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    Generative model selection using a scalable and size-independent complex network classifier

    , Article Chaos ; Volume 23, Issue 4 , 2013 ; 10541500 (ISSN) Motallebi, S ; Aliakbary, S ; Habibi, J ; Sharif University of Technology
    2013
    Abstract
    Real networks exhibit nontrivial topological features, such as heavy-tailed degree distribution, high clusteringsmall-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the generative model that best fits to a target network. In this paper, we investigate this problem and our goal is to select the model that is able to generate graphs similar to a given network instance. By the means of generating synthetic networks with seven outstanding generative models, we have utilized machine learning methods to develop a decision tree for model selection. Our... 

    Combining ontology alignment metrics using the data mining techniques

    , Article 2nd International Workshop on Contexts and Ontologies: Theory, Practice and Applications, C and O 2006 - Collocated with the 17th European Conference on Artificial Intelligence, ECAI 2006, Riva del Garda, 28 August 2006 through 28 August 2006 ; Volume 210 , 2006 ; 16130073 (ISSN) Bagheri Hariri, B ; Sayyadi, H ; Abolhassani, H ; Sheykh Esmaili, K ; Sharif University of Technology
    2006
    Abstract
    Several metrics have been proposed for recognition of relationships between elements of two Ontologies. Many of these methods select a number of such metrics and combine them to extract existing mappings. In this article, we present a method for selection of more effective metrics - based on data mining techniques. Furthermore, by having a set of metrics, we suggest a data-mining-like means for combining them into a better ontology alignment  

    Automatic learning of action priorities

    , Article Proceedings of the Eighth IASTED International Conference on Atificial Intelligence and Soft Computing, Marbella, 1 September 2004 through 3 September 2004 ; 2004 , Pages 255-259 ; 0889864586 (ISBN) Akramifar, S. A ; Ghassem Sani, G. R ; IASTED, TCAIETCSC ; Sharif University of Technology
    2004
    Abstract
    Traditional AI planners often suffer from their poor efficiency. There are many choice points in the planning process, but lack of information precludes proper decision. In this paper, we introduce a new method for adding automatic learning capability to a forward planning system. Our idea is based on a dynamic voting algorithm to choose the best action to proceed to the next state. In every planning cycle, applicable actions (i.e. those actions whose preconditions are satisfied in the current world state) vote to, and compete with each other. As a result of this voting, gradually more useful actions are chosen. This idea has been applied to the blocks world domain, and the preliminary... 

    Evolution of pleasure system in zamin artificial world

    , Article Proceedings of the Fifteenth IASTED Internatinal Conference on Modeling and Simulation, Marina Del Rey, CA, 1 March 2004 through 3 March 2004 ; 2004 , Pages 272-277 ; 10218181 (ISSN) Halavati, R ; Haratizadeh, S ; Bagheri Shouraki, S ; Sharif University of Technology
    2004
    Abstract
    Zamin, which is a high level artificial life environment have been successfully used as a test bed for a number of cognitive and AI studies. Here we have tried to test the evolution of a pleasure computing mechanism in Zamin's artificial creatures and have extended their mental capabilities to cover uncertainty in action selection mechanism. The results show some improvements in both genetic evolution process and learning capabilities. More specifically, we have evolved an internal pleasure system in Zamin creatures for the first time, quite unsupervised. In addition creatures could learn much more efficient behavioral patterns than what they could before  

    On the distortion value of elections with abstention

    , Article Journal of Artificial Intelligence Research ; Volume 70 , 2021 , Pages 567-595 ; 10769757 (ISSN) Seddighin, M ; Latifian, M ; Ghodsi, M ; Sharif University of Technology
    AI Access Foundation  2021
    Abstract
    In Spatial Voting Theory, distortion is a measure of how good the winner is. It has been proved that no deterministic voting mechanism can guarantee a distortion better than 3, even for simple metrics such as a line. In this study, we wish to answer the following question: How does the distortion value change if we allow less motivated agents to abstain from the election? We consider an election with two candidates and suggest an abstention model, which is a general form of the abstention model proposed by Kirchgässner. Our results characterize the distortion value and provide a rather complete picture of the model. ©2021 AI Access Foundation  

