publications

2024

1. FSS

   Robust hybrid learning approach for adaptive neuro-fuzzy inference systems

   Ali Nik-Khorasani, Ali Mehrizi, and Hadi Sadoghi-Yazdi

   Fuzzy Sets and Systems, Apr 2024

   Abs

   

   The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a regression model that combines fuzzy logic and neural networks, making it suitable for modeling the uncertainty of regression problems. However, the non-robust loss function in ANFIS’s hybrid learning algorithm can render it vulnerable to the direct effects of noise and outliers. This paper introduces a new procedure that utilizes robust loss functions to enhance the hybrid learning performance against noise and outliers. Furthermore, a new robust loss function is developed that can completely ignore outliers. A set of robust loss functions with mathematical relations are also suggested. The proposed approach is evaluated on real-world problems, including weather forecasting and stock market prediction. Results indicate that the proposed model can reduce the Mean Square Error (MSE) in regression.

2. MTA

   Enhancing multi-target tracking stability using knowledge graph integration within the Gaussian Mixture Probability Hypothesis Density Filter

   Ali Mehrizi, and Hadi Sadoghi Yazdi

   Multimedia Tools and Applications, Sep 2024

   Abs

   

   This paper proposes a novel approach to enhancing multi-target tracking of vehicles in videos with frequent camera occlusions. Our method integrates prior knowledge about vehicle behavior into a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter framework. This knowledge, extracted as a knowledge graph from historical vehicle trajectories, allows the tracker to maintain persistence even during significant interruptions. The knowledge graph models expected movement patterns and generates pseudo-observations during occlusions, similar to how time series analysis leverages historical data for forecasting. We evaluate the proposed method on both simulated and real-world video datasets using the Optimal Sub Pattern Assignment (OSPA) metric, which assesses tracking accuracy. The results show a 19.5% improvement for simulated data and a 16.5% improvement for real-world video data under ...

3. ACM TIS

   A Self-Distilled Learning to Rank Model for Ad-hoc Retrieval

   Sanaz Keshvari, Farzan Saeedi, Hadi Sadoghi Yazdi, and 1 more author

   ACM Transactions on Information Systems, Sep 2024

   
4. JPDC

   A distributed learning based on robust diffusion SGD over adaptive networks with noisy output data

   Fatemeh Barani, Abdorreza Savadi, and Hadi Sadoghi Yazdi

   Journal of Parallel and Distributed Computing, Sep 2024

   
5. Algorithms

   Correntropy-based constructive one hidden layer neural network

   Mojtaba Nayyeri, Modjtaba Rouhani, Hadi Sadoghi Yazdi, and 3 more authors

   Algorithms, Sep 2024

   
6. EAAI

   Semantic labeling of social big media using distributed online robust classification

   Hadii Sadoghi Yazdi Alireza Naeimi-Sadigh

   Engineering Applications of Artificial Intelligence, , Jun 2024

   Abs HTML PDF Video

   

   Semantic labeling for image datasets is of significant importance in a wide range of social media. However, social datasets with massive amounts of data require effective technologies to increase the quality of classification. In this study, we propose a novel online robust classification using distributed learning method, in which the diffusion method is used over adaptive networks in order to the parallelization of the training process. The loss function of the suggested method is derived from the logarithm cosine function, being more suitable for impulsive noises. Also, the convergence of the proposed method is discussed theoretically. The Extensive experimental results show that the proposed method outperforms the other robust state-of-the-art classification algorithms in the presence of various scenarios, such as: free noise synthetic data sets, pure Gaussian noise, mixture of two Gaussian noises, impulse noise, and Alpha–Beta noise which are added to the synthetic data sets. In addition, the experiments were repeated for two different types of real data sets with real noises and outliers, i.e., UCI and 500PX social media data sets. The obtained results over UCI and 500px data sets on social media with real outliers and mislabeled samples confirmed the acceptable performance of the proposed method.

2023

1. ISCSS

   Randomized Constructive Neural Network Based on Regularized Minimum Error Entropy

   Mojtaba Nayyeri, Hadi Sadoghi Yazdi, Modjtaba Rouhani, and 2 more authors

   In Impact of Scientific Computing on Science and Society, Jun 2023

   
2. PR

   Task weighting based on particle filter in deep multi-task learning with a view to uncertainty and performance

   Emad Aghajanzadeh, Tahereh Bahraini, Amir Hossein Mehrizi, and 1 more author

   Pattern Recognition, Jun 2023

   
3. IS

   Management of the optimizer’s curse concept in single-task diffusion networks

   Atieh Gharib, Hadi Sadoghi-Yazdi, and Amir Hossein Taherinia

   Information Sciences, Jun 2023

   
4. SP

   Diffusion-based Kalman iterative thresholding for compressed sampling recovery over network

   Fahimeh Ansari-Ram, Abbas Ebrahimi-Moghadam, Morteza Khademi, and 1 more author

   Signal Processing, Jun 2023

   

2022

1. SP

   Bayesian framework selection for hyperspectral image denoising

   Tahereh Bahraini, Abbas Ebrahimi-Moghadam, Morteza Khademi, and 1 more author

   Signal Processing, Jun 2022

   
2. ESA

   Density-oriented linear discriminant analysis

   Tahereh Bahraini, Seyed Mohammad Hosseini, Mahbubeh Ghasempour, and 1 more author

