Majid Iranpour

In the paper we present an approach to Introduce automation of brain CT image analysis Because CT... more In the paper we present an approach to Introduce automation of brain CT image analysis Because CT Scan method that used especially for the diagnosis of stroke and can detect bleeding in stroke due to a blocked artery, of course Images from a CT scan resolution is low relatively. Therefore, the grayscale images resolution is scant and makes detection difficult. We can use bioinformatics and artificial coloring techniques by image processing quality added and is more sensitive in outstanding. We have to identify and distinguish the areas of clustering artificial colors with Hopfield clustering that introduced as Pixel clustering based segmentation method and improve it by Hopfield neural network (HNN) based on spectral properties to show different region by artificial coloring and clustering. We want to improve the technique to use this rule by determining best cluster in neural network.

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Connected Component Based Word Spotting on Persian Handwritten image documents

International Journal of Nonlinear Analysis and Applications, 2019

Word spotting is to make searchable unindexed image documents by locating word/words in a doc-ume... more Word spotting is to make searchable unindexed image documents by locating word/words in a doc-ument image, given a query word. This problem is challenging, mainly due to the large numberof word classes with very small inter-class and substantial intra-class distances. In this paper, asegmentation-based word spotting method is presented for multi-writer Persian handwritten doc-uments using attribute-based classi cation and label-embedding. For this purpose, a hierarchicalframework is proposed, in which at rst, the candidate are selected based on connected compo-nents(CCs) sequence. Then, the query word is segmented to constructor CCs, and similar CCs countin the candidate region of document are selected based on their distances to the CCs count of thequery word. As a result, the candidate regions are extracted. In the nal phase, the query wordis located only in the candidate regions of the document. A well known Persian handwritten textdataset, namely FTH, is chosen as a benchmark fo...

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Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm

International journal of engineering. Transactions C: Aspects, 2023

Brain tumors are one of the most common causes of death that have been widely investigated by sch... more Brain tumors are one of the most common causes of death that have been widely investigated by scholars in research areas, including care and prevention. Despite various empirical studies on the brain tumor segmentatin, there is still a need for further investigation. This fact is more needed in the automatic methods of brain tumors detection. In the present study, a new method for improving brain tumor segmentation accuracy based on super-pixel and fast primal dual (PD) algorithms has been proposed. The proposed method detects brain tumor tissue in Flair-MRI imaging in BRATS2012 dataset. This method detects the primary borders of tumors using a super-pixel algorithm, and improves brain tumor borders using fast PD in Markov random field optimization. Then, post-processing processes are used to delete white brain areas. Finally, an active contour algorithm was employed to display tumor area. Different experiments were carried on the proposed method and qualitative and quantitative criteria such as dice similarity measure, accuracy and F-measure were used for evaluation. The obtained results showed the efficiency of the proposed method, such that in the accuracy and sensitivity of 86.59 and 88.57% and F1-Measure 86.37 were obtained, respectively.

Relation Extraction of Protein Complexes from Dynamic Protein-Protein Interaction Network

Journal of biomedical physics & engineering, Dec 1, 2021

rotein-Protein interaction networks have essential position in cell biology to conduct an organis... more rotein-Protein interaction networks have essential position in cell biology to conduct an organism analysis. The protein complex transformation understanding in disease condition is important in modern system biology [1]. From a biological point of view, protein complexes are the molecular entities to do the cellular functionalities such as DNA transcription, signal transduction, mRNA translation [2]. Recently, in order to analyze the dynamicity of PPINs, dynamic PPI net

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Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization

BMC Bioinformatics, Nov 17, 2021

Background The study of DTIs has been attracted many researchers' attention in the field of pharm... more Background The study of DTIs has been attracted many researchers' attention in the field of pharmaceutical science in recent years [1-4]. In this regard, many efforts have been made to investigate drug repositioning as well as the discovery of the interaction between new targets and existing drugs. DTI means binding a drug to a target location, that leads to a change in its behavior or function. On the other hand, the identification of DTIs minimizes the adverse side effects of drugs [1].

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Isolated Persian/Arabic word spotting by label embedding

The Journal of Engineering Research, Jan 16, 2017

The goal of word image retrieval and word spotting, as a particular case of semantic content base... more The goal of word image retrieval and word spotting, as a particular case of semantic content based image retrieval (CBIR) is to find image or text(string) query word in a dataset of images. In this paper we evaluate a holistic approach for Persian handwritten isolated word image retrieval that can be used for word recognition. The aim of recognition is to recognize the content of word image, usually aided by a dictionary or lexicon. To the best of our knowledge, this paper is the first paper about Persian script. This is achieved by a combination of label embedding, attribute based classification and common subspace regression. In this subspace, image and string representation of the same word are close together that allows one to cast recognition and retrieval task as a nearest neighbor problem. Unlike other existing methods in word image retrieval and word spotting, the representation has a fixed length, low dimensionality and it is very fast to compute and compare. We used Farsa and Iranshahr, two common datasets of isolated Persian handwritten words to evaluate method. The Experiment shows promising results for word image retrieval and word spotting and respectively achieved 100% and 97% of recognition rate for Farsa and Iranshahr datasets.

Relation Extraction of Protein Complexes from Dynamic Protein-Protein Interaction Network

Journal of Biomedical Physics & Engineering, 2021

Background: Dynamic protein-protein interaction networks (DPPIN) can confirm the conditional and ... more Background: Dynamic protein-protein interaction networks (DPPIN) can confirm the conditional and temporal features of proteins and protein complexes. In addition, the relation of protein complexes in dynamic networks can provide useful information in understanding the dynamic functionality of PPI networks. Objective: In this paper, an algorithm is presented to discover the temporal association rule from the dynamic PPIN dataset. Material and Methods: In this analytical study, the static protein-protein interaction network is transformed into a dynamic network using the gene expression thresholding to extract the protein complex relations. The number of presented proteins of the dynamic network is large at each time point. This number will increase for extraction of multidimensional rules at different times. By mapping the gold standard protein complexes as reference protein complexes, the number of items decreases from active proteins to protein complexes at each transaction. Extrac...

