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    مهندسی کامپیوتر - هوش مصنوعی

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    مهندسی کامپیوتر - هوش مصنوعی

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    رزومه

    Cell separation algorithm with enhanced search behaviour in miRNA feature selection for cancer diagnosis

    NS Jaddi, MS Abadeh
    Journal Papers , , {Pages }

    Abstract

    Brain Age Estimation based on Brain MRI by an Ensemble of Deep Networks

    Zahra Jahanshiri, Mohammad Saniee Abadeh, Hedieh Sajedi
    Conference Papers2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM) , 2021 January 4, {Pages 06-Jan }

    Abstract

    Estimation of biological brain age is one of the topics that has been much discussed in recent years. One of the most important reasons for this is the possibility of early detection of neurodegenerative disorders such as Alzheimer's and Parkinson's with Brain Age Estimation (BAE). Brain imaging is one of the most important data to estimate the biological age of the brain. Because the brain's natural aging follows a particular pattern, it enables researchers and physicians to predict the human brain's age from its degeneration. Some studies have been done on 2D or 3D brain images data for this purpose. In this study, an ensemble structure, including 3D and 2D Convolutional Neural Networks (CNNs), is used to BAE. The proposed ensemble CNN (E

    Information verification in social networks based on user feedback and news agencies

    Arefeh Yavary, Hedieh Sajedi, Mohammad Saniee Abadeh
    Journal PapersSocial Network Analysis and Mining , Volume 10 , Issue 1, 2020 December 1, {Pages 2 }

    Abstract

    The information on the web is mixed with rumors and unverified information. Additionally, social networks as a special and wide subsection of the web have more potential for spreading and creating misinformation or unverified information. Because of the significance of this issue, and to enhance the information verification performance, in this paper information verification in social networks is investigated. It seems that several features and conditions are effectual on rumor detection. Among possible effective features and properties, we consider two main sources for information verification in social networks that include user feedback and news agencies. User feedbacks as the first source can be user conversational tree. Some patterns c

    CARs-Lands: An associative classifier for large-scale datasets

    Mehrdad Almasi, Mohammad Saniee Abadeh
    Journal PapersPattern Recognition , Volume 100 , 2020 April 1, {Pages 107128 }

    Abstract

    Associative classifiers are one of the most efficient classifiers for large datasets. However, they are unsuitable to be directly used in large-scale data problems. Associative classifiers discover frequent/rare rules or both in order to produce an efficient classifier. Discovery rules need to explore a large solution space in a well-organized manner; hence, learning of the associative classification methods of large datasets is not suitable on large-scale datasets because of memory and time-complexity constraints. The proposed method, CARs-Lands, presents an efficient distributed associative classifier. In CARs-Lands, first, a modified dataset is generated. This new dataset has sub-datasets that are completely appropriate to produce classi

    Aspect-Based Sentiment Analysis using Adaptive Aspect-Based Lexicons

    Mohammad Erfan Mowlaei, Mohammad Saniee Abadeh, Hamidreza Keshavarz
    Journal PapersExpert Systems with Applications , 2020 January 23, {Pages 113234 }

    Abstract

    Reviews expressed in e-commerce websites have formed an important source of information for both consumers and enterprises. Text sentiment analysis approaches aim to detect the sentiments of written reviews in order to achieve a better understanding of public opinion towards entities. Aspect-based sentiment analysis deals with capturing sentiments expressed towards each aspect of entities. A common approach in sentiment analysis problems is to take advantage of lexicons to generate features for classification of reviews. Existing aspect-based approaches fail to properly adapt general lexicons to the context of aspect-based datasets which results in reduced performance. To address this problem, this paper proposes extensions of two lexicon g

    A Hybrid Feature Selection and Deep Learning Algorithm for Cancer Disease Classification

    Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh
    Journal PapersInternational Journal of Computer and Information Engineering , Volume 14 , Issue 2, 2020 January 1, {Pages 55-59 }

