Sharif Digital Repository

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... 

The main goal in many fields in the empirical sciences is to discover causal relationships among a set of variables from observational data. PC algorithm is one of the promising solutions to learn underlying causal structure by performing a number of conditional independence tests. In this paper, we propose a novel GPU-based parallel algorithm, called cuPC, to execute an order-independent version of PC. The proposed solution has two variants, cuPC-E and cuPC-S, which parallelize PC in two different ways for multivariate normal distribution. Experimental results show the scalability of the proposed algorithms with respect to the number of variables, the number of samples, and different graph... 

In this paper, a new reinforcement learning approach is proposed which is based on a powerful concept named Active Learning Method (ALM) in modeling. ALM expresses any multi-input-single-output system as a fuzzy combination of some single-input-singleoutput systems. The proposed method is an actor-critic system similar to Generalized Approximate Reasoning based Intelligent Control (GARIC) structure to adapt the ALM by delayed reinforcement signals. Our system uses Temporal Difference (TD) learning to model the behavior of useful actions of a control system. The goodness of an action is modeled on Reward-Penalty-Plane. IDS planes will be updated according to this plane. It is shown that the... 

Taxonomy learning is one of the major steps in ontology learning process. Manual construction of taxonomies is a time-consuming and cumbersome task. Recently many researchers have focused on automatic taxonomy learning, but still quality of generated taxonomies is not satisfactory. In this paper we have proposed a new compound similarity measure. This measure is based on both knowledge poor and knowledge rich approaches to find word similarity. We also used Machine Learning Technique (Neural Network model) for combination of several similarity methods. We have compared our method with simple syntactic similarity measure. Our measure considerably improves the precision and recall of automatic... 

Due to the unavailability of accurate data and the limitations of the existing methods, reliable input–output tables (IOTs) may not be available for all the regions in a country. This study proposes a novel approach to estimate the regional input coefficients. It harnesses the capabilities of machine-learning (ML) algorithms to estimate the regional input coefficients of one region based on the IOTs of multiple other regions for which reliable data are available. The application of three ML algorithms is investigated using data from Japan. The results highlight the superior performance of the ML models compared with location quotient models. © 2021 Regional Studies Association  

The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated where it considers eventuality. So, it is necessary to consider the highly data-driven technologies and to use new methods of analysis, like machine learning and visualization tools, with the ability of interaction and connection to different data resources with varieties of data regarding the type of big... 

In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; however, few reports have been published on their properties and characteristics of analytical figures of merit. Currently, most performance measures for these types of networks only incorporate some function of prediction error. While useful, these measures are incomplete and cannot be used as an objective comparison among different models. In this report, a new method for calculating the sensitivity of any type of neural network is proposed and studied on both simulated and real... 

This paper develops a novel insight and procedure that includes a variety of algorithms for finding the best solution in a structured multi-agent system with internal communications and a global purpose. In other words, it finds the optimal communication structure among agents and the optimal policy in this structure. First, a unique reinforcement learning algorithm is proposed to find the optimal policy of each agent in a fixed structure with non-linear function approximators like artificial neural networks (ANN) and with eligibility traces. Secondly, a mechanism is presented to perform self-organization based on the information of the learned policy. Finally, an algorithm that can discover... 

Numerous disciplines are engaged in studies involving motor control. In this study, we have used a single link system with a pair of muscles that are excited with alpha and gamma signals to achieve an oscillatory movement with variable amplitude and frequency. The system is highly nonlinear in all its physical and physiological attributes. The major physiological characteristics of this system are simultaneous activation of a pair of nonlinear muscle-like-actuators for control purposes, existence of nonlinear spindle-like sensors and Golgi tendon organ-like sensor, actions of gravity and external loading. Transmission delays are included in the afferent and efferent neural paths to account... 