    Deep learning for the classification of cervical maturation degree and pubertal growth spurts: A pilot study

    , Article Korean Journal of Orthodontics ; Volume 52, Issue 2 , 2022 , Pages 112-122 ; 22347518 (ISSN) Mohammad Rahimi, H ; Motamadian, S. R ; Nadimi, M ; Hassanzadeh Samani, S ; Minabi, M. A. S ; Mahmoudinia, E ; Lee, V. Y ; Rohban, M. H ; Sharif University of Technology
    Korean Association of Orthodontists  2022
    Abstract
    Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two... 

    A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months

    , Article Acute and Critical Care ; Volume 37, Issue 1 , 2022 , Pages 45-52 ; 25866052 (ISSN) Nourelahi, M ; Dadboud, F ; Khalili, H ; Niakan, A ; Parsaei, H ; Sharif University of Technology
    Korean Society of Critical Care Medicine  2022
    Abstract
    Background: Traumatic brain injury (TBI), which occurs commonly worldwide, is among the more costly of health and socioeconomic problems. Accurate prediction of favorable outcomes in severe TBI patients could assist with optimizing treatment procedures, predicting clinical outcomes, and result in substantial economic savings. Methods: In this study, we examined the capability of a machine learning-based model in predicting “favorable” or “unfavorable” outcomes after 6 months in severe TBI patients using only parameters measured on admission. Three models were developed using logistic regression, random forest, and support vector machines trained on parameters recorded from 2,381 severe TBI... 

    Cooperation Control of a Set of Robots with Different Expertise to Accomplish a Duty

    , M.Sc. Thesis Sharif University of Technology Mortazi, Ali Asghar (Author) ; Bagheri Shouraki, Saeed (Supervisor)
    Abstract
    In recent years, there have been many efforts for distributing a big and complex duty among some agents in order to do it more simply. Large number of these methods are in area called distributed systems, where several different agents start to work together to do a difficult or complex duty which each of them cannot do it lonely. Combination of distributed systems and artificial intelligence is known as distributed artificial intelligence.
    One of the theories that has received attention recently, is called Embodiment. According this theory, in cooperation of a set of agents for performing particular task, expertise might not be integrated in a centralized controller, rather it gradually... 

    Pressuremeter test in unsaturated soils: a numerical study

    , Article SN Applied Sciences ; Volume 3, Issue 4 , 2021 ; 25233971 (ISSN) Keshmiri, E ; Ahmadi, M. M ; Sharif University of Technology
    Springer Nature  2021
    Abstract
    The paper presents a numerical analysis of pressuremeter test in unsaturated cohesive soils. In practice, pressuremeter is commonly expanded up to 10–15% cavity strains. At these strains, limit pressure is not usually reached, and its value is estimated by extrapolation. Accordingly, authors suggest using cavity pressure at 10% strain (P10) for the interpretation of pressuremeter test rather than limit pressure. At this strain, it is also assured that plastic strain occurs around the cavity, which is crucial for the interpretations. In unsaturated soils, the moisture at which a soil is tested has a noticeable influence on the pressuremeter cavity pressure, and consequently, on the magnitude... 

    GMWASC: Graph matching with weighted affine and sparse constraints

    , Article CSSE 2015 - 20th International Symposium on Computer Science and Software Engineering, 18 August 2015 ; 2015 ; 9781467391818 (ISBN) Taheri Dezaki , F ; Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    Abstract
    Graph Matching (GM) plays an essential role in computer vision and machine learning. The ability of using pairwise agreement in GM makes it a powerful approach in feature matching. In this paper, a new formulation is proposed which is more robust when it faces with outlier points. We add weights to the one-to-one constraints, and modify them in the process of optimization in order to diminish the effect of outlier points in the matching procedure. We execute our proposed method on different real and synthetic databases to show both robustness and accuracy in contrast to several conventional GM methods  

