المؤلفون: Xinyu Liu, Guoen Xia, Xianquan Zhang, Wenbin Ma, Chunqiang Yu

المصدر: Scientific Reports, Vol 14, Iss 1, Pp 1-28 (2024)

مصطلحات موضوعية: Hybrid neural network, Churn prediction, Deep learning, Medicine, Science

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2045-2322

URL الوصول: https://doaj.org/article/05dfdbc167ff41dd9c18ba756713d0bd

المؤلفون: Ahlstrand, Jim, Boldt, Martin, Borg, Anton, Grahn, Håkan

المصدر: 2024 5th International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2024. :44-51

مصطلحات موضوعية: Customer churn prediction, Machine learning, Telecom B2B customers, Time-series data, Adaptive boosting, Prediction models, Recurrent neural networks, Time series, Churn predictions, Customer churns, F1 scores, Machine-learning, Telecom, Telecom B2B customer, Times series, Training model, Sales

وصف الملف: electronic

URL الوصول: https://urn.kb.se/resolve?urn=urn:nbn:se:bth-27323
https://bth.diva-portal.org/smash/get/diva2:1923857/FULLTEXT01.pdf

المؤلفون: Ferreira, Katia Florinda Narcy

المساهمون: António, Nuno Miguel da Conceição, RUN

مصطلحات موضوعية: Customer Churn, Machine Learning, Supply Chain, Ensemble Methods, E-commerce, Churn Prediction, SDG 4 - Quality education, SDG 8 - Decent work and economic growth, SDG 9 - Industry, innovation and infrastructure, Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação

وصف الملف: application/pdf

الاتاحة: http://hdl.handle.net/10362/174692

المؤلفون: Raji Krishna, D. Jayanthi, D.S. Shylu Sam, K. Kavitha, Naveen Kumar Maurya, T. Benil

المصدر: Results in Engineering, Vol 24, Iss , Pp 103165- (2024)

مصطلحات موضوعية: Churn prediction, Machine learning, Random forest, Classification, Telecom business, Technology

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2590123024014208; https://doaj.org/toc/2590-1230

URL الوصول: https://doaj.org/article/5473aea1eee54810bac2e36b2abb83f3

المؤلفون: Fumo, Dalton

المساهمون: Guedes, Ana, Veritati - Repositório Institucional da Universidade Católica Portuguesa

مصطلحات موضوعية: Churn prediction, Logistic regression, Gradient boosting, Interpretability & explainability, SHAP, Previsão de churn, Regressão logística, Interpretabilidade e explicabilidade, Domínio/Área Científica::Ciências Sociais::Economia e Gestão

وصف الملف: application/pdf

الاتاحة: http://hdl.handle.net/10400.14/46182

المؤلفون: Hui Zhang, Weihua Zhang

المصدر: Heliyon, Vol 10, Iss 17, Pp e37229- (2024)

مصطلحات موضوعية: Customer Relationship Management (CRM), Churn prediction, GWO-attention-ConvLSTM model, Time series, Customer satisfaction analysis, Science (General), Q1-390, Social sciences (General), H1-99

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2405844024132604; https://doaj.org/toc/2405-8440

URL الوصول: https://doaj.org/article/409ecbbb6da14e7baa739965e7b589f3

المؤلفون: Salii, Jill

المساهمون: Paiva, Nuno, Veritati - Repositório Institucional da Universidade Católica Portuguesa

مصطلحات موضوعية: Synthetic data, Churn prediction, Imbalanced datasets, Telecommunication, SMOTENC, ADASYN, TVAE, CTGAN, Lift score, Data quality, Dados sintéticos, Previsão de churn, Conjuntos de dados desbalanceados, Indústria de telecomunicações, Qualidade dos dados, Domínio/Área Científica::Ciências Sociais::Economia e Gestão

وصف الملف: application/pdf

الاتاحة: http://hdl.handle.net/10400.14/45795

المؤلفون: Shimaa Ouf, Kholoud T. Mahmoud, Manal A. Abdel-Fattah

المصدر: Journal of Big Data, Vol 11, Iss 1, Pp 1-27 (2024)

مصطلحات موضوعية: Telecom industry, Churn prediction, Machine learning, XGBOOST classifier, Hybrid framework, Data preprocessing, Computer engineering. Computer hardware, TK7885-7895, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2196-1115

URL الوصول: https://doaj.org/article/2d8ec1edff724b18a550e3b0fa41edae

المؤلفون: Pahul Preet Singh, Fahim Islam Anik, Rahul Senapati, Arnav Sinha, Nazmus Sakib, Eklas Hossain

المصدر: Data Science and Management, Vol 7, Iss 1, Pp 7-16 (2024)

مصطلحات موضوعية: Bank customer attrition, Churn prediction, Machine learning, XGboost, Random forest, Electronic computers. Computer science, QA75.5-76.95

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666764923000401; https://doaj.org/toc/2666-7649

URL الوصول: https://doaj.org/article/7982e3c50eca498ab0d6264ba375b7a1

المؤلفون: Meryem Chajia, El Habib Nfaoui

المصدر: Future Internet, Vol 16, Iss 12, p 453 (2024)

مصطلحات موضوعية: customer churn, customer churn prediction, machine learning, LLMs, embedding, openAI, Information technology, T58.5-58.64

وصف الملف: electronic resource

Relation: https://www.mdpi.com/1999-5903/16/12/453; https://doaj.org/toc/1999-5903