    Abstract

    Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) cla

    DNA methylation-based age prediction using cell separation algorithm

    Najmeh Sadat Jaddi, Mohammad Saniee Abadeh
    Journal PapersComputers in Biology and Medicine , 2020 April 16, {Pages 103747 }

    Abstract

    The age of each individual can be predicted based on the alteration rule of DNA methylation with age. In this paper, an age prediction method is developed in order to solve multivariate regression problems from DNA methylation data, by optimizing the artificial neural network (ANN) model using a new proposed algorithm named the Cell Separation Algorithm (CSA). The CSA imitates cell separation action by using a differential centrifugation process involving multiple centrifugation steps and increasing the rotor speed in each step. The CSA performs similar to the centrifugal force in separating the solutions based on their objective function in different steps, with velocity increasing in each step. Firstly, 25 test functions are used to test

    Information Verification Enhancement Using Entailment Methods

    Mohammad Saniee Abadeh
    Journal PapersData Science: From Research to Application , Volume 45 , 2020 January , {Pages 217 }

    Abstract

    Information verification is a hot topic, especially because of the fact that the rate of information generation is so high and increases every day, mainly in social networks like Twitter. This also causes social networks be invoked as a news agency for most of the people. Accordingly, information verification in social networks becomes more significant. Therefore, in this paper a method for information verification on Twitter is proposed. The proposed method for Tweet verification is going to employ textual entailment methods for enhancement of previous verification methods on Twitter. Aggregating the results of entailment methods in addition to the state-of-the-art methods, can enhance the outcomes of tweet verification. Also, as writing s

    Brain MRI analysis using a deep learning based volutionary approach

    Hossein Shahamat, Mohammad Saniee Abadeh
    Journal PapersNeural Networks , 2020 March 28, {Pages }

    Abstract

    Convolutional neural network (CNN) models have recently demonstrated impressive performance in medical image analysis. However, there is no clear understanding of why they perform so well, or what they have learned. In this paper, a three-dimensional convolutional neural network (3D-CNN) is employed to classify brain MRI scans into two predefined groups. In addition, a genetic algorithm based brain masking (GABM) method is proposed as a visualization technique that provides new insights into the function of the 3D-CNN. The proposed GABM method consists of two main steps. In the first step, a set of brain MRI scans is used to train the 3D-CNN. In the second step, a genetic algorithm (GA) is applied to discover knowledgeable brain regions in

    Gene selection of non-small cell lung cancer data for adjuvant chemotherapy decision using cell separation algorithm

    Najmeh Sadat Jaddi, Mohammad Saniee Abadeh
    Journal PapersApplied Intelligence , 2020 July 1, {Pages 15-Jan }

    Abstract

    Since recommended treatment for Non-small cell lung cancer (NSCLC) after surgery is chemotherapy, the prediction of effectiveness or futileness of adjuvant chemotherapy (ACT) in early stage is important for future decision. Classification of NSCLC in gene expression data is performed to predict effectiveness or futileness of ACT. Selection of genes highly correlated with the class attribute, affects the classification accuracy. In this paper, a new cell separation algorithm is proposed which it imitates the action of cell separation using differential centrifugation process involving multiple centrifugation steps and increasing the rotor speed in each step. The CSA uses the application of centrifugal force to separate the solutions based on

    SEFR: A Fast Linear-Time Classifier for Ultra-Low Power Devices

    Hamidreza Keshavarz, Mohammad Saniee Abadeh, Reza Rawassizadeh
    Journal PapersarXiv preprint arXiv:2006.04620 , 2020 June 8, {Pages }

    Abstract

    One of the fundamental challenges for running machine learning algorithms on battery-powered devices is the time and energy needed for computation, as these devices have constraints on resources. There are energy-efficient classifier algorithms, but their accuracy is often sacrificed for resource efficiency. Here, we propose an ultra-low power binary classifier, SEFR, with linear time complexity, both in the training and the testing phases. The SEFR method runs by creating a hyperplane to separate two classes. The weights of this hyperplane are calculated using normalization, and then the bias is computed based on the weights. SEFR is comparable to state-of-the-art classifiers in terms of classification accuracy, but its execution time and

