Volume 12 (2024)
Volume 11 (2023)
Volume 10 (2022)
Volume 9 (2021)
Volume 8 (2020)
Volume 7 (2019)
Volume 6 (2018)
Volume 5 (2017)
Volume 4 (2016)
Volume 3 (2015)
Volume 2 (2014)
Volume 1 (2013)
H.5. Image Processing and Computer Vision
Automatic Brain Tumor Detection in Brain MRI Images using Deep Learning Methods

Farima Fakouri; Mohsen Nikpour; Abbas Soleymani Amiri

Volume 12, Issue 1 , January 2024, , Pages 27-35


  Due to the increased mortality caused by brain tumors, accurate and fast diagnosis of brain tumors is necessary to implement the treatment of this disease. In this research, brain tumor classification performed using a network based on ResNet architecture in MRI images. MRI images that available in the ...  Read More

H.5. Image Processing and Computer Vision
Using Convolutional Neural Network to Enhance Classification Accuracy of Cancerous Lung Masses from CT Scan Images

Mohammad Mahdi Nakhaie; Sasan Karamizadeh; Mohammad Ebrahim Shiri; Kambiz Badie

Volume 11, Issue 4 , November 2023, , Pages 547-559


  Lung cancer is a highly serious illness, and detecting cancer cells early significantly enhances patients' chances of recovery. Doctors regularly examine a large number of CT scan images, which can lead to fatigue and errors. Therefore, there is a need to create a tool that can automatically detect and ...  Read More

H.3. Artificial Intelligence
Investigating Shallow and Deep Learning Techniques for Emotion Classification in Short Persian Texts

Mahdi Rasouli; Vahid Kiani

Volume 11, Issue 4 , November 2023, , Pages 587-598


  The identification of emotions in short texts of low-resource languages poses a significant challenge, requiring specialized frameworks and computational intelligence techniques. This paper presents a comprehensive exploration of shallow and deep learning methods for emotion detection in short Persian ...  Read More

Document and Text Processing
DcDiRNeSa, Drug Combination Prediction by Integrating Dimension Reduction and Negative Sampling Techniques

Mina Tabatabaei; Hossein Rahmani; Motahareh Nasiri

Volume 11, Issue 3 , July 2023, , Pages 417-427


  The search for effective treatments for complex diseases, while minimizing toxicity and side effects, has become crucial. However, identifying synergistic combinations of drugs is often a time-consuming and expensive process, relying on trial and error due to the vast search space involved. Addressing ...  Read More

Improved Facial Action Unit Recognition using Local and Global Face Features

Amin Rahmati; Foad Ghaderi

Volume 11, Issue 2 , April 2023, , Pages 213-220


  Every facial expression involves one or more facial action units appearing on the face. Therefore, action unit recognition is commonly used to enhance facial expression detection performance. It is important to identify subtle changes in face when particular action units occur. In this paper, we propose ...  Read More

A Deep Learning-based Model for Gender Recognition in Mobile Devices

Fatemeh Alinezhad; Kourosh Kiani; Razieh Rastgoo

Volume 11, Issue 2 , April 2023, , Pages 229-236


  Gender recognition is an attractive research area in recent years. To make a user-friendly application for gender recognition, having an accurate, fast, and lightweight model applicable in a mobile device is necessary. Although successful results have been obtained using the Convolutional Neural Network ...  Read More

H.3. Artificial Intelligence
Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review

Mohammad Hossein Shayesteh; Behrooz Shahrokhzadeh; Behrooz Masoumi

Volume 11, Issue 2 , April 2023, , Pages 259-289


  This paper provides a comprehensive review of the potential of game theory as a solution for sensor-based human activity recognition (HAR) challenges. Game theory is a mathematical framework that models interactions between multiple entities in various fields, including economics, political science, ...  Read More