   Expert Systems with Applications, Jun 2022

   
3. MTA

   Cloning detection scheme based on linear and curvature scale space with new false positive removal filters

   Manaf Mohammed Ali Alhaidery, Amir Hossein Taherinia, and Hadi Sadoghi Yazdi

   Multimedia Tools and Applications, Jun 2022

   
4. ES

   Sparsity-aware support vector data description reinforced by expectation maximization

   Mahdie Eghdami, Hadi Sadoghi Yazdi, and Neshat Salehi

   Expert Systems, Jun 2022

   
5. ASC

   Edge preserving range image smoothing using hybrid locally kernel-based weighted least square

   Tahereh Bahraini, Taha Hamedani, Seyed Mohammad Hosseini, and 1 more author

   Applied Soft Computing, Jun 2022

   
6. IPM

   ListMAP: Listwise learning to rank as maximum a posteriori estimation

   Sanaz Keshvari, Faezeh Ensan, and Hadi Sadoghi Yazdi

   Information Processing & Management, Jun 2022

   
7. ESA

   Robust classification via clipping-based kernel recursive least lncosh of error

   Hadi Sadoghi Yazdi Alireza Naeimi-Sadigh

   Expert Systems with Applications, , Jul 2022

   Abs HTML PDF Video

   

   Classification as a supervised machine learning method predicts the class label from the features distribution and the training process. The traditional classifier algorithms are not robust in the presence of outliers and noisy features. In this study, we suggest a novel robust classifier using kernel recursive least lncosh (RC-KRLL), in which the clipping concept is used to ignore the effect of large noises. Instead of the conventional mean square error cost function, the suggested RC-KRLL method is derived from the lncosh loss function, being more suitable for non-Gaussian noise. The mean, mean-square convergence, and learning curve of the RC-KRLL are discussed theoretically. The simulation results show that the proposed method outperforms the other robust state-of-the-art classification algorithms in the presence of synthetic non-Gaussian noise over UCI data sets, such as the mixture of Gaussian noise with different variances, impulse, and Alpha–Beta noises. The obtained results over 500px data sets on social media with real outliers and mislabeled samples confirmed the acceptable performance of the proposed method.

2021

1. IEEE TC

   Prepare for the worst, hope for the best: Active robust learning on distributions

   Seyed Hossein Ghafarian, and Hadi Sadoghi Yazdi

   IEEE Transactions on Cybernetics, Jul 2021

   
2. IEEE TITS

   Situation assessment-augmented interactive Kalman filter for multi-vehicle tracking

   Maryam Baradaran Khalkhali, Abedin Vahedian, and Hadi Sadoghi Yazdi

   IEEE Transactions on Intelligent Transportation Systems, Jul 2021

   
3. IEEE TGRS

   Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework

   Peyman Azimpour, Tahereh Bahraini, and Hadi Sadoghi Yazdi

   IEEE Transactions on Geoscience and Remote Sensing, Jul 2021

   
4. CEE

   Robustness of convolutional neural network models in hyperspectral noisy datasets with loss functions

   Sepehr Ghafari, Milad Ghobadi Tarnik, and Hadi Sadoghi Yazdi

   Computers & Electrical Engineering, Jul 2021

   
5. SP

   Diversity-based diffusion robust RLS using adaptive forgetting factor

   Alireza Naeimi Sadigh, Hadi Sadoghi Yazdi, and Ahad Harati

   Signal Processing, Jul 2021

   
6. IRIEE

   Variational Bayesian inference in Noise Removal from Hyperspectral Images Using Cluster-Based Latent Variables

   T Bahraini, M Khademi, H Sadoghi Yazdi, and 1 more author

   Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran, Jul 2021

   
7. JVCI

   Image annotation based on multi-view robust spectral clustering

   Mona Zamiri, and Hadi Sadoghi Yazdi

   Journal of Visual Communication and Image Representation, Jul 2021

   
8. SP

   Convergence behavior of diffusion stochastic gradient descent algorithm

   Fatemeh Barani, Abdorreza Savadi, and Hadi Sadoghi Yazdi

   Signal Processing, Jul 2021
9. IEEE TC

   Prepare for the worst, hope for the best: Active robust learning on distributions

   Seyed Hossein Ghafarian, and Hadi Sadoghi Yazdi

   IEEE Transactions on Cybernetics, Jul 2021
10. IEEE TITS

    Situation assessment-augmented interactive Kalman filter for multi-vehicle tracking

    Maryam Baradaran Khalkhali, Abedin Vahedian, and Hadi Sadoghi Yazdi

    IEEE Transactions on Intelligent Transportation Systems, Jul 2021
11. IEEE TGRS

    Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework

    Peyman Azimpour, Tahereh Bahraini, and Hadi Sadoghi Yazdi

    IEEE Transactions on Geoscience and Remote Sensing, Jul 2021
12. CG

    Modified-mean-shift-based noisy label detection for hyperspectral image classification

    Tahereh Bahraini, Peyman Azimpour, and Hadi Sadoghi Yazdi

    Computers & Geosciences, Jul 2021

    
13. SP

    Diversity-based diffusion robust RLS using adaptive forgetting factor

    Ahad Harati Alireza Naeimi-Sadigh

    Signal Processing, , May 2021

    Abs HTML PDF Video

    

14. SP

    Analysis of robust recursive least squares: Convergence and tracking

    Hadi Sadoghi Yazdi Alireza Naeimi-Sadigh

    Signal Processing, , May 2021

    Abs HTML PDF Video

    

