Evaluating the Influential Factors on Virtual Social Network’s Users in Viral Marketing

These days, by attending to the ever-increasing development of social networks and fast development of the virtual relationship with millions of massage interaction possibility in a second by these powerful beds, social networks analyses have been attended by analyzers and experts in different areas, especially in business and marketing fields. Companies and producers, because of marketing development in this part, and service presenters through the world believe that goods advertisement is vital in sailing success and presenting the services and company's profitability. Naturally, the last, past and traditional methods are not suitable. so, the online social networks transfer their advertisements to the customers in different parts of the world with low cost and fast spreading. Therefore, knowing the effective nodes in fast and low-cost spreading and effective recommendations for companies changed to a great challenge by attending to the giant structure of these networks. In this article, the factors, which are over the effective advertisement and services, have been extracted and the needed data have been gathered from Facebook virtual network users (437samples). The gathered data were analyzed in before stages by SPSS software. The stability of the questioners was verified by Cronbach's Alfa coefficient, and finally the suitable results and approaches were presented about recognizing the effective factors in social networks and their impacts scale on social network users which can be effective in viruses marketing area for increasing the business companies' advertisement spreading for reducing the costs of advertisement and comprehension of the advertisement

Evaluating the Influential Factors on Virtual Social Network’s Users in Viral Marketing

These days, by attending to the ever-increasing development of social networks and fast development of the virtual relationship with millions of massage interaction possibility in a second by these powerful beds, social networks analyses have been attended by analyzers and experts in different areas, especially in business and marketing fields. Companies and producers, because of marketing development in this part, and service presenters through the world believe that goods advertisement is vital in sailing success and presenting the services and company's profitability. Naturally, the last, past and traditional methods are not suitable. so, the online social networks transfer their advertisements to the customers in different parts of the world with low cost and fast spreading. Therefore, knowing the effective nodes in fast and low-cost spreading and effective recommendations for companies changed to a great challenge by attending to the giant structure of these networks. In this article, the factors, which are over the effective advertisement and services, have been extracted and the needed data have been gathered from Facebook virtual network users (437samples). The gathered data were analyzed in before stages by SPSS software. The stability of the questioners was verified by Cronbach's Alfa coefficient, and finally the suitable results and approaches were presented about recognizing the effective factors in social networks and their impacts scale on social network users which can be effective in viruses marketing area for increasing the business companies' advertisement spreading for reducing the costs of advertisement and comprehension of the advertisement

Evolution of the World Wide Web: From Web 1.0 to Web 4.0

ABSTRAC

Computational Modeling of Uncertainty Avoidance in Consumer Behavior

Abstract: Human purchasing behavior is affected by many influential factors. Culture at macro-level and personality at microlevel influence consumer purchasing behavior. People of different cultures tend to accept the values of their own group and consequently have different purchasing behavior. Also, people in the same culture have some differences in their purchases which can be described by their personal characteristics. Therefore, this paper studies Uncertainty Avoidance dimension of Hofstede culture model in consumer behavior as well as four personality traits. The consumer model includes three important module including perception, evaluation of the alternatives and post-purchase. Our experimental results show that people of high uncertainty avoidance tend to purchase the high quality products as well as famous brands to reduce the risk of their purchases. On the other hand, people in high uncertainty tolerant culture tend to purchase the new products. The paper discusses about the validity of the proposed model based on empirical data

An Electronic Marketplace Based on Reputation and Learning

In this paper, we propose a market model which is based on reputation and reinforcement learning algorithms for buying and selling agents. Three important factors: quality, price and delivery-time are considered in the model. We take into account the fact that buying agents can have different priorities on quality, price and delivery-time of their goods and selling agents adjust their bids according to buying agents preferences. Also we have assumed that multiple selling agents may offer the same goods with different qualities, prices and delivery-times. In our model, selling agents learn to maximize their expected profits by using reinforcement learning to adjust product quality, price and delivery-time. Also each selling agent models the reputation of buying agents based on their profits for that seller and uses this reputation to consider discount for reputable buying agents. Buying agents learn to model the reputation of selling agents based on different features of goods: reputation on quality, reputation on price and reputation on delivery-time to avoid interaction with disreputable selling agents. The model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/buying agents that model the reputation of buying/selling agent

A Medium Access Control Protocol with Adaptive Parent Selection Mechanism for Large-Scale Sensor Networks

Abstract—In the MAC protocols based on the S-MAC scheme, usually the combination of periodic sleep/listen scheduling and four-way handshake mechanism is employed to reduce idle listening and avoid interference. However, this combination greatly degrades network capacity and results in high end-toend latency. In this paper, we propose Adaptive IAMAC to increase channel utilization and improve communication efficiency, specifically in large-scale sensor networks with low duty cycle. Adaptive IAMAC allows multiple nodes to transmit to their common parent during a frame. Moreover, it includes the adaptive parent selection mechanism, which enables the nodes to change their parent according to the currently overheard control packets at the MAC layer. Through these techniques, Adaptive IAMAC enhances network throughput, reduces end-to-end latency, and moderates the overhead of four-way handshake mechanism. Simulation results confirm that Adaptive IAMAC provides significant improvements over S-MAC in terms of throughput, latency, and energy efficiency