{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:29:22Z","timestamp":1742981362246,"version":"3.40.3"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031576386"},{"type":"electronic","value":"9783031576393"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-57639-3_3","type":"book-chapter","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T14:01:39Z","timestamp":1712671299000},"page":"62-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Refining Detection Mechanism of\u00a0Mobile Money Fraud Using MoMTSim Platform"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8932-5762","authenticated-orcid":false,"given":"Denish","family":"Azamuke","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4718-4209","authenticated-orcid":false,"given":"Marriette","family":"Katarahweire","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6566-5343","authenticated-orcid":false,"given":"Joshua","family":"Muleesi Businge","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1581-8363","authenticated-orcid":false,"given":"Samuel","family":"Kizza","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4493-1317","authenticated-orcid":false,"given":"Chrisostom","family":"Opio","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3304-4144","authenticated-orcid":false,"given":"Engineer","family":"Bainomugisha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,10]]},"reference":[{"issue":"1","key":"3_CR1","doi-asserted-by":"publisher","first-page":"1815963","DOI":"10.1080\/23311886.2020.1815963","volume":"6","author":"BE Akinyemi","year":"2020","unstructured":"Akinyemi, B.E., Mushunje, A.: Determinants of mobile money technology adoption in rural areas of Africa. Cogent Soc. Sci. 6(1), 1815963 (2020). https:\/\/doi.org\/10.1080\/23311886.2020.1815963","journal-title":"Cogent Soc. Sci."},{"issue":"6","key":"3_CR2","doi-asserted-by":"publisher","first-page":"309","DOI":"10.3390\/info11060309","volume":"11","author":"G Ali","year":"2020","unstructured":"Ali, G., Ally Dida, M., Elikana Sam, A.: Evaluation of key security issues associated with mobile money systems in Uganda. Information 11(6), 309 (2020). https:\/\/doi.org\/10.3390\/info11060309","journal-title":"Information"},{"key":"3_CR3","doi-asserted-by":"publisher","unstructured":"Altman, E.: Synthesizing credit card transactions. In: Proceedings of the Second ACM International Conference on AI in Finance, pp. 1\u20139 (2021). https:\/\/doi.org\/10.1145\/3490354.3494378","DOI":"10.1145\/3490354.3494378"},{"key":"3_CR4","doi-asserted-by":"publisher","unstructured":"Azamuke, D., Katarahweire, M., Bainomugisha, E.: Scenario-based synthetic dataset generation for mobile money transactions. In: Proceedings of the Federated Africa and Middle East Conference on Software Engineering, pp. 64\u201372 (2022). https:\/\/doi.org\/10.1145\/3531056.3542774","DOI":"10.1145\/3531056.3542774"},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.procs.2020.06.014","volume":"173","author":"S Bagga","year":"2020","unstructured":"Bagga, S., Goyal, A., Gupta, N., Goyal, A.: Credit card fraud detection using pipeling and ensemble learning. Procedia Comput. Sci. 173, 104\u2013112 (2020). https:\/\/doi.org\/10.1016\/j.procs.2020.06.014","journal-title":"Procedia Comput. Sci."},{"issue":"8","key":"3_CR6","doi-asserted-by":"publisher","first-page":"383","DOI":"10.3390\/info11080383","volume":"11","author":"FE Botchey","year":"2020","unstructured":"Botchey, F.E., Qin, Z., Hughes-Lartey, K.: Mobile money fraud prediction-a cross-case analysis on the efficiency of Support vector machines, Gradient boosted decision trees, and Na\u00efve Bayes algorithms. Information 11(8), 383 (2020). https:\/\/doi.org\/10.3390\/info11080383","journal-title":"Information"},{"issue":"1","key":"3_CR7","doi-asserted-by":"publisher","first-page":"20","DOI":"10.38094\/jastt20165","volume":"2","author":"B Charbuty","year":"2021","unstructured":"Charbuty, B., Abdulazeez, A.: Classification based on decision tree algorithm for machine learning. J. Appl. Sci. Technol. Trends 2(1), 20\u201328 (2021). https:\/\/doi.org\/10.38094\/jastt20165","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002). https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J. Artif. Intell. Res."