    Outliers and impulsive noise are inevitable factors in recursive least squares (RLS). Developing robust RLS is vital in practical applications such as system identification in which outliers in the desired signals may severely divert the solutions. Almost all suggested robust RLS schemes have been designed for the impulsive noise and Gaussian environments. Recently, employing the Maximum Correntropy Criterion (MCC), the RGMCC (Recursive General MCC) algorithm has been given which yields more exact results for system identification problem in non-Gaussian environments. Here, we develop a new Robust RLS (R2LS) scheme based on the MCC. In contrast to RGMCC, the structure of our model, although being complex, makes it possible to conduct convergence and performance analysis in both the stationary and non-stationary environments. Especially, the model is capable to reasonably predict and track the signals when the original signal is contaminated by non-Gaussian noise. To establish convergence and performance, we apply a half-quadratic optimization algorithm in the multiplicative form to successively convert our model to a quadratic problem which can be effectively solved by the classical tools. Numerical experiments are done on real and synthetic datasets; they show that the proposed algorithm outperforms the conventional RLS as well as some of its recent extensions.

2020

1. SPIC

   Probabilistic Kalman filter for moving object tracking

   Fahime Farahi, and Hadi Sadoghi Yazdi

   Signal Processing: Image Communication, May 2020

   
2. ASC

   Robust Heterogeneous C-means

   Atieh Gharib, Hadi Sadoghi-Yazdi, and Amir Taherinia

   Applied Soft Computing, May 2020

   
3. EAAI

   Toward optimum fuzzy support vector machines using error distribution

   Tahereh Bahraini, Saeedeh Ghazi, and Hadi Sadoghi Yazdi

   Engineering Applications of Artificial Intelligence, May 2020
4. IPM

   Identifying crisis-related informative tweets using learning on distributions

   Seyed Hossein Ghafarian, and Hadi Sadoghi Yazdi

   Information Processing & Management, May 2020

   
5. IRIEE

   A New and Robust AMP Algorithm for Non IID Matrices Based on Bayesian Theory in Compressed Sensing

   F Ansari Ram, M Khademi, H Sadoghi Yazdi, and 1 more author

   Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran, May 2020
6. ESA

   A drift aware adaptive method based on minimum uncertainty for anomaly detection in social networking

   Hadi Sadoghi Yazdi, Abbas Ghaemi Bafghi, and  others

   Expert Systems with Applications, May 2020
7. RAS

   Vehicle tracking with Kalman filter using online situation assessment

   Maryam Baradaran Khalkhali, Abedin Vahedian, and Hadi Sadoghi Yazdi

   Robotics and Autonomous Systems, May 2020
8. CILS

   Minimum variance based-Bayes Combination for prediction of soil properties on Vis-NIR reflectance spectroscopy

   Milad Ghobadi Tarnik, Sepehr Ghafari, Tahereh Bahraini, and 1 more author

   Chemometrics and Intelligent Laboratory Systems, May 2020
9. AEJ

   A kernel least mean square algorithm for fuzzy differential equations and its application in earth’s energy balance model and climate

   M Pakdaman, Y Falamarzi, H Sadoghi Yazdi, and 3 more authors

   Alexandria Engineering Journal, May 2020
10. IRIEE

    Semi-Supervised Learning Based on Extreme Learning

    A Mehrizi, H Sadoghi Yazdi, and SJ Seyyed Mahdavi Chabok

    Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran, May 2020
11. IJMLC

    Combination of loss functions for deep text classification

    Hamideh Hajiabadi, Diego Molla-Aliod, Reza Monsefi, and 1 more author

    International Journal of Machine Learning and Cybernetics, May 2020
12. Blind Video Steganalysis by Semi-Supervised Approach for Motion Vectors Based Steganography Algorithms

    J Mortazavi Mehrizi, M Khademi, and H Sadoghi Yazdi

    Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran, May 2020
13. JMVIP

    Automated Detection of Region of Interest using Non-Parametric Distribution Based on Bayesian Risk

    Mahnaz Razavi, Amir Hossein Taherinia, and Hadi Sadoghi Yazdi

    Journal of Machine Vision and Image Processing, May 2020
14. SPIC

    Probabilistic Kalman filter for moving object tracking

    Fahime Farahi, and Hadi Sadoghi Yazdi

    Signal Processing: Image Communication, May 2020
15. ISA Transactions

    Convergence and performance analysis of kernel regularized robust recursive least squares

    Ahad Harati Alireza Naeimi-Sadigh

    ISA Transactions, , Oct 2020

    Abs HTML PDF Video

    

    Kernel recursive least squares (KRLS) is very sensitive to non-Gaussian noise and hence, robust extensions are proposed using maximum correntropy criterion or generalized maximum correntropy. However, because of the complex form of the model, there is no theoretical analysis on the convergence of these filters. In this paper, we propose a new alternative: Kernel Regularized Robust RLS (KRLS). It uses half-quadratic technique to simplify the form of the loss function. Our major contribution is then proving the convergence of the filter to the target weights and desired output. The bounds of regularization factor is also obtained. KRLS is experimentally tested using synthetic and real data and is shown to perform superior compared to other robust alternatives.

