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  • دکتری (1370)

    علوم کامپیوتر

    برادفورد، انگلستان

  • کارشناسی‌ارشد (1364)

    علوم کامپیوتر

    دانشگاه صنعتی شریف،

  • کارشناسی (1358)

    مهندسی کامپیوتر

    دانشگاه شهید بهشتی،

  • طراحی جستجو بنیان معماری سیستمهای نرم‌افزاری
  • کنترل نامتمرکز در سیستم‌ های توزیع شده خودوفقی
  • وارسی حین اجرای نرم افزار
  • امنیت اینترنت اشیا
  • وارسی پروتکل های امنیتی
  • جرمیابی دیجیتال
  • بیوانفورماتیک

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    ارتباط

    رزومه

    Aspects Extraction for Aspect Level Opinion Analysis Based on Deep CNN

    Ali Alemi Matin Pour, Saeed Jalili
    Conference Papers2021 26th International Computer Conference, Computer Society of Iran (CSICC) , 2021 March 3, {Pages 06-Jan }

    Abstract

    Extracting aspect term is essential for aspect level sentiment analysis; Sentiment analysis collects and extracts the opinions expressed in social media and websites' comments and then analyzes them, helping users and stakeholders understand public views on the issues raised better and more quickly. Aspect-level sentiment analysis provides more detailed information, which is very beneficial for use in many various domains. In this paper, the significant contribution is to provide a data preprocessing method and a deep convolutional neural network (CNN) to label each word in opinionated sentences as an aspect or non-aspect word. The proposed method extracts the terms of the aspect that can be used in analyzing the sentiment of the expressed

    Effect of Ultrasound-guided Transversus Abdominis Plane Block for Analgesia after Laparoscopic Cholecystectomy

    S Jalili, M Ghaemi, SF Gheiasi, M Deilami
    Journal Papers , , {Pages }

    Abstract

    File Fragment Type Classification by Bag-Of-Visual-Words.

    M Erfan, S Jalili
    Journal Papers , , {Pages }

    Abstract

    Candidate disease gene prediction using One-Class classification

    A Vasighi Zaker, S Jalili
    Journal Papers , , {Pages }

    Abstract

    Comparison of Anesthesia Results between Wide Awake Local Anesthesia no Tourniquet (WALANT) and Forearm Tourniquet Bier Block in Hand Surgeries: A Randomized Clinical Trial

    R Farzam, M Deilami, S Jalili, K Kamali
    Journal Papers , , {Pages }

    Abstract

    Antioxidant Activity of Pericarp Extract from Different Varieties of Pomegranate Fruit

    S Jalili, A Tabatabee Naini, M Ashrafi, M Aminlari
    Journal PapersJournal of Agricultural Science and Technology , Volume 22 , Issue 1, 2020 January 10, {Pages 95-107 }

    Abstract

    The pomegranate Punicagranatum fruit pericarp, contain polyphenolic compounds including alpha and beta punicalagins and ellagic acid, which exhibit remarkable antioxidant activities. The aim of this study was to purify and quantify the phenolic components from different varieties of Pomegranate Pericarp Extracts (PPEs) and determine their antioxidant properties. Methanolic and aqueous extracts of four pomegranate cultivars (Shahvar, Siahsorfeh, Torshsabz and Abdorahimkhany, from Shiraz, Iran) were prepared and total phenolic content of PPEs was determined. PPE components were further purified by XAD-16 column chromatography followed by LH-20 gel filtration. The eluted components were subjected to HPLC analysis to differentiate and quantify

    DPCT: A dynamic method for detecting protein complexes from TAP-Aware weighted PPI network

    Ali SabziNezhad, Saeed Jalili
    Journal PapersFrontiers in Genetics , Volume 11 , 2020 January , {Pages 567 }

    Abstract

    Detecting protein complexes from Protein-Protein interaction network (PPI) is the essence of discovering the rules of cellular world. There is a large amount of PPI data available generated from high throughput experimental data. The huge size of data persuades us to use computational methods instead of experimental methods to detect protein complexes. In past years, many researchers presented their algorithms to detect protein complexes. Most of the presented algorithms use current static PPI networks. New researches proved the dynamicity of cellular systems and so the PPI is not static over time. In this paper, we introduce DPCT to detect protein complexes from dynamic PPI networks. In the proposed method, TAP and GO data are used to make

    Comparison of anesthesia results between Wide Awake Local Anesthesia no Tourniquet (WALANT) and forearm tourniquet Bier block in hand surgeries: A Randomised Clinical Trial

