Hossein Ebrahimpour-Komleh

University of Kashan · Department of Computer Engineering

Today, online reviews have a great influence on consumers’ purchasing decisions. As a result, spam attacks, consisting of the malicious inclusion of fake online reviews, can be detrimental to both customers as well as organizations. Several methods have been proposed to automatically detect fake opinions; however, the majority of these methods focu...

In recent years, the improvement of medical imaging techniques helps doctors to diagnose tumors. Early detection of tumors will increase the chance of treatment and plays an essential role in patient survival. In this thesis project we would like to find how to be an expert in medical image analysis by explaining fundamental concepts like medical i...

Learning in feed-forward neural networks is a crucial and challenging task. Gradient descent-based approaches are among the most commonly employed learning algorithms but suffer from difficulties such as ending up in local optima. To cope with this, swarm intelligence (SI) algorithms can be employed. Memetic algorithms integrate an SI technique wit...

For Multi-label classification, redundant and irrelevant features degrade the performance of classification. To select the best features based on several conflicting objectives, feature selection can be modeled as a large-scale optimization problem. However, most existing multi-objective feature selection methods select the features based on minimi...

Purpose: Pandemic COVID-19 has created an emergency for the medical community. Researchers require extensive study of scientific literature in order to discover drugs and vaccines. In this situation where every minute is valuable to save the lives of hundreds of people, a quick understanding of scientific articles will help the medical community. A...

The most important action in treating diabetic retinopathy is early diagnosis and its progression degree. This paper presents a two-dimensional Deep Belief Network based on Mixed-restricted Boltzmann Machine capable of receiving multiple two-dimensional inputs. Using multiple inputs provides more appropriate prior information for learning. In this...

The performance of most data science algorithms, and in particular machine learning algorithms, is largely dependent on the performance of their optimisation algorithm. In other words, without an effective optimisation algorithm there is no effective data science algorithm. Conventional optimisation algorithms suffer from drawbacks such as a tenden...

Clustering is a commonly employed approach to image segmentation. To overcome the problems of conventional algorithms such as getting trapped in local optima, in this paper, we propose an improved automatic clustering algorithm for image segmentation based on the human mental search (HMS) algorithm, a recently proposed method to solve complex optim...

Multi-label classification is a machine learning task to construct a model for assigning an entity in the dataset to two or more class labels. In order to improve the performance of multi-label classification, a multi-objective feature selection algorithm has been proposed in this paper. Feature selection as a preprocessing task for Multi-label cla...

Image segmentation is an essential step in image processing and computer vision with many image segmentation algorithms having been proposed in the literature. Among these, clustering is one of the prominent approaches to achieve segmentation. Traditional clustering algorithms have been used extensively for this purpose, although they have disadvan...

Data classification is a fundamental task in data mining. Within this field, the classification of multi-labeled data has been seriously considered in recent years. In such problems, each data entity can simultaneously belong to several categories. Multi-label classification is important because of many recent real-world applications in which each...

Representation learning techniques, as a paradigm shift in feature generation, are considered as an important and inevitable part of state of the art pattern recognition systems. These techniques attempt to extract and abstract key information from raw input data. Representation learning based methods of feature generation are in contrast to handy...

Keywords are a collection of important words in a document that are the core topic of the discussion. This paper proposes a hybrid method for automatically extracting keywords from Persian documents and web pages. In the proposed method, firstly, based on linguistic knowledge, processing was performed at word and letter levels to optimize of the an...

Automated segmentation of abnormal tissues in medical images assists both physicians and medical researchers in the process of diseases diagnostic and research activities respectively. Intelligent techniques of automated segmentation are gaining more popularity in contrast to non-intelligent ones. In these techniques, quality representation of pixe...

This paper develops a new variation of deep belief networks which is evaluated on the basis of supervised classification of human actions and activities. The proposed multi-input 1-dimensional deep belief network (M1DBN) can work based on three inputs which contain different information structures. The multi input features helps M1DBN automatically...

Finding the optimal connection weights in a neural network is one of the most challenging tasks in machine learning and pattern recognition. The main disadvantage of conventionally used algorithms such as back-propagation is that they show a tendency of getting trapped in local rather than global optima. To address this, population-based metaheuris...

Nowadays, medical image modalities are almost available everywhere. These modalities are bases of diagnosis of various diseases sensitive to specific tissue type. Usually physicians look for abnormalities in these modalities in diagnostic procedures. Count and volume of abnormalities are very important for optimal treatment of patients. Segmentatio...

Finding the optimal connection weights in a neural network is one of the most challenging tasks in machine learning and pattern recognition. The main disadvantage of conventionally used algorithms such as back-propagation is that they show a tendency of getting trapped in local rather than global optima. To address this, population-based metaheuris...

Multilevel thresholding is one of the principal methods of image segmentation. These methods enjoy image histogram for segmentation. The quality of segmentation depends on the value of the selected thresholds. Since an exhaustive search is made for finding the optimum value of the objective function, the conventional methods of multilevel threshold...

