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By an increase in demands for multimedia services of mobile, wireless networks shall provide Quality of Service (QoS) requirements for users moving in cells. Multimedia services such as voice, data and image own different features and every QoS are different from the other due to delay and demand for bandwidth.
This thesis presents a new model of call admission control which consists of a few key elements for protecting the network in different conditions of workload in a manner that calls blocking /dropping are kept under desired level. This model consists of two traffic classes of voice and data in order to give apriority to handoff call over new call, the parameters in this model are... 

In single service wireless cellular networks, two types of call are defined; new call and handoff call. New call blocking probability and handoff call dropping probability are two major parameters of QoS. Some call admission control schemes are proposed for handling new and handoff calls in the cell for keeping these QoS parameters under suitable values. In this work, we will introduce some call admission control schemes and will show performance analysis, advantages and disadvantages of them (under different channel holding times and same channel holding times for new calls and handoff calls). Then we will focus on two schemes and based on their ideas, we will propose a new call admission... 

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... 

Worldwide interoperability for Microwave Access (WiMAX) is one of the most dominant cell-based broadband wireless metropolitan access technologies. Packet scheduling algorithm specifies the packet transmission order. In WiMAX standard, packet scheduling algorithm is not defined and its efficient design is left for developers and researchers. The existing researches in the scope of uplink packet scheduling, which is the most challenging packet scheduling scheme, consider only one cell. However the uplink available resources might not be enough when there are many packets that should be scheduled. To solve this problem, we propose an algorithm that uses the load balancing mechanisms that are... 

Nowadays, multi-agent systems as part of the distributed artificial intelligence play an important role in modeling and solving complex industrial and commercial problems. They have distinguishing characteristics such as distributiveness (spatial, temporal, semantic, or functional distribution), robustness, parallel processing, etc. One of the capabilities that can be added to this system is the learning capability. It can help the system to adapt itself to the new environment. This paper proposed a method for the problem of credit assignment in multi-agent domain. Solving the multi-agent credit assignment problem, one can expect individual learning for a single agent in systems of... 

Text classification is one if the widest and most important applications in data mining. Because of the huge number of features in these applications, a method for dimensionality reduction is needed before applying the classification algorithm. Various number of methods for dimensionality reduction and feature selection are proposed. Feature selection based on feature ranking has received much attention by researchers. The major reasons are their scalability, ease of use, and fast computation. Feature ranking methods are divided to different categories and use different measures for scoring features. Recently ensemble methods have entered the field of ranking, and achieved more accuracy... 

The rapid growth of broadband wireless access (BWA) has increased the demand of new  application  such  as  VoIP,  video  conferencing,  online  gaming  each  of  which  has  different requirement for quality of service. Due to limited bandwidth provided for these networks,  one  of  the  most  important  issues  is  how  effective  we  manage  bandwidth  in  order to support requests. The quality of service is an important indicator of the effective management  of  bandwidth.  Using  mechanisms  of  call  admission  control is  a  commonly  accepted method for balance between quality of service and increase of utilization resource  in  cellular  mobile  networks.  In  fact, ... 

Nowadays, with the increasing size of data,it’s impossible to collect data and fast classification by human, and needs for an automated classification and data analysis, is more interested. Data classification is a process of giving the training data along with their class labels to the learning agent, which learns the relation between the instances and the labels. Then make a prediction to the label of the training data.In this thesis we will observe the classification of the multi-label data. Multi-label data have more than one label. In other words, each instance appears with a vector of labels.In this thesis, a method based on nearest neighbor is proposed to classify the multi-label... 

Spam filtering is one of the large scale applications of machine learning. Much research has been carried out in the machine learning field with regards to spam filtering. Most of this work falls in the areas of batch learning or offline incremental learning. In batch learning, the learning process is carried out once on all the learning data. In applications such as spam filtering, in which the learning data is large in comparison to memory resources and data is generated in a stream, using incremental learning is required, in which the learning phase is repeated periodically. In each learning iteration of an offline incremental learning algorithm, a new set of data is learnt by the... 

Recent advances in storage and processing, have provided the ability of automatic gathering of information which in turn leads to fast and contineous flow of data. The data which are produced and stored in this way, are named data streams. data streams have many applications such as processing financial transactions, the recorded data of various sensors or the collected data by web sevices. Data streams are produced with high speed, large size and much dynamism and have some unique properties which make them applicable in precise modeling of many real data mining applications. The main challenge of data streams is the occurrence of concept drift which can be in four types: sudden, gradual,... 

