Esmat Rashedi | Kerman Graduate University of Technology (original) (raw)

Papers by Esmat Rashedi

Research paper thumbnail of Paper: A LONG TERM LEARNING SCHEME IN CBIR SYSTEMS BY DEFINING SEMANTIC TEMPLATES USING INFORMATION OF SIMILARITY-REFINEMENT BASED SHORT TERM LEARNING

Research paper thumbnail of Paper: GEODESIC PATH BASED IMAGE INPAINTING USING WAVELET TRANSFORM

Research paper thumbnail of Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier

Applied Mathematics and Computation, 2015

Research paper thumbnail of Domain adaptation based on incremental adversarial learning

2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)

Domain adaptation is a method of transfer learning. Domain adaptation has a source domain and tar... more Domain adaptation is a method of transfer learning. Domain adaptation has a source domain and target domain with related but different distributions. Unsupervised domain adaptation could be a scenario wherever we've labeled unlabeled target data and source data. In this paper, an incremental adversarial learning method is proposed for unsupervised domain adaptation. In this work, the unknown target labels are predicted and according to these estimated labels, some target data with more similarity to the source data are added to the source data to improve the adaptation between two domains. We use the adversarial discriminative approach as the base unsupervised domain adaptation technique. We do this to handle the large domain shift between the source and target domain distributions. Experimental reports prove that our approach performs much better on several visual domain adaptation tasks.

Research paper thumbnail of Gene selection using pyramid gravitational search algorithm

PLOS ONE, 2022

Genetics play a prominent role in the development and progression of malignant neoplasms. Identif... more Genetics play a prominent role in the development and progression of malignant neoplasms. Identification of the relevant genes is a high-dimensional data processing problem. Pyramid gravitational search algorithm (PGSA), a hybrid method in which the number of genes is cyclically reduced is proposed to conquer the curse of dimensionality. PGSA consists of two elements, a filter and a wrapper method (inspired by the gravitational search algorithm) which iterates through cycles. The genes selected in each cycle are passed on to the subsequent cycles to further reduce the dimension. PGSA tries to maximize the classification accuracy using the most informative genes while reducing the number of genes. Results are reported on a multi-class microarray gene expression dataset for breast cancer. Several feature selection algorithms have been implemented to have a fair comparison. The PGSA ranked first in terms of accuracy (84.5%) with 73 genes. To check if the selected genes are meaningful i...

Research paper thumbnail of The Effects of Exchange Rate Fluctuations on Real GDP in Iran

Research paper thumbnail of Adaptive enhancement and binarization techniques for degraded plate images

Multimedia Tools and Applications, 2017

Vehicle License Plate Recognition (VLPR) is one of the most important aspects of applying compute... more Vehicle License Plate Recognition (VLPR) is one of the most important aspects of applying computer techniques in Intelligent Transport Systems (ITS). They face difficulties like shadows effects, non-uniform illumination intensity, and dirty plates. To tackle these problems, this paper proposes a new VLPR system by producing a contrast enhancement method, a background removal method, and a binarization method. After binarization, an OCR method using artificial neural network (ANN) reads the plate characters. The performance of the proposed system is tested on 4 k Iranian vehicle license plate images. The proposed method causes the correct recognition rate of 91.2%. The results obtained in comparison to those of well-known methods show that the proposed system is robust for moving cars in outside environment and under different illumination conditions.

Research paper thumbnail of Designing a high gain dual-band DA with CRLH-TL

Research paper thumbnail of NewFunctions forMass Calculation inGravitational Search Algorithm

Nowadays, optimization problems are large-scale and complicated, so heuristic optimization algori... more Nowadays, optimization problems are large-scale and complicated, so heuristic optimization algorithms have become common for solving them. Gravitational Search Algorithm (GSA) is one of the heuristic algorithms for solving optimization problems inspired by Newton’s lows of gravity and motion. Definition and calculation of masses in GSA have an impact on the performance of the algorithm. Defining appropriate functions for mass calculation improves the exploitation and exploration power of the algorithm and prevents the algorithm from getting trapped in local optima. In this paper, Sigma scaling and Boltzmann selection functions are examined for mass calculation in GSA. The proposed functions are evaluated on some standard test functions including unimodal functions and multimodal functions. The obtained results are compared with the standard GSA, genetic algorithm, particle swarm optimization algorithm, gravitational particle swarm algorithm and clustered-GSA. Experimental results sh...

