طبقه¬بندی

Open Access Article

1 - Modified orthogonal chaotic colonial competition algorithm and its application in improving pattern recognition in multilayer perceptron neural network
Payman Moallem mehrdad sadeghi hariri MAHDI hashemi

Despite the success of the Colonial Competition Algorithm (ICA) in solving optimization problems, this algorithm still suffers from repeated entrapment in the local minimum and low convergence speed. In this paper, a new version of this algorithm, called Modified Orthog More

Despite the success of the Colonial Competition Algorithm (ICA) in solving optimization problems, this algorithm still suffers from repeated entrapment in the local minimum and low convergence speed. In this paper, a new version of this algorithm, called Modified Orthogonal Chaotic Colonial Competition (COICA), is proposed. In the policy of absorbing the proposed version, each colony seeks the space to move towards the colonizer through the definition of a new orthogonal vector. Also, the possibility of selecting powerful empires is defined through the boltzmann distribution function, and the selection operation is performed through the roulette wheel method. The proposed multilevel perceptron neural network (MLP) algorithm is used to classify standard datasets, including ionosphere and sonar. To evaluate the performance of this algorithm and to evaluate the generalizability of the trained neural network with the proposed version, the K-Fold cross-validation method has been used. The results obtained from the simulations confirm the reduction of network training error as well as the improved generalizability of the proposed algorithm. Manuscript profile

Open Access Article

2 - New changes of local binary pattern and classification and segmentation of texture images of the seabed
Babak Goodarzi Javidan Javidan Mohammad Javad Dehghani

Texture analysis plays an important role in image processing. Due to the highly textured appearance of sonar images, texture analysis methods are a suitable choice for analyzing sea acoustic images. The local binary pattern operator is a very effective multi-resolution More

Texture analysis plays an important role in image processing. Due to the highly textured appearance of sonar images, texture analysis methods are a suitable choice for analyzing sea acoustic images. The local binary pattern operator is a very effective multi-resolution texture descriptor. This descriptor obtains appropriate information from changing brightness and image states. Although many extensions of local binary pattern have been proposed, existing local binary pattern operators are sensitive to noise. Also, sometimes they lead to the description of different structural patterns with homogeneous binary code, which inevitably reduce their discriminability. This research provides an overview of the local binary pattern method, which includes several of the newer variables. Then, to overcome the inefficiencies of various types of local binary patterns, a robust binary pattern framework called robust local binary pattern is presented, in which the value of each central pixel is replaced by the average gray intensity values of houses from a three by three square. The proposed method is a fast tool with high accuracy in classifying seabed images, and comparing the simulation results with other well-known methods shows the efficiency of the proposed algorithm. Manuscript profile

Open Access Article

3 - ۹۳ / ۵٬۰۰۰ Integration of data envelopment analysis model and decision tree in order to evaluate units based on information technology
Amir Amini علی رضا علی نژاد سمیه شفقی¬زاده

Every organization needs an evaluation system to measure this usefulness in order to know the performance and usefulness of its units, and this issue is more important for financial institutions, including companies based on information technology. Data envelopment anal More

Every organization needs an evaluation system to measure this usefulness in order to know the performance and usefulness of its units, and this issue is more important for financial institutions, including companies based on information technology. Data envelopment analysis is a non-parametric method for measuring the efficiency and productivity of decision making units (DMUs). On the other hand, data mining techniques allow DMUs to explore and discover meaningful information, which was previously hidden in large databases. This paper proposes a general framework combining data envelopment analysis with regression trees to evaluate the efficiency and productivity of DMUs. The result of the hybrid model is a set of rules that can be used by policy makers to discover the reasons for efficient and inefficient DMUs. As a case study using the proposed method to investigate the factors related to productivity, a sample including 18 branches of Iranian insurance in Tehran was selected and after modeling based on the advanced input-oriented LVM model with poor accessibility in data coverage analysis with Undesirable output was calculated and with the decision tree technique, rules are extracted to discover the reasons for productivity increase and productivity regression. Manuscript profile

Open Access Article

4 - Converting protein sequence to image for classification with convolutional neural network
reza ahsan mansour ebrahimi dianat dianat

Since methods for sequencing machine learning sequences were not successful in classifying healthy and cancerous proteins, it is imperative to find a way to represent these sequences to classify healthy and ill individuals with deep learning approaches. In this study di More

