Sharif Digital Repository

The classification of different tumor types is of great importance in cancer diagnosis and drug discovery. However, most previous cancer classification studies are clinical based and have limited diagnostic ability. Cancer classification using gene expression data is known to contain the keys for addressing the fundamental problems relating to cancer diagnosis. The recent advent of DNA microarray technique has made simultaneous monitoring of thousands of gene expressions possible. With this abundance of gene expression data, researchers have started to explore the possibilities of cancer classification using gene expression data and quite a number of Pattern Recognition approaches have been... 

Decision making often has different effects and results with unequal importance. Most of classifiers try to minimize the rate of misclassified instances. These classifiers assume equal costs for different misclassification types. However, this assumption is not true in many real world problems and different misclassification types have different costs. These differences can be applied by introducing the cost in the process of learning. In this manner, total cost of misclassification will be the evaluation metric of classification. In order to apply this metric to the problems, new learning algorithms are needed. Cost-sensitive learning is the related area of machine learning which deals with... 

Measuring emotions of music is one of the methods to determine music content. Music emotion detection is applicable in music retrieval, recognition of music genre and also music data management softwares. Music emotion is considered in different sciences such as physiology, psychology, musicology and engineering. First, we collected a database of different types of music with various emotions. These data have been labeled according to their emotions. In this project, four emotions (Angry, happy, relax and sad) have been used as labels based on Thayer’s two dimension emotion model. There are two basic steps for music emotion recognition similar to other recognition systems: Feature extraction... 

Computer-Aided Diagnosis (CAD) systems are widely used for detection of various kinds of abnormalities in mammography images. In this work, mass classification is investigated and its steps are explained in detail, for each step a main method is presented and other methods are also discussed. For mass segmentation a relatively new method based on level set and Morphological Component Analysis (MCA) is used.After this step, various kinds of features such as shape, geometrical, and textural ones are introduced. Moreover, a set of proposed features based on wavelet transformation,for this application are presented. The proposed features can describe margin and texture characterizations of a... 

Traditional classification techniques of machine learning assume that data have stationary distributions. This assumption for recent challenges where tremendous amount of data are generated at unprecedented rates with evolving patterns, is not true anymore. Classification of data streams has become an important area of machine learning, as the number of applications facing these challenges increases. Examples of such data streams applications include text streams, surveillance video streams, credit card fraud detection, market basket analysis, information filtering, computer security, etc. An appropriate method for such problems should adapt to drifting concepts by revising and refining the... 

Concept drift is a challenging problem in the context of data stream processing. As a result of increasing applications of data streams, including network intrusion detection, weather forecasting, and detection of unconventional behavior in financial transactions; numerous studies have been conducted in the field of concept drift detection. In order to solve the problem of concept drift detection, an ideal method should be able to quickly and correctly identify a variety of changes, adapt quickly to new concepts, in the presence of limitations of memory and processing power. In this thesis, a new explicit concept drift detection method based on ensemble classifiers has been proposed for data... 

In this study hybrid classification models by combining genetic algorithm and classifiers such as neural network and decision tree are presented and efficiency of these models is tested on 5 different databases against other proposed models in this area. This comparison shows that the model obtained by combining genetic algorithm and neural network obtains better results than other models. Afterwards this model is used as a decision support system in diagnosis heart disease and in addition to determining the parameters of neural network such as the number of hidden layers and the number of neurons in each layer, efficient features in diagnosis heart disease are also determined. Among the... 

The main focus of this research is to resolve the semantic ambiguity in Persian. In this study, a semi-supervised machine learning method is proposed to choose the most proper meaning of a target word in the context. Several statistical methods are compared, and the most accurate one is chosen for developing a sense tagger. An initial seed data is built by searching collocation lists for each sense. After developing the sense tagger and initial seed set, a bootstrapping method is used to sense tag all occurences of a target word in corpus with 90% accuracy  

Modulation Recognition of communication signals has been an important theme in the field of wireless communication. Modulation Recognition has various applications for both military and civil purposes. Recently there has been considerable attention to Digital Modulation Recognition, due to the vast application of this kind of Modulation Recognition tasks. In this thesis, we proposed a Digital Modulation Recognition Algorithm, which is able to identify various types of digital modulations in low SNRs. These include BASK, BFSK, BPSK, 4-ASK, 4-FSK, 4-PSK, 8-FSK, 8-PSK, MQAM (M=16, 32, 64). The proposed method uses a general pattern recognition scheme, consisting of a feature extraction phase... 

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... 

Nowadays, the world is based on the interaction between individuals, groups and different systems. The actual networks that have a complex structure and behavior are called complex networks. Complex networks are one of the new knowledge that studies the connections. The complex systems represented as graph, with non-trivial topological features—features that do not occur in simple networks.With the vast development of computer networks, complex networks appear in different categories such as social networks, citation networks, collaboration networks and communication networks. Data mining is the process of exploring hidden knowledge in data bases and it has applications in complex networks.... 