    A Survey on Single and Multi Omics Data Mining Methods in Cancer Data Classification

    Zahra Momeni, Esmail Hassanzadeh, Mohammad Saniee Abadeh, Riccardo Bellazzi
    Journal Papers , 2020 June 7, {Pages 103466 }

    Abstract

    Data analytics is routinely used to support biomedical research in all areas, with particular focus on the most relevant clinical conditions, such as cancer. Bioinformatics approaches, in particular, have been used to characterize the molecular aspects of diseases. In recent years, numerous studies have been performed on cancer based upon single and multi-omics data. For example, Single-omics-based studies have employed a diverse set of data, such as gene expression, DNA methylation, or miRNA, to name only a few instances. Despite that, a significant part of literature reports studies on gene expression with microarray datasets. Single-omics data have high numbers of attributes and very low sample counts. This characteristic makes them para

    Brain MR Image Classification for ADHD Diagnosis Using Deep Neural Networks

    Sahar Abdolmaleki, Mohammad Saniee Abadeh
    Conference Papers2020 International Conference on Machine Vision and Image Processing (MVIP) , 2020 February 18, {Pages 05-Jan }

    Abstract

    Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorder in childhood and adolescence. ADHD diagnosis currently includes psychological tests and depends on ratings of behavioral symptoms, which can be unreliable. Thus, an objective diagnostic tool based on non-invasive imaging can improve the understanding and diagnosis of ADHD. The purpose of this study is classifying brain images by using Artificial Intelligence methods such as clinical decision support system for the diagnosis of ADHD. For this purpose and according to a medical imaging classification system, firstly, image pre-processing is done. Then, a deep multi-modal 3D CNN is trained on GM from structural and fALFF from functional MRI

    Using Data Complexity Measures and an Evolutionary Cultural Algorithm for Gene Selection in Microarray Data

    Saeed Sarbazi-Azad, Mohammad Saniee Abadeh, Mohammad Erfan Mowlaei
    Journal PapersSoft Computing Letters , 2020 October 21, {Pages 100007 }

    Abstract

    Cancer detection using gene expression data has been a major trend of research for the last decade. Microarray gene expression data is one of the most challenging types of data due to high dimensionality and rarity of available samples. Feature redundancy greatly contributes to the difficulty of prediction task. Therefore, it is essential to apply feature selection to datasets to reduce the number of features selected for the classification task. In this paper, a novel two-staged framework is proposed to confront curse of dimensionality in microarray data using data complexity measures and a customized cultural algorithm, incorporating a static belief space into the genetic algorithm in order to reduce the search space and prioritize import

    Cancer miRNA biomarkers classification using a new representation algorithm and evolutionary deep learning

    Niousha Bagheri Khoulenjani, Mohammad Saniee Abadeh, Saeed Sarbazi-Azad, Najmeh Sadat Jaddi
    Journal PapersSoft Computing , 2020 October 12, {Pages 17-Jan }

    Abstract

    The diagnosis of cancer is presently undergoing a change of paradigm for the diagnostic panel using molecular biomarkers. MicroRNA (miRNA) is one of the most important genomic datasets presenting the genome sequences. Since several studies have shown the relationship between miRNAs and cancers, data mining and machine learning methods can be incorporated to extract a large amount of knowledge from cancer genomic datasets. However, previous research works on the identification of cancers from miRNAs have made it possible to diagnose cancer, and the accuracy of some classes is not quite satisfactory. Therefore, this research is aimed at promoting a super-class (meta-label) approach and deep learning in a three-phase method to diagnose cancers

    Predicting Hospital Length of Stay of Neonates Admitted to the NICU Using Data Mining Techniques

    Ardeshir Mansouri, Mohammadreza Noei, Mohammad Saniee Abadeh
    Conference Papers2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) , 2020 October 29, {Pages 629-635 }

