Abbas Bagherian Kasgari

About Abbas Bagherian Kasgari

Abbas Bagherian Kasgari, With an exceptional h-index of 4 and a recent h-index of 4 (since 2020), a distinguished researcher at Allameh Tabataba'i University, specializes in the field of Machine Learning, Deep Learning, Data Science, Image Processing, Anomaly Detection.

His recent articles reflect a diverse array of research interests and contributions to the field:

A Comprehensive Analysis of Two Decades in Intelligent Surveillance Systems for Financial Fraud Detection Research

Brain Tumor Segmentation Based on Zernike Moments, Enhanced Ant Lion Optimization, and Convolutional Neural Network in MRI Images

Point-of-interest preference model using an attention mechanism in a convolutional neural network

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Extensible Visual Business Intelligence for Analyzing XBRL Big Data on Blockchain

Abbas Bagherian Kasgari Information

University

Allameh Tabataba'i University

Position

PhD Department of Management & Accounting

Citations(all)

367

Citations(since 2020)

360

Cited By

13

hIndex(all)

4

hIndex(since 2020)

4

i10Index(all)

2

i10Index(since 2020)

2

Email

University Profile Page

Allameh Tabataba'i University

Abbas Bagherian Kasgari Skills & Research Interests

Machine Learning

Deep Learning

Data Science

Image Processing

Anomaly Detection

Top articles of Abbas Bagherian Kasgari

A Comprehensive Analysis of Two Decades in Intelligent Surveillance Systems for Financial Fraud Detection Research

Authors

Iman Raeesi Vanani,Abbas Bagherian Kasgari,Maghsoud Amiri,Saeid Homayoun

Journal

Journal of Development and Capital

Published Date

2023/11/20

Objective The primary objective of this research is to conduct a comprehensive analysis of the evolution, effectiveness, and future potential of intelligent surveillance systems in fraud detection over the past two decades. Fraudulent activities have become increasingly complex, requiring equally sophisticated countermeasures. At the forefront of this battle against financial malfeasance are intelligent fraud detection systems. This research embarks on a profound exploration of studies undertaken in this domain. Beyond merely identifying fraudulent transactions, these systems play a crucial role in upholding the financial integrity of organizations and fostering confidence among investors and other stakeholders. The aftermath of fraud can be devastating, potentially destabilizing financial institutions, intensifying investment volatility, and negatively influencing economic health. Through an innovative approach, this study …

Brain Tumor Segmentation Based on Zernike Moments, Enhanced Ant Lion Optimization, and Convolutional Neural Network in MRI Images

Authors

Abbas Bagherian Kasgari,Ramin Ranjbarzadeh,Annalina Caputo,Soroush Baseri Saadi,Malika Bendechache

Published Date

2023/10/8

Gliomas that form in glial cells in the spinal cord and brain are the most aggressive and common kinds of brain tumors (intra-axial brain tumors) due to their rapid progression and infiltrative nature. The procedure of recognizing tumor margins from healthy tissues is still an arduous and time-consuming task in the clinical routine. In this study, a robust and efficient machine learning-based pipeline is suggested for brain tumor segmentation. Moreover, we employ four MRI modalities for increasing the final accuracy of the segmentation results, namely, Flair, T1, T2, and T1ce. Firstly, eight feature maps are extracted from each modality using the Zernike moments approach. The Zernike moments can create a feature map using two parameters, namely, n and m. So, by changing these values, we are able to generate different sets of edge feature maps. Then, eight edge feature maps for each modality are selected to …

Point-of-interest preference model using an attention mechanism in a convolutional neural network

Authors

Abbas Bagherian Kasgari,Sadaf Safavi,Mohammadjavad Nouri,Jun Hou,Nazanin Tataei Sarshar,Ramin Ranjbarzadeh

Journal

Bioengineering

Published Date

2023/4/20

In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of the features related to individual users and their corresponding contexts. To overcome this issue, we propose a deep learning model based on an attention mechanism in this study. The suggested technique employs an attention mechanism that focuses on the pattern’s friendship, which is responsible for concentrating on the relevant features related to individual users. To compute context-aware similarities among diverse users, our model employs six features of each user as inputs, including user ID, hour, month, day, minute, and second of visiting time, which explore the influences of both spatial and temporal features for the users. In addition, we incorporate geographical information into our attention mechanism by creating an eccentricity score. Specifically, we map the trajectory of each user to a shape, such as a circle, triangle, or rectangle, each of which has a different eccentricity value. This attention-based mechanism is evaluated on two widely used datasets, and experimental outcomes prove a noteworthy improvement of our model over the state-of-the-art strategies for POI recommendation.