Increasing information in real world needs the special instrument for data saving, cleaning and processing. Data cleaning is so important steps in machine learning application that include various kind of procedures such as, duplicate detection, fill out missing value and outlier detection. Outliers are observation, which deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism. Many researches has been carried out in the machine learning field with regards to the outlier detection that has applications in real world, like: Intrusion detection for network security, fraud detection in credit cards, fault detection for security in critical... 

Nowadays, living in information era has forced us to face with a great deal of problems of which the input data is received like a nonstop endless stream. Intrusion detection in networks or filtering spam emails out of legal ones are instances of such problems. In such areas, traditional classification algorithms show function improperly, thus it is necessary to make use of novel algorithms that can tackle these problems. Among classification algorithms, decision trees have significant advantages such as being independent of any parameter and acting robust against outliers or unrelated attributes. Moreover, results of a decision tree are quite easy to interpret and extract rules from.... 

Expert finding in bibliographic networks has received increasing attention in recent years. This task concerns with finding relevant researchers for a given topic. In this thesis, we propose a model to determine authority of authors who have participated in the Communities. This model has a little improvement over community based baseline model. However, due to the low performance of community based models, the proposed authority based model cannot improve the document based baseline models either. Therefore, we try to improve document based models, instead of community based models and have proposed two other models which are based on authors’ topic dominance for expert finding. Document... 

This thesis investigates the expertise ranking problem. Recently, the expertise ranking problem has attracted lots of attention in Information Retrieval community. The broad usages of expert ranking algorithms in commercial search engines indicate its importance and usability. Expertise ranking problem is concerned with finding people who are knowledgeable in a given topic. The main research questions in this thesis are related to three important questions related to expert ranking problem. The first question is what the sources of evidences are and how we can infer expertise of a person on a given topic. The second question is concerned with the modeling of information related to each... 

Estimation of distribution algorithms (EDA) are optimization methods that search the solution space by building and sampling probabilistic models. The linkage tree genetic algorithm (LTGA), which can be considered an estimation of distribution algorithm, uses hierarchical clustering to build a hierarchical linkage model called the linkage tree, and gene-pool optimal mixing algorithm to generate new solutions. While the LTGA performs very well on problems with separable sub-problems, its performance deteriorates on ones with overlapping sub-problems. This thesis presents a comparison of the effect of different pre-constructed models in the LTGA's performance. Several important factors that... 

Expert retrieval when the number of experts are limited is an open problem. Undoubtedly, becoming an expert in a field is a time consuming and expensive task; thus finding the best candidates is a crucial task. In addition, passage of time and growth of knowledge could change the view of a person towards life and his work, which may lead to the change of his or her field of work. When considering the changes each person makes in his or her life, it becomes obvious that they are not far from the original status. Therefore, recommending all possible options around a person could really help the task of decision making. This research is addressing two similar issues of finding experts, in a... 

Nowadays, recommender systems have become powerful tools that engage users in an online manner, over the Internet. Collaborative filtering (CF) is a well established method for building recommender systems and has been applied to several applications. While CF has its advantages,its use is hindered by challenges such as low accuracy for new users (newcomers). With the growth of online social networks, networkbased recommender systems emerged. These systems take advantage of the information available in social networks and the user’s past activity to recognize user behavior and recommend items that are more relevant to each user. One of the most important advantages of network-based... 

Nowadays Systems in different fields of research like computer science, biology, social networks, information networks, and economics are modeled as graphs. The graphs which model real world systems have very different topological characteristics than those of classic networks. One of the prominent characteristics of these networks, is that its not practical to describe a general model for their structure and behavior. As a consequence of this complexity in modeling and structure, these networks are called complex networks. One of the most important observations in complex networks is the presence of communities, it means that in such networks one can separate vertices in disjoint sets, such... 

Genome sequences, high dimensional digital pictures and on-line text news are all examples of high dimensional data sets. As technology keeps advancing new challenges arise from applications of high dimensional datasets. Amongst these challenges, the problem of constraint clustering for high dimensional data is of great importance. This problem deals with 2 major challenges.The first challenge is the concentration effect of Lp norms, which means as the dimensionality increases, ratio of distance between the closest points to the distance of furthest points approaches 1. This in turn makes the concept of nearest neighbour meaning less. It also means the discriminative property of such... 

Taking into account the fast and ever-increasing pace of data growth, clustering algorithms emerge as the key tools for data analysis in new researches. Clustering remain as a method for decomposing data into clusters, in such a way that similar data coalesce in the same group. Different algorithms conduct clustering according to a series of initial hypotheses, without being informed about the clusters’ form and aims. Hence, in case with no conformity between initial hypothesis and the clustering aim, one cannot expect adequate response from the clustering algorithm. Exploitation of side information in clustering can play an impactful role in introduction of real models into clustering...