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Open Access Article
1 - Hybridization of Facial Features and Use of Multi Modal Information for 3D Face Recognition
Nita Thakare -
Open Access Article
2 - Document Analysis And Classification Based On Passing Window
ZAHER BAMASOOD -
Open Access Article
3 - High-Scale Image Clustering with Semantic Cues Modeling and Spatial Simulation
Mahdi JalaliIn recent years, image annotation is one of the active research topics. In this article, a semi-supervised cooperative clustering technique is proposed for image annotation. Clustering methods are very popular because they do not require annotations. In order to achieve MoreIn recent years, image annotation is one of the active research topics. In this article, a semi-supervised cooperative clustering technique is proposed for image annotation. Clustering methods are very popular because they do not require annotations. In order to achieve the highest efficiency, the clustering results of six systems with different color space and similarity criteria are cooperatively combined with the majority vote. When the number of votes for an image is low, relevant feedback is used to annotate it. One of the most important parts of the image retrieval system and clustering algorithm is determining the appropriate similarity criteria between images. Nowadays, the linear similarity criterion is mostly used to determine the similarity between images, but the nonlinear models can have much better performance due to their proximity to the human vision system, for this purpose, the KMRBF nonlinear similarity criterion is used to simulate vision. Humans and improvement of recovery results are suggested. Experiments on the Corel image database and satellite images show that the proposed method has good performance. According to the results obtained in the satellite image database, the YIQ color space has a higher accuracy (82.5%). Also, the three color spaces CIELab, HSV and YIQ have higher efficiency, because in these color spaces, luminance is separated from chrominance and these color spaces are closer to the human vision system. Manuscript profile -
Open Access Article
4 - Spatial -Spectral Feature Extraction using Three-Dimensional Singular Spectrum Analysis for Hyperspectral Image Classification
Ehsan Dashtifard Azar Mahmoodzadeh Ahmad Keshavarz Hamed AgahiFeature extraction has a valuable role in hyperspectral images processing. In recent years, various methods have been presented to extract efficient features of hyperspectral images. Recent studies have successfully used conventional singular spectrum analysis in the sp MoreFeature extraction has a valuable role in hyperspectral images processing. In recent years, various methods have been presented to extract efficient features of hyperspectral images. Recent studies have successfully used conventional singular spectrum analysis in the spectral domain and two-dimensional singular spectrum analysis in the spatial domain for feature extraction in hyperspectral images. However, a lack of success in joint spectral-spatial feature extraction is a problem with both algorithms. This study uses a three-dimensional singular spectrum analysis extension to overcome this problem. The implementation of proposal model on hyperspectral images removes the noise components during spectral-spatial feature extraction process and significantly improves features identification capability. This study conducts experiments using two publically available datasets. Experimental results show that our proposed method has a promising performance so that it has obtained a classification accuracy of at least 1.93% and 1.27% respectively on the hyperspectral dataset of Indian Pines and Pavia University compared to other recent methods. Manuscript profile -
Open Access Article
5 - Recombining Features of Frequency Domain and Location for Machine Recognition of Sign Language
Sirous TannazIn this article, a system for recognizing Persian sign language alphabets is presented. This system is able to recognize 32 hand postures for Persian alphabets and translate it into Persian text. For this purpose, images of hand positions have been considered for each l MoreIn this article, a system for recognizing Persian sign language alphabets is presented. This system is able to recognize 32 hand postures for Persian alphabets and translate it into Persian text. For this purpose, images of hand positions have been considered for each letter of the alphabet. The database contains 600 images of different people taken by a digital camera. We have transferred all the image data to the binary domain and resized them with a single scale. Image data preprocessing includes image cropping and noise removal. After pre-processing, 3 algorithms are proposed to extract features. The proposed algorithms include the image segmentation algorithm, the distance between border contour points and the center of gravity algorithm, and Radon transformation. Algorithm of the distances between the border contour points and the center of gravity shows how the points are placed on the peripheral curve of the hand in relation to each other and to the center of gravity, and therefore provides suitable structural information for describing states. The next algorithm is based on image segmentation. In this algorithm, the ratio of the number of white pixels to the total number of pixels is calculated in each of the areas. In Radon transformation, in addition to obtaining the general information of the image in each of the modes, we have increased the accuracy of the detection by using the proposed method and discarding additional information. The proposed methods also provided good results on other image databases. Manuscript profile -
Open Access Article
6 - Use of Classification Models for Optimize Link Prediction in the Ego-Social Networks
