Sharif Digital Repository

Water demand forecasting and modeling is very important and needful in water resource planning and management as well as water consumption forecasting. The forecasting helps the managers to design and operate various infrastructures of water supply such as tanks and other distribution equipments. Nowadays, intelligent systems are very efficient and practical tools because of their high ability in forecasting and independency from limitative assumptions in classic methods. In this thesis, one of the newest methods, called support vector regression method, is used to forecast monthly demands of water consumption in Tehran, Iran. To develop the method, data is first preprocessed through... 

An ontology is a formal description of a domain by definition a set of concepts and the relationships between those concepts. Ontologies defined by different applications usually describe their domains in different terminologies,even when they cover the same domain. Thus, we need to resolve the semantic heterogeneities problem and align terms in different ontologies. Ontology alignment plays the central role in these areas to identify correspondences among elements of two or more ontologies. It should be noted that,finding an optimize mapping between two ontologies with respect to search space is an NP-hard [6] problem.
In This thesis we focused on large ontologies and alignment among... 

Due to the application of data streams in various data sources such as Web click streams, Web pages, and data generated by sensors and satellites, data streams have attracted a huge attention recently. A data stream is an ordered sequence of points that must be accessed in order and can be read only once or a small number of times. For mining such data, the ability to process in one pass along with limited memory usage is very important. Data stream clustering also has received a huge attention in recent years and numerous algorithms are developed in this field. None of them has paid attention to the feature selection problem as an effective factor in clustering quality especially when the... 

Setar is a traditional Persian musical instrument. Nowadays, Setar is one of the most common musical instruments in Persian music. A systematic method for Setar production modification has not been introduced yet. This work is the first scientific work about analysis of this instrument. The aim of this study is to undertake early steps for Setar quality improvement. In this thesis, after analysis of the wood using in Setar construction, numerical and experimental methods have been utilized to analyze this instrument. After that, some works has been performed to extract an objective function which can judge about Setar quality. The results show good agreement between experimental and... 

Data mining (DM) is one of the newest techniques which is used in decision making. DM helps the specialist to find the valuable knowledge which is hidden in data, with different techniques. DM has many research areas such as scientific research, medical fields, health care, fraud detection, marketing and customer relationship, sport, and games, and where ever there is data. Unfortunately, in our country, DM is not engaged seriously and there are fallacies of DM that weaken its efficiency. In this thesis, which is prepared in two sections, at the first section different techniques of DM are described and in the second section DM process is performed for a real world problem. For this purpose,... 

Wireless sensor networks (WSNs) are tiny devices with limited computation and power supply. For such devices, data transmission is a very energy-consuming operation. Data aggregation eliminates redundancy and minimizes the number of transmissions in order to save energy. This research explores the efficiency of data aggregation by focusing on different aspects of the problem such as energy efficiency, latency and accuracy. To achieve this goal, we first investigate data aggregation efficiency with constraint on delay, which can be compatible with other important system properties such as energy consumption and accuracy. By simulation, we will show that, depending on the application, we can... 

Cellular automata is a model for physical systems that has homogenous and simple components. Simple components, which are called cells, have local interactions creating complicated global emergent of behaviour. In the field of cellular automata, there are two basic problems: forward and inverse problems. Characterizations of cellular automata rule are studied in forward problem, but in inverse problem, there exists a description of cellular automata and we should find a rule or a set of rules that satisfy the given description. This problem belongs to the class of NP problems and hence heuristic algorithms such as evolutionary algorithms have been used for solving it. Since rules space and... 

Cellular learning automata (CLA) is a distributed computational model that is introduced recently. This model is combination of cellular automata (CA) and learning automata (LA) and is used in many applications such as image processing, channel assignment in cellular networks, VLSI placement, rumor diffusion and modeling of commerce networks, and obtained acceptable results in these applications. This model consists of computational units called cells and each cell has one or more learning automata. In each stage, each automaton chooses an action from its actions set and applies it to the environment. Each cell has some neighboring cells that constitute its local environment. The local rule... 

Microarray which is also known as Biochip is a flat substrate of glass with the size of 1 ×1 cm on which a numerous number of biosensors are placed in an array format. Microarray DNAs are used to measure expression level of thousands of genes. Repeating these experiments in different conditions can result in patterns of expression. After preparation, the florescent sample is hybridized with the sensors of microarray surface and fluoresce intensities of the spots are measured by a special camera called CCD. The obtained pictures are examined by a computer and the spot lights converted into numerical data by image processing algorithms. Putting these numbers into matrices of size m×n is... 