2019

1. IEEE TITS

   Multi-target state estimation using interactive Kalman filter for multi-vehicle tracking

   Maryam Baradaran Khalkhali, Abedin Vahedian, and Hadi Sadoghi Yazdi

   IEEE Transactions on Intelligent Transportation Systems, Oct 2019

   
2. IEEE TPDS

   cCUDA: Effective co-scheduling of concurrent kernels on GPUs

   S-Kazem Shekofteh, Hamid Noori, Mahmoud Naghibzadeh, and 2 more authors

   IEEE Transactions on Parallel and Distributed Systems, Oct 2019

   
3. Sparse Bayesian approach for metric learning in latent space

   Reza Monsefi, Davood Zabihzadeh, and Hadi Sadoghi Yazdi

   Knowledge-Based Systems, Oct 2019
4. KBS

   Sparse Bayesian approach for metric learning in latent space

   Davood Zabihzadeh, Reza Monsefi, and Hadi Sadoghi Yazdi

   Knowledge-Based Systems, Oct 2019

   
5. EAAI

   Crowd analysis using bayesian risk kernel density estimation

   Mahnaz Razavi, Hadi Sadoghi Yazdi, and Amir Hossein Taherinia

   Engineering Applications of Artificial Intelligence, Oct 2019
6. CSSP

   Incorporating Nonparametric Knowledge to the Least Mean Square Adaptive Filter

   Soheila Ashkezari-Toussi, and Hadi Sadoghi-Yazdi

   Circuits, Systems, and Signal Processing, Oct 2019
7. ACM TACO

   Metric selection for gpu kernel classification

   S-Kazem Shekofteh, Hamid Noori, Mahmoud Naghibzadeh, and 2 more authors

   ACM Transactions on Architecture and Code Optimization (TACO), Oct 2019
8. MTA

   Robust sentiment fusion on distribution of news

   Mohammad Kamel, Farzaneh Namdar Siuky, and Hadi Sadoghi Yazdi

   Multimedia Tools and Applications, Oct 2019
9. EAAI

   Functional gradient approach to probabilistic minimax active learning

   Seyed Hossein Ghafarian, and Hadi Sadoghi Yazdi

   Engineering Applications of Artificial Intelligence, Oct 2019
10. MSSP

    Collaborative linear dynamical system identification by scarce relevant/irrelevant observations

    Behzad Bakhtiari, and Hadi Sadoghi Yazdi

    Multidimensional Systems and Signal Processing, Oct 2019
11. Cities

    Emotional maps based on social networks data to analyze cities emotional structure and measure their emotional similarity

    Soheila Ashkezari-Toussi, Mohammad Kamel, and Hadi Sadoghi-Yazdi

    Cities, Oct 2019
12. IET IS

    Event reconstruction using temporal pattern of file system modification

    Somayeh Soltani, Seyed Amin Hosseini Seno, and Hadi Sadoghi Yazdi

    IET Information Security, Oct 2019
13. SP

    Robust diffusion LMS over adaptive networks

    Soheila Ashkezari-Toussi, and Hadi Sadoghi-Yazdi

    Signal Processing, Oct 2019
14. ESA

    working with mashhadcheragh

    Hadi Sadoghi Yazdi

    Expert Systems with Applications, Oct 2019
15. IRIEE

    Automatic Image Annotation by Block Principal Pivoting

    H Rikabi, N Soufi, H Sadoghi Yazdi, and 1 more author

    Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran, Oct 2019
16. IEEE TITS

    Multi-target state estimation using interactive Kalman filter for multi-vehicle tracking

    Maryam Baradaran Khalkhali, Abedin Vahedian, and Hadi Sadoghi Yazdi

    IEEE Transactions on Intelligent Transportation Systems, Oct 2019
17. AI

    relf: robust regression extended with ensemble loss function

    Hamideh Hajiabadi, Reza Monsefi, and Hadi Sadoghi Yazdi

    Applied Intelligence, Oct 2019
18. I

    Cautious Classification of Hyper Rectangular, Hyper Circular, and Hyper Oval with a Maximum Symmetric Margin Relative to the Data Edge

    Yahya Forghani, M Hejazi, and H Sadoghi Yazdi

    Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran, Oct 2019
19. Cities

    Emotional maps based on social networks data to analyze cities’ emotional structure and measure their emotional similarity

    Soheila Ashkezari-Toussi, Mohammad Kamel, and Hadi Sadoghi-Yazdi

    Cities, , Jan 2019

    Abs HTML PDF Video

    

    The present study utilizes the social media data to sample from people’s emotion in different places based on their facial expressions, for analyzing emotions distribution in a city and comparing the emotional similarity between cities. Experiencing various emotions g1ives birth to different facial expressions which usually have similar patterns for individuals. Extracting these patterns from face images and analyzing them makes it possible to extract related emotion associated with the shared photos. Since geo-tagged images include metadata about the geographical location, estimation of emotions spatial distribution is possible. The distribution of four emotions: anger, disgust, happiness, and surprise in twelve cities, included Athens, Beijing, Berlin, Brussels, Buenos Aires, Copenhagen, Helsinki, Melbourn, New York, Ottawa, Paris, and Prague is investigated by analyzing geo-tagged photos shared on Flickr. After facial expressions extraction, the spatial distribution functions of the emotions are calculated by kernel density estimation method. Emotional maps for cities are created using extracted features of Fourier transform spectrum of the spatial distributions which are robust against rotation and translation. Afterward, similar cities based on their emotional structure are clustered into appropriate groups. The results are beneficial for urban planners and social researchers to analyze the effect of the environment on peoples’ emotions. Furthermore, this could guide them to make right policies to improve the quality of life. Considering the four studied emtions, New York has the highest ratio in anger, Ottawa has the highest proportion of happiness and Copenhagen has the highest ratio in both surprise and disgust among the studied cities. In addition, an attempt is made to calculate the percentage of different emotions and dispersion of them as important emotional factors in understanding the emotional structure of cities. To measure the dispersion of happiness the coefficient of variation is computed. The results show that happiness dispersion in Berlin has a more uniform distribution according to the collected data. It seems both coefficients of variation and emotions percentage may be jointly effective in urban planning and cities comparison based on people emotional mode.

