Sharif Digital Repository

In recent years, Intelligent Transportation Systems (ITS) have received special attentions both in research and in commercial areas. Increased infrastructure facilities, like surveillance cameras, has made this concept even more attainable than before. In this respect, the ability to automatically extract information from traffic images, as one of the key inputs of ITSs, is of great importance. With an increased number of surveillance cameras and the need for more accurate information regarding the road users and their interactions, in order to better city traffic management, building and repairing roads, trip time estimation, number of people per roads estimation and etc, using human... 

The application of deep learning algorithms in the processing of telecommunication signals is increasing. One of the issues in this area is the automatic recognition of radio signal modulation. Recognizing the type of signal modulation as the first stage of baseband signal processing on the receiver side is a key issue in military and civilian applications. The problem with classical algorithms for solving this problem is their strong dependence on channel characteristics and factors resulting from the transmitter and receiver being non-ideal. However, modern algorithms based on deep neural networks have been able to make the model somewhat resistant to these factors. However, the detection... 

Proper documentation plays an important role in successful software management and maintenance. Software repositories such as GitHub host an enormous number of software entities with various features. Developers collaboratively implement, use, and share these repositories in the community. Software repositories use issue tracking systems to keep track of issue reports, both to manage workload and document the highlight of teams’ effort. An issue report can contain a request for new features, a reported problem, or simply a question by users of a software product. As the number of these issues increases, it becomes harder to manage them. Github provides labels for tagging issues, however,... 

Web services are some kind of software applications which are available on the Web. Growing the popularity of Web services, led to increasing number of providers and as a result a great deal of Web services. This huge number of services made the searching and discovery tasks hard and effort-full jobs. In order to have a better discovery it is better to first classify Web services into some categories and then search in the relevant class. Although this classification can be done based on matching key words through the service registration information, such syntax-level service facilities can’t achieve the satisfaction results both in the precision and the recall sides. Human experience shows... 

In recent years, the spread of neurological disorders worldwide has been increasing, especially in developing countries. Due to the unknown function, complexity, and high importance of the brain, such disorders have been pervasive, severe, prolonged, and impose enormous costs on the individual, the family, and the community. Thus, increasing the knowledge about the brain and its areas in various activities is too vital and can facilitate the diagnosis and treatment of many different and unknown neuro- logical disorders. Different kinds of research have been done to automatically process and find the active and vital areas in various states and brain activities. The problem with most of these... 

BCI is an interface between brain and machine, particularly computer which translates brain signals into understandable instructions for machine. BCI records signals and determines what the subject is doing or thinking. BCI in the point of view of pattern recognition is a classification problem. For this aim, different tasks are referred to different classes. The more number of classes, the higher complexity we encounter in classification so surveying of different kinds of features, feature selection and reduction methods have highly importance. In this project we want to design a 4-class classification that each class is referred to a direction of wrist movement. During the time that the... 

The importance of using a fast and automatic method to classify variable stars for large amounts of data is undeniable. There have been many attempts for classifying variable stars by traditional algorithms, which require long pre-processing time. In recent years, neural networks as classifiers have come to notice. This thesis proposes the Hierarchical Classification technique, which contains several models with the same network structure. Our pre-processing method produces input data by using light curves and the period. We use OGLE-IV variable stars database to train and test the performance of Convolutional Neural Networks based on the Hierarchical Classification technique. We see that... 

In this research, an optical processing method for DNA classification is presented in order to overcome the problems in the previous methods. With improving in the operational capacity of the sequencing process, which has increased the number of genomes, comparing sequences with a complete database of genomes is a serious challenge to computational methods. Most current classification programs suffer from either slow classification speeds, large memory requirements, or both. To achieve high speed and accuracy at the same time, we suggest using optical processing methods. The performance of electronic processing-based computing, especially in the case of large data processing, is usually... 

The rapid increase in the size of digital multimedia data collections has resulted in wide availability of multimedia contents to the general users. Effective and efficient management of these collections is an important task that has become a focus in the research of multimedia signal processing and pattern recognition. In this thesis, we address the problem of automatic classification of music, as one of the main multimedia signals. In this context, music genres are crucial descriptors that are widely used to organize the large music collections. The two main components of automatic music genre classification systems are feature extraction and classification. While features are a compact... 

In this thesis, we have discussed image classification by sparse representation. Sparse representation is used in two different ways for image classification. The first goal of sparse representation is to make an efficient classifier, that can learn the subspace, in which the data lies. In this field we have surveyed various methods. We also proposed a method, called ”Locality Preserving Dictionary Learning” that works approximately better than state of the art similar methods, specially when training data is limited. We have reported the result of lassification on four datasets including MNIST, USPS, COIL2 and ISOLET. Another use of sparse representation, is to extract local features from... 