Environment Identification and recognizing surrounding objects is an exigent need in future applications. For example one of the emerging technologies in car industry is driverless cars. In driverless cars, navigation system should be able to detect and recognize pedestrians, traffic signs, roads, surrounding cars and so on. Therefore, conventional single-object recognition systems are not capable of handling the needs of advanced machine vision based applications. In recent years, designing and analyzing multi-class object detection and recognition systems have become a big challenge in machine vision. In this thesis our goal is to identify and analyze the existing problems in designing... 

Cardio Vascular Diseases (CVD) is currently the most important cause of mortality in the world. Coronary artery disease (CAD-with 3 major epicardial vessels) is one of the significant CVD. Coronary angiography remains the gold standard for identifying the presence and absence of stenosis due to CAD. It is performed by inserting a catheter into the coronary artery, directly injecting radiopaque contrast material there and recording radiographic images in different view of arteries. Images of this invasive way show severity and area of stenosis.
A sign of CAD is endothelial dysfunction (inner artery layer) in peripheral arteries like the brachial artery. Due to a significant correlation... 

Event extraction is one of the important tasks in Natural Language Processing (NLP). Many NLP applications like question answering, information extraction and summarization need to have some knowledge about events of input documents. There are several definitions for events in NLP domains. In this dissertation, the event is viewed as an element in a network of temporal information. Therefore, the project has been based on the ISO-TimeML specification language, which is the standard scheme for temporal information processing in natural texts. Event extraction based on ISO-TimeML has been performed for a number of languages including English, French, Spanish, and Korean. However, for Persian... 

Semi-structured documents are a new type of textual documents which has gained a lot of attention nowadays to itself. A specific document modeling for boosting classifiers is needed more than ever which reflects major document specifications. The main goal of this thesis is presenting new adaptive model based on semi-structured documents features. We also aim to use some problem solving approaches such as Statistical approach, Machine Learning and few Algorithmic solutions. In some cases 10% precision optimization can be seen compare to the best approaches available nowadays  

Scene classification and understanding is one of the most important fields in computer vision. Its applications are such as exploring robot navigation enviroment, content-based image retrieval (CBIR), organization in image databases, highly semantic describing images and videos and content extraction of videos.Many methods and algorithm are proposed till today to deal with diversity of this field by emphesizing on feature based methods or machine learning based methods. In this research we have focoused on proposing a new algorithm which is using principals of NBNN image classification method but major changes in how to exract distance metric from Nearest neighbour and how to use local... 

In this thesis, the efficiency of bump modeling has been investigated on brain signals, in a variety of aspects including analysis, detection, classification and prediction. The aim of bump modeling is to provide an optimized representation of the signal in time-frequency domain. This would be done by discriminating oscillatory bursts from background signal and then showing them by half-ellipsoid functions called bump. Consequently, the problem of dealing with large numbers of parameters and hence complicated calculations, which are serious concerns in similar methods, can be overcome. This is in addition to the benefits of using time-frequency representation of the signal.The aim of bump... 

Deontic logic is used to formalize legal reasoning. To apply this logic in law, we describe tersely some efforts to improve this logic by relativizing its operations with respect to different people and groups of society. Until now, this logic was restricted to formalize “what must be”. We extend this logic to dyadic logic to formalize “what must be done”.
In practice, legal reasoning leads to non-monotonic logics, the most applicable one in law is defeasible logic. So it is necessary to combine deontic and defeasible logics to formalize legal reasoning in a more appropriate way. To do that, we must adjust possible worlds of these two logics. In this way, we find a method for... 

Data mining is the one of top ten developing knowledge in the world. This study followed three fold objectives; Firstly, An efficient model based on data mining algorithms is proposed to predict the duration of hospitalization time for patients of digestive system disease that need short term care. Duration of hospitalization is an important criterion to be used for predicting the hospital resources. In order to, a combined model based on CHAID and C.5 decision trees and a neural network is suggested. The suggested model predict the duration of hospitalization with 82% accuracy. The second object of this study is to propose an algorithm based on likelihood ratio. The suggested algorithm... 

Cardiovascular diseases are very common nowadays and are one of the main reasons of death. Being among the major types of these diseases, correct and in time diagnosis of Coronary Artery Disease (CAD) is very important. The best and most accurate CAD diagnosis method by now is recognized as Angiography, which has many side effects and is costly. Thus researchers are seeking for inexpensive, though still accurate, methods. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to increase accuracy. In this thesis, a data set is introduced which utilizes several new and effective features for CAD diagnosis, as well as a...