20. SP

    Robust diffusion LMS over adaptive networks

    Soheila Ashkezari-Toussi, and Hadi Sadoghi-Yazdi

    Signal Processing, , Jul 2019

    Abs HTML PDF Video

    

    The present study proposes the Robust DLMS (RDLMS) algorithm for a robust estimation over adaptive networks. Instead of minimizing the mean square error (MSE), the RDLMS algorithm is derived from minimizing the pseudo-Huber function which is a continuous derivative and smooth approximation of the Huber function. Performance of the RDLMS algorithm is examined in the presence of Gaussian and α-stable non-Gaussian noise, in stationary and non-stationary environments. The results show that in the presence of non-Gaussian noise the proposed algorithm is robust and outperforms the diffusion LMS, the diffusion maximum correntropy criteria, and the diffusion least mean fourth algorithms. In addition, RDLMS is similar to the diffusion sign-error LMS and diffusion robust LMS. On the other hand, when the environment noise is Gaussian, the performance of RDLMS is similar to the DLMS while outperforms the other aforementioned algorithms.

21. CSSP

    Incorporating Nonparametric Knowledge to the Least Mean Square Adaptive Filter

    Soheila Ashkezari-Toussi, and Hadi Sadoghi-Yazdi

    Circuits, Systems, and Signal Processing, , Apr 2019

    Abs HTML PDF Video

    

    In the framework of the maximum a posteriori estimation, the present study proposes the nonparametric probabilistic least mean square (NPLMS) adaptive filter for the estimation of an unknown parameter vector from noisy data. The NPLMS combines parameter space and signal space by combining the prior knowledge of the probability distribution of the process with the evidence existing in the signal. Taking advantage of kernel density estimation to estimate the prior distribution, the NPLMS is robust against the Gaussian and non-Gaussian noises. To achieve this, some of the intermediate estimations are buffered and then used to estimate the prior distribution. Despite the bias-compensated algorithms, there is no need to estimate the input noise variance. Theoretical analysis of the NPLMS is derived. In addition, a variable step-size version of NPLMS is provided to reduce the steady-state error. Simulation results in the system identification and prediction show the acceptable performance of the NPLMS in the noisy stationary and non-stationary environments against the bias-compensated and normalized LMS algorithms.

2018

1. Circuits Syst. Signal Process.

   Kalman Filter Reinforced by Least Mean Square for Systems with Unknown Inputs

   Hadi Sadoghi Yazdi Mohammad Ali Majidi

   Circuits, Systems, and Signal Processing, , Feb 2018

   Abs HTML PDF Video

   

   This paper addresses the state estimation problem of linear discrete-time time-varying stochastic systems with unknown inputs (UIs). It is shown that the globally optimal unbiased minimum-variance filters may not satisfy the minimum-variance property, and hence they cannot eliminate noises appropriately. If this is the case, the well-known Kalman filter may give a better solution, which however may also not be the best one due to that the imbedded unknown input model may not be practical. To remedy the filtering degradation problem, a robust filter named as the KFLMS, which has good noise rejection property for such systems, is developed in this paper, where the UI estimates are obtained by using least mean square algorithm and the state estimation is achieved via the previous proposed two-stage Kalman filtering approach. Numerical examples are provided to show the effectiveness of the proposed results. Specifically, simulation results illustrate the goodness of the new method in the sense of lower root mean square error and better noise rejection property.

2. Neurocomputing

   Bayesian filter based on the wisdom of crowds

   Behzad Bakhtiari, and Hadi Sadoghi Yazdi

   Neurocomputing, Feb 2018

   
3. arXiv

   Sentimental content analysis and knowledge extraction from news articles

   Mohammad Kamel, Neda Keyvani, and Hadi Sadoghi Yazdi

   arXiv preprint arXiv:1808.03027, Feb 2018

   
4. CSSP

   Kalman filter reinforced by least mean square for systems with unknown inputs

   Mohammad Ali Majidi, Chien-Shu Hsieh, and Hadi Sadoghi Yazdi

   Circuits, Systems, and Signal Processing, Feb 2018
5. JMVLS

   Fuzzy Relevance Feedback in the Semantic Image Retrieval.

