Sharif Digital Repository

There is a significant amount of data in the healthcare domain and it is unfeasible to process such volume of data manually in order to diagnose the diseases and develop a treatment method in the short term. Diabetes mellitus has attracted the attention of data miners for a couple of reasons among which significant effects on the health and well-being of the contracted people and the economic burdens on the health care system are of prime importance. Researchers are trying to find a statistical correlation between the causes of this disease and factors like patient's lifestyle, hereditary information, etc. The purpose of data mining is to discover rules that facilitate the early diagnosis... 

The endeavors towards achieving a sustainable management of supply chains have brought about new cardinal logistic aims in addition to the common cost minimization objective. Traditional and sometimes disoriented constraints and assumptions such as demand certainty, infinite shelf life of products and so forth have resulted in a noticeable number of improper decisions made by supply chains’ members. Furthermore, intensified environmental concerns, consumers’ awareness of health problems, growth of demand for high quality products, scarcity of natural resources, etc. have brought many challenges and complexities to inventory management and routing problems. Due to supply chain managers’ and... 

Nowadays, data is the most important asset for health organizations in which the process of collecting, storing and analyzing of data leads to success of health organizations. Many companies have turned to data mining for the beneficial use of these data. The main purpose of data mining is to obtain useful knowledge from existing data. One of the diseases that is very significant for data miners is cardiovascular disease. Cardiovascular disease is the most important cause of death in the world. Therefore, it is necessary to improve the diagnostic and predictive measures of these patients. In this study, a database containing of characteristics of patients with chest pain who referred to... 

Poor decision making in health care has always had irreparable consequences for society. Also, expensive medical tests cause lots of problems for patients. A huge amount of data is produced daily by hospitals, which unfortunately are not used to improve decision making and predicting disease. Data mining can be an appropriate tool for extracting knowledge from a huge amount of data by using a variety of techniques such as prediction. The leading cause of death in the world is heart disease so this study has been designed to predict the incidence of that. Regarding the literature review, the Naïve Bayes method had predicted heart disease accurately. According to the specialist's opinion, some... 

Although a considerable number of problems whose analysis depends on a set of complex mathematical relations exist in the literature due to recent developments in the field of decision making, still very simplified and unrealistic assumptions are involved in many. Simulation is one of the most powerful tools to deal with this kind of problems and enjoys being free of any restricting assumptions which may generally be considered in a stochastic system. In addition, simulation optimization techniques are categorized into two broad classes of model-based and metamodel-based methods. In the first class, simulation and optimization component interact with each other causing an increase in... 

Today with the development of technology in medical science, there is a need to develop new methods to analyze and process the medical images. Furthermore, increasing use of machines and computers to accomplish prediction goals delineates that these tools had promising results. Because of all the above, this research focuses on processing and analyzing medical images with using data mining tools in order to identify MS and tumor disease which have been ubiquitous in last decades, fast and meticulous. To do so, we introduce a new clustering algorithm based on the modularity measure of graph networks as well as a new machine learning algorithm based on Kalman filter for Tensor-based data.... 

Huge availability of data in last decade has raised the opportunity to use data for decision making. The idea of using existing data to achieve more coherent reality solution has led to a branch of optimization called data-driven optimization. Presence of uncertain variables makes it crucial to design robust optimization methods for this area. On the other hand, in many real-world problems, the closed-form of the objective function is not available and a meta-model based framework is necessary. Motivated by this, we are using a Gaussian process in a Bayesian optimization framework to design a method that is consistent with the data in predefined confidence level. The goodness of the... 

Blood is a very valuable material for the life of human beings and should be transfused to the patient as soon as possible in case of necessity. It is s perishable product and different blood products have different shelf lives. Therefore, it is important to manage properly blood product inventories in hospitals to avoid shortage and outdate as much as possible. In this research, a bi-objective mixed-integer programming model is developed which aims to simultaneously minimize the total cost of the supply chain network and the total environmental impacts of the activities of the supply chain network. Since the nature of the problem is uncertain, a robust possibilistic programming approach is... 

The research in this study aims to analyze data in healthcare, especially the diagnosis of several diseases caused by heart failure. Analyzing and analyzing this data can lead to the discovery of relationships and patterns that can play an important role in the decision-making process of relevant officials in any field. Today, medical data around the world is stored in large volumes for future research. Various infrastructures and software have been set up in many health centers and research centers affiliated with those organizations.In this research, the general process of work is such that the data related to the heart sounds, which are in the four broad categories of S1 to S4, are... 

Road accidents are unfortunate events that cause more thanl16000 deaths each year in Iran. Intercity accidents require a comprehensive plan to reduce casualties because the number of roads users are increasing and the accidents account for nearlyl65% of fatalities. In this study, we first tried to identify the status of Iran through a study of traffic accidents in the world, and then the research and activities carried out in Iran were analyzed to find new and effective solutions. Using the daily fatalities data froml2008 tol2014, and using the new methodology presented in this research based on the Discrete Fourier Transformation (DFT), the Box-Jenkins models and the Secant method, the... 

Unstructured data is an important resource in data mining which In spite of their large volume, they haven’t been analyzed so much. Natural language data are a typical kind of unstructured data which humans can easily understand them but normally it is not possible for machines to process these kind of data. To make these data usable for prediction, pre-processing is required to prepare them for feeding into machine learning algorithms. Therefore, feature extraction is needed for texts in order to make presentative features from them that can unveil the hidden pattern. In this study, in addition to the variables that extracted from the technical indicators, the texts from telegram channels... 