URL الوصول: https://doaj.org/article/bb4be8eb98d94042aea17b5a17374f06

المؤلفون: Lince, Miguel Ângelo Nunes

المساهمون: Henriques, Roberto André Pereira, RUN

مصطلحات موضوعية: Casual inference, Price elasticity, Churn prediction, Machine Learning, Motor insurance, Pricing optimization, SDG 8 - Decent work and economic growth, SDG 9 - Industry, innovation and infrastructure, SDG 11 - Sustainable cities and communities, Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação

وصف الملف: application/pdf

الاتاحة: http://hdl.handle.net/10362/163577

المؤلفون: Nien-Ting Lee, Hau-Chen Lee, Joseph Hsin, Shih-Hau Fang

المصدر: IEEE Open Journal of the Computer Society, Vol 5, Pp 27-38 (2024)

مصطلحات موضوعية: Customer behavior, customer churn prediction, machine learning, buy till you die (BTYD), Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/10334013/; https://doaj.org/toc/2644-1268

URL الوصول: https://doaj.org/article/ce41d65d51904749ad97013d2bd7bda1

المؤلفون: Somak Saha, Chamak Saha, Md. Mahidul Haque, Md. Golam Rabiul Alam, Ashis Talukder

المصدر: IEEE Access, Vol 12, Pp 4471-4484 (2024)

مصطلحات موضوعية: Squeeze and excitation, spatial attention, residual block, churn prediction, ChurnNet, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/10380579/; https://doaj.org/toc/2169-3536

URL الوصول: https://doaj.org/article/9f418e610fdb4838b55fed44e4b1d612

المؤلفون: João B. G. Brito, Guilherme B. Bucco, Rodrigo Heldt, João L. Becker, Cleo S. Silveira, Fernando B. Luce, Michel J. Anzanello

المصدر: Financial Innovation, Vol 10, Iss 1, Pp 1-29 (2024)

مصطلحات موضوعية: Customer churn prediction, Imbalanced dataset treatment, Feature engineering, RFM, Public finance, K4430-4675, Finance, HG1-9999

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2199-4730

URL الوصول: https://doaj.org/article/846d239c10db46cabe3d26b1214e0a03

المؤلفون: Bijan Moradi, Mehran Khalaj, Ali Taghizadeh Herat, Asghar Darigh, Alireza Tamjid Yamcholo

المصدر: AIMS Mathematics, Vol 9, Iss 2, Pp 2781-2807 (2024)

مصطلحات موضوعية: customer churn prediction, telecommunication industry, dataset balancing, feature selection, ensemble learning, whale optimization algorithm, Mathematics, QA1-939

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2473-6988

URL الوصول: https://doaj.org/article/8d602adc050b44c488b628bf37295a5a

المؤلفون: Awais Manzoor, M. Atif Qureshi, Etain Kidney, Luca Longo

المصدر: IEEE Access, Vol 12, Pp 70434-70463 (2024)

مصطلحات موضوعية: Churn prediction, machine learning, artificial intelligence, business decision making, customer defection, marketing analytics, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/10531735/; https://doaj.org/toc/2169-3536

URL الوصول: https://doaj.org/article/61a83a8788d24a068ede83ea2edf9a39

المؤلفون: You-Jung Han, Jihoon Moon, Jiyoung Woo

المصدر: IEEE Access, Vol 12, Pp 101971-101984 (2024)

مصطلحات موضوعية: Churn prediction in gaming, social activity analysis, graph neural networks, MMORPG user behavior, online gaming retention strategies, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/10600684/; https://doaj.org/toc/2169-3536

URL الوصول: https://doaj.org/article/dd99b439ba8641728f0cd31f43c18c4b

المؤلفون: Liu, Zhenkun, Jiang, Ping, de Bock, Koen W., Wang, Jianzhou, Zhang, Lifang, Niu, Xinsong

المساهمون: Nanjing University of Posts and Telecommunications Nanjing (NJUPT), Dongbei University of Finance and Economics, Dalian (DUFES), Audencia Business School, Audencia Recherche, Macau University of Science and Technology (MUST), Nanjing University of Finance and Economics (NUFE), Chinese Academy of Sciences Beijing (CAS)

المصدر: ISSN: 0040-1625 ; Technological Forecasting and Social Change ; https://hal.science/hal-04273578 ; Technological Forecasting and Social Change, 2024, 198 (January 2024), pp.122945. ⟨10.1016/j.techfore.2023.122945⟩.

مصطلحات موضوعية: Bayesian optimization, Customer churn prediction, Extreme gradient boosting tree, Profit maximization, Profit-driven customer churn prediction, Sensitivity analysis, [SHS.GESTION]Humanities and Social Sciences/Business administration, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]

الاتاحة: https://hal.science/hal-04273578
https://hal.science/hal-04273578v1/document
https://hal.science/hal-04273578v1/file/Manuscript%20HAL%20preprint.pdf
https://doi.org/10.1016/j.techfore.2023.122945

المؤلفون: Dias, João Pedro Rolim

المساهمون: António, Nuno Miguel da Conceição, RUN

مصطلحات موضوعية: Data Mining, Customer Churn, Churn Prediction, Machine Learning, Supervised Learning, SaaS, Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação

وصف الملف: application/pdf

الاتاحة: http://hdl.handle.net/10362/159898

المؤلفون: Sweidan, D., Johansson, Ulf, Gidenstam, A., Alenljung, B.

المصدر: International Conference on Machine Learning and Applications (ICMLA). :635-640

مصطلحات موضوعية: Sales, Case-studies, Churn rates, Correlation, Customer churn prediction, Customer churns, Digital retailing, Feature importance, High costs, RFM analysis, Top probability, Forecasting, correlations, top probabilities

وصف الملف: print

URL الوصول: https://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-60233
https://ieeexplore.ieee.org/document/10068870