 febbraio 2022): 859–83. http://dx.doi.org/10.1007/s10115-022-01657-w.