Soheila Nemati Mehdi sadeghzadeh maziyar ganjooSocial propositional systems are a new generation of systems that use the social network as a user modeling platform to maximize some challenges by using rich interactive data volumes. To make Social networking sites offer new friends to registered users based on local MoreSocial propositional systems are a new generation of systems that use the social network as a user modeling platform to maximize some challenges by using rich interactive data volumes. To make Social networking sites offer new friends to registered users based on local graph features. The main purpose of the link prediction problem on social networks is to suggest a list of users to a particular user that they will probably be communicating in the future. In this research, a prediction method for the link is presented based on the characteristics of classification models. Here, the prediction problem of the link is transformed into a classifying problem with two positive and negative classes, where the positive class represents the relationship and the negative class indicates that the two users are not communicating. Three classical classes DT, NN and NB are used for classification work. To create the dataset, the features of credibility, optimism, number of neighbors, the number of paths of different lengths, the number of shared tweets, the number of internal and external axes are used. Although self-centered grids do not have much overlap in the rings, experiments show that the consideration of self-directed pathways significantly improves predictive performance. The DT classification has recorded the best performance with an average accuracy of 99.85%. Manuscript profile -
Open Access Article
7 - Reducing Image Size and Noise Removal in Fast Object Detection using Wavelet Transform Neural Network
mahmoud jeddi Ahmad Khoogar Ali Mehdipoor Omrani -
Open Access Article
8 - Detection of Blood Vessels in Retina Images using Gray Level Grouping Method
Majid Eskandari Shahraki Mehran Emadi -
Open Access Article
9 - Improving the speed and accuracy of arrhythmia classification based on morphological features of ECG signal
Kamran Dehgany habib abadi Mohammad Yousefi -
Open Access Article
10 - Improving Image Quality Based on Feature Extraction and Gaussian Model
Alireza Alirezaei Shahraki Mehran Emadi -
Open Access Article
11 - Efficiency assessment of micro-climate change in land cover distinguishing compared to some supervised classification techniques for an arid urban environment
NAJMAE SATARI Malihe Erfani Fatemeh JahanishakibThe distinction between barren and build-up areas is one of the most important issues in land use/land cover mapping ‎in arid and semi-arid climates. In this regard, many researchers have tried to increase the accuracy of classification ‎using different methods MoreThe distinction between barren and build-up areas is one of the most important issues in land use/land cover mapping ‎in arid and semi-arid climates. In this regard, many researchers have tried to increase the accuracy of classification ‎using different methods that, some of which are complex and time-consuming. Therefore, the present study conducted ‎aimed to apply micro-climate change through the implementation of Local Climate Zoning (LCZ) algorithm in land ‎use identification with emphasis on the separation of build-up areas in one of the arid cities of Iran, and the efficiency ‎of the method by investigation the classification accuracy was compared with various supervised methods including ‎maximum likelihood, minimum distance, Fisher, KNN, fuzzy, artificial neural network and support vector machine. ‎The study area is Zahedan city, which has a very significant growth of build-up areas in recent decades. For this ‎purpose, four periods of Landsat satellite images year 2020 were used. Training samples were extracted from Google ‎Earth and the validation of the classification results was performed using 218 random points. The accuracy results ‎showed that the use of LCZ algorithm with overall accuracy and kappa coefficient of 96.33% and 0.95, respectively is ‎the highest and then the support vector machine and Fisher methods with overall accuracy of 86.61 and 83.03 and ‎kappa coefficient of 0.82 and 0.75, respectively. Therefore, for land use / land cover studies, the LCZ method that ‎considers the micro-climate, is proposed.‎ Manuscript profile -
Open Access Article
12 - Improve the Quality of Mammogram Images by Image Processing Techniques
Mahdi Hariri Hassan NajafyAbstract: Due to the spread of breast cancer, its early detection from mammogram images using computerized methods have been considered an effective method to reduce the death rate of patients.In this research, a method based on image processing techniques is presented MoreAbstract: Due to the spread of breast cancer, its early detection from mammogram images using computerized methods have been considered an effective method to reduce the death rate of patients.In this research, a method based on image processing techniques is presented to improve the quality of mammography images. Therefore, in this research, we try to improve the quality of mammography images with image processing techniques to create a medical system. The research has two stages of pre-processing, including equalizing the dimensions and adjusting the histogram of the images, and a stage of feature extraction using Contourlet and Curlet transforms from mammography images, which provides three categories of morphological and histological, statistical, and frequency features to improve diagnosis. And it increases the accuracy of diagnosis. The proposed improvement method was implemented on the MIAS dataset and a subset of the extracted features was selected for the input of the classifier. Comparing the performance of the proposed