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For: Holzinger A, Plass M, Kickmeier-rust M, Holzinger K, Crişan GC, Pintea C, Palade V. Interactive machine learning: experimental evidence for the human in the algorithmic loop: A case study on Ant Colony Optimization. Appl Intell 2019;49:2401-14. [DOI: 10.1007/s10489-018-1361-5] [Cited by in Crossref: 105] [Cited by in F6Publishing: 52] [Article Influence: 21.0] [Reference Citation Analysis]
Number Citing Articles
1 Xu G, Lin H, Cheng Y, Li S. An Improved Ant Colony Optimization for Solving Task Scheduling Problem in Radar Signal Processing System. J Sign Process Syst 2023. [DOI: 10.1007/s11265-023-01838-y] [Reference Citation Analysis]
2 Fuchs A, Passarella A, Conti M. Modeling, Replicating, and Predicting Human Behavior: A Survey. ACM Trans Auton Adapt Syst 2023. [DOI: 10.1145/3580492] [Reference Citation Analysis]
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4 Sharef NM, Nasharuddin NA, Mohamed R, Zamani NW, Osman MH, Yaakob R. Applications of Data Analytics and Machine Learning for Digital Twin-based Precision Biodiversity: A Review. 2022 International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS) 2022. [DOI: 10.1109/icacnis57039.2022.10055149] [Reference Citation Analysis]
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6 Niraula D, Cui S, Pakela J, Wei L, Luo Y, Ten Haken RK, El Naqa I. Current status and future developments in predicting outcomes in radiation oncology. Br J Radiol 2022;95:20220239. [PMID: 35867841 DOI: 10.1259/bjr.20220239] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Gavrilovic N, Sibalija T, Domazet D. Design and implementation of discrete Jaya and discrete PSO algorithms for automatic collaborative learning group composition in an e-learning system. Applied Soft Computing 2022;129:109611. [DOI: 10.1016/j.asoc.2022.109611] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
8 Wu X, Xiao L, Sun Y, Zhang J, Ma T, He L. A survey of human-in-the-loop for machine learning. Future Generation Computer Systems 2022;135:364-81. [DOI: 10.1016/j.future.2022.05.014] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
9 Grimmeisen B, Chegini M, Theissler A. VisGIL: machine learning-based visual guidance for interactive labeling. Vis Comput 2022. [DOI: 10.1007/s00371-022-02648-2] [Reference Citation Analysis]
10 Pfeifer B, Saranti A, Holzinger A. GNN-SubNet: disease subnetwork detection with explainable graph neural networks. Bioinformatics 2022;38:ii120-6. [PMID: 36124793 DOI: 10.1093/bioinformatics/btac478] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
11 Mosqueira-rey E, Hernández-pereira E, Alonso-ríos D, Bobes-bascarán J, Fernández-leal Á. Human-in-the-loop machine learning: a state of the art. Artif Intell Rev. [DOI: 10.1007/s10462-022-10246-w] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
12 Han X, Huwan T, Khan MA. The Modular Design of an English Pronunciation Level Evaluation System Based on Machine Learning. Security and Communication Networks 2022;2022:1-11. [DOI: 10.1155/2022/6804131] [Reference Citation Analysis]
13 Marín-gonzález F, Senior-naveda A, Ferrer LY, Perozo Sierralta B. Generation of Programmatic Contents by Competencies: A Technological and Systemic View of Higher Education. Front Educ 2022;7:915377. [DOI: 10.3389/feduc.2022.915377] [Reference Citation Analysis]
14 Yao Y. Human-machine co-intelligence through symbiosis in the SMV space. Appl Intell. [DOI: 10.1007/s10489-022-03574-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
15 Müller H, Holzinger A, Plass M, Brcic L, Stumptner C, Zatloukal K. Explainability and Causability for Artificial Intelligence-Supported Medical Image Analysis in the Context of the European In Vitro Diagnostic Regulation. New Biotechnology 2022. [DOI: 10.1016/j.nbt.2022.05.002] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
16 Fuentes-alvarez R, Hernandez JH, Matehuala-moran I, Alfaro-ponce M, Lopez-gutierrez R, Salazar S, Lozano R. Assistive robotic exoskeleton using recurrent neural networks for decision taking for the robust trajectory tracking. Expert Systems with Applications 2022;193:116482. [DOI: 10.1016/j.eswa.2021.116482] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