Over the past few decades, with the exponential increase in data volume, scientists and researchers have tried to discover relationships and algorithms for productivity and find useful information from this amount of data in various fields. Their efforts in data analysis have led to the development of algorithms in the big data field. The result of researchers' working in multiple fields has come to aid the people of science and technology. Among the most important of these areas, we can mention the health and medical sectors, financial sectors, services, manufacturing sectors, etc. The purpose of this study is to enter the financial industry and use data mining tools. One of the newest and... 

This study proposes a deep reinforcement learning approach to solve a perishable inventory allocation problem in a two-echelon supply chain. The inventory allocation problem is studied considering the stochastic nature of demand and supply. The examined supply chain includes two retailers and one distribution center (DC) under a vendor-managed inventory (VMI) system. This research aims to minimize the wastages and shortages occurring at the retailer's sites in the examined supply chain. With regard to continuous action space in the considered inventory allocation problem, the Advantage Actor-Critic algorithm is implemented to solve the problem. Numerical experiments are implemented on... 

Identifying anomalous events is one of the vital topics in research as it often leads to the detection of actionable and critical information such as intrusions, faults, and system failures. With its importance, there has been a substantial body of work for network anomaly detection using supervised and unsupervised machine learning techniques with their own strengths and weaknesses. In this work, we take advantage of both worlds of unsupervised and supervised learning methods. The basic process model we present in this paper includes (i) clustering the training data set to create referential labels, (ii) building a supervised learning model with the automatically produced labels, and (iii)... 

Ⅾue to the absenⅽe of the ⅼabeⅼs or so−ⅽaⅼⅼeⅾ target variabⅼe، ⅽⅼustering vaⅼiⅾation، ⅾespite ⅽⅼassifiⅽation, is not that straightforward. So، the ⅽⅼuster evaⅼuation is a ⅽhaⅼⅼenging task both in researⅽh projeⅽts anⅾ appⅼiⅽations. Whiⅼe ⅿany ⅽⅼustering vaⅼiⅾity inⅾiⅽes are aⅾⅾresseⅾ in the ⅼiterature, ⅿost of theⅿ, even those wiⅾeⅼy useⅾ in the appⅼiⅽation, ⅽannot hanⅾⅼe arbitrary shapes. In this paper, a noveⅼ ⅽⅼustering vaⅼiⅾity inⅾex is proposeⅾ, whiⅽh is ⅿuⅽh ⅿore powerfuⅼ in ⅽapturing the ⅾata’s reaⅼ struⅽture anⅾ ⅾeaⅼing with arbitrary shapes. An aⅼⅿost noveⅼ separation ⅿeasure is proposeⅾ to represent the signifiⅽant or insignifiⅽant separation regarⅾing the ⅽⅼuster’s struⅽture... 

In the era of artificial intelligence, achieving high accuracy in machine learning models is crucial for their practical applications. This thesis presents a novel approach to improve the accuracy of machine learning models by learning to defer to a team of human experts. The primary goal of this work is to build upon and extend previous research, proposing a model that outperforms existing models in the literature. Inspired by the "Mixture of Experts" framework, we introduce a neural network-based allocation system responsible for assigning cases to each member of the team, which consists of a machine learning model and multiple human experts. The allocation system intelligently determines... 

One of the influential factors affecting the future price trends of a stock is the public sentiment surrounding that particular stock. In recent years, researchers have employed Natural Language Processing (NLP) techniques to analyze textual data present on social networks, aiming to investigate public opinions. However, there has been limited attention given to validating the users expressing opinions concerning the stock market. Much of the opinions shared on social networks lack a thorough examination and analysis of the market, often being solely based on the author's sentiments. This research endeavors to validate active users on the social network 'X' (Twitter) by developing a... 

This study investigates optimal strategies for price, warranty length and production rate of a new product to maximize profit of a producer during lifecycle of the product. We consider both durable products and non-durable ones. Customers buy non-durable products many times but if we consider the planning horizon relatively short, each customer buys one product during the period. So, the market for non-durable products is static and for durable ones is dynamic. The objective function includes both demand and cost functions, where production cost, warranty cost and inventory costs are involved. A solution approach using the maximum principle is described and some propositions are discussed... 

In this study, a brief definition of data mining and its variants were mentioned. Then the methods and algorithms for clustering and their application in the field of healthcare is studied. Concidering the available data for anemia disease, including numeric and categorical attributes, the k-medoids clustering algorithm was selected. This algorithm is one of the simple, powerful and most widely used methods for clustering. The drawbacks of this algorithm are as follow: requires a user input on the number of clusters, depends on the initial data and traps in the local optima. In this thesis, an improved method of clustering-based on Random Forest and k-medoids algorithms has been developed.... 

This effort attempts to study the multiple change-point problem in the area of non-linear profiles. Two methods for estimating the times of multiple change-points is proposed. In the first method, a model consisting of two networks, which is based on artificial neural networks, is proposed. These networks are distinctive only in their training data. One network is trained for ascending segment of the profile and the other is trained for descending segments of the profile. In the second method, Bayesian approach is proposed for estimating multiple change-point. While using Bayesian approach the parameters of the Non-linear model must be estimated. However, this issue is complicated or...