Loading...
Search for: deep-learning
0.006 seconds
Total 146 records

    Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks

    , Article Journal of Nuclear Cardiology ; 2020 Shiri, I ; AmirMozafari Sabet, K ; Arabi, H ; Pourkeshavarz, M ; Teimourian, B ; Ay, M. R ; Zaidi, H ; Sharif University of Technology
    Springer  2020
    Abstract
    Introduction: The purpose of this work was to assess the feasibility of acquisition time reduction in MPI-SPECT imaging using deep leering techniques through two main approaches, namely reduction of the acquisition time per projection and reduction of the number of angular projections. Methods: SPECT imaging was performed using a fixed 90° angle dedicated dual-head cardiac SPECT camera. This study included a prospective cohort of 363 patients with various clinical indications (normal, ischemia, and infarct) referred for MPI-SPECT. For each patient, 32 projections for 20 seconds per projection were acquired using a step and shoot protocol from the right anterior oblique to the left posterior... 

    Deep Learning Approach for Domain Adaptation

    , M.Sc. Thesis Sharif University of Technology Aminzadeh, Majid (Author) ; Soleymani Baghshah, Mahdieh (Supervisor)
    Abstract
    A predefined assumption in many learning algorithms is that the training and test data must be in thesame feature space and have the same distribution.However, this assumption may not hold in all of these algorithms and in the real world there might be difference between the source and the targer domian, whether in the feature space or the distribution. Moreover, there might be a few number of labled data of the target domain which causes difficulty in learning an accurate classifier. In such cases, transferring knowledge can be useful if can be done successfully and transfer learning was introduced for this purpose. Domain Adaptation is one of the transfer leaning problems that assume some... 

    Deep Learning For Recommender Systems

    , M.Sc. Thesis Sharif University of Technology Abbasi, Omid (Author) ; Soleimani, Mahdieh (Supervisor)
    Abstract
    Collaborative fltering (CF) is one of the best and widely employed approaches in Recommender systems (RS). This approach tries to fnd some latent features for users and items so it would predict user rates with these features. Early CF methods used matrix factorization to learn users and items latent features. But these methods face cold start as well as sparsity problem. Recent years methods employ side information along with rating matrix to learn users and items latent features. On the other hand, deep learning models show great potential for learning effective representations especially when auxiliary information is sparse. Due to this feature of deep learning, we use deep learning to... 

    Robust Face Verification under Occlusion in Video

    , M.Sc. Thesis Sharif University of Technology Hajbabaei, Mohammad Reza (Author) ; Ghaemmaghami, Shahrokh (Supervisor)
    Abstract
    Nowadays, using of digital cameras is streaming across the world dramatically. Application of these devices is very diverse. One of the most interesting application of those is face verification. For example, imagine your smartphone has an application which verifies faces in front of its front camera, if that face be your face (with variation from original) then application automatically unlocks your phone. Face verification systems are also deployed in airports to verify passport photos and in smart homes. One of the most regular problems in face verification is occlusion. When your face is occluded with natural or random changes we can say your face is occluded. All of the recent papers... 

    An intelligent cloud-based data processing broker for mobile e-health multimedia applications

    , Article Future Generation Computer Systems ; 2016 ; 0167739X (ISSN) Peddi, S. V. B ; Kuhad, P ; Yassine, A ; Pouladzadeh, P ; Shirmohammadi, S ; Shirehjini, A. A. N ; Sharif University of Technology
    Elsevier  2016
    Abstract
    Mobile e-health applications provide users and healthcare practitioners with an insightful way to check users/patients' status and monitor their daily calorie intake. Mobile e-health applications provide users and healthcare practitioners with an insightful way to check users/patients' status and monitor their daily activities. This paper proposes a cloud-based mobile e-health calorie system that can classify food objects in the plate and further compute the overall calorie of each food object with high accuracy. The novelty in our system is that we are not only offloading heavy computational functions of the system to the cloud, but also employing an intelligent cloud-broker mechanism to... 

    Semantic Analysis and Event Detection Using Deep Learning for Stock Prediction

    , M.Sc. Thesis Sharif University of Technology Basirian Jahromi, Ali (Author) ; Sameti, Hossein (Supervisor) ; Bokaei, Mohammad Hadi (Supervisor)
    Abstract
    News plays a very important role in stock market trading. Nowadays news from a different part of the world and about different fields can be accessed easily, and for a successful trade, it is necessary to analyze accurately and use this big data and information as soon as possible. For this reason, this thesis tries to present and study models based on Deep Learning networks and Natural Language Processing for financial news analysis and predicting stock indices movement. This research takes advantage of a language model for learning and representing news text, and beside this language model it uses deep learning networks at multiple levels to extract proper features from each news in a day... 

