This work is licensed under the Creative Commons Attribution 4.0 International License.
Ntumba, C., Aguayo, S., & Maina, K. (2023). Revolutionizing retail: a mini review of e-commerce evolution. Journal of Digital Marketing and Communication, 3(2), 100-110.NtumbaC.AguayoS.MainaK. (2023). Revolutionizing retail: a mini review of e-commerce evolution. Journal of Digital Marketing and Communication, 3(2), 100-110.Search in Google Scholar
Tsantekidis, A., Passalis, N., & Tefas, A. (2022). Recurrent neural networks. In Deep learning for robot perception and cognition (pp. 101-115). Academic Press.TsantekidisA.PassalisN.TefasA. (2022). Recurrent neural networks. In Deep learning for robot perception and cognition (pp. 101-115). Academic Press.Search in Google Scholar
Shiri, F. M., Perumal, T., Mustapha, N., & Mohamed, R. (2023). A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU. arXiv preprint arXiv:2305.17473.ShiriF. M.PerumalT.MustaphaN.MohamedR. (2023). A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU. arXiv preprint arXiv:2305.17473.Search in Google Scholar
Das, S., Tariq, A., Santos, T., Kantareddy, S. S., & Banerjee, I. (2023). Recurrent neural networks (RNNs): architectures, training tricks, and introduction to influential research. Machine learning for Brain disorders, 117-138.DasS.TariqA.SantosT.KantareddyS. S.BanerjeeI. (2023). Recurrent neural networks (RNNs): architectures, training tricks, and introduction to influential research. Machine learning for Brain disorders, 117-138.Search in Google Scholar
Zha, W., Liu, Y., Wan, Y., Luo, R., Li, D., Yang, S., & Xu, Y. (2022). Forecasting monthly gas field production based on the CNN-LSTM model. Energy, 260, 124889.ZhaW.LiuY.WanY.LuoR.LiD.YangS.XuY. (2022). Forecasting monthly gas field production based on the CNN-LSTM model. Energy, 260, 124889.Search in Google Scholar
Huang, R., Wei, C., Wang, B., Yang, J., Xu, X., Wu, S., & Huang, S. (2022). Well performance prediction based on Long Short-Term Memory (LSTM) neural network. Journal of Petroleum Science and Engineering, 208, 109686.HuangR.WeiC.WangB.YangJ.XuX.WuS.HuangS. (2022). Well performance prediction based on Long Short-Term Memory (LSTM) neural network. Journal of Petroleum Science and Engineering, 208, 109686.Search in Google Scholar
She, J., Gong, S., Yang, S., Yang, H., & Lu, S. (2022, July). Xigmoid: An approach to improve the gating mechanism of RNN. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-10). IEEE..SheJ.GongS.YangS.YangH.LuS. (2022, July). Xigmoid: An approach to improve the gating mechanism of RNN. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-10). IEEE..Search in Google Scholar
Jorge, I., Mesbahi, T., Samet, A., & Boné, R. (2023). Time series feature extraction for lithium-ion batteries state-of-health prediction. Journal of Energy Storage, 59, 106436.JorgeI.MesbahiT.SametA.BonéR. (2023). Time series feature extraction for lithium-ion batteries state-of-health prediction. Journal of Energy Storage, 59, 106436.Search in Google Scholar
Vichi, M., Cavicchia, C., & Groenen, P. J. (2022). Hierarchical means clustering. Journal of Classification, 39(3), 553-577.VichiM.CavicchiaC.GroenenP. J. (2022). Hierarchical means clustering. Journal of Classification, 39(3), 553-577.Search in Google Scholar
Moseley, B., & Wang, J. R. (2023). Approximation bounds for hierarchical clustering: Average linkage, bisecting k-means, and local search. Journal of Machine Learning Research, 24(1), 1-36.MoseleyB.WangJ. R. (2023). Approximation bounds for hierarchical clustering: Average linkage, bisecting k-means, and local search. Journal of Machine Learning Research, 24(1), 1-36.Search in Google Scholar
Goldstein, A., & Hajaj, C. (2022). The hidden conversion funnel of mobile vs. desktop consumers. Electronic Commerce Research and Applications, 53, 101135.GoldsteinA.HajajC. (2022). The hidden conversion funnel of mobile vs. desktop consumers. Electronic Commerce Research and Applications, 53, 101135.Search in Google Scholar
Golik-Górecka, G. (2023). Web analytics–the dominant problem of marketing automation and sales funnel. Marketing Instytucji Naukowych i Badawczych, 50(4), 73-92.Golik-GóreckaG. (2023). Web analytics–the dominant problem of marketing automation and sales funnel. Marketing Instytucji Naukowych i Badawczych, 50(4), 73-92.Search in Google Scholar