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Isolated Persian/Arabic word spotting by label embedding

The Journal of Engineering Research, 2017

The goal of word image retrieval and word spotting, as a particular case of semantic content base... more The goal of word image retrieval and word spotting, as a particular case of semantic content based image retrieval (CBIR) is to find image or text(string) query word in a dataset of images. In this paper we evaluate a holistic approach for Persian handwritten isolated word image retrieval that can be used for word recognition. The aim of recognition is to recognize the content of word image, usually aided by a dictionary or lexicon. To the best of our knowledge, this paper is the first paper about Persian script. This is achieved by a combination of label embedding, attribute based classification and common subspace regression. In this subspace, image and string representation of the same word are close together that allows one to cast recognition and retrieval task as a nearest neighbor problem. Unlike other existing methods in word image retrieval and word spotting, the representation has a fixed length, low dimensionality and it is very fast to compute and compare. We used Farsa ...

Predicting drug-target interaction based on bilateral local models using a decision tree-based hybrid support vector machine

International Journal of Nonlinear Analysis and Applications, 2021

Identifying the interaction between the drug and the target proteins plays a very important role ... more Identifying the interaction between the drug and the target proteins plays a very important role in the drug discovery process. Because prediction experiments of this process are time consuming, costly and tedious, Computational prediction can be a good way to reduce the search space to examine the interaction between drug and target instead of using costly experiments. In this paper, a new solution based on known drug-target interactions based on bilateral local models is introduced. In this method, a hybrid support vector machine based on the decision tree is used to decide and optimize the two-class classification. Using this machine to manage data related to this application has performed well. The proposed method on four criteria datasets including enzymes (Es), ion channels (IC), G protein coupled receptors (GPCRs) and nuclear receptors (NRs), based on AUC, AUPR, ROC and running time has been evaluated. The results show an improvement in the performance of the proposed method.

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An integrated clustering algorithm based on firefly algorithm and self-organized neural network

Progress in Artificial Intelligence, 2022

FRBF Neural Network base for Face Recognition using Zernike Moments and PCA

International Journal of Computer Applications, 2015

In this paper a method to face recognition in digital images based on statistical features and fu... more In this paper a method to face recognition in digital images based on statistical features and fuzzy neural networks will be introduced. In order to increase system performance, and analysis of the basic components, Zernike moments used as features have been used and various combinations of these features have been introduced. Work is based on the use of fuzzy neural network of FRBF with a teaching method based on fuzzy training in face recognition with high accuracy. In FHLA algorithm used in learning, in addition to determining weights between hidden layer and output layer parameters, including center RBF neurons and the width shall be determined during the training process. In this way of education, initial values of parameters using fuzzy logic and troubleshooting methods and fuzzy clustering hidden layer neurons are obtained by number of FCM techniques. To determine the final values of communication parameters and weights, the gradient method and the LLS is used as optimization methods. Test results show that this technique has very good accuracy in identifying faces on the database composed of 1000.

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Breast Cancer Detection from FNA Using SVM and RBF Classifier

In this paper, we consider the benefits of applying support vector machines (SVMs) and radial bas... more In this paper, we consider the benefits of applying support vector machines (SVMs) and radial basis function (RBF) for breast cancer detection. The Wisconsin diagnosis breast cancer (WDBC) dataset is used in the classification experiments; the dataset was generated from fine needle aspiration (FNA) samples through image processing. The 1-norm C-SVM (L1-SVM) and 2-norm C-SVM (L2-SVM) are applied, for which the grid search based on gradient descent based on validation error estimate (GDVEE) are developed to improve the detection accuracy. Experimental results demonstrate that SVM classifiers with the proposed automatic parameter tuning systems and the RBF classifier can be used as one of most efficient tools for breast cancer detection, with the detection accuracy up to 98%.

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Breast cancer detection from fna using svm and rbf classifier

In this paper, we consider the benefits of applying support vector machines (SVMs) and radial bas... more In this paper, we consider the benefits of applying support vector machines (SVMs) and radial basis function (RBF) for breast cancer detection. The Wisconsin diagnosis breast cancer (WDBC) dataset is used in the classification experiments; the dataset was generated from fine needle aspiration (FNA) samples through image processing. The 1-norm C-SVM (L1-SVM) and 2-norm C-SVM (L2-SVM) are applied, for which the grid search based on gradient descent based on validation error estimate (GDVEE) are developed to improve the detection accuracy. Experimental results demonstrate that SVM classifiers with the proposed automatic parameter tuning systems and the RBF classifier can be used as one of most efficient tools for breast cancer detection, with the detection accuracy up to 98%.

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FRBF Neural Network base for Face Recognition using Zernike Moments and PCA

by Majid Iranpour and Mehran Emadi

International Journal of Computer Applications, 2015

Breast Cancer Detection from FNA

In this paper, we consider the benefits of applying support vector machines (SVMs) and radial bas... more In this paper, we consider the benefits of applying support vector machines (SVMs) and radial basis function (RBF) for breast cancer detection. The Wisconsin diagnosis breast cancer (WDBC) dataset is used in the classification experiments; the dataset was generated from fine needle aspiration (FNA) samples through image processing. The 1-norm C-SVM (L1-SVM) and 2-norm C-SVM (L2-SVM) are applied, for which

Verb Detection in Persian Corpus

International Journal of Digital Content Technology and Its Applications, 2009

increase the performance as the second phase. Experimental results show that our technique can ac... more

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