An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges M Imani, H Ghassemian Information fusion 59, 59-83, 2020 | 247 | 2020 |
A survey of emotion recognition methods with emphasis on E-Learning environments M Imani, GA Montazer Journal of Network and Computer Applications 147, 102423, 2019 | 183 | 2019 |
Convolutional and Recurrent Neural network Based Model for Short-Term Load Forecasting H Eskandari, M Imani, M Parsa Moghaddam Electric Power Systems Research 195, 2021 | 150 | 2021 |
Classification of heart sound signal using curve fitting and fractal dimension M Hamidi, H Ghassemian, M Imani Biomedical Signal Processing and Control 39, 351-359, 2018 | 135 | 2018 |
Fast Facial emotion recognition Using Convolutional Neural Networks and Gabor Filters M Mohammad Taghi Zadeh, M Imani, B Majidi 5th Conference on Knowledge-Based Engineering and Innovation, 2019 | 111* | 2019 |
Electrical load-temperature CNN for residential load forecasting M Imani Energy 227, 120480, 2021 | 106 | 2021 |
Electrical Load Forecasting Using Customers Clustering and Smart Meters in Internet of Things M Imani, H Ghassemian 9th International Symposium on Telecommunications (IST2018), 2018 | 105 | 2018 |
Band clustering-based feature extraction for classification of hyperspectral images using limited training samples M Imani, H Ghassemian IEEE Geoscience and remote sensing letters 11 (8), 1325-1329, 2013 | 99 | 2013 |
Feature extraction using weighted training samples M Imani, H Ghassemian IEEE Geoscience and Remote Sensing Letters 12 (7), 1387-1391, 2015 | 84 | 2015 |
Feature space discriminant analysis for hyperspectral data feature reduction M Imani, H Ghassemian ISPRS Journal of Photogrammetry and Remote Sensing 102, 1-13, 2015 | 83 | 2015 |
Residential load forecasting using wavelet and collaborative representation transforms M Imani, H Ghassemian Applied Energy 253, 113505, 2019 | 69 | 2019 |
Feature extraction using attraction points for classification of hyperspectral images in a small sample size situation M Imani, H Ghassemian IEEE Geoscience and Remote Sensing Letters 11 (11), 1986-1990, 2014 | 49 | 2014 |
RX anomaly detector with rectified background M Imani IEEE Geoscience and Remote Sensing Letters 14 (8), 1313-1317, 2017 | 48 | 2017 |
Ridge regression-based feature extraction for hyperspectral data M Imani, H Ghassemian International Journal of Remote Sensing 36 (6), 1728-1742, 2015 | 42 | 2015 |
Principal component discriminant analysis for feature extraction and classification of hyperspectral images M Imani, H Ghassemian 2014 Iranian Conference on Intelligent Systems (ICIS), 1-5, 2014 | 42 | 2014 |
Binary coding based feature extraction in remote sensing high dimensional data M Imani, H Ghassemian Information Sciences 342, 191-208, 2016 | 41 | 2016 |
Anomaly detection using morphology-based collaborative representation in hyperspectral imagery M Imani European Journal of Remote Sensing 51 (1), 457-471, 2018 | 35 | 2018 |
Two dimensional linear discriminant analyses for hyperspectral data M Imani, H Ghassemian Photogrammetric Engineering & Remote Sensing 81 (10), 777-786, 2015 | 31 | 2015 |
GLCM, Gabor, and Morphology Profiles Fusion for Hyperspectral Image Classification M Imani, H Ghassemian 24th Iranian Conference on Electrical Engineering (ICEE 2016), 2016 | 30 | 2016 |
Particulate matter (PM2. 5 and PM10) generation map using MODIS Level-1 satellite images and deep neural network M Imani Journal of environmental management 281, 111888, 2021 | 28 | 2021 |