   Malihe Javidi, Hadi Sadoghi Yazdi, and Hamid Reza Pourreza

   Journal of Multiple-Valued Logic & Soft Computing, Feb 2018
6. IRIEE

   Geometrical Self-Organizing Map Classifier Based on Active Learning for Steganalysis in the Video Environment by Spending at Least a Label

   H Sadoghi Yazdi, A Mohiaddini, and M Khademi

   Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran, Feb 2018
7. KBS

   Sparse online feature maps

   Nima Salehi-M, Reza Monsefi, and Hadi Sadoghi Yazdi

   Knowledge-Based Systems, Feb 2018
8. EAAI

   Sparse Bayesian similarity learning based on posterior distribution of data

   Davood Zabihzadeh, Reza Monsefi, and Hadi Sadoghi Yazdi

   Engineering Applications of Artificial Intelligence, Feb 2018
9. IRIEE

   Handover Management between Femtocell and Macrocell Using Geo-Based Spectral Clustering

   T Bahraini, M Zamiri, and H Sadoghi Yazdi

   Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran, Feb 2018
10. ESA

    Robust semi-supervised growing self-organizing map

    Amir Hossein Taherinia Ali Mehrizi

    Expert Systems with Applications, Aug 2018

    Abs

    

    This paper presents a robust semi-supervised growing self-organizing map (GSOM) approach. The proposed method aims to address the challenges faced in machine learning tasks, specifically classification in the presence of both labeled and unlabeled data. By integrating unsupervised and supervised learning components, the proposed GSOM method enhances the adaptability and accuracy of learning, even with a limited amount of labeled data. The robustness of the proposed approach is demonstrated through extensive experiments across several benchmark datasets, showing superior performance compared to traditional semi-supervised techniques.

2017

1. IEEE and GRSL

   Improving signal subspace identification using weighted graph structure of data

   Saeid Gholinejad, Rouzbeh Shad, Hadi Sadoghi Yazdi, and 1 more author

   IEEE Geoscience and Remote Sensing Letters, Aug 2017

   
2. IEEE NNLS

   Universal approximation by using the correntropy objective function

   Mojtaba Nayyeri, Hadi Sadoghi Yazdi, Alaleh Maskooki, and 1 more author

   IEEE transactions on neural networks and learning systems, Aug 2017

   
3. IEEE GRST

   Improving signal subspace identification using weighted graph structure of data

   Saeid Gholinejad, Rouzbeh Shad, Hadi Sadoghi Yazdi, and 1 more author

   IEEE Geoscience and Remote Sensing Letters, Aug 2017

   
4. IEEE NNLS

   Universal approximation by using the correntropy objective function

   Mojtaba Nayyeri, Hadi Sadoghi Yazdi, Alaleh Maskooki, and 1 more author

   IEEE transactions on neural networks and learning systems, Aug 2017
5. IJMC

   Label denoising based on Bayesian aggregation

   Parsa Bagherzadeh, and Hadi Sadoghi Yazdi

   International Journal of Machine Learning and Cybernetics, Aug 2017
6. KSCE

   Introducing prior knowledge for a hybrid accident prediction model

   Ali Mansourkhaki, Abolfazl Karimpour, and Hadi Sadoghi Yazdi

   KSCE Journal of Civil Engineering, Aug 2017
7. Enhancing performance of graphics processing units through better scheduling of concurrent kernels

   SeyedKazem Shekofteh, Hamid Noori, Mahmoud Naghibzadeh, and 2 more authors

   In 2017 ISC High Performance, Aug 2017
8. PICET

   Non-stationary concept of accident prediction

   Ali Mansourkhaki, Abolfazl Karimpour, and Hadi Sadoghi Yazdi

   In Proceedings of the Institution of Civil Engineers-Transport, Aug 2017

2016

1. ISC

   Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise

   Reza Izanloo, Seyed Abolfazl Fakoorian, Hadi Sadoghi Yazdi, and 1 more author

   In 2016 Annual Conference on Information Science and Systems (CISS), Aug 2016

   
2. ICCKE

   Hierarchical cooperation of experts in learning from crowds

   Mahla Esmaeily, Hadi Sadoghi Yazdi, Saeid Abbassi, and 1 more author

   In 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), Aug 2016

   
3. IJMC

   Large symmetric margin instance selection algorithm

   Javad Hamidzadeh, Reza Monsefi, and Hadi Sadoghi Yazdi

   International Journal of Machine Learning and Cybernetics, Aug 2016
4. arXiv

   Active robust learning

   Hossein Ghafarian, and Hadi Sadoghi Yazdi

   arXiv preprint arXiv:1608.07159, Aug 2016
5. IRIEE

   Sentiment Analysis of Persian Documents using Optimal Transform Domain

   A Pourmasoumi, H Sadoghi Yazdi, H Ghaemi, and 1 more author

   Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran, Aug 2016
6. arXiv

   Outlier absorbing based on a Bayesian approach

   Parsa Bagherzadeh, and Hadi Sadoghi Yazdi

   arXiv preprint arXiv:1607.00466, Aug 2016
7. JIST

   Efficient Land-cover segmentation using Meta fusion

   Hadi Mahdipour Hossein Abad, Morteza Khademi, and Hadi Sadoghi Yazdi

   Journal of Information Systems and Telecommunication, Aug 2016
8. IDA

   Semi-supervised GSOM integrated with extreme learning machine

   Hadi Sadoghi Yazdi, and Ali Mehrizi

   Intelligent Data Analysis, Aug 2016
9. JIST

   Efficient Land-cover Segmentation Using Meta Fusion

   Morteza Khademi, and Hadi Sadoghi Yazdi

   Journal of Information Systems and Telecommunication (JIST), Aug 2016
10. IDA

    Semi-supervised GSOM integrated with extreme learning machine

    Ali Mehrizi, and Hadi Sadoghi Yazdi

    Intelligent Data Analysis, Aug 2016
11. arXiv

    Fuzzy Constraints Linear Discriminant Analysis

    Hamid Reza Hassanzadeh, Hadi Sadoghi Yazdi, and Abedin Vahedian

    arXiv preprint arXiv:1612.09593, Aug 2016
12. IRIEE

    Particle Filter with Adaptive Observation Model

    H Haeri, and H Sadoghi Yazdi

    Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran, Aug 2016
13. IDA

    Semi-supervised GSOM integrated with extreme learning machine

    Hadi Sadoghi Yazdi Ali Mehrizi

    Intelligent Data Analysis, Jun 2016

    Abs

    

    Semi-supervised learning with a growing self-organizing map (GSOM) is commonly used to cope with the machine learning problems...