    Ramin Farzam, Saeed Jalili, Mohammad Deilami, Koorosh Kamali
    Journal PapersThe Archives of Bone and Joint Surgery , 2020 September 10, {Pages }

    Abstract

    Background: There is still some debate regarding the most proper anesthetic technique in minor hand surgeries. We hypothesized that both WALANT and forearm torniquet Bier block methods provide effective anesthesia in minor hand surgeries without significant difference. Method: 85 patients consented to participate in this study and were randomized into WALANT and single tourniquet forearm bier block groups. In WALANT group, patients received adrenaline-contained lidocaine without tourniquet and in Bier group, lidocaine was administered accordingly after applying a forearm tourniquet . Due to difference in intervention methods, the study was non-blinded. Need for additional analgesia during surgery, visual analogue scale (VAS) for pain in

    On the computational power of the light: A plan for breaking data encryption standard

    Javad Salimi Sartakhti, Saeed Jalili
    Journal PapersTheoretical Computer Science , Volume 773 , 2019 June 14, {Pages 71-78 }

    Abstract

    The successful of the light-based solutions for some NP-complete problems, such as Hamiltonian path problem, have demonstrated the power of light-based computing. The capabilities of the light-based computing such as massive parallelism of light, allow it to solve hard computational problems in polynomial time, while the conventional computers require exponential time. In this study we show how the light-based solution can be applied to break the Data Encryption Standard (DES). Under the assumption of having one given (plain-text, cipher-text) pair, our method recovers the DES key in a efficient time. We describe how to implement XOR gates, circular shifts, P-boxes, and S-boxes of DES in a light-based approach. The proposed solution encrypt

    Effective defense against fingerprinting attack based on autocorrelation property minimization approach

    Hojjat Jahani, Saeed Jalili
    Journal PapersJournal of Intelligent Information Systems , 2019 March , {Pages 22-Jan }

    Abstract

    The website fingerprinting attack is one of the most important traffic analysis attacks that is able to identify a visited website in an anonymizing network such as Tor. It is shown that the existing defense methods against website fingerprinting attacks are inappropriate. In addition, they use large bandwidth and time overhead. In this study, we show that the autocorrelation property is the most important success factor of the website fingerprinting attack. We offer a new effective defense model to resolve this security vulnerability of the Tor anonymity network. The proposed defense model prevents information leakage from the passing traffic. In this regard, a novel mechanism is developed to make the traffic analysis a hard task. This mec

    Efficient Distributed k-clique Mining for Large Networks Using MapReduce

    Saeed Shahrivari, Saeed Jalili
    Journal PapersIEEE Transactions on Knowledge and Data Engineering , 2019 August 20, {Pages }

    Abstract

    Mining cliques of a network is an important problem that has many applications in different fields like social networks, bioinformatics, and web analysis. In most applications, mining fixed sized cliques, known as k-cliques, is enough. However, mining cliques of a large network is very challenging using current solutions, and it takes a considerable time using a commodity machine. Also, very large networks cannot be efficiently loaded into memory of a single machine. To overcome these limitations, we have proposed a solution named KCminer, which is based on state space search and can be totally fitted into the MapReduce framework. Using the MapReduce framework, it is possible to run KCminer on cloud computing platforms and hence, process ve

    Comparison of effects of propofol and ketofol (Ketamine-Propofol mixture) on emergence agitation in children undergoing tonsillectomy

    Saeed Jalili, Ali Esmaeeili, Koorosh Kamali, Vahideh Rashtchi
    Journal PapersAfrican health sciences , Volume 19 , Issue 1, 2019 January , {Pages 1736-1744 }

    Abstract

    Background: The aim of this study was to compare the effect of propofol and ketofol (ketamine-propofol mixture) on EA in children undergoing tonsillectomy.Method: In this randomized clinical trial, 87 ASA class I and II patients, aged 3-12 years, who underwent tonsillectomy, were divided into two groups to receive either propofol 100 ?g/kg/min (group p, n= 44) or ketofol: ketamine 25 ?g/kg/min+ propofol 75 ?g/kg/min (group k, n= 43). Incidence and severity of EA was evaluated using the Pediatric Anesthesia Emergence Delirium (PAED) scales on arrival at the recovery room, and 10 and 30 min after that time.Results: There was no statistically significant difference in demographic data between the two groups. In the ketofol group, the need for

    Voxel selection framework based on meta‐heuristic search and mutual information for brain decoding