    Envy-free mechanisms with minimum number of cuts

    , Article 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 4 February 2017 through 10 February 2017 ; 2017 , Pages 312-318 Alijani, R ; Farhadi, M ; Ghodsi, M ; Seddighin, M ; Tajik, A. S ; Amazon; Artificial Intelligence; Baidu; et al.; IBM; Tencent ; Sharif University of Technology
    AAAI press  2017
    Abstract
    We study the problem of fair division of a heterogeneous resource among strategic players. Given a divisible heterogeneous cake, we wish to divide the cake among n players in a way that meets the following criteria: (I) every player (weakly) prefers his allocated cake to any other player's share (such notion is known as envy-freeness), (II) the mechanism is strategy-proof (truthful), and (III) the number of cuts made on the cake is minimal. We provide methods, namely expansion process and expansion process with unlocking, for dividing the cake under different assumptions on the valuation functions of the players. Copyright © 2017, Association for the Advancement of Artificial Intelligence... 

    Fuzzy type-2 nash equilibrium

    , Article 2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008, Vienna, 10 December 2008 through 12 December 2008 ; 2008 , Pages 398-402 ; 9780769535142 (ISBN) Chakeri, A. R ; Habibi, J ; Heshmat, Y ; Sharif University of Technology
    2008
    Abstract
    In the past decade, fuzzy logic has been widely used to manage uncertainty in games. In this paper, we employ fuzzy logic to determine the priority of a payoff using linguistic preference relation. Preferences are derived according to the difference between payoffs using fuzzy IF-THEN rule. We introduce the concept of linguistic Nash equilibrium based on priority of each payoff. We assign a fuzzy set type2 to each cell to determine how much a cell has possibility to have a specific degree of being Nash. © 2008 IEEE  

    Rotated general regression neural network

    , Article 2007 International Joint Conference on Neural Networks, IJCNN 2007, Orlando, FL, 12 August 2007 through 17 August 2007 ; 2007 , Pages 1959-1964 ; 10987576 (ISSN) ; 142441380X (ISBN); 9781424413805 (ISBN) Gholamrezaei, M ; Ghorbanian, K ; Sharif University of Technology
    2007
    Abstract
    A rotated general regression neural network is presented as an enhancement to the general regression neural network. A variable kernel estimate for multivariate densities is considered. A coordinate transformation is adopted which circumvent the difficulty of predicting multimodal distribution with large variance differences between modes which is associated with the general regression neural network. The proposed technique trains the network in a way that the variance differences between modes is kept small and in the same order. Further, the technique reduces the number of indispensable training parameters to two parameters and lowers the load of the computation as well as the time for... 

    How resiliency and hope can predict stress of covid-19 by mediating role of spiritual well-being based on machine learning

    , Article Journal of Religion and Health ; Volume 60, Issue 4 , 2021 , Pages 2306-2321 ; 00224197 (ISSN) Nooripour, R ; Hosseinian, S ; Hussain, A. J ; Annabestani, M ; Maadal, A ; Radwin, L. E ; Hassani Abharian, P ; Pirkashani, N. G ; Khoshkonesh, A ; Sharif University of Technology
    Springer  2021
    Abstract
    Nowadays, artificial intelligence (AI) and machine learning (ML) are playing a tremendous role in all aspects of human life and they have the remarkable potential to solve many problems that classic sciences are unable to solve appropriately. Neuroscience and especially psychiatry is one of the most important fields that can use the potential of AI and ML. This study aims to develop an ML-based model to detect the relationship between resiliency and hope with the stress of COVID-19 by mediating the role of spiritual well-being. An online survey is conducted to assess the psychological responses of Iranian people during the Covid-19 outbreak in the period between March 15 and May 20, 2020, in... 