Image segmentation is one of the fundamental techniques in image analysis. One group of segmentation techniques is based on clustering principles, where association of image pixels is based on a similarity criterion. Conventional clustering algorithms, such as k-means, can be used for this purpose but have several drawbacks including dependence on...

In this study we consider multi-view capabilities of convolutional neural networks as one of the best methods of representation learning. Multi-view learning as a machine learning technique deals with the task of learning from multiple distinct views or multiple distinct feature sets. Moreover, multi-view feature learning attempts to abstract and s...

In the present study, a new algorithm is developed for neural network training by combining a gradient-based and a meta-heuristic algorithm. The new algorithm benefits from simultaneous local and global search, eliminating the problem of getting stuck in local optimum. For this purpose, first the global search ability of the grey wolf optimizer (GW...

Multi-objective optimization is an inseparable area of optimization and plays a crucial role in terms of practicality. Almost all multi-objective optimization problems in the real world are suitable as opposed to goals with several ideal models around. Rather than one optimal solution, these issues have a set of optimal solutions known as the Paret...

Digital technologies are one of the main components in smart cities. Images are one of the principal data types in digital technologies. Images can be seen in a variety of applications such as intelligent transport systems, tourism applications, and real-time science understanding. Therefore, it is important to provide efficient image processing al...

Diabetic retinopathy is proved to be one of the most important eye disorders in recent decades that late diagnosis of it may cause low vision or even blindness. Specialist are able to detect retinopathy in retinal images using machine learning as a decision support system which helps accelerate and facilitate the diagnosis. The automated diabetic r...

Population-based metaheuristic algorithms have become popular in recent years with them getting used in different fields such as business, medicine, and agriculture. The present paper proposes a simple but efficient population-based metaheuristic algorithm called Human Mental Search (HMS). HMS algorithm mimics the exploration strategies of the bid...

Classification of biomedical data plays a significant role in prediction and diagnosis of disease. The existence of redundant and irrelevant features is one of the major problems in biomedical data classification. Excluding these features can improve the performance of classification algorithm. Feature selection is the problem of selecting a subset...

Multilevel thresholding is one of the most broadly used approaches to image segmentation. However, the traditional techniques of multilevel thresholding are time-consuming, especially when the number of the threshold values is high. Thus, population-based metaheuristic (P-metaheuristic) algorithms can be used to overcome this limitation. P-metaheur...

Diabetic retinopathy is proved to be one of the most important eye disorders in recent decades that late diagnosis of it may cause low vision or even blindness. Specialists are able to detect retinopathy in retinal images using machine learning as a decision support system which helps accelerate and facilitate the diagnosis. The automated diabetic...

Feature selection problem is one of the most important issues in machine learning and statistical pattern recognition. This problem is important in many applications such as text categorization because there are many redundant and irrelevant features in these applications which may reduce the classification performance. Indeed, feature selection is...

For many years, researchers have studied high accuracy methods for recognizing the handwriting and achieved many significant improvements. However, an issue that has rarely been studied is the speed of these methods. Considering the computer hardware limitations, it is necessary for these methods to run in high speed. One of the methods to increase...

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural netw...

Abstract—Image thresholding considered as a popular method for image segmentation. So far, many approaches have been proposed for image thresholding. Maximum entropy thresholding has been widely applied in the literature. This paper proposes a multilevel image thresholding (MECOAT) using cuckoo optimization algorithm (COA). COA is a new nature base...

Chronic kidney disease is a universal common obstacle which its outcomes can be prevented or delayed by early detection and cure. Classification of kidney disease is vital for global improvement and accomplishment of practical guidance. Therefore, data mining and machine learning techniques can be used to discover knowledge and identify patterns fo...

In today’s modern world, there is no place for passwords and security has the highest priority in current systems. Biometrics like face and voice can be circumvented by fraudulent methods but fingerprint has a high security in this aspect. In this paper a new fingerprint matching approach is introduced. The proposed method with various modification...

Text classification is extensively used to organize documents in a digital form. According to the growth number of digitally documents, automated text classification has become more Heralds in the recent years. High dimensionality of the feature space is a common problem in text classification. Most of them are irrelevant and redundant which may re...

Face Recognition has attracted many researchers in the last three decades. There have been great improvements in controlled environments and static images. But there are still many challenges in uncontrolled environments and online applications. In this paper, a new parallel face recognition approach has been proposed that is almost robust to illum...

Fingerprint as a biometric has the most applications in verification and identification systems, because of its specific properties. In identification systems, input image is compared with all of images stored in the database. In huge databases, the comparison will take large amounts of time; Consider FBI databases, for instance. Image classificat...

Feature subset selection plays an important role in data mining. The aim of feature selection is to remove redundant and irrelevant features without reducing the accuracy. Cuckoo optimization algorithm (COA) is a new population based algorithm which is inspired by the lifestyle of a species of bird called cuckoo. In this paper, we introduce a new a...

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural net...