    Osama Hourani, Nasrollah Moghadam Charkari, Saeed Jalili
    Journal PapersInternational Journal of Imaging Systems and Technology , Volume 29 , Issue 4, 2019 December , {Pages 663-676 }

    Abstract

    Visual stimulus decoding is an increasingly important challenge in neuroscience. The goal is to classify the activity patterns from the human brain; during the sighting of visual objects. One of the crucial problems in the brain decoder is the selecting informative voxels. We propose a meta‐heuristic voxel selection framework for brain decoding. It is composed of four phases: preprocessing of fMRI data; filtering insignificant voxels; postprocessing; and meta‐heuristics selection. The main contribution is benefiting a meta‐heuristics search algorithm to guide a wrapper voxel selection. The main criterion to nominate a voxel is based on its mutual information with the provided stimulus label. The results show impressive accuracy rates

    A Binary Particle Swarm Optimization Approach for Gene Expression Biclustering Problem

    Bilal Taher, Muhammad Fares, Saeed Jalili
    Journal PapersarXiv preprint arXiv:1911.11223 , 2019 November 25, {Pages }

    Abstract

    Microarray techniques are widely used in Gene expression analysis. These techniques are based on discovering submatrices of genes that share similar expression patterns across a set of experimental conditions with coherence constraint. Actually, these submatrices are called biclusters and the extraction process is called biclustering. In this paper we present a novel binary particle swarm optimization model for the gene expression biclustering problem. Hence, we apply the binary particle swarm optimization algorithm with a proposed measure, called Discretized Column-based Measure (DCM) as a novel cost function for evaluating biclusters where biological relevance, MSR and the size of the bicluster are considered as evaluation metrics for our

    An Algorithm Based on Theory of Constraints and Branch and Bound for Solving Integrated Product-Mix-Outsourcing Problem

    Esmaeil Mehdizadeh, Saeed Jalili
    Journal PapersJournal of Optimization in Industrial Engineering , Volume 12 , Issue 1, 2019 March 1, {Pages 167-172 }

    Abstract

    One of the most important decision making problems in many production systems is identification and determination of products and their quantities according to available resources. This problem is called product-mix. However, in the real-world situations, for existing constrained resources, many companies try to provide some products from external resources to achieve more profits. In this paper, an integrated product-mix-outsourcing problem (IPMO) is considered to answer how many products should be produced inside of the system or purchased from external resources. For this purpose, an algorithm based on Theory of Constraints (TOC) and Branch and Bound (B&B) algorithm is proposed. For investigation of the proposed algorithm, a numerical ex

    Disease genes prediction by HMM based PU-learning using gene expression profiles

    Ozra Nikdelfaz, Saeed Jalili
    Journal PapersJournal of biomedical informatics , Volume 81 , 2018 May 1, {Pages 102-111 }

    Abstract

    Predicting disease candidate genes from human genome is a crucial part of nowadays biomedical research. According to observations, diseases with the same phenotype have the similar biological characteristics and genes associated with these same diseases tend to share common functional properties. Therefore, by applying machine learning methods, new disease genes are predicted based on previous ones. In recent studies, some semi-supervised learning methods, called Positive-Unlabeled Learning (PU-Learning) are used for predicting disease candidate genes. In this study, a novel method is introduced to predict disease candidate genes through gene expression profiles by learning hidden Markov models. In order to evaluate the proposed method, it

    HM3alD: Polymorphic Malware Detection Using Program Behavior-Aware Hidden Markov Model

    Asghar Tajoddin, Saeed Jalili
    Journal PapersApplied Sciences , Volume 8 , Issue 7, 2018 July , {Pages 1044 }

    Abstract

    Malware have been tremendously growing in recent years. Most malware use obfuscation techniques for evasion and hiding purposes, but they preserve the functionality and malicious behavior of original code. Although most research work has been mainly focused on program static analysis, some recent contributions have used program behavior analysis to detect malware at run-time. Extracting the behavior of polymorphic malware is one of the major issues that affects the detection result. In this paper, we propose HM 3 alD, a novel program behavior-aware hidden Markov model for polymorphic malware detection. The main idea is to use an effective clustering scheme to partition the program behavior of malware instances and then apply a novel hidden

    Multi-kernel one class link prediction in heterogeneous complex networks

    Hadi Shakibian, Nasrollah Moghadam Charkari, Saeed Jalili
    Journal PapersApplied Intelligence , 2018 January , {Pages 18-Jan }