},{"key":"3_CR9","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"3_CR10","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2022.876814","volume":"9","author":"D Chicco","year":"2022","unstructured":"Chicco, D., Jurman, G.: An invitation to greater use of Matthews correlation coefficient in robotics and artificial intelligence. Front. Robot. AI 9, 876814 (2022). https:\/\/doi.org\/10.3389\/frobt.2022.876814","journal-title":"Front. Robot. AI"},{"issue":"1","key":"3_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-021-00244-z","volume":"14","author":"D Chicco","year":"2021","unstructured":"Chicco, D., T\u00f6tsch, N., Jurman, G.: The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData Mining 14(1), 1\u201322 (2021). https:\/\/doi.org\/10.1186\/s13040-021-00244-z","journal-title":"BioData Mining"},{"key":"3_CR12","unstructured":"Collier, N.: RePast: an extensible framework for agent simulation. Nat. Resour. Environ. Issues 8(4) (2001)"},{"key":"3_CR13","unstructured":"Department of Computer Science, Makerere University: MoMTSim financial simulation platform (2023). version 1.0.0. www.github.com\/aiinfinancegroup\/MoMTSim"},{"issue":"1","key":"3_CR14","doi-asserted-by":"publisher","first-page":"49","DOI":"10.33215\/sjom.v5i1.770","volume":"5","author":"Z Faraji","year":"2022","unstructured":"Faraji, Z.: A review of machine learning applications for credit card fraud detection with a case study. SEISENSE J. Manag. 5(1), 49\u201359 (2022). https:\/\/doi.org\/10.33215\/sjom.v5i1.770","journal-title":"SEISENSE J. Manag."},{"key":"3_CR15","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1007\/978-3-540-39964-3_62","volume-title":"On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE","author":"G Guo","year":"2003","unstructured":"Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. LNCS, vol. 2888, pp. 986\u2013996. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-39964-3_62"},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"Hajek, P., Abedin, M.Z., Sivarajah, U.: Fraud detection in mobile payment systems using an XGBoost-based framework. Inf. Syst. Front. 1\u201319 (2022). https:\/\/doi.org\/10.1007\/s10796-022-10346-6","DOI":"10.1007\/s10796-022-10346-6"},{"issue":"1","key":"3_CR17","doi-asserted-by":"publisher","first-page":"38","DOI":"10.2214\/AJR.18.20224","volume":"212","author":"GS Handelman","year":"2019","unstructured":"Handelman, G.S., et al.: Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. Am. J. Roentgenol. 212(1), 38\u201343 (2019). https:\/\/doi.org\/10.2214\/AJR.18.20224","journal-title":"Am. J. Roentgenol."},{"key":"3_CR18","doi-asserted-by":"publisher","unstructured":"Itoo, F., Meenakshi, Singh, S.: Comparison and analysis of Logistic regression, Na\u00efve Bayes and KNN machine learning algorithms for credit card fraud detection. Int. J. Inf. Technol. 13, 1503\u20131511 (2021). https:\/\/doi.org\/10.1007\/s41870-020-00430-y","DOI":"10.1007\/s41870-020-00430-y"},{"key":"3_CR19","unstructured":"Karpov, Y.G.: AnyLogic: a new generation professional simulation tool. In: VI International Congress on Mathematical Modeling, Nizni-Novgorog, Russia (2004)"},{"key":"3_CR20","doi-asserted-by":"publisher","unstructured":"Kolluri, J., Kotte, V.K., Phridviraj, M., Razia, S.: Reducing overfitting problem in machine learning using novel L1\/4 regularization method. In: 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI) (48184), pp. 934\u2013938. IEEE (2020). https:\/\/doi.org\/10.1109\/ICOEI48184.2020.9142992","DOI":"10.1109\/ICOEI48184.2020.9142992"},{"issue":"18","key":"3_CR21","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1161\/CIRCULATIONAHA.106.682658","volume":"117","author":"MP LaValley","year":"2008","unstructured":"LaValley, M.P.: Logistic regression. Circulation 117(18), 2395\u20132399 (2008)","journal-title":"Circulation"},{"issue":"3","key":"3_CR22","doi-asserted-by":"publisher","first-page":"300","DOI":"10.3390\/e23030300","volume":"23","author":"M Lokanan","year":"2021","unstructured":"Lokanan, M., Liu, S.: Predicting fraud victimization using classical machine learning. Entropy 23(3), 300 (2021). https:\/\/doi.org\/10.3390\/e23030300","journal-title":"Entropy"},{"key":"3_CR23","doi-asserted-by":"publisher","unstructured":"Lokanan, M.E.: Predicting money laundering using machine learning and artificial neural networks algorithms in banks. J. Appl. Secur. Res. 1\u201325 (2022). https:\/\/doi.org\/10.1080\/19361610.2022.2114744","DOI":"10.1080\/19361610.2022.2114744"},{"issue":"2","key":"3_CR24","doi-asserted-by":"publisher","DOI":"10.1002\/ail2.85","volume":"4","author":"ME Lokanan","year":"2023","unstructured":"Lokanan, M.E.: Predicting mobile money transaction fraud using machine learning algorithms. Appl. AI Lett. 4(2), e85 (2023). https:\/\/doi.org\/10.1002\/ail2.85","journal-title":"Appl. AI Lett."