14. IJMLC

    Large symmetric margin instance selection algorithm

    Hadi Sadoghi Yazdi Javad Hamidzadeh

    International Journal of Machine Learning and Cybernetics, , Feb 2016

    Abs HTML PDF Video

    

    In instance-based classifiers, there is a need for storing a large number of samples as a training set. In this paper, we propose a large symmetric margin instance selection algorithm, namely LAMIS. LAMIS removes non-border (interior) instances and keeps border ones. This paper presents an instance selection process through formulating it as a constrained binary optimization problem and solves it by employment filled function algorithm. Instance-based learning algorithms are often confronted with the problem of deciding which instances must be stored for use during an actual test. Storing too many instances can result in large memory requirements and slow execution. In LAMIS, the core of instance selection process is based on keeping the hyperplane that separates a two-class data, to provide large margin separation. LAMIS selects the most representative instances, satisfying both objectives: high accuracy and reduction rates. The performance has been evaluated on real world data sets from UCI repository by the ten-fold cross-validation method. The results of experiments have been compared with state-of-the-art methods, where the overall results, show the superiority of the proposed method in terms of classification accuracy and reduction percentage.

15. ISC

    Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise

    Reza Izanloo, Seyed Abolfazl Fakoorian, Hadi Sadoghi Yazdi, and 1 more author

    In 2016 Annual Conference on Information Science and Systems (CISS), Feb 2016

    

2015

1. WN

   Anomaly detection and foresight response strategy for wireless sensor networks

   Mohammad GhasemiGol, Abbas Ghaemi-Bafghi, Mohammad Hossein Yaghmaee-Moghaddam, and 1 more author

   Wireless Networks, Feb 2015

   
2. Neurocomputing

   Constrained semi-supervised growing self-organizing map

   Amin Allahyar, Hadi Sadoghi Yazdi, and Ahad Harati

   Neurocomputing, Feb 2015

   
3. NPL

   Fuzzy min–max neural network for learning a classifier with symmetric margin

   Yahya Forghani, and Hadi Sadoghi Yazdi

   Neural Processing Letters, Feb 2015

   
4. CI

   Robust Support Vector Machines with Low Test Time

   Yahya Forghani, and Hadi Sadoghi Yazdi

   Computational Intelligence, Feb 2015
5. PR

   IRAHC: instance reduction algorithm using hyperrectangle clustering

   Javad Hamidzadeh, Reza Monsefi, and Hadi Sadoghi Yazdi

   Pattern Recognition, Feb 2015
6. PR

   IRAHC: instance reduction algorithm using hyperrectangle clustering

   Hadi Sadoghi Yazdi Javad Hamidzadeh

   Pattern Recognition, , May 2015

   Abs HTML PDF Video

   

   In instance-based classifiers, there is a need for storing a large number of samples as training set. In this work, we propose an instance reduction method based on hyperrectangle clustering, called Instance Reduction Algorithm using Hyperrectangle Clustering (IRAHC). IRAHC removes non-border (interior) instances and keeps border and near border ones. This paper presents an instance reduction process based on hyperrectangle clustering. A hyperrectangle is an n-dimensional rectangle with axes aligned sides, which is defined by min and max points and a corresponding distance function. The min–max points are determined by using the hyperrectangle clustering algorithm. Instance-based learning algorithms are often confronted with the problem of deciding which instances must be stored to be used during an actual test. Storing too many instances can result in a large memory requirements and a slow execution speed. In IRAHC, core of instance reduction process is based on set of hyperrectangles. The performance has been evaluated on real world data sets from UCI repository by the 10-fold cross-validation method. The results of the experiments have been compared with state-of-the-art methods, which show superiority of the proposed method in terms of classification accuracy and reduction percentage.

2014

1. IEEE BHI

   Temporal and spatial monitoring and prediction of epidemic outbreaks

   Amin Zamiri, Hadi Sadoghi Yazdi, and Sepideh Afkhami Goli

   IEEE journal of biomedical and health informatics, May 2014

   
2. NCA

   Multiclass classifier based on boundary complexity

   Hamid Reza Ghaffari, and Hadi Sadoghi Yazdi

   Neural Computing and Applications, May 2014
3. IEEE JBHI

   Temporal and spatial monitoring and prediction of epidemic outbreaks

   Amin Zamiri, Hadi Sadoghi Yazdi, and Sepideh Afkhami Goli

   IEEE journal of biomedical and health informatics, May 2014
4. IJMLC

   Online neural network model for non-stationary and imbalanced data stream classification

   Adel Ghazikhani, Reza Monsefi, and Hadi Sadoghi Yazdi

   International journal of machine learning and cybernetics, May 2014
5. Neurocomputing

