303 research outputs found
Improving the Performance of Wide Area Networks
Research in to the performance of wide area data networks is described in this thesis. A model of wide area network packet delays is developed and used to direct the research in to methods of improving performance.
Wide area networks are slow and expensive compared to the computer systems that rely on them for communication. Typically data networks are packet switched in order to make efficient use of resources. This can lead to contention, and the mechanisms for resolving contention can bring about further delays when demand for resources is high. In this thesis, network users are viewed as interacting decision makers with conflicting interests, and Game Theory is used to analyse the effects users have on each other’s performance. It is asserted in this thesis that wide area network performance is an ethical issue as well as a technical one.
Compression is examined as a technique for reducing network traffic load. While load reductions can reduce the time packets spend waiting in buffer queues experimental results show the compression process itself can present a bottleneck if CPU resources are limited.
The other inhibiting factor with regard to wide area network performance is the time it takes for a signal to propagate through a transmission medium. Propagation delays are bounded by the speed of light and becomes significant as the distance between computer systems increases. Mirrors and Caches are methods of bringing data closer to the user, thereby reducing propagation delays and capping traffic loads on long haul communication facilities. The performance benefits of replicating data within a wide area network environment are studied in this thesis
Spam Classification Using Machine Learning Techniques - Sinespam
Most e-mail readers spend a non-trivial amount of time regularly deleting junk e-mail (spam)
messages, even as an expanding volume of such e-mail occupies server storage space and
consumes network bandwidth. An ongoing challenge, therefore, rests within the development
and refinement of automatic classifiers that can distinguish legitimate e-mail from spam. Some
published studies have examined spam detectors using Naïve Bayesian approaches and large
feature sets of binary attributes that determine the existence of common keywords in spam,
and many commercial applications also use Naïve Bayesian techniques.
Spammers recognize these attempts to prevent their messages and have developed tactics to
circumvent these filters, but these evasive tactics are themselves patterns that human readers
can often identify quickly. This work had the objectives of developing an alternative approach
using a neural network (NN) classifier brained on a corpus of e-mail messages from several
users. The features selection used in this work is one of the major improvements, because the
feature set uses descriptive characteristics of words and messages similar to those that a
human reader would use to identify spam, and the model to select the best feature set, was
based on forward feature selection. Another objective in this work was to improve the spam
detection near 95% of accuracy using Artificial Neural Networks; actually nobody has reached
more than 89% of accuracy using ANN
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