In the past few years, the available knowledge in graph-based processing has made significant progress, and as a result, powerful tools have been created. In this regard, graph learning with the assumption of data smoothness on the final result can be considered a successful example. Briefly, in graph learning, to describe the relationship between the problem components, a graph is learned using the available data whose nodes represent the problem components, and its edges represent how much these components are connected. The usefulness of this method lies in the possibility of using the obtained graph as the input to currently known methods of classification and achieving better results... 

Cerebral palsy (CP) is one of the most common motor disability among children that caused by damage to brain or defective development of brain before, during or just after birth. Due to this damage, children with CP have cognitive and motor problems. As an important activity of daily living, walking enhances independence, social activities and quality of life. Much of therapy for children is aimed to improve their gait. A usual technique to evaluate how a person walk and detect abnormal features in walking pattern is gait analysis. Lots of studies have been done on gait kinematics of CPs and classified them based on their kinematis. The purpose of this thesis is studying muscles activity of... 

Emotion recognition from speech signals has become one of the most popular researches in recent years. In order to increase human-machine interaction, a proper connection must be established between them. To achieve this goal, a machine must be able to understand the situation and respond accordingly. Part of this process involves understanding the user's emotional state. In recent years, various methods have been proposed to increase the efficiency of the speech emotion recognition system. These methods include collecting various audio databases, extracting efficient features from speech signals, using feature selection algorithms, designing different classifiers, as well as combining... 

Importance role of railway in transporting freight attract researchers and operators of railroad to consider using the new technology and methods to making this mode of transportation more efficient. Most railways network use one prevalent method to move freight wagons from origin to destination. In this method, freight wagons on its route from their origin to their destination passing through different type of stations. Some of these stations are origin or destination. Some of station use to just refueling, passing or crossing freight trains. Third types of stations are classification stations. In these stations its passible freight wagons are reclassified. In other word freight wagons... 

Graphs are widely used for representing structured data and analysis of them is an important area that appears in a broad domain of applications. Graph processing is of great importance in analyzing and predicting social media users' behavior, examining financial markets, detecting malware programs, and designing recombinant drugs. For example, consider a graph in which nodes and edges show the financial institutions and the financial connection between these institutions, respectively. Financial connection refers to the investment of one institute by another. Based on the graph structure, predicting trade stability and balance is extremely significant in macro decisions.In the last few... 

In this thesis we propose a two-layer hybrid fuzzy genetic algorithm for designing anomaly based an Intrusion Detection System. Our proposed algorithm is based on two basic Genetic Based Machine Learning Styles (i.e. Pittsburgh and Michigan). The Algorithm supports multiple attack classifications; it means that the algorithm is able to detect five classes of network patterns consisting of Denial of Service, Remote to Local, User to Root, Probing and Normal class.
Our proposed algorithm has two approaches. In the first approach we choose Pittsburgh style as the base of the algorithm that provides a global search. Then combine it with Michigan style to support local search. In this... 

The tree structure of neuron morphologies has excited neuroscientists since their discovery in the 19-th century. Many theories assign computational meaning to morphologies, but it is still hard to generate realistic looking morphologies. There are a few growth models for generating neuron morphologies that correctly reproduce some features (e.g. branching angles) of morphologies, but they tend to fall short on other features. Here we present an approach that builds a generative model by extracting a set of human-chosen features from a database of neurons by using the naïve Bayes approach. Then by starting from a neuron with a soma we use statistical sampling techniques to generate... 

In machine learning, classification is of great importance. Unlike the traditional single-label classification in which one instance can have only one label, in multi-label classification tasks, an instance can be associated with a set of labels. Multi-label classifiers have to address many problems including: considering correlations between labels, handling large-scale datasets with many instances and a large set of labels, and having only a fraction of valid label assignments in the training set. To tackle datasets with a large set of labels, recently embedding-based methods have been proposed which seek to represent the label assignments in an intermediate space. Subsequently, given the... 

Classification of Traffic Internet obtained early interest in the computer science community. Various methods have been presented for classifying the traffic of Internet to manage both security and Quality of Service (QoS). Nonetheless, traditional methods of classification including scheme of Transmission Control Protocol/Internet Protocol (TCP/IP) have not been accepted because of their complicated management. Classification method of network through learning algorithms of machine is the most popular classification method of traffic at this time. ELM was proposed as a modern algorithm of learning for the Single-hidden Layer Feed Forward Neural Networks (SLFNs). Meanwhile, learning process... 

The emerging field of “metabolomics” focuses on investigating into the changes of low-molecular-weight – less than 1500 Daltons – molecules, or metabolites, and it has significantly developed in the field of detecting diseases, particularly cancer in recent years. Regarding the importance of breast cancer (BC), especially among women, developing simple, trusted metabolic approaches are crucial. In the present work, utilizing multivariate class-modelling techniques combined to nuclear magnetic resonance (NMR) in order to predict breast cancer based on analyzing the blood serum of healthy and BC patients is presented. To do so, using 42 blood samples, 18 BC patients and 24 healthy individuals,...