    Learning and abstraction in a fuzzy artificial world

    , Article Proceedings of the Seventh IASTED International Conference on Artificial Intelligence and Soft Computing, Banff, 14 July 2003 through 16 July 2003 ; Volume 7 , 2003 , Pages 75-80 ; 0889863679 (ISBN) Zadeh, S. H ; Shouraki, S. B ; Halavati, R ; Sharif University of Technology
    2003
    Abstract
    Zamin is an artificial world suitable for cognitive studies on the origin of complex behaviors. Zamin's main organisms (called Aryos) live in a simple world and use their Fuzzy CBR-based brains to learn Zamin's living rules and react to their environment, in order to stay alive. Based on previous works on Zamin, we have made some changes on the organisms' behavior and internal structure, in order to increase their abstraction and learning capabilities. We tested the results and found that with respect to old type Aryos, new ones, can overcome to environmental difficulties more easily, and considerably improve their species' population  

    Using machine learning to predict mortality for covid-19 patients on day 0 in the ICU

    , Article Frontiers in Digital Health ; Volume 3 , 2022 ; 2673253X (ISSN) Jamshidi, E ; Asgary, A ; Tavakoli, N ; Zali, A ; Setareh, S ; Esmaily, H ; Jamaldini, S. H ; Daaee, A ; Babajani, A ; Sendani Kashi, M. A ; Jamshidi, M ; Jamal Rahi, S ; Mansouri, N ; Sharif University of Technology
    Frontiers Media S.A  2022
    Abstract
    Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies. Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission. Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest... 

    Advances in heuristic signal processing and applications

    , Book ; Chatterjee, Amitava ; Nobahari, Hadi ; Siarry, Patrick
    Springer  2013
    Abstract
    There have been significant developments in the design and application of algorithms for both one-dimensional signal processing and multidimensional signal processing, namely image and video processing, with the recent focus changing from a step-by-step procedure of designing the algorithm first and following up with in-depth analysis and performance improvement to instead applying heuristic-based methods to solve signal-processing problems. In this book the contributing authors demonstrate both general-purpose algorithms and those aimed at solving specialized application problems, with a special emphasis on heuristic iterative optimization methods employing modern evolutionary and swarm... 

    ADIAN: A distributed intelligent ad-hoc network

    , Article 8th International Conference on Distributed Computing and Networking, ICDCN 2006, Guwahati, 27 December 2006 through 30 December 2006 ; Volume 4308 LNCS , 2006 , Pages 27-39 ; 03029743 (ISSN); 9783540681397 (ISBN) Shahbazi, S ; Ghassem Sani, G ; Rabiee, H ; Ghanbari, M ; Dehghan, M ; Sharif University of Technology
    2006
    Abstract
    Mobile Ad-hoc Networks are networks that have a dynamic topology without any fixed infrastructure. To transmit information in ad-hoc networks, we need robust protocols that can cope with constant changes in the network topology. The known routing protocols for mobile ad-hoc networks can be classified in two major categories: proactive routing protocols and reactive routing protocols. Proactive routing protocols keep the routes up-to-date to reduce delay in real-time applications but they have high control overhead. The control overhead in reactive routing protocols is much less than proactive routing protocols; however, the routes are discovered on demand, which is not suitable for real-time... 

    Improving the Efficiency of Sat-Based Planning by Enhancing the Representations

    , M.Sc. Thesis Sharif University of Technology Jamali, Sima (Author) ; Ghasem Sani, Gholamreza (Supervisor)
    Abstract
    Automated planning is a branch of artiticial intelligence that studies intelligent agents’ decision making process. The objective is to design agents that are able to decide on their own, about how to perform tasks that are assigned to them. In the past 20 years, a popular and appealing method for solving planning problems has been to use satisfiability (SAT) techniques. In this method, the planning ptoblem with a preset length would be encoded into a satisfiability problem, which is then solved by a general satisfiability solver. The solution to the planning problem is then extracted from the solution of the SAT problem. The length of the problem is proportional to the number of steps in... 

    Examination and Critique of Susan George’s Ideas Concerning the Relation of Religion and Technology

    , M.Sc. Thesis Sharif University of Technology Hajikazem, Hojjatollah (Author) ; Taghavi, Mostafa (Supervisor)
    Abstract
    This article investigate about Susan George’s ideas concerning the relation of religion and technology and examine her point of view. George, searches synergy between religion and technology and tries to develop basis of artificial intelligence by religion. She provide answers to some philosophical concerns about technology through religion and tries to prove that technological morality needs religion. We show that some of her Assumptions is not through her conclusions. George, notices to the help of technology to religion for providing a new expression. We examine her clams about relation between technology and Islam