},{"key":"3_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2022.100269","volume":"8","author":"ME Lokanan","year":"2022","unstructured":"Lokanan, M.E., Sharma, K.: Fraud prediction using machine learning: the case of investment advisors in Canada. Mach. Learn. Appl. 8, 100269 (2022). https:\/\/doi.org\/10.1016\/j.mlwa.2022.100269","journal-title":"Mach. Learn. Appl."},{"key":"3_CR26","unstructured":"Lopez-Rojas, E., Elmir, A., Axelsson, S.: PaySim: a financial mobile money simulator for fraud detection. In: 28th European Modeling and Simulation Symposium, EMSS, Larnaca, pp. 249\u2013255. Dime University of Genoa (2016)"},{"key":"3_CR27","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1007\/978-3-030-22868-2_51","volume-title":"Intelligent Computing","author":"EA Lopez-Rojas","year":"2019","unstructured":"Lopez-Rojas, E.A., Barneaud, C.: Advantages of the PaySim simulator for improving financial fraud controls. In: Arai, K., Bhatia, R., Kapoor, S. (eds.) CompCom 2019. Advances in Intelligent Systems and Computing, vol. 998, pp. 727\u2013736. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-22868-2_51"},{"issue":"4","key":"3_CR28","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1504\/IJSPM.2018.093756","volume":"13","author":"EA Lopez-Rojas","year":"2018","unstructured":"Lopez-Rojas, E.A., Axelsson, S., Baca, D.: Analysis of fraud controls using the PaySim financial simulator. Int. J. Simul. Process Model. 13(4), 377\u2013386 (2018). https:\/\/doi.org\/10.1504\/IJSPM.2018.093756","journal-title":"Int. J. Simul. Process Model."},{"key":"3_CR29","unstructured":"Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K.: MASON: a new multi-agent simulation toolkit. In: Proceedings of the 2004 Swarmfest Workshop, Michigan, USA, vol. 8, pp. 316\u2013327 (2004)"},{"issue":"7","key":"3_CR30","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1177\/0037549705058073","volume":"81","author":"S Luke","year":"2005","unstructured":"Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K., Balan, G.: MASON: a multiagent simulation environment. Simulation 81(7), 517\u2013527 (2005). https:\/\/doi.org\/10.1177\/0037549705058073","journal-title":"Simulation"},{"issue":"5","key":"3_CR31","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1080\/00273171.2015.1036965","volume":"50","author":"DM McNeish","year":"2015","unstructured":"McNeish, D.M.: Using lasso for predictor selection and to assuage overfitting: a method long overlooked in behavioral sciences. Multivar. Behav. Res. 50(5), 471\u2013484 (2015). https:\/\/doi.org\/10.1080\/00273171.2015.1036965","journal-title":"Multivar. Behav. Res."},{"key":"3_CR32","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.eswa.2018.06.011","volume":"110","author":"S Nami","year":"2018","unstructured":"Nami, S., Shajari, M.: Cost-sensitive payment card fraud detection based on dynamic Random forest and K-nearest neighbors. Expert Syst. Appl. 110, 381\u2013392 (2018). https:\/\/doi.org\/10.1016\/j.eswa.2018.06.011","journal-title":"Expert Syst. Appl."},{"key":"3_CR33","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3209827","author":"IK Nti","year":"2022","unstructured":"Nti, I.K., Somanathan, A.R.: A scalable RF-XGBoost framework for financial fraud mitigation. IEEE Trans. Comput. Soc. Syst. (2022). https:\/\/doi.org\/10.1109\/TCSS.2022.3209827","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"3_CR34","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1007\/978-3-030-03146-6_86","volume-title":"Intelligent Data Communication Technologies and Internet of Things (ICICI)","author":"A Parmar","year":"2019","unstructured":"Parmar, A., Katariya, R., Patel, V.: A review on Random forest: an ensemble classifier. In: Hemanth, J., Fernando, X., Lafata, P., Baig, Z. (eds.) ICICI 2018, pp. 758\u2013763. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-03146-6_86"},{"issue":"2","key":"3_CR35","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1504\/IJSPM.2018.093756","volume":"30","author":"J Perols","year":"2011","unstructured":"Perols, J.: Financial statement fraud detection: an analysis of statistical and machine learning algorithms. Auditing A J. Pract. Theory 30(2), 19\u201350 (2011). https:\/\/doi.org\/10.1504\/IJSPM.2018.093756","journal-title":"Auditing A J. Pract. Theory"},{"issue":"1","key":"3_CR36","doi-asserted-by":"publisher","first-page":"3763","DOI":"10.48550\/arXiv.2010.16061","volume":"2","author":"DM Powers","year":"2011","unstructured":"Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2(1), 3763 (2011). https:\/\/doi.org\/10.48550\/arXiv.2010.16061","journal-title":"J. Mach. Learn. Technol."