Wireless sensor networks consist of many small sensor nodes which suffer from some limitations like energy level, band width, processing ability and memory; therefore, decreasing energy consumption, increasing network lifetime and scalability are the main challenges in the area of routing in wireless sensor networks. In the field of routing in sensor networks, many algorithms have been suggested, one group of which is clusterbased hierarchical algorithms. The main goals of such algorithms are reducing energy consumption, distributing consumed energy within whole network and increasing scalability of algorithm. There are some problems in most of cluster-based routing algorithms which cause... 

Clustering has great applications in various fields such as marketing, health, insurance, bioinformatics and etc. The assumption of regular parametric clusters is a common problem in current popular methods. This assumption is not valid in most real applications and greatly increases the clustering errors even to an unacceptable rate. Inspired by sardine fish, in this thesis we propose a new model with high elasticity factor that can cluster data without cluster shape constraints. This method uses the sardine fish model to augment clustering space dimension in order to achieve greater separability. The proposed method had some problems that were fixed and the final algorithm was finalized.... 

Nowadays, clustering is one of the most common data mining tasks used for data categorization upon their similarities and analysis of these data groups in both industry and academia is of interest. Clustering does not need any supervision. The theme of this thesis is using some prior knowledge to improve clustering algorithms. The prior knowledge is in the form of some constraints determined by supervision to allowing preventing some combination data to be in one cluster or allocate some data in the same cluster. Here, prior knowledge in the form of constraints is used to modify a heuristic optimization algorithm (harmony search) and a novel "semi-supervised harmony clustering" is proposed.... 

Energy constraint is the most critical problem in wireless sensor networks (WSNs). To address this issue, clustering has been introduced as an efficient way for data aggregation and routing. However, there also exist some disadvantages associated with the clustering scheme, such as additional overheads during the cluster-head election and cluster formation process. Most available clustering algorithms do not efficiently take into consideration the geographical information of nodes in the cluster-head election. This leads to uneven distribution of cluster-heads and unbalanced cluster sizes, which brings about uneven energy dissipation in clusters. In this research, an Efficient Distributed... 

Graph partitioning, or graph clustering, is an essential researa problem in many areas. In this thesis, we focus on the partitioning problem of unweighted undirected graph, that is, graphs for which the weight of all edges is 1. We will investigate spectral methods for solving the graph partitioning and we compare them. In addition to theoretical analysis,We also implement some of spectral algorithms in matlab and apply them on standard graph data sets. Finally, the experimental
results obtained are offering  

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  

Recently, data stream has been popular in many contexts like click sequences in web pages, obtained data from sensor networks and satellite data. A data stream is an ordered list of points that should be used once. Clustering of these kinds of data is one of the most difficult issues in data mining. Due to the high amount of data, the traditional clustering algorithms are not suitable for this family of problems. Many data stream clustering algorithms have been proposed in recent years considered the scalability (largeness) of data, but most of them didn’t attend to the following issues. •The quality of clustering can be bad over the time. •Some of the algorithms cannot handle arbitrary... 

Wireless sensor network is composed of a large number of sensor nodes which are densely deployed to monitor the environment and collect information from physical events. There are different modelsfor sending collected data to users; one of them is query-driven models, in which according to user interest and via declarative queries collected data is delivered. Wireless sensor networks are energy constrained; regarding this issue, it is necessary to have an energy efficient design. Data aggregation or in network processing is one existing solution to reduce energy consumption. Data correlation or redundancy is also can be used with this method to save energy even more. In this thesis, an... 

The problem of partitioning vertices of a graph has been studied with different formulations according to their application. In this thesis we try to review some of these formulations and existing algorithms. In addition we try to investigate the correct definition for a specific application in image processing, namely image segmentation. At the end, we propose a new algorithm for partitioning vertices of a weighted graph according to the mentioned application and compare its performance with some similar algorithms  

This thesis is concerned with studying several aspects of the generalized isoperimetric problems on weighted graphs. In this regard, as a generalization of well-known Cheeger constant, we define k-th isoperimetric constant as the minimum of normalized outgoing flow over all k-subpartitions (disjoint subsets) of the vertex set. Also, we investigate basic properties of these parameters and particularly we prove a Federer-Fleming-type theorem. We then introduce a generalized Cheeger conjecture which is a generalization of celebrated Cheeger inequality and relates defined isoperimetric constants to higher eigenvalues of Laplace operator. In order to find a partial proof for this conjecture, we... 

In order to manage a complex system, one way is to divide it into smaller parts. That’s the reason airspace in each country should be divided into sectors. Currently, these sectors have fixed areas and each is controlled by a number of air traffic controllers. However, the traffic crossing these sectors is considerably dynamic, which means the traffic density and its distribution varies seasonally, weakly and even through the 24 hours of a day. Moreover, weather conditions and security and military issues make constraints to some of these sectors. This sometimes results in some high traffic sectors while some others have very low traffic density which causes two main problems. One is that,...