Classification of biomedical data plays a significant role in prediction and diagnosis of disease. The existence of redundant and irrelevant features is one of the major problems in biomedical data classification. Excluding these features can improve the performance of classification algorithm. Feature selection is the problem of selecting a subs...

Introduction: In recent years, hepatitis diseases have become prevalent in the world. The correct diagnosis of hepatitis disease is not a straight task. The goal of this paper is to introduce a new intelligent system for automatic hepatitis diagnosis based on machine learning approaches. Materials and Methods:the proposed approach consists of three...

Fingerprint classification is an important phase in increasing the speed of a fingerprint verification system and narrow down the search of fingerprint database. Fingerprint verification is still a challenging problem due to the difficulty of poor quality images and the need for faster response. The classification gets even harder when just one cor...

Hough transform is one of the most widely used algorithms in image processing. The major problems of Hough's transform are its time consuming and its abundant requirement of computational resources. In this paper, we try to solve this problem by paralleling this algorithm and implementing it on GPUs(Graphic Process unit) using CUDA(Compute Unified...

Hough transform is one of the most widely used algorithms in image processing. The major problems of Hough's transform are its time consuming and its abundant requirement of computational resources. In this paper, we try to solve this problem by paralleling this algorithm and implementing it on GPUs(Graphic Process unit) using CUDA(Compute Unified...

Feature selection process is one of the main steps in data mining and knowledge discovery. Feature selection is a process to remove redundant and irreverent features without reducing the classification accuracy. This paper tries to select the best features set using imperialist competitive algorithm. Imperialist competitive algorithm is a novel pop...

The illumination variation is one of the main challenges in real-world face recognition systems. Face recognition under uneven illumination is still an open problem. In this paper, we proposed a novel illumination invariant face recognition approach based on Self Quotient Image and weighted Local Binary Pattern. We improved the performance of the s...

Cuckoo Optimization Algorithm (COA) is a new evolutionary algorithm which is inspired by the life of a species of bird, called cuckoo. COA has presented excellent capabilities in various optimization problems. In this paper, we want to adapt COA to train the weights of feedforward neural networks. For this purpose, COA has been applied on three kno...

This article has tried to take care to identify and pick tomatoes through image–processing methods. Through boosting the vision of a computer to gain discipline and arrangement for picking tomatoes, though there is chaos in an agricultural setting, an intelligent processing system is applied, which is equipped with wave's length wireless camera use...

In this article, it has been tried to take care of identifying and picking tomato through image–processing method. Through boosting the vision of a computer to gain discipline and arrangement for picking tomato, though there is a chaos in an agriculture setting besides smart processing system is applied which is equipped with wave's length wireless...

In this paper, an effective method for human eye tracking and also decreasing the current challenges and problems in its algorithms, possibly as real time and for unconstrained environments has been proposed. In this method, firstly face has been detected and segmented from the remaining parts to make the searching area in tracking stage, narrower...

This paper presents a novel face recognition approach, based on Local Binary Pattern (LBP) and Haar wavelet transform. We propose a fast and robust three-layer weighted Haar and weighted LBP histogram (WHWLBP) representation for face recognition. In this method, face image is decomposed using first level Haar wavelet decomposition, and then a multi...

Abstract-In this article, it has been tried to take care of identifying and picking tomato through image-processing method. Through boosting the vision of a computer to gain discipline and arrangement for picking tomato, though there is a chaos in an agriculture setting besides smart processing system is applied which is equipped with wave's length...

The recent area of Wireless Sensor Networks (WSNs) has brought new challenges to developers of net-work protocols. Energy consumption and network coverage are two important challenges in wireless sensor networks. We investigate intelligent techniques for node positioning to reduce energy consumption with coverage preserved in wireless sensor networ...

In this paper, we introduce an interactive whiteboard system using image processing and computer vision techniques. This system is more portable than other similar interactive systems, because the system only needs a computer, laser pointer and a camera to communicate with the user. Using a standard laser pointer, user can send his/her commands to...

Fractals are popular because of their ability to create complex images using only several simple codes. This is possible by capturing image redundancy and presenting the image in compressed form using the self similarity feature. For many years fractals were used for image compression. In the last few years they have also been used for face recogni...

Faces are complex patterns that often differ in only subtle ways. Face recognition algorithms have difficulty in coping with differences in lighting, cameras, pose, expression, etc. We propose a novel approach for facial recognition based on a new feature extraction method called fractal image-set encoding. This feature extraction method is a speci...

Faces are complex patterns that often differ in only subtle ways. Face recognition algorithms have difficulty in coping with differences in lighting, cameras, pose, expression, etc. We propose a novel approach for facial recognition based on a new feature extraction method called fractal image-set encoding. This feature extraction method is a speci...

Face recognition has developed into a major research area in pattern recognition and computer vision. Face recognition is different from classical pattern recognition problems such as character recognition. In classical pattern recognition, there are relatively few classes, and many samples per class. With many samples per class, algorithms can cla...