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Jafar Tanha
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2020 – today
- 2024
- [j27]Adil Abdullah Abdulhussein Alshawi, Jafar Tanha
, Mohammad Ali Balafar
An Attention-Based Convolutional Recurrent Neural Networks for Scene Text Recognition. IEEE Access 12: 8123-8134 (2024) - [j26]Mehdi Nozad Bonab
A Semi-Supervised Learning Approach to Quality-Based Web Service Classification. IEEE Access 12: 50489-50503 (2024) - [j25]Mohammad Saber Iraji, Jafar Tanha
A novel individual-relational consistency for bad semi-supervised generative adversarial networks (IRC-BSGAN) in image classification and synthesis. Appl. Intell. 54(20): 10084-10105 (2024) - [j24]Amin Golzari Oskouei, Negin Samadi
Feature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering. Appl. Soft Comput. 161: 111712 (2024) - [j23]Seyed Ehsan Roshan
A deep ensemble medical image segmentation with novel sampling method and loss function. Comput. Biol. Medicine 172: 108305 (2024) - [j22]Shirin Khezri
An experimental review of the ensemble-based data stream classification algorithms in non-stationary environments. Comput. Electr. Eng. 118: 109420 (2024) - [j21]Sahar Hassanzadeh Mostafaei
A novel deep learning model based on transformer and cross modality attention for classification of sleep stages. J. Biomed. Informatics 157: 104689 (2024) - [j20]Imaneh Khodayari-Samghabadi, Leili Mohammad Khanli
A Fast Multi-Network K-Dependence Bayesian Classifier for Continuous Features. Pattern Recognit. 150: 110299 (2024) - [j19]Amin Golzari Oskouei
SSFCM-FWCW: Semi-Supervised Fuzzy C-Means method based on Feature-Weight and Cluster-Weight learning. Softw. Impacts 21: 100678 (2024) - [j18]Ali Jameel Hashim
NEAE: NeuroEvolution AutoEncoder for anomaly detection in internet traffic data. J. Supercomput. 80(5): 6746-6777 (2024) - [j17]Mohammad Saber Iraji
Image classification with consistency-regularized bad semi-supervised generative adversarial networks: a visual data analysis and synthesis. Vis. Comput. 40(10): 6843-6865 (2024) - 2023
- [j16]Jafar Tanha
The Bombus-terrestris bee optimization algorithm for feature selection. Appl. Intell. 53(1): 470-490 (2023) - [j15]Seyed Ehsan Roshan
IMBoost: A New Weighting Factor for Boosting to Improve the Classification Performance of Imbalanced Data. Complex. 2023: 2176891:1-2176891:19 (2023) - [j14]Hadi Alhares, Jafar Tanha
AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19. Evol. Syst. 14(6): 1101-1115 (2023) - [j13]Sahar Hassanzadeh Mostafaei
OUBoost: boosting based over and under sampling technique for handling imbalanced data. Int. J. Mach. Learn. Cybern. 14(10): 3393-3411 (2023) - [c13]Sahar Hassanzadeh Mostafaei, Jafar Tanha, Amir Sharafkhaneh, Ritwick Agrawal, Zohair Hassanzadeh Mostafaei:
Biological Signals for Diagnosing Sleep Stages Using Machine Learning Models. CSICC 2023: 1-7 - [c12]Bahar Sar-Saifee, Jafar Tanha
A Hybrid Deep Learning Network for Sentiment Analysis on SemEval-2017 Dataset. CSICC 2023: 1-7 - 2022
- [j12]Mohammad Saber Iraji
Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method. Comput. Biol. Medicine 151(Part): 106276 (2022) - [j11]Jafar Tanha
CPSSDS: Conformal prediction for semi-supervised classification on data streams. Inf. Sci. 584: 212-234 (2022) - [j10]Shadi Alijani, Jafar Tanha