Research paper thumbnail of A novel diagnostic and prognostic approach for unresponsive patients with anthroponotic cutaneous leishmaniasis using artificial neural networks

PLOS ONE, 2021

Cutaneous leishmaniasis (CL) imposes a major health burden throughout the tropical and subtropica... more Cutaneous leishmaniasis (CL) imposes a major health burden throughout the tropical and subtropical regions of the globe. Unresponsive cases are common phenomena occurred upon exposure to the standard drugs. Therefore, rapid detection, prognosis and classification of the disease are crucial for selecting the proper treatment modality. Using machine learning (ML) techniques, this study aimed to detect unresponsive cases of ACL, caused by Leishmania tropica, which will consequently be used for a more effective treatment modality. This study was conducted as a case-control setting. Patients were selected in a major ACL focus from both unresponsive and responsive cases. Nine unique and relevant features of patients with ACL were selected. To categorize the patients, different classifier models such as k-nearest neighbors (KNN), support vector machines (SVM), multilayer perceptron (MLP), learning vector quantization (LVQ) and multipass LVQ were applied and compared for this supervised lea...

Research paper thumbnail of Representation learning in a deep network for license plate recognition

Multimedia Tools and Applications, 2020

Research paper thumbnail of A Multimodal Emotion Recognition System Using Facial Landmark Analysis

Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2018

Research paper thumbnail of Optimal pipe dimensioning in water distribution networks using Gravitational Search Algorithm

ISH Journal of Hydraulic Engineering, 2019

Research paper thumbnail of Design and Construction of Electronic Nose for Multi-purpose Applications by Sensor Array Arrangement Using IBGSA

Journal of Intelligent & Robotic Systems, 2017

Research paper thumbnail of A comprehensive survey on gravitational search algorithm

Swarm and Evolutionary Computation, 2018

Research paper thumbnail of Gravitational search algorithm with both attractive and repulsive forces

Research paper thumbnail of Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models

Journal of Hydroinformatics, 2016

Pier scour phenomena in the presence of debris accumulation have attracted the attention of engin... more Pier scour phenomena in the presence of debris accumulation have attracted the attention of engineers to present a precise prediction of the local scour depth. Most experimental studies of pier scour depth with debris accumulation have been performed to find an accurate formula to predict the local scour depth. However, an empirical equation with appropriate capacity of validation is not available to evaluate the local scour depth. In this way, gene-expression programming (GEP), evolutionary polynomial regression (EPR), and model tree (MT) based formulations are used to develop to predict the scour depth around bridge piers with debris effects. Laboratory data sets utilized to perform models are collected from different literature. Effective parameters on the local scour depth include geometric characterizations of bridge piers and debris, physical properties of bed sediment, and approaching flow characteristics. The efficiency of the training stages for the GEP, MT, and EPR models ...

Research paper thumbnail of Harmony Search Algorithm

Recent Developments in Intelligent Nature-Inspired Computing

Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation p... more Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation process of musicians in finding a pleasing harmony. In recent years, due to some advantages, HS has received a significant attention. HS is easy to implement, converges quickly to the optimal solution and finds a good enough solution in a reasonable amount of computational time. The merits of HS algorithm have led to its application to optimization problems of different engineering areas. In this chapter, the concepts and performance of HS algorithm are shown and some engineering applications are reviewed. It is observed that HS has shown promising performance in solving difficult optimization problems and different versions of this algorithm have been developed. In the next years, it is expected that HS is applied to more real optimization problems.

Research paper thumbnail of A hierarchical algorithm for vehicle license plate localization

Multimedia Tools and Applications, 2017

Research paper thumbnail of Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm

Biomedical Signal Processing and Control, 2017

Abstract This paper presents an automatic method for finding optimal channels in Brain Computer I... more Abstract This paper presents an automatic method for finding optimal channels in Brain Computer Interfaces (BCIs). Detecting the effective channels in BCI systems is an important problem in reducing the complexity of these systems. In this research, Improved Binary Gravitation Search Algorithm (IBGSA) is used to automatically detect the effective electroencephalography (EEG) channels in left or right hand classification. To do this, at first, data is filtered with a bandpass filter in order to reduce the amount of different types of merged noise. Then, the electrooculography (EOG) and electromyography (EMG) artifacts are corrected based on Blind Source Separation (BSS) algorithm. Data is epoched according to the left or right hand motor imageries and central beta frequency band is isolated for Event Related Synchronization (ERS) analysis. Feature extraction process is carried out by analyzing EEG signals in time and wavelet domains. The logarithmic power of each channel is computed in time domain and the features of mean, mode, median, variance, and standard deviation are calculated in wavelet domain. IBGSA is employed to detect the optimal channels to achieve better classification results. Support Vector Machine (SVM) is used as the classifier. The maximum accuracy of 80% and average accuracy of 76.24% were obtained for eight subjects in BCI competition IV dataset. The results of this research confirm that automatically detecting effective channels can enhance the practical implementation of BCI based systems and reduce the complexity.