    Deep Learning for Action Recognition

    , M.Sc. Thesis Sharif University of Technology Aslan Beigi, Fatemeh (Author) ; Vosoughi Vahdat, Bijan (Supervisor) ; Mohammadzadeh, Narjesolhoda (Supervisor)
    Abstract
    Computers, laptops, tablets and even cell phones are capable of recording, producing, storing and sharing videos. With the increasing availability of movies and more and easier access to them, the need for understanding videos has increased. Due to the limited human ability in analyzing videos, there is an increasing demand for intelligent systems to analyze videos and recognize the actions in them.Action recognition is the classification of the action performed by the individual in the video, and there are different types of action recognition depending on the nature of the data and the way it will be processed. Vision-based human action recognition is affected by several challenges due to... 

    Pitch Detection Using Deep Learning

    , M.Sc. Thesis Sharif University of Technology Khademhosseini, Mohammad (Author) ; Marvasti, Farrokh (Supervisor) ; Ghaemmaghami, Shahrokh (Co-Supervisor)
    Abstract
    Pitch frequency is one of the most important attributes of speech, which has been found to be quite challenging in noisy conditions. In this paper, we propose a pitch detection method based on separation of the low pitch from high pitch signals, depending on the pitch frequency below or over 200Hz, respectively, using a deep convolutional neural network. The pitch frequency is initially estimated, employing a conventional pitch detection method. From this initial estimation and using a deep convolutional neural network which determines the signals type (high-pitch or low-pitch), the pitch candidates are derived. To choose the true pitch values, we use three features in addition to soft... 

    Automated Generation of Commit Messages in Code Repositories

    , M.Sc. Thesis Sharif University of Technology Ganji, Siavash (Author) ; Heydarnoori, Abbas (Supervisor)
    Abstract
    Software requirements are changing continuously and hence during software evolution and maintenance, source codes changes are being committed in the software repositories. Reading source codes to understand the changes is a very time consuming and tedious activity. Commit messages contain information about code changes that let developers be aware of the essence of the changes without reading the source codes. Unfortunately, due to the pressure of deadlines and lack of time, developers neglect to write these messages. Commit messages can speed up the process of software understanding for developers and also play an important role in software documentation. Therefore, an automated method for... 

    Visual Question Answering

    , M.Sc. Thesis Sharif University of Technology Salari, Arsalan (Author) ; Manzuri, Mohammad Taghi (Supervisor)
    Abstract
    Visual Question Answering (VQA) deep-learning systems tend to capture superficial statistical correlations in the training data because of strong language priors and fail to generalize to test data with a significantly different question-answer(QA) distribution. To address this issue, we introduce a Visually Directed Question Encoder to replace the commonly used RNNs in base models. our method uses visual features alongside word embeddings of question words to encode each word. As a result, the model is forced to look at the visual information relevant to each word and it no longer produces answers based on just the question itself. We evaluate our approach on the VQA generalization task... 

    A survey on deep learning based approaches for action and gesture recognition in image sequences

    , Article 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017, 30 May 2017 through 3 June 2017 ; 2017 , Pages 476-483 ; 9781509040230 (ISBN) Asadi Aghbolaghi, M ; Clapes, A ; Bellantonio, M ; Escalante, H. J ; Ponce Lopez, V ; Baro, X ; Guyon, I ; Kasaei, S ; Escalera, S ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2017
    Abstract
    The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions. We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research. © 2017 IEEE  

    Automatic access control based on face and hand biometrics in a non-cooperative context

    , Article Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2018 ; Volume 2018-January , 2018 , Pages 28-36 ; 9781538651889 (ISBN) Sabet Jahromi, M. N ; Bonderup, M. B ; Asadi Aghbolaghi, M ; Avots, E ; Nasrollahi, K ; Escalera, S ; Kasaei, S ; Moeslund, T. B ; Anbarjafari, G ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Abstract
    Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they... 

    Designing a Vehicle Counting and Classification System

    , M.Sc. Thesis Sharif University of Technology Mousavi, Zeinab (Author) ; Gholampour, Iman (Supervisor)
    Abstract
    In recent years, Intelligent Transportation Systems (ITS) have received special attentions both in research and in commercial areas. Increased infrastructure facilities, like surveillance cameras, has made this concept even more attainable than before. In this respect, the ability to automatically extract information from traffic images, as one of the key inputs of ITSs, is of great importance. With an increased number of surveillance cameras and the need for more accurate information regarding the road users and their interactions, in order to better city traffic management, building and repairing roads, trip time estimation, number of people per roads estimation and etc, using human... 