Since methods for sequencing machine learning sequences were not successful in classifying healthy and cancerous proteins, it is imperative to find a way to represent these sequences to classify healthy and ill individuals with deep learning approaches. In this study different methods of protein sequence representation for classification of protein sequence of healthy individuals and leukemia have been studied. Results showed that conversion of amino acid letters to one-dimensional feature vectors in classification of 2 classes was not successful and only one disease class was detected. By changing the feature vector to colored numbers, the accuracy of the healthy class recognition was slightly improved. The binary protein sequence representation method was more efficient than the previous methods with the initiative of sequencing the sequences in both one-dimensional and two-dimensional (image by Gabor filtering). Protein sequence representation as binary image was classified by applying Gabor filter with 100% accuracy of the protein sequence of healthy individuals and 98.6% protein sequence of those with leukemia. The findings of this study showed that the representation of protein sequence as binary image by applying Gabor filter can be used as a new effective method for representation of protein sequences for classification Manuscript profile

Open Access Article

5 - Analysing students' learning through morning exercise using data mining techniques
behzad lak narges abbasi

Since school has identified as one of the major agents in the socialization process, it has found remarkable position in the educational system of any country. Improving student learning is also a key factor to enhance the educational system quality in schools. As regul More

Since school has identified as one of the major agents in the socialization process, it has found remarkable position in the educational system of any country. Improving student learning is also a key factor to enhance the educational system quality in schools. As regular exercise has profoundly positive impact on learning, this paper mainly aims to provide an approach to enhance students' learning process through morning exercise based on artificial neural network (ANN) technique and intelligent water drop optimization algorithm. This study is a quantitative research, which is purposefully a descriptive-analytical and methodologically a practical study. To that end, ANN technique was used to classify and extract the results, as well as, intelligent water drop optimization algorithm was employed for feature selection. In ANN, eleven neurons were selected as the appropriate number of hidden layer neurons; a combination of two linear and sigmoidal activation functions were employed as interlayer transmission functions; a training function was applied to train the network; and a maximum 3000 duplicates was proposed for the training algorithm on dataset. The accuracy of the proposed method was 68%, which has improved by about 2.2% compared to the basic method, i.e., exercise has a positive effect on students' learning. The results showed a proper performance of the optimal classification on the dataset with homogeneous parameters as well as a better performance of the artificial neural networks than the novel methods. Accordingly, the proposed method can have an appropriate improvement in terms of output accuracy in strengthening the learning process. Manuscript profile

Open Access Article

6 - Assessment of Spatial and temporal changes in land use using remote sensing (case study: Jayransoo rangeland, North Khorasan)
Mohabat Nadaf Reza Omidipour Hossein Sobhani

10.61186/jert.43616.8.14.131

<p>Awareness of changes process, as well as the proper management of land use in natural ecosystems, is of great importance in conservation natural resources. In this regard, the use of remote sensing has become a common approach due to the provision an extent spatial a More

<p>Awareness of changes process, as well as the proper management of land use in natural ecosystems, is of great importance in conservation natural resources. In this regard, the use of remote sensing has become a common approach due to the provision an extent spatial and temporal information. In this research, in order to land use mapping, first, the accuracy of three common methods of pixel-based (maximum likelihood), machine learning (support vector machine) and object-oriented methods were compared. Then, the spatial and temporal changes of land use in a period of 26 years (1997-2023) assessed using six Landsat satellite imagery. The accuracy of image classification methods was evaluated using Kappa coefficient and overall accuracy indices and the change trend was evaluated using crosstab and spatial evaluation methods. Based on the results, the support vector machine method had the highest kappa coefficient (0.71 to 0.98) and overall accuracy (86 to 99%) for all studied courses. According to the results, poor rangeland had a decreasing trend, and the land uses of very poor rangeland, bare soil, and rainfed agriculture had increasing trends. The area of poor rangeland decreased from 962 hectares (44.36%) in 1997 to 489 hectares (22.57%) in 2023, while very poor rangeland increased from 1138 hectares (52.48%) to 1606 hectares (74.05 percent) in the same period. The results of this research indicated that the trend of land use changes in Jayransoo rangeland is towards the destruction of rangelands and with the passage of time this trend is intensifying. Also, based on the results obtained from this research, it is suggested to use machine learning based classification method to prepare land use mapping in future research.</p> Manuscript profile

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