Wenz, A., Jäckle, A., Burton, J., & Couper, M. P. (2022). The effects of personalized feedback on participation and reporting in mobile app data collection. Social Science Computer Review, 40(1), 165-178.WenzA.JäckleA.BurtonJ.CouperM. P. (2022). The effects of personalized feedback on participation and reporting in mobile app data collection. Social Science Computer Review, 40(1), 165-178.Search in Google Scholar
Fernandes, P., Madaan, A., Liu, E., Farinhas, A., Martins, P. H., Bertsch, A., … & Martins, A. F. (2023). Bridging the gap: A survey on integrating (human) feedback for natural language generation. Transactions of the Association for Computational Linguistics, 11, 1643-1668.FernandesP.MadaanA.LiuE.FarinhasA.MartinsP. H.BertschA.MartinsA. F. (2023). Bridging the gap: A survey on integrating (human) feedback for natural language generation. Transactions of the Association for Computational Linguistics, 11, 1643-1668.Search in Google Scholar
Zhu, X., & Goldberg, A. (2009). Introduction to semi-supervised learning. Morgan & Claypool Publishers.ZhuX.GoldbergA. (2009). Introduction to semi-supervised learning. Morgan & Claypool Publishers.Search in Google Scholar
Valkenborg, D., Geubbelmans, M., Rousseau, A. J., & Burzykowski, T. (2023). Supervised learning. American Journal of Orthodontics and Dentofacial Orthopedics, 164(1), 146-149.ValkenborgD.GeubbelmansM.RousseauA. J.BurzykowskiT. (2023). Supervised learning. American Journal of Orthodontics and Dentofacial Orthopedics, 164(1), 146-149.Search in Google Scholar
Zabor, E. C., Reddy, C. A., Tendulkar, R. D., & Patil, S. (2022). Logistic regression in clinical studies. International Journal of Radiation Oncology* Biology* Physics, 112(2), 271-277.ZaborE. C.ReddyC. A.TendulkarR. D.PatilS. (2022). Logistic regression in clinical studies. International Journal of Radiation Oncology* Biology* Physics, 112(2), 271-277.Search in Google Scholar
James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). Unsupervised learning. In An introduction to statistical learning: with applications in Python (pp. 503-556). Cham: Springer International Publishing.JamesG.WittenD.HastieT.TibshiraniR.TaylorJ. (2023). Unsupervised learning. In An introduction to statistical learning: with applications in Python (pp. 503-556). Cham: Springer International Publishing.Search in Google Scholar
Greenacre, M., Groenen, P. J., Hastie, T., d’Enza, A. I., Markos, A., & Tuzhilina, E. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1), 100.GreenacreM.GroenenP. J.HastieT.d’EnzaA. I.MarkosA.TuzhilinaE. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1), 100.Search in Google Scholar
Gu, S., Yang, L., Du, Y., Chen, G., Walter, F., Wang, J., & Knoll, A. (2022). A review of safe reinforcement learning: Methods, theory and applications. arXiv preprint arXiv:2205.10330.GuS.YangL.DuY.ChenG.WalterF.WangJ.KnollA. (2022). A review of safe reinforcement learning: Methods, theory and applications. arXiv preprint arXiv:2205.10330.Search in Google Scholar
Zimmermann, R., & Auinger, A. (2023). Developing a conversion rate optimization framework for digital retailers—case study. Journal of marketing analytics, 11(2), 233-243.ZimmermannR.AuingerA. (2023). Developing a conversion rate optimization framework for digital retailers—case study. Journal of marketing analytics, 11(2), 233-243.Search in Google Scholar
Gupta, V., Mishra, V. K., Singhal, P., & Kumar, A. (2022, December). An overview of supervised machine learning algorithm. In 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 87-92). IEEE.GuptaV.MishraV. K.SinghalP.KumarA. (2022, December). An overview of supervised machine learning algorithm. In 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 87-92). IEEE.Search in Google Scholar
Berman, R., & Van den Bulte, C. (2022). False discovery in A/B testing. Management Science, 68(9), 6762-6782.BermanR., Van den BulteC. (2022). False discovery in A/B testing. Management Science, 68(9), 6762-6782.Search in Google Scholar
Nhu, C. N., & Park, M. (2022). Dynamic network slice scaling assisted by attention-based prediction in 5g core network. IEEE Access, 10, 72955-72972.NhuC. N.ParkM. (2022). Dynamic network slice scaling assisted by attention-based prediction in 5g core network. IEEE Access, 10, 72955-72972.Search in Google Scholar