SQAP: A simple QoS supportive adaptive polling protocol for wireless LANs

A Simple QoS supportive Adaptive Polling (SQAP) protocol for wireless LANs is introduced. SQAP operates under an infrastructure wireless LAN, where an Access Point (AP) polls the wireless nodes in order to grant them permission to transmit. The polled node sends data directly to the destination node. We consider bursty traffic conditions, under which the protocol operates efficiently. The polling scheme is based on an adaptive algorithm according to which it is most likely that an active node is polled. Also, SQAP takes into account packet priorities, so it supports QoS by means of the Highest Priority First packet buffer discipline and the priority distinctive polling scheme. Lastly, the protocol combines efficiency and fairness, since it prohibits a singe node to dominate the medium permanently. SQAP is compared to the efficient learning automata-based polling (LEAP) protocol, and is shown to have superior performance. © 2005 Elsevier B.V. All rights reserved

A new deadlock recovery mechanism for fully adaptive routingalgorithms

Routing algorithms used in wormhole switched networks must all provide a solution to the deadlock problem. If the routing algorithm allows deadlock cycles to form, then it must provide a deadlock recovery mechanism. Because deadlocks are anomalies that occur while routing, the deadlock recovery mechanism should not allocate any expensive hardware resources for the sake of handling such a rare event. Rather, it should only dedicate a minimal set of required resources to the recovery process in order to engage most of the hardware resources to the task of routing normal packets. This paper proposes a new deadlock recovery mechanism to be used with the True Fully Adaptive Routing algorithm. The new deadlock recovery mechanism takes advantage of the concept behind wormhole switching. The scheme is efficient in terms of hardware requirements, causes fewer deadlocks and can compete with other expensive deadlock recovery scheme

Modeling a teacher in a tutorial-like system using Learning Automata

The goal of this paper is to present a novel approach to model the behavior of a Teacher in a Tutorial- like system. In this model, the Teacher is capable of presenting teaching material from a Socratic-type Domain model via multiple-choice questions. Since this knowledge is stored in the Domain model in chapters with different levels of complexity, the Teacher is able to present learning material of varying degrees of difficulty to the Students. In our model, we propose that the Teacher will be able to assist the Students to learn the more difficult material. In order to achieve this, he provides them with hints that are relative to the difficulty of the learning material presented. This enables the Students to cope with the process of handling more complex knowledge, and to be able to learn it appropriately. To our knowledge, the findings of this study are novel to the field of intelligent adaptation using Learning Automata (LA). The novelty lies in the fact that the learning system has a strategy by which it can deal with increasingly more complex/difficult Environments (or domains from which the learning as to be achieved). In our approach, the convergence of the Student models (represented by LA) is driven not only by the response of the Environment (Teacher), but also by the hints that are provided by the latter. Our proposed Teacher model has been tested against different benchmark Environments, and the results of these simulations have demonstrated the salient aspects of our model. The main conclusion is that Normal and Below-Normal learners benefited significantly from the hints provided by the Teacher, while the benefits to (brilliant) Fast learners were marginal. This seems to be in-line with our subjective understanding of the behavior of real-life Students

FS2RNN: Feature Selection Scheme for Web Spam Detection Using Recurrent Neural Networks

In modern era, Internet plays a key role in accessing and fetching web information and web resources from World Wide Web (WWW). The websites act as a medium for retrieving information from the web. Although it increases the data retrieval and users interactions, it also opens the gate for various types of attacks. For example, spams in the websites attract various Internet users. It has been observed from the literature that many authors attempted to detect the web spam using various machine learning techniques. However, none of these techniques used deep learning architecture for detection of hidden patterns. Hence, in this paper, a deep learning algorithm, i.e., Recurrent Neural Networks (RNN), has been used for the classification of spam nodes. We devise here a framework called FS2RNN: Feature Selection Scheme using Recurrent Neural Networks. In this framework, the dataset is preprocessed before applying RNN in which principal component analysis (PCA) is used for dimension reduction on the data set and recursive feature elimination (RFE) is used for feature selection. The accuracy of the proposed framework, when compared before and after preprocessing, is improved by 24.2 %, which is excellent result