Research paper thumbnail of Paper: A LONG TERM LEARNING SCHEME IN CBIR SYSTEMS BY DEFINING SEMANTIC TEMPLATES USING INFORMATION OF SIMILARITY-REFINEMENT BASED SHORT TERM LEARNING

Research paper thumbnail of Paper: GEODESIC PATH BASED IMAGE INPAINTING USING WAVELET TRANSFORM

Research paper thumbnail of Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier

Applied Mathematics and Computation, 2015

Research paper thumbnail of Domain adaptation based on incremental adversarial learning

2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)

Domain adaptation is a method of transfer learning. Domain adaptation has a source domain and tar... more Domain adaptation is a method of transfer learning. Domain adaptation has a source domain and target domain with related but different distributions. Unsupervised domain adaptation could be a scenario wherever we've labeled unlabeled target data and source data. In this paper, an incremental adversarial learning method is proposed for unsupervised domain adaptation. In this work, the unknown target labels are predicted and according to these estimated labels, some target data with more similarity to the source data are added to the source data to improve the adaptation between two domains. We use the adversarial discriminative approach as the base unsupervised domain adaptation technique. We do this to handle the large domain shift between the source and target domain distributions. Experimental reports prove that our approach performs much better on several visual domain adaptation tasks.

Research paper thumbnail of Gene selection using pyramid gravitational search algorithm

PLOS ONE, 2022

Genetics play a prominent role in the development and progression of malignant neoplasms. Identif... more Genetics play a prominent role in the development and progression of malignant neoplasms. Identification of the relevant genes is a high-dimensional data processing problem. Pyramid gravitational search algorithm (PGSA), a hybrid method in which the number of genes is cyclically reduced is proposed to conquer the curse of dimensionality. PGSA consists of two elements, a filter and a wrapper method (inspired by the gravitational search algorithm) which iterates through cycles. The genes selected in each cycle are passed on to the subsequent cycles to further reduce the dimension. PGSA tries to maximize the classification accuracy using the most informative genes while reducing the number of genes. Results are reported on a multi-class microarray gene expression dataset for breast cancer. Several feature selection algorithms have been implemented to have a fair comparison. The PGSA ranked first in terms of accuracy (84.5%) with 73 genes. To check if the selected genes are meaningful i...

Research paper thumbnail of The Effects of Exchange Rate Fluctuations on Real GDP in Iran

Research paper thumbnail of Adaptive enhancement and binarization techniques for degraded plate images

Multimedia Tools and Applications, 2017

Vehicle License Plate Recognition (VLPR) is one of the most important aspects of applying compute... more Vehicle License Plate Recognition (VLPR) is one of the most important aspects of applying computer techniques in Intelligent Transport Systems (ITS). They face difficulties like shadows effects, non-uniform illumination intensity, and dirty plates. To tackle these problems, this paper proposes a new VLPR system by producing a contrast enhancement method, a background removal method, and a binarization method. After binarization, an OCR method using artificial neural network (ANN) reads the plate characters. The performance of the proposed system is tested on 4 k Iranian vehicle license plate images. The proposed method causes the correct recognition rate of 91.2%. The results obtained in comparison to those of well-known methods show that the proposed system is robust for moving cars in outside environment and under different illumination conditions.

Research paper thumbnail of Designing a high gain dual-band DA with CRLH-TL

Research paper thumbnail of NewFunctions forMass Calculation inGravitational Search Algorithm

Nowadays, optimization problems are large-scale and complicated, so heuristic optimization algori... more Nowadays, optimization problems are large-scale and complicated, so heuristic optimization algorithms have become common for solving them. Gravitational Search Algorithm (GSA) is one of the heuristic algorithms for solving optimization problems inspired by Newton’s lows of gravity and motion. Definition and calculation of masses in GSA have an impact on the performance of the algorithm. Defining appropriate functions for mass calculation improves the exploitation and exploration power of the algorithm and prevents the algorithm from getting trapped in local optima. In this paper, Sigma scaling and Boltzmann selection functions are examined for mass calculation in GSA. The proposed functions are evaluated on some standard test functions including unimodal functions and multimodal functions. The obtained results are compared with the standard GSA, genetic algorithm, particle swarm optimization algorithm, gravitational particle swarm algorithm and clustered-GSA. Experimental results sh...