Efficient Stance Ordering to Improve Rumor Veracity Detection

Z. MohammadHosseini; A. Jalaly Bidgoly

Volume 11, Issue 1 , January 2023, , Pages 69-76


  Social media is an inseparable part of human life, although published information through social media is not always true. Rumors may spread easily and quickly in the social media, hence, it is vital to have a tool for rumor veracity detection. Papers already proved that users’ stance is an important ...  Read More

Whitened gradient descent, a new updating method for optimizers in deep neural networks

H. Gholamalinejad; H. Khosravi

Volume 10, Issue 4 , November 2022, , Pages 467-477


  Optimizers are vital components of deep neural networks that perform weight updates. This paper introduces a new updating method for optimizers based on gradient descent, called whitened gradient descent (WGD). This method is easy to implement and can be used in every optimizer based on the gradient ...  Read More

Audio-visual emotion recognition based on a deep convolutional neural network

Kh. Aghajani

Volume 10, Issue 4 , November 2022, , Pages 529-537


  Emotion recognition has several applications in various fields, including human-computer interactions. In recent years, various methods have been proposed to recognize emotion using facial or speech information. While the fusion of these two has been paid less attention in emotion recognition. In this ...  Read More

H.3.8. Natural Language Processing
A Transformer-based Approach for Persian Text Chunking

P. Kavehzadeh; M. M. Abdollah Pour; S. Momtazi

Volume 10, Issue 3 , July 2022, , Pages 373-383


  Over the last few years, text chunking has taken a significant part in sequence labeling tasks. Although a large variety of methods have been proposed for shallow parsing in English, most proposed approaches for text chunking in Persian language are based on simple and traditional concepts. In this paper, ...  Read More

Video Prediction Using Multi-Scale Deep Neural Networks

N. Shayanfar; V. Derhami; M. Rezaeian

Volume 10, Issue 3 , July 2022, , Pages 423-431


  In video prediction it is expected to predict next frame of video by providing a sequence of input frames. Whereas numerous studies exist that tackle frame prediction, suitable performance is not still achieved and therefore the application is an open problem. In this article multiscale processing is ...  Read More

Automatic Detection of Lung Nodules on Computer Tomography Scans with a Deep Direct Regression Method

Kh. Aghajani

Volume 10, Issue 2 , April 2022, , Pages 207-215


  Deep-learning-based approaches have been extensively used in detecting pulmonary nodules from computer Tomography (CT) scans. In this study, an automated end-to-end framework with a convolution network (Conv-net) has been proposed to detect lung nodules from CT images. Here, boundary regression has been ...  Read More

A Hybrid Deep Network Representation Model for Detecting Researchers’ Communities

A. Torkaman; K. Badie; A. Salajegheh; M. H. Bokaei; Seyed F. Fatemi

Volume 10, Issue 2 , April 2022, , Pages 233-243


  Recently, network representation has attracted many research works mostly concentrating on representing of nodes in a dense low-dimensional vector. There exist some network embedding methods focusing only on the node structure and some others considering the content information within the nodes. In this ...  Read More

Customer Behavior Analysis to Improve Detection of Fraudulent ‎Transactions using Deep Learning

F. Baratzadeh; Seyed M. H. Hasheminejad

Volume 10, Issue 1 , January 2022, , Pages 87-101


  With the advancement of technology, the daily use of bank credit cards has been increasing exponentially. Therefore, the fraudulent use of credit cards by others as one of the new crimes is also growing fast. For this reason, detecting and preventing these attacks has become an active area of study. ...  Read More

Text Sentiment Classification based on Separate Embedding of Aspect and Context

A. Lakizadeh; E. Moradizadeh

Volume 10, Issue 1 , January 2022, , Pages 139-149


  Text sentiment classification in aspect level is one of the hottest research topics in the field of natural language processing. The purpose of the aspect-level sentiment analysis is to determine the polarity of the text according to a particular aspect. Recently, various methods have been developed ...  Read More