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Authors

Ramin Ranjbarzadeh,Abbas Bagherian Kasgari,Saeid Jafarzadeh Ghoushchi,Shokofeh Anari,Maryam Naseri,Malika Bendechache

Journal

Scientific Reports

Published Date

2021/5/25

Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each …

Extensible Visual Business Intelligence for Analyzing XBRL Big Data on Blockchain

Authors

Abbas Bagherian Kasgari,Hamed Mousavi,Saeid Homayoun

Published Date

2020

The state-of-the-art patented EVBI (Extensible Visual Business Intelligence) provides a new visual programming language for modeling Business Intelligence applications over all databases at the intra-and inter-organizational levels. This paper describes the EVBI (Extensible Visual Business Intelligence) based on a case study research method in for illustrating how the solution aid for analyzing XBRL big data securely on Blockchain. The EVBI makes it easy for senior executives to design and develop supervisory reports using an extensible visual modeler desktop, without help of technical staff such as programmers. The results would be easy to use, as well. A one-click reporting component can be served to non-technical users, which at the organizational level, senior executives of the country can access all supervised entities and organizations, even without interacting with supervised entities, at any time. Ability to model processes and processing algorithms as visual multilayer models, enable the implementation of SQL Procedures visually and without the need for technical staff, which in addition to the ability to store knowledge, the ability to develop knowledgeable teams stored in models is realized by the state-of-the-art idea. The output can be displayed in any environment, including, but not limited to web pc/smartphone applications.

See List of Professors in Abbas Bagherian Kasgari University(Allameh Tabataba'i University)

Abbas Bagherian Kasgari FAQs

What is Abbas Bagherian Kasgari's h-index at Allameh Tabataba'i University?

The h-index of Abbas Bagherian Kasgari has been 4 since 2020 and 4 in total.

What are Abbas Bagherian Kasgari's top articles?

The articles with the titles of

A Comprehensive Analysis of Two Decades in Intelligent Surveillance Systems for Financial Fraud Detection Research

Brain Tumor Segmentation Based on Zernike Moments, Enhanced Ant Lion Optimization, and Convolutional Neural Network in MRI Images

Point-of-interest preference model using an attention mechanism in a convolutional neural network

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Extensible Visual Business Intelligence for Analyzing XBRL Big Data on Blockchain

are the top articles of Abbas Bagherian Kasgari at Allameh Tabataba'i University.

What are Abbas Bagherian Kasgari's research interests?

The research interests of Abbas Bagherian Kasgari are: Machine Learning, Deep Learning, Data Science, Image Processing, Anomaly Detection

What is Abbas Bagherian Kasgari's total number of citations?

Abbas Bagherian Kasgari has 367 citations in total.

What are the co-authors of Abbas Bagherian Kasgari?

The co-authors of Abbas Bagherian Kasgari are Maghsoud Amiri (Top 2% of highly cited researcher), Saeid Jafarzadeh Ghoushchi, Malika Bendechache, Iman Raeesi Vanani, Annalina Caputo, Mohammad Taghi Taghavifard.

    Co-Authors

    H-index: 42
    Maghsoud Amiri (Top 2% of highly cited researcher)

    Maghsoud Amiri (Top 2% of highly cited researcher)

    Allameh Tabataba'i University

    H-index: 28
    Saeid Jafarzadeh Ghoushchi

    Saeid Jafarzadeh Ghoushchi

    Urmia University of Technology

    H-index: 21
    Malika Bendechache

    Malika Bendechache

    Dublin City University

    H-index: 17
    Iman Raeesi Vanani

    Iman Raeesi Vanani

    Allameh Tabataba'i University

    H-index: 16
    Annalina Caputo

    Annalina Caputo

    Dublin City University

    H-index: 14
    Mohammad Taghi Taghavifard

    Mohammad Taghi Taghavifard

    Allameh Tabataba'i University

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