17 Chou Y, Moreira C, Bruza P, Ouyang C, Jorge J. Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications. Information Fusion 2022;81:59-83. [DOI: 10.1016/j.inffus.2021.11.003] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 19.0] [Reference Citation Analysis]
18 [DOI: 10.1145/3477314.3507310] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Theissler A, Thomas M, Burch M, Gerschner F. ConfusionVis: Comparative evaluation and selection of multi-class classifiers based on confusion matrices. Knowledge-Based Systems 2022. [DOI: 10.1016/j.knosys.2022.108651] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
20 Asher N, De Lara L, Paul S, Russell C. Counterfactual Models for Fair and Adequate Explanations. MAKE 2022;4:316-49. [DOI: 10.3390/make4020014] [Reference Citation Analysis]
21 Ulusan A, Narayan U, Snodgrass S, Ergun O, Harteveld C. “Rather Solve the Problem from Scratch”: Gamesploring Human-Machine Collaboration for Optimizing the Debris Collection Problem. 27th International Conference on Intelligent User Interfaces 2022. [DOI: 10.1145/3490099.3511163] [Reference Citation Analysis]
22 Díaz-rodríguez N, Lamas A, Sanchez J, Franchi G, Donadello I, Tabik S, Filliat D, Cruz P, Montes R, Herrera F. EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: The MonuMAI cultural heritage use case. Information Fusion 2022;79:58-83. [DOI: 10.1016/j.inffus.2021.09.022] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 10.0] [Reference Citation Analysis]
23 Pfeifer B, Secic A, Saranti A, Holzinger A. GNN-SubNet: disease subnetwork detection with explainable Graph Neural Networks.. [DOI: 10.1101/2022.01.12.475995] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Finzel B, Saranti A, Angerschmid A, Tafler D, Pfeifer B, Holzinger A. Generating Explanations for Conceptual Validation of Graph Neural Networks: An Investigation of Symbolic Predicates Learned on Relevance-Ranked Sub-Graphs. Kunstliche Intell (Oldenbourg) 2022;36:271-85. [PMID: 36590103 DOI: 10.1007/s13218-022-00781-7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Kori A, Natekar P, Srinivasan B, Krishnamurthi G. Interpreting Deep Neural Networks for Medical Imaging Using Concept Graphs. AI for Disease Surveillance and Pandemic Intelligence 2022. [DOI: 10.1007/978-3-030-93080-6_15] [Reference Citation Analysis]
26 Sinha BB, Dhanalakshmi R. Recent advancements and challenges of Internet of Things in smart agriculture: A survey. Future Generation Computer Systems 2022;126:169-84. [DOI: 10.1016/j.future.2021.08.006] [Cited by in Crossref: 35] [Cited by in F6Publishing: 43] [Article Influence: 35.0] [Reference Citation Analysis]
27 Bröring A, Kulkarni V, Zirkler A, Buschmann P, Fysarakis K, Mayer S, Soret B, Nguyen LD, Popovski P, Samarakoon S, Bennis M, Härri J, Rooker M, Fritz G, Bucur A, Spanoudakis G, Ioannidis S. IntellIoT: Intelligent IoT Environments. Internet of Things 2022. [DOI: 10.1007/978-3-031-20936-9_5] [Reference Citation Analysis]
28 Chouvarda I. Connected health technologies for knowledge extraction and knowledge-based medicine in cardiac care. Personalized Health Systems for Cardiovascular Disease 2022. [DOI: 10.1016/b978-0-12-818950-4.00001-x] [Reference Citation Analysis]
29 Mishra AK. PeC-HiCA: A Perception Centric Human-in-loop Cognitive Architecture. Procedia Computer Science 2022;213:768-773. [DOI: 10.1016/j.procs.2022.11.132] [Reference Citation Analysis]
30 Holzinger A, Saranti A, Molnar C, Biecek P, Samek W. Explainable AI Methods - A Brief Overview. xxAI - Beyond Explainable AI 2022. [DOI: 10.1007/978-3-031-04083-2_2] [Cited by in Crossref: 6] [Article Influence: 6.0] [Reference Citation Analysis]
31 Ticala C, Pintea C, Crisan GC, Matei O, Hajdu-macelaru M, Pop PC. Aspects on Image Edge Detection Based on Sensitive Swarm Intelligence. Lecture Notes in Computer Science 2022. [DOI: 10.1007/978-3-031-15471-3_39] [Reference Citation Analysis]
32 Roehr TM, Harnack D, Wöhrle H, Wiebe F, Schilling M, Lima O, Langosz M, Kumar S, Straube S, Kirchner F. A development cycle for automated self-exploration of robot behaviors. AI Perspect 2021;3:1. [DOI: 10.1186/s42467-021-00008-9] [Reference Citation Analysis]