   LMIRA: large margin instance reduction algorithm

   Javad Hamidzadeh, Reza Monsefi, and Hadi Sadoghi Yazdi

   Neurocomputing, May 2014
6. NN

   Robust support vector machine-trained fuzzy system

   Yahya Forghani, and Hadi Sadoghi Yazdi

   Neural Networks, May 2014
7. JCAM

   A recursive algorithm for optimizing differentiation

   Ali Mashreghi, and Hadi Sadoghi Yazdi

   Journal of computational and applied mathematics, May 2014
8. Neurocomputing

   LMIRA: large margin instance reduction algorithm

   Hadi Sadoghi Yazdi Javad Hamidzadeh

   Neurocomputing, , Dec 2014

   Abs HTML PDF Video

   

   In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classification or training could be reduced. In this paper, we propose a Large Margin Instance Reduction Algorithm, namely LMIRA. LMIRA removes non-border instances and keeps border ones. In the proposed method, the instance reduction process is formulated as a constrained binary optimization problem and then it is solved by employing a filled function algorithm. Instance-based learning algorithms are often confronted with the difficulty of choosing those instances which must be stored to be used during an actual test. Storing too many instances can result in large memory requirements and slow execution. In LMIRA, core of instance reduction process is based on keeping the hyperplane that separates a two-class data and provides large margin separation. LMIRA selects the most representative instances, satisfying both following objectives: high accuracy and reduction rates. The performance has been evaluated on real world data sets from UCI repository by the ten-fold cross-validation method. The results of experiments are compared with state-of-the-art methods, which show the superiority of proposed method in terms of classification accuracy and reduction percentage.

2013

1. Neurocomputing

   Ensemble of online neural networks for non-stationary and imbalanced data streams

   Adel Ghazikhani, Reza Monsefi, and Hadi Sadoghi Yazdi

   Neurocomputing, Dec 2013

   

2012

1. NCA

   DDC: distance-based decision classifier

   Hadi Sadoghi Yazdi Javad Hamidzadeh

   Neural Computing and Applications , , Nov 2012

   Abs HTML PDF Video

   

   This paper presents a new classification method utilizing distance-based decision surface with nearest neighbor projection approach, called DDC. Kernel type of DDC has been extended to take into account the effective nonlinear structure of the data. DDC has some properties: (1) does not need conventional learning procedure (as k-NN algorithm), (2) does not need searching time to locate the k-nearest neighbors, and (3) does not need optimization process unlike some classification methods such as Support Vector Machine (SVM). In DDC, we compute the weighted average of distances to all the training samples. Unclassified sample will be classified as belonging to a class that has the minimum obtained distance. As a result, by such a rule we can derive a formula that can be used as the decision surface. DDC is tested on both synthetic and real-world data sets from the UCI repository, and the results were compared with k-NN, RBF Network, and SVM. The experimental results indicate DDC outperforms k-NN in the most experiments and the results are comparable to or better than SVM with some data sets.

2011

1. IJSPIPPR

   Feature selection in spectral clustering

   Soheila Ashkezari Toussi, and Hadi Sadoghi Yazdi

   International Journal of Signal Processing, Image Processing and Pattern Recognition, , 2011

   Abs HTML PDF Video

   

   Spectral clustering is a powerful technique in clustering specially when the structure of data is not linear and classical clustering methods lead to fail. In this paper, we propose a spectral clustering algorithm with a feature selection schema based on extracted features of Kernel PCA. In the proposed algorithm, selecting appropriate vectors is dependent upon entropy of clusters on these vectors and weighting method is influenced by sum of the existence gap between clusters and entropy of the vectors. Tuning the parameters has a great effect on the results of spectral clustering techniques. In the ideal case, comparing our method with NJW and Kernel K-Means indicate the successful of the proposed algorithm.

2. IJSPIPPR

   Edge/corner programming

   Hadi Sadoghi Yazdi, and Soheila Ashkezari Toussi

   International Journal of Signal Processing, Image Processing and Pattern Recognition, , Apr 2011

   Abs HTML PDF Video

   

   Many vision tasks are based on edge/corner detection. We propose some rules and utilize them to find suitable window for edge/corner detection by using quadratic programming. Experimental results over synthetic images and real images show satisfactory outcomes.

2010

1. ASC

   Unsupervised adaptive neural-fuzzy inference system for solving differential equations

   Hadi Sadoghi Yazdi, and Reza Pourreza

   Applied Soft Computing, Apr 2010

   

2009

1. IEEE conf.

   ECG arrhythmia classification with support vector machines and genetic algorithm

   Jalal A Nasiri, Mahmoud Naghibzadeh, H Sadoghi Yazdi, and 1 more author

   In 2009 Third UKSim European symposium on computer modeling and simulation, Apr 2009

   
2. IJCEE

   Fuzzy complex system of linear EquationsApplied to circuit analysis

   Taher Rahgooy, Hadi Sadoghi Yazdi, and Reza Monsefi

   International Journal of Computer and Electrical Engineering, Apr 2009

   

2008

1. IEEE conf.

   An eigenspace-based approach for human fall detection using integrated time motion image and neural network

   Homa Foroughi, Aabed Naseri, Alireza Saberi, and 1 more author

   In 2008 9th International Conference on Signal Processing, Apr 2008

   
2. IEEE conf.

   A Short Term and Long Term Learning Based on Fuzzy Transaction Repository and Feature Re-Weighting

   Hamid Reza Pourreza, Hadi Sadoghi Yazdi, and Homa Foroughi

   In IEEE Int. Conf. on Intelligent Communication and Processing, Apr 2008