An ensemble of deep learning algorithms for popularity prediction of flickr images. Multim. Tools Appl. 81(3): 3253-3274 (2022) - [c11]Negin Samadi
Spreader node detection based on the Perron-Frobenius theorem in complex networks. CSICC 2022: 1-5 - 2021
- [j9]Mohammad Saber Iraji
COVID-19 Detection Using Deep Convolutional Neural Networks and Binary Differential Algorithm-Based Feature Selection from X-Ray Images. Complex. 2021: 9973277:1-9973277:10 (2021) - [j8]Shirin Khezri
A novel semi-supervised ensemble algorithm using a performance-based selection metric to non-stationary data streams. Neurocomputing 442: 125-145 (2021) - [j7]Mona Emadi, Jafar Tanha
A Selection Metric for semi-supervised learning based on neighborhood construction. Inf. Process. Manag. 58(2): 102444 (2021) - [i1]Mohammad Saber Iraji, Mohammad-Reza Feizi-Derakhshi, Jafar Tanha:
Deep learning for COVID-19 diagnosis based feature selection using binary differential evolution algorithm. CoRR abs/2104.07279 (2021) - 2020
- [j6]Shirin Khezri
STDS: self-training data streams for mining limited labeled data in non-stationary environment. Appl. Intell. 50(5): 1448-1467 (2020) - [j5]Jafar Tanha
Boosting methods for multi-class imbalanced data classification: an experimental review. J. Big Data 7(1): 70 (2020) - [c10]Mona Emadi, Jafar Tanha
Margin-Based Semi-supervised Learning Using Apollonius Circle. TTCS 2020: 48-60
2010 – 2019
- 2019
- [j4]Jafar Tanha
A multiclass boosting algorithm to labeled and unlabeled data. Int. J. Mach. Learn. Cybern. 10(12): 3647-3665 (2019) - 2018
- [j3]Jafar Tanha
MSSBoost: A new multiclass boosting to semi-supervised learning. Neurocomputing 314: 251-266 (2018) - 2017
- [j2]Jafar Tanha
Semi-supervised self-training for decision tree classifiers. Int. J. Mach. Learn. Cybern. 8(1): 355-370 (2017) - 2015
- [c9]Jafar Tanha, Jesse de Does, Katrien Depuydt:
Combining higher-order N-grams and intelligent sample selection to improve language modeling for Handwritten Text Recognition. ESANN 2015 - [c8]Jafar Tanha
Crossing the lines: making optimal use of context in line-based Handwritten Text Recognition. ICDAR 2015: 956-960 - [c7]Jafar Tanha, Jesse de Does, Katrien Depuydt:
An LDA-based Topic Selection Approach to Language Model Adaptation for Handwritten Text Recognition. RANLP 2015: 646-653 - 2014
- [j1]Jafar Tanha
Boosting for multiclass semi-supervised learning. Pattern Recognit. Lett. 37: 63-77 (2014) - [c6]Jafar Tanha
An Intelligent Sample Selection Approach to Language Model Adaptation for Hand-Written Text Recognition. ICFHR 2014: 349-354 - 2013
- [c5]Jafar Tanha
Multiclass Semi-Supervised Boosting Using Similarity Learning. ICDM 2013: 1205-1210 - 2012
- [c4]Jafar Tanha
An AdaBoost Algorithm for Multiclass Semi-supervised Learning. ICDM 2012: 1116-1121 - [c3]Jafar Tanha
Multiclass Semi-supervised Learning for Animal Behavior Recognition from Accelerometer Data. ICTAI 2012: 690-697 - 2011
- [c2]Jafar Tanha
Disagreement-Based Co-training. ICTAI 2011: 803-810 - 2010
- [c1]Hamideh Afsarmanesh
A High Level Architecture for Personalized Learning in Collaborative Networks. PRO-VE 2010: 601-608
Coauthor Index
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last updated on 2024-10-16 21:27 CEST by the dblp team
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