Abstract Cohen-Grossberg neural networks which include Hopfield neural networks and Cellular neural networks as special cases and have important applications in many fields are researched by many authors. There is few results on synchronization control of delayed Cohen-Grossberg neural networks. In this paper, the synchronization control of delayed Cohen- Grossberg neural networks with diffusion terms is discussed by constructing suitable Lyapunov... Cohen-Grossberg neural networks which include Hopfield neural networks and Cellular neural networks as special cases and have important applications in many fields are researched by many authors. There is few results on synchronization control of delayed Cohen-Grossberg neural networks. In this paper, the synchronization control of delayed Cohen- Grossberg neural networks with diffusion terms is discussed by constructing suitable Lyapunov functional and using inequality techniques. Instead of the linear control term, we consider a class of nonlinear control term. Some sufficient conditions are given to ensure the synchronization control of the drive-response delayed Cohen-Grossberg neural networks with reaction- diffusion terms. A numerical example is presented to verify that the results in the paper are correct. Read More Read Less

Abstract This paper investigates the quality of geographical information provided by a location-based service, such as Foursquare, and its geographical distribution. According to the result, geographical information described by autonomous individuals is heavily clustered in metropolitan areas. In order to reduce the geographically-biased distribution of information, location-based content systems have been developed with mobile devices for people in local communities. Members... This paper investigates the quality of geographical information provided by a location-based service, such as Foursquare, and its geographical distribution. According to the result, geographical information described by autonomous individuals is heavily clustered in metropolitan areas. In order to reduce the geographically-biased distribution of information, location-based content systems have been developed with mobile devices for people in local communities. Members of a local community, e.g., shopping districts and tourist spots, have strong incentives to provide high quality information to their customers. Hence, the systems are provided to people in these communities so that a vast amount of geo-local content is going to be published on the Internet. To deploy the developed system, two local areas are investigated in terms of spatial analysis on social media. The areas are a rural town and a mid-size city. According to the analysis, the members in the rural town have to generate local content concerning their locality because word-of-mouth information is very scarce. Read More Read Less

Abstract In this paper, a new classification method for enhancing the performance of K-Nearest Neighbor is proposed which uses robust neighbors in training data. The robust neighbors are detected using a validation process. This method is more robust than traditional equivalent methods. This new classification method is called Modified K-Nearest Neighbor. Inspired the traditional KNN algorithm, the main idea is classifying... In this paper, a new classification method for enhancing the performance of K-Nearest Neighbor is proposed which uses robust neighbors in training data. The robust neighbors are detected using a validation process. This method is more robust than traditional equivalent methods. This new classification method is called Modified K-Nearest Neighbor. Inspired the traditional KNN algorithm, the main idea is classifying the test samples according to their neighbor tags. This method is a kind of weighted KNN so that these weights are determined using a different procedure. The procedure computes the fraction of the same labeled neighbors to the total number of neighbors. The proposed method is evaluated on a variety of several standard UCI data sets. Experiments show the excellent improvement in accuracy in comparison with KNN method. Read More Read Less

Abstract Due to the recent spread of streaming delivery of movies, streaming using Peer-to-Peer (P2P) streaming technology has attracted much attention. In P2P streaming systems, to distribute the network load, peers from which the user receives data is selected at random. For this, clients have to wait until their desired data is delivered. Therefore, there are many researches to reduce the... Due to the recent spread of streaming delivery of movies, streaming using Peer-to-Peer (P2P) streaming technology has attracted much attention. In P2P streaming systems, to distribute the network load, peers from which the user receives data is selected at random. For this, clients have to wait until their desired data is delivered. Therefore, there are many researches to reduce the waiting time. However, because of the complexity of the implementation, they usually evaluate these methods using computer simulations. In actual environment, indeed, interruption time is not always reduced by increasing clients to deliver data. To evaluate the effectiveness of P2P streaming systems, it is important to implement an actual P2P streaming system. In this paper, we evaluate a P2P streaming system. By using our implemented P2P streaming system, we investigate situations that its system is effective. As a result of our evaluation, we confirmed that interruption time is reduced effectively. Read More Read Less