33 Fernández-edreira D, Liñares-blanco J, Fernandez-lozano C. Machine Learning analysis of the human infant gut microbiome identifies influential species in type 1 diabetes. Expert Systems with Applications 2021;185:115648. [DOI: 10.1016/j.eswa.2021.115648] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
34 Ticala C, Pintea C, Matei O. Sensitive Ant Algorithm for Edge Detection in Medical Images. Applied Sciences 2021;11:11303. [DOI: 10.3390/app112311303] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
35 Carneiro D, Guimarães M, Carvalho M, Novais P. Using meta‐learning to predict performance metrics in machine learning problems. Expert Systems. [DOI: 10.1111/exsy.12900] [Reference Citation Analysis]
36 Zheng Z, Sieber R. Putting humans back in the loop of machine learning in Canadian smart cities. Transactions in GIS. [DOI: 10.1111/tgis.12869] [Reference Citation Analysis]
37 Smith AG, Petersen J, Terrones-Campos C, Berthelsen AK, Forbes NJ, Darkner S, Specht L, Vogelius IR. RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy. Med Phys 2021. [PMID: 34783028 DOI: 10.1002/mp.15353] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
38 Xu C, Liu W, Chen Y. A DES-based group decision model for group decision making with large-scale alternatives. Appl Intell. [DOI: 10.1007/s10489-021-02950-x] [Reference Citation Analysis]
39 Jiang Y, Atif Y. A selective ensemble model for cognitive cybersecurity analysis. Journal of Network and Computer Applications 2021;193:103210. [DOI: 10.1016/j.jnca.2021.103210] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
40 Cabitza F, Campagner A, Simone C. The need to move away from agential-AI: Empirical investigations, useful concepts and open issues. International Journal of Human-Computer Studies 2021;155:102696. [DOI: 10.1016/j.ijhcs.2021.102696] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
41 Kim S, Oh C, Cho WI, Shin D, Suh B, Lee J. Trkic G00gle: Why and How Users Game Translation Algorithms. Proc ACM Hum -Comput Interact 2021;5:1-24. [DOI: 10.1145/3476085] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
42 Zhang Z, Xu Z, Luan S, Li X. A Hybrid Max–Min Ant System by Levy Flight and Opposition-Based Learning. Int J Patt Recogn Artif Intell 2021;35:2151013. [DOI: 10.1142/s0218001421510137] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
43 Hsu YC, Wang JD, Huang PH, Chien YW, Chiu CJ, Lin CY. Integrating domain knowledge with machine learning to detect obstructive sleep apnea: Snore as a significant bio-feature. J Sleep Res 2021;:e13487. [PMID: 34549473 DOI: 10.1111/jsr.13487] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
44 Bitrián P, Buil I, Catalán S. Enhancing user engagement: The role of gamification in mobile apps. Journal of Business Research 2021;132:170-85. [DOI: 10.1016/j.jbusres.2021.04.028] [Cited by in Crossref: 35] [Cited by in F6Publishing: 22] [Article Influence: 17.5] [Reference Citation Analysis]
45 Ostheimer J, Chowdhury S, Iqbal S. An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles. Technology in Society 2021;66:101647. [DOI: 10.1016/j.techsoc.2021.101647] [Cited by in Crossref: 5] [Cited by in F6Publishing: 8] [Article Influence: 2.5] [Reference Citation Analysis]
46 Mobbs D, Wise T, Suthana N, Guzmán N, Kriegeskorte N, Leibo JZ. Promises and challenges of human computational ethology. Neuron 2021;109:2224-38. [PMID: 34143951 DOI: 10.1016/j.neuron.2021.05.021] [Cited by in Crossref: 17] [Cited by in F6Publishing: 16] [Article Influence: 8.5] [Reference Citation Analysis]
47 Holzinger A, Malle B, Saranti A, Pfeifer B. Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI. Information Fusion 2021;71:28-37. [DOI: 10.1016/j.inffus.2021.01.008] [Cited by in Crossref: 126] [Cited by in F6Publishing: 137] [Article Influence: 63.0] [Reference Citation Analysis]
48 Confalonieri R, Weyde T, Besold TR, Moscoso del Prado Martín F. Using ontologies to enhance human understandability of global post-hoc explanations of black-box models. Artificial Intelligence 2021;296:103471. [DOI: 10.1016/j.artint.2021.103471] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 8.5] [Reference Citation Analysis]