In this study, we have used a single link system with a pair of muscles that are excited with alpha and gamma signals to achieve an oscillatory movement with variable amplitude and frequency. This paper proposes a reinforcement learning method with an Actor-Critic architecture instead of middle and low level of central nervous system (CNS). The Actor in this structure is a two layer feedforward neural network and the Critic is a model of the cerebellum. The Critic is trained by State-Action-Reward-State-Action (SARSA) method. The system showed excellent tracking capability and after 280 epochs the RMS error for position and velocity profiles were 0.02, 0.04 radian and radian/sec,... 

Previous researches have shown the success of using reinforcement learning in solving combinatorial optimization problems. The main idea of these methods is to learn (near) optimal evaluation function to improve local searches and find (near) optimal solutions. Stage algorithm introduced by Boyan & Moore, is one of the most important algorithm in this area. In the other hand fuzzy methods have been used in all fields of science to solve problems but still never used in combinatorial optimization problems. In this paper we focus on Bin Packing Problem. We introduce two basic fuzzy algorithms (ALM and IDS) and then solve our problem with these fuzzy algorithms. We run ALM and IDS algorithms on... 

This paper is concerned with the design and simulation of an intelligent stop and go cruise control system in an automated vehicle. In this paper Active learning method is used to extract driver's behavior and to derive control rules for cruise control system. First, there is a brief introduction to ALM (Active Learning Method) and its specifications. Then a one-line space for driving is assumed and its parameters are extracted. By using IDS, the processing engine of ALM, effective parameters in controller are derived. A simulation program is written to produce learning samples and also to evaluate controller's parameters. To apply controller's output, appropriate acceleration of the... 

Pretrained representations have recently gained attention in various machine learning applications. Nonetheless, the high computational costs associated with training these models have motivated alternative approaches for representation learning. Herein we introduce TripletProt, a new approach for protein representation learning based on the Siamese neural networks. Representation learning of biological entities which capture essential features can alleviate many of the challenges associated with supervised learning in bioinformatics. The most important distinction of our proposed method is relying on the protein-protein interaction (PPI) network. The computational cost of the generated... 

We propose a novel hybrid learning approach to gain situation awareness in smart environments by introducing a new situation identifier that combines an expert system and a machine learning approach. Traditionally, expert systems and machine learning approaches have been widely used independently to detect ongoing situations as the main functionality in smart environments in various domains. Expert systems lack the functionality to adapt the system to each user and are expensive to design based on each setting. On the other hand, machine learning approaches fail in the challenge of cold start and making explainable decisions. Using both of these approaches enables the system to use user's... 

In the nascent and challenging problem of the blind separation of the sources (BSS) supported by graphs, i.e., graph signals, along with the statistical independence of the sources, additional dependency information can be interpreted from their graph structure. To the best of our knowledge, in these cases, only GraDe and GraphJADE methods have been proposed to exploit the graph dependencies and/or Graph Signal Processing (GSP) techniques to improve the separation quality. Despite the significant advantages of these graph-based methods, they assume that the underlying graphs are known, which is a serious drawback, especially in many real-world applications. To address this issue, in this... 

Purpose: Cardiac arrhythmia is one of the most common heart diseases that can have serious consequences. Thus, heartbeat arrhythmias classification is very important to help diagnose and treat. To develop the automatic classification of heartbeats, recent advances in signal processing can be employed. The Hidden Markov Model (HMM) is a powerful statistical tool with the ability to learn different dynamics of the real time-series such as cardiac signals. Materials and Methods: In this study, a hierarchy of HMMs named Layered HMM (LHMM) was presented to classify heartbeats from the two-channel electrocardiograms. For training in the first layer, the morphology of the heartbeats was used as... 

Due to the unavailability of accurate data and the limitations of the existing methods, reliable input–output tables (IOTs) may not be available for all the regions in a country. This study proposes a novel approach to estimate the regional input coefficients. It harnesses the capabilities of machine-learning (ML) algorithms to estimate the regional input coefficients of one region based on the IOTs of multiple other regions for which reliable data are available. The application of three ML algorithms is investigated using data from Japan. The results highlight the superior performance of the ML models compared with location quotient models. © 2021 Regional Studies Association