    Abstract

    Hospitals face many pressures, including limited budgets and resources. The Intensive Care Unit (ICU) mostly includes patients who are in critical condition and require costly sources of treatment and has attracted much attention from the medical community. The ability to predict the length of stay for newborns in the Neonatal Intensive Care Unit (NICU) can assist the health care system in allocating needed resources and also has clinical value as an indicator of newborn’s health status. This research utilized the Medical Information Mart for Intensive Care III database (MIMIC III), and the performance of different machine learning models on NICU patients was discussed. Data was filtered, extracted, and preprocessed from the database, and

    Brain MRI analysis using a deep learning based evolutionary approach

    H Shahamat, MS Abadeh
    Journal Papers , , {Pages }

    Abstract

    Promises of Meta-Learning for Device-Free Human Sensing: Learn to Sense

    Farid Ghareh Mohammadi, M Hadi Amini
    Conference PapersProceedings of the 1st ACM International Workshop on Device-Free Human Sensing , 2019 November 10, {Pages 44-47 }

    Abstract

    In this study, we explore potential opportunities for leveraging meta-learning algorithms to enable device-free sensing. We refer to this solution as" meta-sensing", which is mainly learning to sense by discovering the available information rather than deploying additional sensors. We specifically are interested in application of zero-shot learning, a specific algorithm that required no a priori information to learn. This class of methods aim at learning to learn, ie, meta-sensing does not only learn from the available data, but also learns how to learn over time without requiring extra sensing inputs. Meta-sensing learns and predicts through data transformation with respect to the test data. It executes the process of mapping data and upda

    Information verification improvement by textual entailment methods

    Arefeh Yavary, Hedieh Sajedi, Mohammad Saniee Abadeh
    Journal PapersSN Applied Sciences , Volume 1 , Issue 9, 2019 September 1, {Pages 1048 }

    Abstract

    Information verification is significant because the rate of information generation is high and growing every day, generally in social networks. This also causes social networks to be invoked as a news agency for most of the people. Accordingly, information verification in social networks becomes more significant. Therefore, in this paper, a method for information verification on Twitter is proposed. The proposed method employs textual entailment methods for enhancement of verification methods on Twitter. Aggregating the results of entailment methods in addition to the state-of-the-art methods can enhance the outcomes of tweet verification. In addition, as writing style of tweets is not perfect and formal enough for textual en

    An Introduction to Advanced Machine Learning: Meta Learning Algorithms, Applications and Promises

    Farid Ghareh Mohammadi, M Hadi Amini, Hamid R Arabnia
    Journal PapersarXiv preprint arXiv:1908.09788 , 2019 August 26, {Pages }

    Abstract

    In [1, 2], we have explored the theoretical aspects of feature extraction optimization processes for solving largescale problems and overcoming machine learning limitations. Majority of optimization algorithms that have been introduced in [1, 2] guarantee the optimal performance of supervised learning, given offline and discrete data, to deal with curse of dimensionality (CoD) problem. These algorithms, however, are not tailored for solving emerging learning problems. One of the important issues caused by online data is lack of sufficient samples per class. Further, traditional machine learning algorithms cannot achieve accurate training based on limited distributed data, as data has proliferated and dispersed significantly. Machine learnin

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    • كارشناسي ارشد
      داده كاوي ( واحد)
      دانشکده مهندسی برق و کامپیوتر، گروه آمار كاربردي
    • كارشناسي ارشد
      داده كاوي پيشرفته ( واحد)

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    • كارشناسي ارشد
      داده كاوي ( واحد)
      دانشکده مهندسی برق و کامپیوتر، گروه بيو فيزيك
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      رايانش تكاملي ( واحد)
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      جهانشيري, زهرا
      يك روش تكاملي چند هدفه جهت تخمين سن بيولوژيكي مغز با استفاده از تصاوير MRI
    • 1397
      حمزه لو, مريم
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      صامت عمراني, مسلم
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      بهراد, فاطمه
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      جهدكاران, ارش
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