DENOVA: Predicting Five-Factor Model using Deep Learning based on ANOVA

M. Nasiri; H. Rahmani

Volume 9, Issue 4 , November 2021, , Pages 451-463


  Determining the personality dimensions of individuals is very important in psychological research. The most well-known example of personality dimensions is the Five-Factor Model (FFM). There are two approaches 1- Manual and 2- Automatic for determining the personality dimensions. In a manual approach, ...  Read More

Multi-Sentence Hierarchical Generative Adversarial Network GAN (MSH-GAN) for Automatic Text-to-Image Generation

E. Pejhan; M. Ghasemzadeh

Volume 9, Issue 4 , November 2021, , Pages 475-485


  This research is related to the development of technology in the field of automatic text to image generation. In this regard, two main goals are pursued; first, the generated image should look as real as possible; and second, the generated image should be a meaningful description of the input text. our ...  Read More

Automatic Grayscale Image Colorization using a Deep Hybrid Model

K. Kiani; R. Hematpour; R. Rastgoo

Volume 9, Issue 3 , July 2021, , Pages 321-328


  Image colorization is an interesting yet challenging task due to the descriptive nature of getting a natural-looking color image from any grayscale image. To tackle this challenge and also have a fully automatic procedure, we propose a Convolutional Neural Network (CNN)-based model to benefit from the ...  Read More

Convolutional Neural Network Equipped with Attention Mechanism and Transfer Learning for Enhancing Performance of Sentiment Analysis

H. Sadr; Mir M. Pedram; M. Teshnehlab

Volume 9, Issue 2 , April 2021, , Pages 141-151


  With the rapid development of textual information on the web, sentiment analysis is changing to an essential analytic tool rather than an academic endeavor and numerous studies have been carried out in recent years to address this issue. By the emergence of deep learning, deep neural networks have attracted ...  Read More

Diagnosis of Multiple Sclerosis Disease in Brain MRI Images using Convolutional Neural Networks based on Wavelet Pooling

A. Alijamaat; A. Reza NikravanShalmani; P. Bayat

Volume 9, Issue 2 , April 2021, , Pages 161-168


  Multiple Sclerosis (MS) is a disease that destructs the central nervous system cell protection, destroys sheaths of immune cells, and causes lesions. Examination and diagnosis of lesions by specialists is usually done manually on Magnetic Resonance Imaging (MRI) images of the brain. Factors such as small ...  Read More

Automatic Persian Text Emotion Detection using Cognitive Linguistic and Deep Learning

Seyedeh S. Sadeghi; H. Khotanlou; M. Rasekh Mahand

Volume 9, Issue 2 , April 2021, , Pages 169-179


  In the modern age, written sources are rapidly increasing. A growing number of these data are related to the texts containing the feelings and opinions of the users. Thus, reviewing and analyzing of emotional texts have received a particular attention in recent years. A System which is based on combination ...  Read More

IRVD: A Large-Scale Dataset for Classification of Iranian Vehicles in Urban Streets

H. Gholamalinejad; H. Khosravi

Volume 9, Issue 1 , January 2021, , Pages 1-9


  In recent years, vehicle classification has been one of the most important research topics. However, due to the lack of a proper dataset, this field has not been well developed as other fields of intelligent traffic management. Therefore, the preparation of large-scale datasets of vehicles for each country ...  Read More

A Novel Hierarchical Attention-based Method for Aspect-level Sentiment Classification

A. Lakizadeh; Z. Zinaty

Volume 9, Issue 1 , January 2021, , Pages 87-97


  Aspect-level sentiment classification is an essential issue in sentiment analysis that intends to resolve the sentiment polarity of a specific aspect mentioned in the input text. Recent methods have discovered the role of aspects in sentiment polarity classification and developed various techniques to ...  Read More

A Deep Model for Super-resolution Enhancement from a Single Image

N. Majidi; K. Kiani; R. Rastgoo

Volume 8, Issue 4 , November 2020, , Pages 451-460


  This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model ...  Read More