Sharif Digital Repository

The voluminous of the e-mails are spam. Several algorithms are represented for spam detection based on batch learning. In this paper, a new algorithm based on incremental learning is introduced. The algorithm composes new knowledge from new training data with previous knowledge by combining classifiers based on weighted majority voting. The experiment results show that the proposed algorithm outperforms other related incremental algorithms and non-incremental algorithms  

Incremental learning is a learning algorithm that can get new information from new training sets without forgetting the acquired knowledge from the previously used training sets. In this paper, an incremental learning algorithm based on ensemble learning is proposed. Then, an application of the proposed algorithm for spam filtering is discussed. The proposed algorithm called incremental RotBoost, assumes the environment is stationary. It trains new weak classifiers for newly arriving data, which are added to the ensemble of classifiers. To evaluate the performance of the proposed algorithm, several computer experiments are conducted. The results of computer experiments show the ability of... 

Success in forecasting and analyzing sales for given goods or services can mean the difference between profit and loss for an accounting period and, ultimately, the success or failure of the business itself. Therefore, reliable prediction of sales becomes a very important task. This article presents a novel sales forecasting approach by the integration of genetic fuzzy systems (GFS) and data clustering to construct a sales forecasting expert system. At first, all records of data are categorized into k clusters by using the K-means model. Then, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. In order to evaluate our K-means... 

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all... 

Forecasting stock price time series is very important and challenging in the real world because they are affected by many highly interrelated economic, social, political and even psychological factors, and these factors interact with each other in a very complicated manner. This article presents an approach based on Genetic Fuzzy Systems (GFS) for constructing a stock price forecasting expert system. We use a GFS model with the ability of rule base extraction and data base tuning for next day stock price prediction to extract useful patterns of information with a descriptive rule induction approach. We evaluate capability of the proposed approach by applying it on stock price forecasting... 

Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays, because of their flexibility, symbolic reasoning, and explanation capabilities. Meanwhile, accurate forecasts on tourism demand and study on the pattern of the tourism demand from various origins is essential for the tourism-related industries to formulate efficient and effective strategies on maintaining and boosting tourism industry in a country. In this paper we develop a hybrid AI model to deal with tourist arrival forecasting problems. The hybrid model adopts Adaptive Neuro-Fuzzy Inference System (ANFIS) and... 

The need for independent power generation has increased in recent years, especially with the growing demand in microgrid systems. In a microgrid with several generations of different types and with all kinds of loads of variable nature, an optimal power balance in the system has to be achieved. This optimal objective, which results in minimal energy losses over a specific period of time, requires an optimal location and sizing of the distributed generations (DGs) in a microgrid. This paper proposes a new optimization method in which both optimal location of the DGs and their generation profile according to the load demand profile as well as the type of DG are determined during the life time... 

Packet loss recovery is an important part of P2P video streaming networks due to inevitable packet loss in today's internet and interdependency of data units in compressed video streams. In addition, the architecture of P2P streaming networks, in which the data delivered to the receivers through chain of peers, can intensify the impact of the internet packet loss on the quality of perceived video at the receivers. FEC and ARQ are the two most important techniques that can be used to overcome the side effect of the internet packet loss in P2P video streaming networks. Based on these two techniques, different packet loss recovery strategies can be applied in different overlay hops of a given... 

Co(salen)-type [where salen = di-(salicylal)-ethylenediimine] complexes were shown to be efficient catalysts in the oxidation of 2-phenylpropane (cumene) by dioxygen primarily to 2-phenyl-2-propanol (cumyl alcohol), 2-phe-nylpropene (a-methylstyrene), and 1-phenylethanone (acetophenone) applying 1H NMR spectroscopy and gas chromatography-mass spectrometry (GC-MS). The effect of substitution on the ligand was also monitored in both oxygen-absorption and the catalytic reaction. Based on these results, the trend observed for the production of a-methylstyrene and cumyl alcohol were parallel to dioxygen uptake by the catalyst in neat cumene, while acetophenone productions obeyed a non-linear... 

In this paper joint optimization of layers in the layered video coding is investigated. Through theoretical analysis and simulations, it is shown that, due to higher interactions between the layers in a SNR scalable codec, this type of layering technique benefits most from joint optimization of the layers. A method for joint optimization is then proposed, and its compression efficiency is contrasted against the separate optimization and an optimized single layer coder. It is shown that, in joint optimization of SNR scalable coders when the quantization step size of the enhancement layer is larger than half the step size of the base layer, an additional improvement is gained by not sending... 

Peer-to-peer streaming has recently gained attention as an effective solution to support large scale media streaming applications over the Internet. One of the main challenges of peer-to-peer video streaming is the cumulative impact of the Internet packet loss due to the decoding dependency of the compressed video frames. In this paper we study the impact of the Internet packet loss on the performance of peer-to-peer video streaming systems, and analyze the efficiency of various packet loss recovery policies in such systems. Our analytical and simulation results show how the Internet packet loss can affect the performance of peer- to-peer video streaming systems and how different packet loss... 