49 Patil DO, Hamde ST. Automated detection of brain tumor disease using empirical wavelet transform based LBP variants and ant-lion optimization. Multimed Tools Appl 2021;80:17955-17982. [DOI: 10.1007/s11042-020-10434-2] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
50 Hudec M, Mináriková E, Mesiar R, Saranti A, Holzinger A. Classification by ordinal sums of conjunctive and disjunctive functions for explainable AI and interpretable machine learning solutions. Knowledge-Based Systems 2021;220:106916. [DOI: 10.1016/j.knosys.2021.106916] [Cited by in Crossref: 16] [Cited by in F6Publishing: 9] [Article Influence: 8.0] [Reference Citation Analysis]
51 Al-Taie Z, Liu D, Mitchem JB, Papageorgiou C, Kaifi JT, Warren WC, Shyu CR. Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential. J Biomed Inform 2021;118:103792. [PMID: 33915273 DOI: 10.1016/j.jbi.2021.103792] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
52 Roski J, Maier EJ, Vigilante K, Kane EA, Matheny ME. Enhancing trust in AI through industry self-governance. J Am Med Inform Assoc 2021;28:1582-90. [PMID: 33895824 DOI: 10.1093/jamia/ocab065] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
53 Shahid O, Nasajpour M, Pouriyeh S, Parizi RM, Han M, Valero M, Li F, Aledhari M, Sheng QZ. Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance. J Biomed Inform 2021;117:103751. [PMID: 33771732 DOI: 10.1016/j.jbi.2021.103751] [Cited by in Crossref: 28] [Cited by in F6Publishing: 29] [Article Influence: 14.0] [Reference Citation Analysis]
54 Ogawa K, Hartono P. Collaborative General Purpose Convolutional Neural Networks. Journal of Signal Processing 2021;25:53-61. [DOI: 10.2299/jsp.25.53] [Reference Citation Analysis]
55 Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, Gallivanone F, Cozzi A, D'Amico NC, Sardanelli F. AI applications to medical images: From machine learning to deep learning. Phys Med 2021;83:9-24. [PMID: 33662856 DOI: 10.1016/j.ejmp.2021.02.006] [Cited by in Crossref: 74] [Cited by in F6Publishing: 54] [Article Influence: 37.0] [Reference Citation Analysis]
56 Holzinger A. Explainable AI and Multi-Modal Causability in Medicine. i-com 2021;19:171-9. [DOI: 10.1515/icom-2020-0024] [Cited by in Crossref: 28] [Cited by in F6Publishing: 32] [Article Influence: 14.0] [Reference Citation Analysis]
57 Dorgham OM, Alweshah M, Ryalat MH, Alshaer J, Khader M, Alkhalaileh S. Monarch butterfly optimization algorithm for computed tomography image segmentation. Multimed Tools Appl 2021;80:30057-90. [DOI: 10.1007/s11042-020-10147-6] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
58 Yang F, Yu Z, Chen L, Gu J, Li Q, Guo B. Human-Machine Cooperative Video Anomaly Detection. Proc ACM Hum -Comput Interact 2021;4:1-18. [DOI: 10.1145/3434183] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
59 Krak IV, Barmak OV, Manziuk E. Visual Analytics to Build a Machine Learning Model. Advances in Computer and Electrical Engineering 2021. [DOI: 10.4018/978-1-7998-3970-5.ch015] [Reference Citation Analysis]
60 Asher N, Paul S, Russell C. Fair and Adequate Explanations. Lecture Notes in Computer Science 2021. [DOI: 10.1007/978-3-030-84060-0_6] [Reference Citation Analysis]
61 Schuir J, Brinkhege R, Anton E, Oesterreich TD, Meier P, Teuteberg F. Augmenting Humans in the Loop: Towards an Augmented Reality Object Labeling Application for Crowdsourcing Communities. Lecture Notes in Information Systems and Organisation 2021. [DOI: 10.1007/978-3-030-86797-3_14] [Reference Citation Analysis]
62 Carneiro D, Guimarães M, Sousa M. Optimizing Instance Selection Strategies in Interactive Machine Learning: An Application to Fraud Detection. Hybrid Intelligent Systems 2021. [DOI: 10.1007/978-3-030-73050-5_13] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
63 Nikolić Z, Miladinov Z, Vasiljević S, Katanski S, Tamindžić G, Milošević D, Petrović G. Legume vigour. Acta agriculturae Serbica 2021;26:19-26. [DOI: 10.5937/aaser2151019n] [Reference Citation Analysis]
64 Carneiro D, Guimarães M, Carvalho M, Novais P. Optimizing Model Training in Interactive Learning Scenarios. Advances in Intelligent Systems and Computing 2021. [DOI: 10.1007/978-3-030-72657-7_15] [Reference Citation Analysis]
