Sharif Digital Repository

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... 

Image Registration is spatially alignment of two images in a wide range of applications such as remote sensing, computer assisted surgery, and medical image analysis and processing. In general, registration algorithms can be categorized as either intensity based or feature based. The feature based methods use the alignment between the extracted features in two images. The simplest feature is images intensity which is directly used in the intensity based method via similarity measure. This similarity measure quantifies the matching of two images.Similarity measure is main core of image registration algorithms. Spatially varying intensity dis-tortion is an important challenge in a wide range... 

One of the most important issues in medical image processing is the registration of images with various imaging modalities, because in this case, one can take advantage of these imaging modalities and sometimes fuse and use the useful information of each one in the form of a single image.As it was said, MRI and ultrasound images each have their own disadvantages and advantages, and by considering these two modalities, they have tried to integrate the good features of these two. As we know, one of the destructive cases in the MRI image is the inhomogeneity of the image, a inhomogeneity due to the fact that the main magnetic field is not constant and makes the parts of the image brighter or... 

In this project we tried to to solve the problem of clustring images of a large image database. Considering that there is no prior information regarding domain of the images, we will review unsupervised clustring methods. For this, we will discuss about image description vector and similarity measures. At last, our contribution will be about finding the optimum number of clusters with the least of user invervention. Results of runnig our method on a databse with 1000 images is reported and compared to a similar method named CLUE. Our result shows considerable improvements when user feedback taken to account.
 

Nowadays, a need for intelligent system that can assist web users in acquiring their desired information has been highlighted. In particular a mechanism for web user profiling which involves knowing the users and providing them with their preferred information is recommended to any website. To address this issue, Web Usage Mining (WUM) techniques have attracted many attentions. WUM is a kind of web mining process that tries to extract interesting usage patterns from the data that are obtained from the interaction of users with the web. The most important drawback of conventional, data mining based, WUM system is their inability to continuously learn the evolving patterns of web usage. On the... 

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... 

Image registration is the process of matching two or more images captured at different times, angles or even sensors. Image registration is finding a transformation between two images. Image registration has many applications in different fields of Image Processing and machine vision. In the field of remote sensing, image registration can be used for environmental monitoring, generating graphical maps and gathering data for geogeraphical information systems, also in the field of medical image processing it can be used to detect tumor growth. Generally whenever there is need to extract information from many images; image registration is considered as an important pre-processing step.... 

Medical imaging is of interest because of information that will provide for doctors and registration is inevitable when we need to compare two or more images, taken from a subject at different times or with different sensors or when comparing two or more subjects together. Registration methods can be categorized in two major groups; methods based on feature and methods based on intensity. Methods in first group have three steps in common: feature extraction, finding matches and transform estimation. In second group it’s important to define a similarity measure and find the transform that minimizes this measure.
Manifold learning algorithms are mostly used as a dimensionality reduction... 

Image registration is defined as matching two or more than two images which are taken from a same scene in different times, from various views and using different sensors. By matching, we mean finding the transformation function between the images. Image registration
has many application in medical domains such as analyzing changes in body limbs, supervision of the tumors growth, and robotic surgery. The challenges of image registration are unavailability of the transformation function, existence of the outliers in the images, changes in the images intensity, changes in the image geometry, noise, etc. Image registration consists of four steps: (i) feature extraction, (ii) choosing a...