Sharif Digital Repository

Research in the field of financial market prediction has always been an intriguing subject for academic researchers and stock traders, despite its associated complexities and challenges. Accurately forecasting stock prices and market indices is considered a complex task due to their nonlinear and dynamic nature, requiring analysis of intricate time series data. Over time, various models such as regression models, classification methods, statistical techniques, and artificial intelligence algorithms have been used to predict these variables. With the advancement of technology and the development of AI-based models, particularly machine learning models, along with the availability of vast... 

What people write about their experience on web pages or social media about a product they have used or a service they have received can influence the reputation and the popularity of a certain brand with a great deal. If the reviews that exist about a product or a service of a certain company are mainly positive, it can increase the profit and improve the image of the company. On the other hand, mostly negative reviews can decrease the profit and destroy a company's image irreversibly. Unfortunately, because of this great influence that online reviews have over general public's decision to use a a product or a service of a brand, some companies hire people to write undeserving positive... 

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... 

In this research, it has been tried to use deep learning methods and embedding vector, in addition to user-item data, from user side information such as age, gender, city, etc., and also for item information such as product name, product category, etc. can be used to better understand customer behavior patterns and provide a relatively rich recommender system. The proposed model in this research has two phases, the first phase tries to identify the user and item feature vector and form the user similarity matrix and the user-item correlation matrix. The outputs of phase one are used as inputs of phase two. In the second phase of the model, using these inputs, Top-N recommendation are... 

Emergence of crypto-currency and blockchain technology revolutionize supply chain processes. In addition, customer needs for more information on products or services from origin to destination, highlights the necessity of transparency, originality and traceability in supply chains. This research is aimed to develop a blockchain based system ascertaining supply chain transparency and originality. To this aim, a joint pricing and closed-loop supply chain network design problem is selected as a good platform to implement it. Due to increasing concerns on environmental issues and maximizing job opportunities, sustainability is also considered in the proposed problem. To ascertain transparency... 

Bankruptcy is an important issue in the economy that can have extensive financial and social consequences on individuals and society. Timely warning to managers and providing analysis may prevent bankruptcy. Many studies have been conducted on the application and implementation of machine learning techniques to predict bankruptcy. Many bankruptcy prediction models produce incomprehensible outputs for the user. Therefore, they are called black box algorithms. Implementation of advanced models inevitably requires interpretability for users to understand the result and trust. Since most machine learning methods are "black box", explainable AI, which aims to provide explanations to users, has... 

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... 

There are many cases in industrial and non-industrial sections where the quality characteristics are in the form of profiles. A profile is the functional relationship between a response variable and one or more predictor variables used to describe the quality of a process. Profile monitoring is the implementation of statistical process control techniques for this purpose. According to the type of relationship between response variable and predictor variables, profiles are classified into many categories such as: simple linear profiles, multiple linear profiles, nonlinear profiles and generalized linear profiles. Most of the research efforts in the area of profile monitoring have been... 

One of the most trivial factors for every service company is to fulfill customer demands and to satisfy them. For this purpose, it is needed to explore the business fundamentals. Transportation companies, however seem to be more important, because in one hand, the demand for their services is too many. In the other hand due to the intense competition between rivals, every business tries to stand in the crowd; thus, it is of high importance to correctly predict the travel time as well as fare. The importance of time is not only because of having a fare pricing system, but also it is important for scheduling, assigning drivers, and covering city or a particular district. Travel time... 

Integrated modeling of learning makes happen simultaneous investigation and analysis of the interaction among different scales, abstraction levels and contexts within an organization. Enquiry of the organization, organizational unit and individual employee scales at levels of low and high abstraction within temporal, spatial and event-driven contexts may have conspicuous impact on perspicuousness of learning concept, as well as the creation of productive policy making opportunities which improves system performance measurements and their influencing causes. By development in multi-method approach to system modeling, integrated representation of learning has got recently feasible. Thus, a... 

There are many exact and comprehensive project management standards that have been presented till now. On the basis of those standards numerous corporations and organizations have collected various methodologies in order to ensure the accuracy of their project management processes. Apart from utilization of a predefined methodology or collecting of a customized one in the organization, there is an important question: in each project what is the suitable project management system (PMS). In this thesis a tool with the help of simulation technique is developed to support the improvement of the organization’s PMS. The processes of a methodology which is on the basis of PMBOK standard are... 

This paper presents some approaches based on data mining techniques to solve the prediction task of Computational Intelligence and Learning (CoIL) Challenge 2000. The prediction task of the contest is a direct mailing problem and the goal is to improve its response rate. The main issue in this competition is the incompatibility of the dataset in which the distribution of the classes of the target attribute is highly unbalanced. This in turn causes high error rate in identifying the minority class samples. Three different level methods including data-level, algorithm-level, and hybrid method are used to overcome this issue. The specificity and sensitivity criteria are employed to compare 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... 

Manufacturing environments has changed in recent decades. Today’s it has specification such as shorten life cycle of products, decrease new product design and manufacturing cycle, increase flexibility, decrease response time for market demands and increase competitive level among manufacturers. For achieve this specification integration of CAD, CAPP and CAM is essential. Process planning is important in manufacturing systems so that down stream activity costs and optimality depends on process plans. To develop a process plan for a prismatic part it is required to interpret part design data; select manufacturing processes; select machines, tools and fixtures; decompose the material volume to... 

Intensity-modulated radiation therapy is a well-known technique to treat cancer patients worldwide. A treatment plan in this technique requires decision-making for three main problems: selection of beam angles, intensity map calculation, and leaf sequencing. Previous works have investigated these problems sequentially. In this research, we present a new integrated framework for simultaneous decision-making of directions, intensities, and apertures shape, called direct angle and aperture optimization, and develop a mixed-integer nonlinear mathematical model for the problem. At first, the problem's single-objective model is established using the quadratic dose penalty function. After that,...