Research paper thumbnail of A novel diagnostic and prognostic approach for unresponsive patients with anthroponotic cutaneous leishmaniasis using artificial neural networks

PLOS ONE, 2021

Cutaneous leishmaniasis (CL) imposes a major health burden throughout the tropical and subtropica... more Cutaneous leishmaniasis (CL) imposes a major health burden throughout the tropical and subtropical regions of the globe. Unresponsive cases are common phenomena occurred upon exposure to the standard drugs. Therefore, rapid detection, prognosis and classification of the disease are crucial for selecting the proper treatment modality. Using machine learning (ML) techniques, this study aimed to detect unresponsive cases of ACL, caused by Leishmania tropica, which will consequently be used for a more effective treatment modality. This study was conducted as a case-control setting. Patients were selected in a major ACL focus from both unresponsive and responsive cases. Nine unique and relevant features of patients with ACL were selected. To categorize the patients, different classifier models such as k-nearest neighbors (KNN), support vector machines (SVM), multilayer perceptron (MLP), learning vector quantization (LVQ) and multipass LVQ were applied and compared for this supervised lea...

Research paper thumbnail of Representation learning in a deep network for license plate recognition

Multimedia Tools and Applications, 2020

Research paper thumbnail of A Multimodal Emotion Recognition System Using Facial Landmark Analysis

Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2018

Research paper thumbnail of Optimal pipe dimensioning in water distribution networks using Gravitational Search Algorithm

ISH Journal of Hydraulic Engineering, 2019

Research paper thumbnail of Design and Construction of Electronic Nose for Multi-purpose Applications by Sensor Array Arrangement Using IBGSA

Journal of Intelligent & Robotic Systems, 2017

Research paper thumbnail of A comprehensive survey on gravitational search algorithm

Swarm and Evolutionary Computation, 2018

Research paper thumbnail of Gravitational search algorithm with both attractive and repulsive forces

Research paper thumbnail of Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models

Journal of Hydroinformatics, 2016

Pier scour phenomena in the presence of debris accumulation have attracted the attention of engin... more Pier scour phenomena in the presence of debris accumulation have attracted the attention of engineers to present a precise prediction of the local scour depth. Most experimental studies of pier scour depth with debris accumulation have been performed to find an accurate formula to predict the local scour depth. However, an empirical equation with appropriate capacity of validation is not available to evaluate the local scour depth. In this way, gene-expression programming (GEP), evolutionary polynomial regression (EPR), and model tree (MT) based formulations are used to develop to predict the scour depth around bridge piers with debris effects. Laboratory data sets utilized to perform models are collected from different literature. Effective parameters on the local scour depth include geometric characterizations of bridge piers and debris, physical properties of bed sediment, and approaching flow characteristics. The efficiency of the training stages for the GEP, MT, and EPR models ...

Research paper thumbnail of Harmony Search Algorithm

Recent Developments in Intelligent Nature-Inspired Computing

Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation p... more Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation process of musicians in finding a pleasing harmony. In recent years, due to some advantages, HS has received a significant attention. HS is easy to implement, converges quickly to the optimal solution and finds a good enough solution in a reasonable amount of computational time. The merits of HS algorithm have led to its application to optimization problems of different engineering areas. In this chapter, the concepts and performance of HS algorithm are shown and some engineering applications are reviewed. It is observed that HS has shown promising performance in solving difficult optimization problems and different versions of this algorithm have been developed. In the next years, it is expected that HS is applied to more real optimization problems.

Research paper thumbnail of A hierarchical algorithm for vehicle license plate localization

Multimedia Tools and Applications, 2017

Research paper thumbnail of Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm

Biomedical Signal Processing and Control, 2017

Abstract This paper presents an automatic method for finding optimal channels in Brain Computer I... more Abstract This paper presents an automatic method for finding optimal channels in Brain Computer Interfaces (BCIs). Detecting the effective channels in BCI systems is an important problem in reducing the complexity of these systems. In this research, Improved Binary Gravitation Search Algorithm (IBGSA) is used to automatically detect the effective electroencephalography (EEG) channels in left or right hand classification. To do this, at first, data is filtered with a bandpass filter in order to reduce the amount of different types of merged noise. Then, the electrooculography (EOG) and electromyography (EMG) artifacts are corrected based on Blind Source Separation (BSS) algorithm. Data is epoched according to the left or right hand motor imageries and central beta frequency band is isolated for Event Related Synchronization (ERS) analysis. Feature extraction process is carried out by analyzing EEG signals in time and wavelet domains. The logarithmic power of each channel is computed in time domain and the features of mean, mode, median, variance, and standard deviation are calculated in wavelet domain. IBGSA is employed to detect the optimal channels to achieve better classification results. Support Vector Machine (SVM) is used as the classifier. The maximum accuracy of 80% and average accuracy of 76.24% were obtained for eight subjects in BCI competition IV dataset. The results of this research confirm that automatically detecting effective channels can enhance the practical implementation of BCI based systems and reduce the complexity.