},{"issue":"15","key":"3_CR37","doi-asserted-by":"publisher","first-page":"5916","DOI":"10.1016\/j.eswa.2013.05.021","volume":"40","author":"Y Sahin","year":"2013","unstructured":"Sahin, Y., Bulkan, S., Duman, E.: A cost-sensitive decision tree approach for fraud detection. Expert Syst. Appl. 40(15), 5916\u20135923 (2013). https:\/\/doi.org\/10.1016\/j.eswa.2013.05.021","journal-title":"Expert Syst. Appl."},{"key":"3_CR38","doi-asserted-by":"publisher","unstructured":"Sahin, Y., Duman, E.: Detecting credit card fraud by ANN and logistic regression. In: 2011 International Symposium on Innovations in Intelligent Systems and Applications, pp. 315\u2013319. IEEE (2011). https:\/\/doi.org\/10.1109\/INISTA.2011.5946108","DOI":"10.1109\/INISTA.2011.5946108"},{"key":"3_CR39","doi-asserted-by":"publisher","unstructured":"Sailusha, R., Gnaneswar, V., Ramesh, R., Rao, G.R.: Credit card fraud detection using machine learning. In: 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1264\u20131270. IEEE (2020). https:\/\/doi.org\/10.1109\/ICICCS48265.2020.9121114","DOI":"10.1109\/ICICCS48265.2020.9121114"},{"key":"3_CR40","doi-asserted-by":"publisher","unstructured":"Sundarkumar, G.G., Ravi, V., Siddeshwar, V.: One-class support vector machine based undersampling: application to churn prediction and insurance fraud detection. In: 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1\u20137. IEEE (2015). https:\/\/doi.org\/10.1109\/ICCIC.2015.7435726","DOI":"10.1109\/ICCIC.2015.7435726"},{"key":"3_CR41","doi-asserted-by":"publisher","unstructured":"Tang, Q., et al.: Prediction of casing damage in unconsolidated sandstone reservoirs using machine learning algorithms. In: 2019 IEEE International Conference on Computation, Communication and Engineering (ICCCE), pp. 185\u2013188. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCCE48422.2019.9010785","DOI":"10.1109\/ICCCE48422.2019.9010785"},{"key":"3_CR42","doi-asserted-by":"publisher","unstructured":"Thennakoon, A., Bhagyani, C., Premadasa, S., Mihiranga, S., Kuruwitaarachchi, N.: Real-time credit card fraud detection using machine learning. In: 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 488\u2013493. IEEE (2019). https:\/\/doi.org\/10.1109\/CONFLUENCE.2019.8776942","DOI":"10.1109\/CONFLUENCE.2019.8776942"},{"key":"3_CR43","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.procs.2020.06.070","volume":"174","author":"Z Tian","year":"2020","unstructured":"Tian, Z., Xiao, J., Feng, H., Wei, Y.: Credit risk assessment based on Gradient boosting decision tree. Procedia Comput. Sci. 174, 150\u2013160 (2020). https:\/\/doi.org\/10.1016\/j.procs.2020.06.070","journal-title":"Procedia Comput. Sci."},{"key":"3_CR44","unstructured":"Tisue, S., Wilensky, U.: NetLogo: a simple environment for modeling complexity. In: International Conference on Complex Systems, vol. 21, pp. 16\u201321. Citeseer (2004)"},{"key":"3_CR45","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Tong, J., Wang, Z., Gao, F.: Customer transaction fraud detection using XGBoost model. In: 2020 International Conference on Computer Engineering and Application (ICCEA), pp. 554\u2013558. IEEE (2020). https:\/\/doi.org\/10.1109\/ICCEA50009.2020.00122","DOI":"10.1109\/ICCEA50009.2020.00122"}],"container-title":["Communications in Computer and Information Science","Pan-African Conference on Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-57639-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T14:03:16Z","timestamp":1712671396000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-57639-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031576386","9783031576393"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-57639-3_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"10 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PanAfriConAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pan African Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Addis Ababa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ethiopia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"panafricon2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/panafriconai.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"134","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}