method on different classifications, this method shows an accuracy rate of 86.3, which is a better result than other methods.MethodThis research is looking for a method that can improve the accuracy of the final diagnosis. Therefore, after the pre-processing stage, which includes rescaling and adjusting the texture of the image, highlighter transforms in the frequency domain such as Curvelet and Contourlet are used to highlight and increase the differentiation of areas with masses in the image for decision-making.Local features based on image zoning are used for image segmentation, and these methods are also used to increase the contrast of mammogram images concerning their surroundings. The improvement methods used in this research use features based on the wavelet domain.ResultsThe input image to the system is subjected to the feature extraction process and three main categories of frequency, morphology, and histology features are extracted from it. This process is done through the cycle, size equalization, histogram adjustment, contourlet transform, and curvelet transform. Due to the sensitivity of the systems, it has been tried to extract the features with various levels and known matrices such as the matrix of events and gray groups.The classification results evaluated the said method. The best results on the data set were the proposed method, which reached an accuracy rate of 86.3 and showed a good improvement on the displayed data set. Manuscript profile -
Open Access Article
13 - Reliability Measurement’s in Depression Detection Using a Data Mining Approach Based on Fuzzy-Genetics
Mohammad Nadjafi Sepideh Jenabi Adel Najafi Ghasem Kahe -
Open Access Article
14 - Contours Extraction Using Line Detection and Zernike Moment
Vahid Rostami Mahdieh Raesi -
Open Access Article
15 - A New Method for Diagnosing Patients Suspected of Bone Marrow Metastasis in the Presence of Outliers
Mahmood Shahrabi Amirhossein Amiri Hamidreza Saligheh Rad Sedigheh Ghofrani -
Open Access Article
16 - Fake News Detection Using Feature Extraction, Resampling Methods, and Deep Learning
Mirmorsal Madani Homayun Motameni Hosein Mohamadi -
Open Access Article
17 - Intelligent Breast Cancer Detection in Thermography Images Using Extreme Learning Machine and Image Processing
Hamid Reza Khodadad Homayoun Mahdavi-NasabSince breast cancer is known as one of the main mortality reasons of women around the world, it would be most important to provide and develop early recognition methods for timely diagnosis, especially without using invasive imaging techniques and with fast response. Th MoreSince breast cancer is known as one of the main mortality reasons of women around the world, it would be most important to provide and develop early recognition methods for timely diagnosis, especially without using invasive imaging techniques and with fast response. Thermography with the contactless property and low cost, combined with a fast and reliable method to classify imaging results can be an ideal option to achieve this goal. This research aims at using breast thermography and a recent neural network method called extreme learning machine (ELM) as an intelligent and efficient classifier to recognize the cancer. We have also studied the development of this classifier as the kernel-based multilayer ELM. In the proposed method, at first, breast images are segmented using an automated segmentation to generate the region of interest (ROI) of the left and right breasts. Then, by extracting texture, color, and shape features, and presenting them separately or in combination with the neural network, the performance and efficiency of the method are evaluated. By experimenting with various models of local texture extraction, such as local binary pattern (LBP) and local ternary pattern (LTP), as well as using RGB and YCbCr color templates, the best results of this research in the database for mamma research with infrared image (DMR-IR) database belonged to the proposed combined LBP-Mix and the LTP texture features achieving more than 96% accuracy and 100% precision. Manuscript profile -
Open Access Article
18 - Effective Visual Saliency Detection Method Using Reduced Color and Texture Features
Masoud khazaee Fadafen Naser Mehrshad Seyyed Mohammad RazaviIn this study, an effective and efficient algorithm for detection a saliency map is presented based on the modeling of the rapid response of the human visual system to changes in the intensity, texture and color. Some cases such as inspiration from performance of human MoreIn this study, an effective and efficient algorithm for detection a saliency map is presented based on the modeling of the rapid response of the human visual system to changes in the intensity, texture and color. Some cases such as inspiration from performance of human visual system, requiring no training, reduce number of image colors, reduce color channels and Proper use of the least texture information in this algorithm have increased its efficiency. In the proposed method in the first step , Due to sensitivity of the human visual system to higher contrast signals, only higher contrast channel has been used to extract the color saliency map, Then the intensity saliency map as well as the texture saliency map are extracted using the intensity component in lab color space using Simple cell computational model of the visual cortex and finally, with the perfect combination of the saliency maps of the color, the intensity, and the texture, object saliency map is obtained. The proposed method and existing methods have been tested on MSRA10K and ECSSD databases. The results of the implementations show that the proposed hybrid algorithm for the detection saliency map using the dominant color and texture features, On the ECSSD database, the mean absolute error, F-measure score and the area under the ROC curve are 0.173, 0.789 and 0.891, respectively, and on the MSRA10K database are 0.178, 0.790 and 0.919, respectively, compared to other models, it indicates better performance of the proposed method than other methods. Manuscript profile -