    Abstract

    The heterogeneity of a network causes major challenges for link prediction in heterogeneous complex networks. To deal with this problem, supervised link prediction could be applied to integrate heterogeneous features extracted from different nodes/relations. However, supervised link prediction might be faced with highly imbalanced data issues which results in undesirable false prediction rate. In this paper, we propose a new kernel-based one-class link predictor in heterogeneous complex networks. Assuming a set of available meta-paths, a graph kernel is extracted based on each meta-path. Then, they are combined to form a single kernel function. Afterwards, one class support vector machine (OC-SVM) would be applied on the positi

    Online Tor Privacy Breach Through Website Fingerprinting Attack

    Hojjat Jahani, Saeed Jalili
    Journal PapersJournal of Network and Systems Management , 2018 January , {Pages Jan-38 }

    Abstract

    Tor is one of the most widely used anonymization networks based on onion router that it preserves user’s privacy and secure data flow over the Internet communications. Due to the growing utilization of Tor, Identifying its weaknesses and fixing them is crucial. This study focuses on the website fingerprinting attack and offers a new procedure based on FFT to calculate the similarity distance between two instances and form a distance matrix. By applying the proposed method, we demonstrate that either accuracy grows significantly or the time complexity reduces such that it is applicable in an online manner. In order to evaluate the capability of the proposed method to defeat user privacy, we applied it in an open-world scenario

    C-PUGP: A Cluster-based Positive Unlabeled learning method for disease Gene Prediction and prioritization

    Akram Vasighizaker, Saeed Jalili
    Journal PapersComputational biology and chemistry , 2018 June 1, {Pages }

    Abstract

    Disease gene detection is an important stage in the understanding disease processes and treatment. Some candidate disease genes are identified using many machine learning methods Although there are some differences in these methods including feature vector of genes, the method used to selecting reliable negative data (non-disease genes), and the classification method, the lack of negative data is the most significant challenge of them. Recently, candidate disease genes are identified by semi-supervised learning methods based on positive and unlabeled data. These methods are reasonably accurate and achieved more desirable results versus preceding methods. In this article, we propose a novel Positive Unlabeled (PU) learning technique based up

    دروس نیمسال جاری

    • كارشناسي ارشد
      يادگيري ماشين (3 واحد)
      دانشکده مهندسی برق و کامپیوتر، گروه مهندسي كامپيوتر
    • كارشناسي ارشد
      سيستم هاي توزيع شده (3 واحد)

    دروس نیمسال قبل

    • كارشناسي ارشد
      يادگيري ماشين (3 واحد)
      دانشکده مهندسی برق و کامپیوتر، گروه مهندسي كامپيوتر
    • كارشناسي ارشد
      سيستم هاي توزيع شده (3 واحد)
    • 1396
      همتي نادنيلوئي, امين
      روش پوياي تشخيص كمپلكس هاي پروتييني با رويكرد خوشه بندي تكاملي و با استفاده از شبكه در هم كنش پروتيين-پروتيين، گراف هستان شناسي و تي اي پي
    • 1396
      عالمي متين پور, علي
    • 1397
      غيورباغباني, سيده يگانه
    • 1397
      حسين زاده, مهدي
    • 1397
      سروري, علي
    • 1397
      يزدان پرست, زهرا
    • 1397
      قرباني چمازكتي, زهرا
    • 1397
      مولايي, سپيده
    • 1398
      رستمي درونكلا, طه
    • 1398
      مولي ورديخاني, مريم
    • 1396
      همتي نادنيلوئي, امين
    • 1396
      عالمي متين پور, علي
    • 1397
      غيورباغباني, سيده يگانه
    • 1397
      حسين زاده, مهدي
    • 1397
      سروري, علي
    • 1397
      يزدان پرست, زهرا
    • 1397
      قرباني چمازكتي, زهرا
    • 1397
      مولايي, سپيده
    • 1398
      رستمي درونكلا, طه
    • 1398
      مولي ورديخاني, مريم
    • 1390
      شحاده, عبداله
      شناسايي معنايي كدهاي برنامه مشابه در برنامه هاي شي گرا با رويكرد تكاملي
    • 1393
      عابديان, كوشان
    • 1393
      علوي سلطاني, سيدحسين
    • 1396
      يوسفي نژادراوري, فاطمه
    • 1390
      شحاده, عبداله
    • 1393
      عابديان, كوشان
    • 1393
      علوي سلطاني, سيدحسين
    • 1396
      يوسفي نژادراوري, فاطمه
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