65 Fister I, Fister D, Fister I. Topology-based generation of sport training sessions. J Ambient Intell Human Comput 2021;12:667-678. [DOI: 10.1007/s12652-020-02048-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
66 Ananthachari P, Makhtumov N. Interactive Machine Learning Approach for Staff Selection Using Genetic Algorithm. Intelligent Human Computer Interaction 2021. [DOI: 10.1007/978-3-030-68449-5_37] [Reference Citation Analysis]
67 Sander J, Kuwertz A. Supplementing Machine Learning with Knowledge Models Towards Semantic Explainable AI. Advances in Intelligent Systems and Computing 2021. [DOI: 10.1007/978-3-030-74009-2_1] [Reference Citation Analysis]
68 Ventirozos F, Jacobo-romero M, Clinch S, Batista-navarro R. Interactive Clustering of Cooking Recipe Instructions: Towards the Automatic Detection of Events Involving Kitchen Devices. 2021 IEEE 15th International Conference on Semantic Computing (ICSC) 2021. [DOI: 10.1109/icsc50631.2021.00064] [Reference Citation Analysis]
69 Bryant PT. Learning. Augmented Humanity 2021. [DOI: 10.1007/978-3-030-76445-6_8] [Reference Citation Analysis]
70 Matei O, Erdei R, Pintea C. Selective Survey: Most Efficient Models and Solvers for Integrative Multimodal Transport. Informatica. [DOI: 10.15388/21-infor449] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
71 Pianosi F, Dobson B, Wagener T. Use of Reservoir Operation Optimization Methods in Practice: Insights from a Survey of Water Resource Managers. J Water Resour Plann Manage 2020;146:02520005. [DOI: 10.1061/(asce)wr.1943-5452.0001301] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
72 Koch M, Ziegler J, Reuter C, Schlegel T, Prilla M. Mensch-Computer-Interaktion als zentrales Gebiet der Informatik – Bestandsaufnahme, Trends und Herausforderungen. Informatik Spektrum 2020;43:381-387. [DOI: 10.1007/s00287-020-01299-8] [Reference Citation Analysis]
73 Obukhov AD, Krasnyanskiy MN. Automated organization of interaction between modules of information systems based on neural network data channels. Neural Comput & Applic 2021;33:7249-69. [DOI: 10.1007/s00521-020-05491-5] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 1.3] [Reference Citation Analysis]
74 Razakatiana M, Kolski C, Mandiau R, Mahatody T. Game Theory-based Human-Assistant Agent Interaction Model. Proceedings of the 8th International Conference on Human-Agent Interaction 2020. [DOI: 10.1145/3406499.3415071] [Reference Citation Analysis]
75 Vescan A, Pintea C, Pop PC. Test Case Prioritization—ANT Algorithm With Faults Severity. Logic Journal of the IGPL 2022;30:277-88. [DOI: 10.1093/jigpal/jzaa061] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
76 Gui Z, Peng D, Wu H, Long X. MSGC: Multi-scale grid clustering by fusing analytical granularity and visual cognition for detecting hierarchical spatial patterns. Future Generation Computer Systems 2020;112:1038-56. [DOI: 10.1016/j.future.2020.06.053] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
77 Confalonieri R, Coba L, Wagner B, Besold TR. A historical perspective of explainable Artificial Intelligence. WIREs Data Mining Knowl Discov 2021;11. [DOI: 10.1002/widm.1391] [Cited by in Crossref: 35] [Cited by in F6Publishing: 36] [Article Influence: 11.7] [Reference Citation Analysis]
78 Gambino O, Rundo L, Pirrone R, Vitabile S. HCI for biomedical decision-making: From diagnosis to therapy. J Biomed Inform 2020;111:103593. [PMID: 33069887 DOI: 10.1016/j.jbi.2020.103593] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
79 Han S, Ahn D, Park S, Yang J, Lee S, Kim J, Yang H, Park S, Cha M. Learning to Score Economic Development from Satellite Imagery. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020. [DOI: 10.1145/3394486.3403347] [Cited by in Crossref: 5] [Article Influence: 1.7] [Reference Citation Analysis]
80 Cygert S, Czyżewski A. Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform. Applied Sciences 2020;10:5763. [DOI: 10.3390/app10175763] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
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