Open Access Article
19 - An Overview of Drugs Addiction Diagnosis Methods on Brain Activity and Structure Based on Electroencephalogram Signals
Atefeh Tobeiha Neda Behzadfar Mohammad Reza Yousefi Homayoun Mahdavi-NasabDrug addiction causes structural and functional changes in the human brain and can be considered a chronic disease. So far, various studies have been performed to examine addiction on the brain signal, which differs in terms of method, experimental conditions, samples, MoreDrug addiction causes structural and functional changes in the human brain and can be considered a chronic disease. So far, various studies have been performed to examine addiction on the brain signal, which differs in terms of method, experimental conditions, samples, and results. A review of previous studies and experimental methods is necessary to better examine the issues and challenges in the design of addiction studies. The electroencephalogram (EEG) signal, as a non-invasive instrument, has the potential to monitor the functional and cognitive activity of the brain. EEG can be used to examine the relationship between changes in the brain caused by drug use. This article will review the changes in brain signals caused by substance use as well as after quitting drugs. The results show that the use of narcotic drugs reduces attention processing and causes functional disorders and brain abnormalities. In addicted people, an increase in the activity of the beta subunit and the second alpha subunit, a delay in the event, and a decrease in the amplitude of P300 have been observed. Also, the power ratio of alpha to theta subunit has decreased in T6 and a significant difference has been observed in the power ratio between delta subunit and alpha subunit. The findings showed that people's desire and history of drug consumption affect the power of the electroencephalogram signal. The neural activity in the sub-alpha band of people who quit an addiction is also significantly weaker in the parietal (BA3 and BA7), frontal (BA4 and BA6), and limbic. Manuscript profile -
Open Access Article
20 - Hand Static Gesture Recognition Using HMM Based on Curvature Gradient of Occluding Contour
Nasibeh Alijanpour Shalmani Hossein Ebrahimnezhad Afshin EbrahimiToday, the connection between human and computer is possible using mouse, keyboard and etc. These devices have some limitations like application speed. Making easy the interaction between human and computer is final objective of technology. In this paper, we propose one MoreToday, the connection between human and computer is possible using mouse, keyboard and etc. These devices have some limitations like application speed. Making easy the interaction between human and computer is final objective of technology. In this paper, we propose one method for gesture recognition using curvature of B-Spline curves. First, the image of hand is extracted from different frames and some numbers of points are selected on the contour of hand in equal distances. Then, B-spline curves for groups of 4 points are calculated. Next, the slope of curvature for B-spline curves is calculated and used as feature vector of HMM classifier. The proposed method is inariant to rotation, movement and size of image because of using the slope of curvature. The results on 15 video sequences (with 150 frames in average) give recognition rate of 92.21%. Manuscript profile -
Open Access Article
21 - Determination of the Type of The Imagined Movement of Organs in People with Mobility Disabilities Using Corrected Common Spatial Patterns
Alireza Pirasteh Manouchehr Shamseini Ghiyasvand Majid Pouladian -
Open Access Article
22 - A Brief Overview on Analysis and Feature Extraction of Electroencephalogram Signals
Neda Behzadfar -
Open Access Article
23 - Diagnostic Study for Neurodegenerative Disorders Based on Handwriting Analysis
Leila Soleimanidoust Abdalhossein Rezai Hamideh Barghamadi Iman AhanianOne of the most frequently acknowledged personal behavioral traits in the biometric system is the handwritten exam. Numerous fields, including e-health, psychological issues, medical diag-nosis, and many more, can benefit from handwriting analysis. In this study, a hand MoreOne of the most frequently acknowledged personal behavioral traits in the biometric system is the handwritten exam. Numerous fields, including e-health, psychological issues, medical diag-nosis, and many more, can benefit from handwriting analysis. In this study, a handwriting-based computer diagnostic method for identifying neurodegenerative disorders is established. The sug-gested computer diagnosis system uses the SFTA feature extraction approach, and the findings are classified using SVM, kNN, and D-Tree algorithms. MATLAB R2021b and the handwritten tests gathered at Botucatu Medical School, So Paulo State University—Brazil—are used to assess the performance of the suggested computer diagnosis method. The best results were related into two models of classifier, Optimizable model of SVM and kNN. The accuracy, sensitivity and specificity are 89.2%, 88.3% and 90.0% for SVM and 89.2%,90.0% and 88.3% for kNN over Meander handwritten exam. These results indicate that the use of SFTA feature extraction method, SVM classification algorithm and handwritten database in the proposed computer diagnosis system give acceptable results. Manuscript profile -
Open Access Article
24 - Robot control system using SMR signals detection
faeze asadi -
Open Access Article
25 - A Survey on botnet detection methods in the Internet of Things
Taha Shojarazavi hamid barati Ali Barati
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