One of the man concerns of peer-to-peer video streaming over the internet is the cumulative impact of the Internet packet loss due to the decoding dependency of the compressed video frames. In this paper we study the efficiency of various packet loss recovery policies that can be used in peer-to-peer streaming. Our analytical and simulation results show how different packet loss recovery policies can be effective for peer-to-peer video streaming. © 2007 IEEE  

In recent years, the demand for video transmission over wireless communication networks is growing very fast. The H.264 video compression standard which offers high quality at low bit rates, is a suitable codec for applications that require efficient video transmission over wireless networks. While the compressed videos are transmitted through error-prone networks, error robustness becomes an important issue. In this paper, a joint source-channel Lagrange optimization method in which the distortion of the decoder is estimated without using feedback which can be used for both multicast and point-to-point applications is proposed. The experimental results show that the new algorithm has a good... 

Transmission of video over error prone and still bandwidth limited wireless channels demand high compression efficiency and resilience to packet losses and errors. Scalable or layered video coding applied to highly compression efficient codecs is an ideal solution to the problem. However, scalability reduces compression efficiency of the coders. In this paper we show how compression efficiency of two-layer SNR scalable video coders can be retained via joint base-enhancement layer optimization. Simulation results show that joint base-enhancement layer optimization significantly outperforms separate optimization of the layers, and it closely follows the compression performance of the... 

In this paper we propose a rate-efficient peer-to-peer architecture for video multicasting over the Internet. The limited capacity of the Internet hosts and the heterogeneous property of their access links are the main challenges of the peer-to-peer video multicasting over the Internet. Although, the rate-optimized overlay tree construction is a NP-hard problem, we propose a number of distributed and efficient protocols for rate-efficient overlay tree construction. Our proposed protocols include efficient join, improvement, overlay tree refinement and an optimum rate allocation protocol. The simulation results show the efficiency of the proposed protocols in rate-efficient overlay tree... 

In this paper, a new dynamic proxy solution based on overlay multicasting network for layered video streaming is presented. In our proposed approach, all clients in an Internet service provider network interested in receiving a video stream, provided by a media server in the Internet, organize themselves into an overlay tree. The root of the overlay tree acts as a proxy and receives video stream from original server and distributes it in the local overlay tree. In addition to introducing a new architecture, our main contribution in this paper is proposing a number of distributed protocols for construction and improvement of local overlay tree in video streaming applications. The experimental... 

In this paper, a new method for spatial concealment of missing areas in image and video signals transmitted over error prone infrastructures, is presented. The concealment process is performed in three steps. First, a novel technique is used to estimate the significant edges of missing areas after performing a directional edge analysis on the correctly received neighboring blocks of the missing areas. This technique uses the moments of the neighboring edge magnitudes to obtain an adaptive threshold for rejecting non-significant directions. Second, based on the predicted significant directions, an approximation is obtained for each missing pixel. Finally, for each pixel, we compute a weighted... 

In this study, a method for enhancing low-contrast images is proposed. This method, called Gaussian mixture model-based contrast enhancement (GMMCE), brings into play the Gaussian mixture modelling of histograms to model the content of the images. On the basis of the fact that each homogeneous area in natural images has a Gaussian-shaped histogram, it decomposes the narrow histogram of low-contrast images into a set of scaled and shifted Gaussians. The individual histograms are then stretched by increasing their variance parameters, and are diffused on the entire histogram by scattering their mean parameters, to build a broad version of the histogram. The number of Gaussians as well as their... 

The in vitro endothelial response of human umbilical vein endothelial cells was investigated on a poly (caprolactone)-based polyurethane surface vs an in situ TiO2-polyurethane nanocomposite surface, which has been produced as scaffolds for artificial vascular graft. The in situ synthesis of TiO2 nanoparticles in polyurethane provided surface properties that facilitated cellular adhesion, cell sensing, cell probing and especially cell migration. Cells on the nanocomposite surface have elongated morphology and were able to produce more extracellular matrix. All of these advantages led to an increase in the rate of endothelialization of the nanocomposite scaffold surface vs pure polyurethane.... 

Two-dimensional boron nitride quantum dots (2D BNQDs) with excellent chemical stability, high photoluminescence efficiency, and low toxicity are a new class of advanced materials for biosensing and bioimaging applications. To overcome the current challenge about the lack of facile, scalable, and reproducible synthesis approach of BNQDs, we introduce a green and facile approach based on mechanochemical exfoliation of bulk h-BN particles in ethanol. Few-layered hydroxylated-functionalized QDs with a thickness of 1-2 nm and a lateral dimension of 2-6 nm have been prepared. The synthesized nanocrystals exhibit a strong fluorescence emission at 407 and 425 nm with a quantum efficiency of ∼6.2%....