| An optimized model using LSTM network for demand forecasting H Abbasimehr, M Shabani, M Yousefi Computers & Industrial Engineering, 106435, 2020 | 354 | 2020 |
| Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization H Abbasimehr, R Paki Chaos, Solitons & Fractals 142, 110511, 2021 | 151 | 2021 |
| ChatGPT: Applications, Opportunities, and Threats A Bahrini, M Khamoshifar, H Abbasimehr, RJ Riggs, M Esmaeili, ... 2023 Systems and Information Engineering Design Symposium (SIEDS), 274-279, 2023 | 117 | 2023 |
| Improving time series forecasting using LSTM and attention models H Abbasimehr, R Paki Journal of Ambient Intelligence and Humanized Computing 13 (1), 673-691, 2022 | 101 | 2022 |
| A neuro-fuzzy classifier for customer churn prediction H Abbasimehr, M Setak, MJ Tarokh Int J Comput Appl 19 (8), 35-41, 2011 | 59 | 2011 |
| A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting H Abbasimehr, R Paki, A Bahrini Neural Computing and Applications, 1-15, 2021 | 51 | 2021 |
| A framework for identification of high-value customers by including social network based variables for churn prediction using neuro-fuzzy techniques H Abbasimehr, M Setak, J Soroor International Journal of Production Research 51 (4), 1279-1294, 2013 | 50 | 2013 |
| A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection H Fanai, H Abbasimehr Expert Systems with Applications, 119562, 2023 | 48 | 2023 |
| A comparative assessment of the performance of ensemble learning in customer churn prediction. H Abbasimehr, M Setak, MJ Tarokh Int. Arab J. Inf. Technol. 11 (6), 599-606, 2014 | 37 | 2014 |
| A new methodology for customer behavior analysis using time series clustering: A case study on a bank’s customers H Abbasimehr, M Shabani Kybernetes, 2019 | 33 | 2019 |
| A new framework for predicting customer behavior in terms of RFM by considering the temporal aspect based on time series techniques H Abbasimehr, M Shabani Journal of Ambient Intelligence and Humanized Computing, 2020 | 29 | 2020 |
| An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentation H Abbasimehr, A Bahrini Expert Systems with Applications 192, 116373, 2022 | 28 | 2022 |
| Combining data mining and group decision making in retailer segmentation based on LRFMP variables A Parvaneh, MJ Tarokh, H Abbasimehr International Journal of Industrial Engineering & Production Research 25 (3 …, 2014 | 25 | 2014 |
| Integrating AHP and data mining for effective retailer segmentation based on retailer lifetime value A Parvaneh, H Abbasimehr, MJ Tarokh Journal of Optimization in Industrial Engineering 5 (11), 25-31, 2012 | 25 | 2012 |
| Improving the performance of deep learning models using statistical features: The case study of COVID‐19 forecasting H Abbasimehr, R Paki, A Bahrini Mathematical Methods in the Applied Sciences, 2021 | 20 | 2021 |
| A novel interval type-2 fuzzy AHP-TOPSIS approach for ranking reviewers in online communities H Abbasimehr, MJ Tarokh Scientia Iranica 23 (5), 2355-2373, 2016 | 18 | 2016 |
| A novel XGBoost-based featurization approach to forecast renewable energy consumption with deep learning models H Abbasimehr, R Paki, A Bahrini Sustainable Computing: Informatics and Systems 38, 100863, 2023 | 15 | 2023 |
| Trust prediction in online communities employing neurofuzzy approach H Abbasimehr, MJ Tarokh Applied Artificial Intelligence 29 (7), 733-751, 2015 | 15 | 2015 |
| A high level security mechanism for internet polls S Mohammadi, H Abbasimehr 2010 2nd International Conference on Signal Processing Systems 3, V3-101-V3-105, 2010 | 12 | 2010 |
| A novel time series clustering method with fine-tuned support vector regression for customer behavior analysis H Abbasimehr, FS Baghery Expert Systems with Applications, 117584, 2022 | 11 | 2022 |
Reza PakiPolitecnico Di MilanoVerified email at mail.polimi.it