    The Application of Deep Learning on Network Traffic Classification

    , M.Sc. Thesis Sharif University of Technology Lotfollahi, Mohammad (Author) ; Jafari Siavoshani, Mahdi (Supervisor)
    Abstract
    Almost all of the network traffic classification systems use pre-defined extracted features by the experts in computer network. These features include regular expressions, port number, information in the header of different layers and statistical feature of the flow. The main problem of the traffic analysis and anomaly detection system lies in finding appropriate features. The feature extraction is a time consuming process which needs an expert to be done. It is notable that the classification of special kinds of traffic like encrypted traffic is impossible using some subset of mentioned features.The lack of integration in feature detection and classification is also another important issue... 

    Design and Efficient Implementation of Deep Learning Algorithm for ECG Classification

    , M.Sc. Thesis Sharif University of Technology Oveisi, Mohammad Hossein (Author) ; Hashemi, Matin (Supervisor)
    Abstract
    Cardiovascular diseases are the leading cause of death globally so early diagnosis of them is important. Many researchers focused on this field. First signs of cardiac diseases appear in the electrocardiogram signal. This signal represents the electrical activity of the heart so it’s primarily used for the detection and classification of cardiac arrhythmias. Permanent monitoring of this signal is not possible for specialists so we should do this by means of Artificial Intelligence. In this thesis, we use recurrent neural networks to classify electrocardiogram’s arrhythmias. This deep learning method, use two sources of data to learn from. The first part of data is global for everyone and the... 

    Design and Efficient Implementation of ECG-based Detection Algorithm for Dangerous Myocardial Problems

    , M.Sc. Thesis Sharif University of Technology Saadatnejad, Saeed (Author) ; Hashemi, Matin (Supervisor) ; Vosooghi Vahdat, Bizhan (Co-Advisor)
    Abstract
    Cardiovascular diseases are the first leading cause of death in the world also in IRAN. Early detection of such problems can decrease the costs also can help to cure the patient but it needs continuous monitoring and automated classification of hearbeats. Mobile devices and wearable gadgets are good solutions which can help patients before visiting the doctor.In this research, an algorithm is introduced which with the help of ECG signal detects dangerous myocardial problems. Our approach is using deep learning method which were not considered much before. In the proposed algorithm ECG signal is processed in order to get features and with dimensionality reduction, input of the network gets... 

    Fine-grained Image Classification

    , M.Sc. Thesis Sharif University of Technology Souri, Yaser (Author) ; Kasaei, Shohreh (Supervisor)
    Abstract
    Fine-grained image classification is image classification where the considered classes are all sub-classes of a certain, more general class. In this setting of the problem, the classes are visually very similar to each other, such that an unskilled human cannot discriminate between them. In this case, proposed methods for the ordinary image classification problem do not obtain good classification accuracy. So proposing new methods for solving this problem is necessary. In this thesis two new methods, based on recent advances in deep learning are proposed for solving the fine-grained image classification problem. First by improving several parts of one of the recent proposed methods for this... 

    Performance Improvement of Machine Learning based Intrusion Detection Systems

    , M.Sc. Thesis Sharif University of Technology Ramin, Shirali Hossein Zadeh (Author) ; Jafari Siavoshani, Mahdi (Supervisor)
    Abstract
    The rapid growth of computer networks has increased the importance of analytics and traffic analysis tools for these networks, and the increasing importance of these networks has increased the importance of security of these networks and the intrusion detection in these networks. Many studies aimed at providing a powerful way to quickly and accurately detect computer network intrusions, each of which has addressed this issue.The common point of all these methods is their reliance on the features extracted from network traffic by an expert. This strong dependence has prevented these methods from being flexible against new attacks and methods of intrusion or changes in the current normal... 

    Automatic Image Annotation Using Deep Learning

    , M.Sc. Thesis Sharif University of Technology Bahramipoor, Misagh (Author) ; Jamzad, Mansour (Supervisor)
    Abstract
    With the advances in technology, Nowadays digital cameras are everywhere. As a result very large amount of images are on the web. Searching through these images intelligently and purposively is an essential need. Recently the possibility of retrieving images with some conceptual words along side of content based image retrieval has been studied in computer vision. For this purpose it’s required that for each image several words that describe its content be assigned automatically. One of the main problems for this task is semantic gap, meaning that the low level features such as color, texture,… don’t have the ability to describe the high level concepts in images which are comprehensible by... 

    Insert Graphical Elements in Multiview Soccer Videos

    , M.Sc. Thesis Sharif University of Technology Ashgar, Nafiseh (Author) ; Kasaei, Shohreh (Supervisor)
    Abstract
    In recent decades, many researchers have focused on inferring camera calibration from soccer videos. This task is usually used to provide more information to the audience by adding graphical elements to the field. Indeed, the problem of inserting graphical elements in sport field videos is the problem of calculating projection matrix in continuous frames with which we can insert graphical elements. Basic challenges in this regard are the lack of information in some frames, bad lighting conditions, noise and blur, quick changes of camera viewpoint and radial distortion. Despite previous methods which aimed to propose an algorithm for a specific region of the field, we have introduced a novel...