Sharif Digital Repository

With the rapid development of the economy, more people have started investing in the stock market. Predicting price changes can reduce the risk of investing in stocks. Technical data such as price and volume in the stock market is usually used to predict stock prices, and less often other types of data such as market data or fundamental data are used. In this study, we want to determine what impact each of the available data types has on stock prices. For example, data of buy and sell for per capita, capital inflows and outflows for small and large natural and legal investors, information related to the stocks themselves, indicators, fundamental data such as earnings per share (EPS) and... 

In this work, we aim to find a way to identify and forecast transients in nuclear power plants with the aid of semi-supervised machine learning algorithm. Forecasting and identifying transients in nuclear power plants at the early stages of formation are essential for safety considerations and precautionary measures. The use of machine learning algorithms provides an intelligent control mechanism that, along with the main operator of the power plant, raises the transient detection and identification rate. Our algorithm of choice is to change the way data is presented, which is a semi-supervised learning approach. The algorithm consists of two methods: quantum dynamics clustering... 

The ultimate goal of this study is to evaluate the condition of concrete structures using computer vision methods and using powerful tools such as machine learning methods based on visual information. This assessment is performed by detecting damage in these structures. With the aging of structures such as dams, bridges and tall buildings, structural health monitoring is an important task in ensuring their safety and stability. Therefore, rapid assessment of the health of structures and diagnosis of damage after destructive events is of great value in terms of providing resilience of structures. Visual inspection of structures by experts is one of the basic methods of evaluating structures.... 

Increasing the popularity of smartphones and the great ovation of users of mobile apps has turned the app stores to massive software repositories. Therefore, using these repositories can be useful for improving the quality of the program. Since the bridge between users and developers of mobile apps is the comments that users write in the app store, special attention to these comments from developers can make a dramatic improvement in final quality of mobile apps. Hence, in recent years, numerous studies have been conducted around the topic of opinion mining, whose intention was to extract and exert important information from user’s reviews. One of the shortcomings of these studies is the... 

In this thesis, we review some methods related to model-free discrete reinforcement learning and their corresponding algorithms. Our main goal is to present existing methods in an integrated and formal setup, without compromising their mathematical accuracy or comprehensibility. We have done our best to fix the inconsistencies existing in notations and definitions appearing in different areas of the vast literature. We discuss dynamic programming methods, including policy iteration and value iteration and temporal difference methods as well as policy-based methods such as policy gradient, advantage actor-critic, TRPO, and PPO. Among value-based methods, we discuss Q-learning and C51 where we... 

Entanglement is one of the key resources of quantum information science which makes identification of entangled states essential to a wide range of quantum technologies and phenomena.This problem is however both computationally and experimentally challenging.Here we use autoencoder neural networks to find semi-optimal measurements for detection of entangled states. We show that it is possible to find high-performance entanglement detectors with as few as three measurements. Also, with the complete information of the state, we develop a neural network that can identify all two-qubits entangled states almost perfectly.This result paves the way for automatic development of efficient... 

Gamma ray spectroscopy has a special place in the industrial applications of nuclear radiation. Currently, the most common device for gamma ray detection and spectroscopy is the sodium iodide scintillation detector. Long life and high efficiency and reasonable price of these detectors are the reasons for the development of the use of these detectors in industries and laboratories. But these detectors in the classification of energy sensitive detectors are considered as detectors with low resolution. The presence of broad peaks in the gamma ray spectrum of this detector increases the possibility of interference of peaks related to different energies and makes it difficult to identify the... 

For several years, self-organizing networks have been introduced by 3GPP to reduce the costs of mobile operators. Self-organizing networks, by introducing a kind of intelligence to the mobile network, will cause optimal performance and, as a result, reduce the operating costs of operators. Improving network coverage and increasing user data rates are always among the priorities of operators to increase profits and customer satisfaction. Coverage and capacity optimization is one of the most important functions introduced in self-organized networks. In this study, in addition to the coverage and capacity optimization function, the load balance optimization and the contradictory interaction of... 

Cryptocurrencies are not only regarded as a trustworthy method of financial transaction validated by a decentralized cryptographic system as opposed to a centralized authority, but also as one of the most popular and lucrative forms of trade and investing. Predicting the price of a cryptocurrency is a challenging topic in time-series research. Its intricacy is due to the volatility and large swings of cryptocurrencies' price. The emergence of brand-new cryptocurrencies, which might present a profitable trading opportunity but lack sufficient historical data for technical analysis, prompted us to develop a trading strategy that could be applied universally. The forecast of the next timestep's... 

Ability to differentiate induced pluripotent stem cells to cardiomycocytes has attracted attentions,considering crucial role of the heart in the human body and great potential applications of these cells like disease modeling, new treatment methods and basic research. We are able to analyze the performance of beating cells through recording extracellular field potentials of cardiomyocytes using multi-electrode array (MEA) technology. This analysis is an essential step to use cardiac cells in any future development and experiment. Currently, the electrophysiology experts analyze recorded extracellular field potentials of induced cardiomyocytes by observing all the episodes of each record.... 

In recent years, studying of biological tissues by mass spectrometry imaging (MSI) has been considered due to its selectivity in identifying different compounds in biological tissues, no need for sample preparation, and the possibility of creating the distribution map of these compounds. The complexity of biological tissues due to their heterogeneity, the large volume of data generated, and the effects of competition of other species for ionization in MSI experiments have doubled the importance of using chemometrics to interpret these data. The aim of this work is to quantitatively study Chlordcone as a carcinogenic pesticide and to extract its spatial distribution pattern in mouse liver... 

Ever increasing demand for higher data rate in wireless communication in the face of limited or underutilized spectral resources has motivated the introduction of cognitive radio for dynamic access to spectrum. In dynamic spectrum access a new type of users called secondary users measure the spectrum to see if it is occupied by licensed users (primary users or PU). When channel is empty secondary users can use it to transmit signal. This approach is called spectrum sensing. Hidden PU problem can severely defect detection ability of non-cooperativ spectrum sensing systems. Cooperative spectrum sensing (CSS) uses spatial diversity of spectrum sensors to tackle this problem. There are two kinds... 

In this project an optimized and robust orientation estimation method using IMU and magnetic sensors is presented. Magnetic distortion effects in orientation estimation is also one of the main purposes. Proposed sensor fusion algorithm is based on a complementary filter which provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. To develop the basic sensor fusion algorithm some procedures including a simple calculation to deal better with non-gravitational accelerations, decrease the effect of magnetometer in the presence of distortions and online gyroscope bias estimation is added. Also, a method for classification the different types of... 

Among the equipment used in supermarkets, refrigeration systems should be the focus of energy efficiency initiatives; Because they are the biggest consumers of energy and refrigerant with significant maintenance costs. Fault detection and diagnosis (FDD) can provide considerable potential for energy savings as well as reduced maintenance costs. Although there have been numerous investigations of FDD for HVAC systems, there has been very little research on the application of FDD for supermarket refrigeration systems. Therefore, this thesis focuses on the application of FDD to these systems and helps to fill these research gaps. The studied system is a commercial refrigerator system on a... 

The main responsibility of a primary cement job is to provide hydraulic isolation between various zones. Cement-sheath evaluation is concerned with the question of whether zonal isolation exists. Cement Evaluation Logs are used to evaluate the quality of cement sheath behind the casing or liner after cement job. these logs are a part of well integrity logs. Cement Evaluation Log results are complex and can be misunderstood, and it can lead to bad decisions that could have severe consequences. Cement evaluation logs must therefore be interpreted by experienced professionals. To help these interpreters, we propose a system for automatically interpreting cement evaluation logs, which they can... 

Hypertension precvalence is 24 and 20.5 percent in men and women, respectively. Continuous Blood Pressure monitoring can provide invaluable information about individuals’ health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This work presents an efficient algorithm, based on the Pulse Arrival Time (PAT), extracted from Electrocardiogram (ECG) and Photopletysmograph (PPG), for the continuous and cuff-less estimation of the Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial Pressure (MAP) values. Methods: The proposed framework estimates the BP values through processing vital... 

Persistent homology is one of the main tools in Topological Data Analysis. Indeed, to deal with a huge dataset while noise sensitivity is important, persistent homology can reflect some information about data in the form of persistent homology groups and persistence diagrams. Note that statistical or linear algebraic tools are not suitable to work with huge datasets with very high dimensions. In this thesis, we discuss the concept of persistent homology and investigate some of its properties such as the stability of the persistence diagrams. Indeed, persistence diagrams are obtained from the generating sets of the persistent homology groups. Further, we discuss an application of persistent... 

In this paper, a machine learning-based atomistic-continuum multi-scale scheme is introduced to model the materials' geometrically and materially nonlinear behavior. The kinematic and energetic consistency principles are employed to link the atomistic and continuum scales. In order to establish the kinematic consistency principle, the periodic boundary condition is implemented for the atomistic RVE. The Ni-based superalloy, including 0 to 3% porosity, is considered for the models. Several parameter analysis is done to distinguish the proper atomistic RVE to be used in multi-scale models. The data set, including the stress-strain samples, is generated through molecular dynamics analysis... 

Continuous Blood Pressure monitoring can provide invaluable information about individuals’ health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This work presents an efficient algorithm, based on the Pulse Arrival Time (PAT), for the continuous and cuff-less estimation of the Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial Pressure (MAP) values. The proposed framework estimates the BP values through processing vital signals and extracting two types of features, which are based on either physiological parameters or whole-based representation of vital signals. Finally, the... 

With the development of online two-sided platforms and increasing competition between these companies, issues such as customer targeting or recommendation systems have become more important to organizations. So far, various tools have been used for this purpose, but one of the most effective methods is the data analytics based on the stored data, through which personalized promotions can be automatically sent to the customers by implementing optimization models and algorithms. In this research, we present a model that re-adjust the commissions received from drivers based on detecting hidden patterns in their behavior in order to maximize the company's profit and then offer a suitable...