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Email visualizationCardoso, Celso Renato da Rocha January 2010 (has links)
Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 2010
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Methoden der Spambekämpfung und -vermeidung /Eggendorfer, Tobias. January 2007 (has links)
Zugl.: Hagen, FernUniversiẗat, Diss., 2007.
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Teachers and computer-mediated communication : a study of the development of collegiality among secondary school teachers using electronic mailGruenberg, Jorge January 2000 (has links)
No description available.
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Language and power : a critical analysis of email text in professional communicationAlsree, Zubaida S. A. January 1997 (has links)
No description available.
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A backprogagation neutral network in an address block classification system /Grzech, Matthew Phillip, January 1991 (has links)
Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1991. / Vita. Abstract. Includes bibliographical references (leaves 57-59). Also available via the Internet.
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The socio-economic impact of unsolicited bulk email (spam) on New Zealand organisations and employees : comparative case studies. A dissertation submitted in partial fulfilment of the requirements for the degree of Master of Computing at Unitec New Zealand /Foster, Brian. January 2007 (has links)
Thesis (M.Comp.)--Unitec New Zealand, 2007. / Includes bibliographical references (leaves 223-230).
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E-Mail-Kategorisierung und Spam-Detektion mit SENTRAX [Mustererkennung mit Assoziativmatrizen]Frobese, Dirk T. January 2009 (has links)
Zugl.: Hildesheim, Univ., Diss., 2009
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Graph-based email prioritizationNussbaum, Ronald. January 2008 (has links)
Thesis (M.S.)--Michigan State University. Computer Science and Engineering, 2008. / Title from PDF t.p. (viewed on July 29, 2009) Includes bibliographical references (p. 45-47). Also issued in print.
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Investigating unsupervised feature learning for email spam classificationDiale, Melvin January 2017 (has links)
A dissertation submitted in partial ful llment of the requirements for the degree
Master of Science.
School of Computer Science and Applied Mathematics,
Faculty of Science,
University of the Witwatersrand, Johannesburg.
November 2017 / In the cyberspace, spam emails are used as a way to divulge sensitive information of
victims through social engineering. There are various classi cation systems that have
been employed previously to identify spam emails. The primary objective of email spam
classi cation systems is to classify incoming email as either legitimate (non-spam) or
spam emails. The spam classi cation task can thus be regarded as a two-class classi
cation problem. This kind of a problem involves the use of various classi ers such
as Decision Trees (DTs) and Support Vector Machines (SVMs). DTs and SVMs have
been shown to perform well on email spam classi cation tasks. Several studies have
failed to mention how these classi ers were optimized in terms of their hyperparameters.
As a result, poor performance was encountered with complex datasets. This is
because SVM classi er is dependent on the selection of the kernel function and the optimization
of kernel hyperparameters. Additionally, many studies on spam email ltering
task use words and characters to compute Term-Frequency (TF) based feature space.
However, TF based feature space leads to sparse representation due to the continuous
vocabulary growth. This problem is linked with the curse of dimensionality. Overcoming
dimensionality issues involves the use of feature reduction techniques. Traditional
feature reduction techniques, for instance, Information Gain (IG) may cause feature
representations to lose important features for identifying spam emails. This proposed
study demonstrates the use of Distributed Memory (DM), Distributed Bag of Words
(DBOW), Cosine Similarity (CS) and Autoencoder for feature representation to retain
a better class separability. Generated features enable classi ers to identify spam emails
in a lower dimension feature space. The use of the Autoencoder for feature reduction led
to improved classi cation performance. Furthermore, a comparison of kernel functions
and CS measure is taken into consideration to evaluate their impacts on classi ers when
employed for feature transformation. The study further shows that removal of more
frequent words, which have been regarded as noisy words and stemming process, may
negatively a ect the performance of the classi ers when word order is taken into consideration.
In addition, this study investigates the performance of DTs and SVM classi ers
on the publicly available datasets. This study makes a further investigation on the selection
of optimal kernel function and optimization of kernel hyperparameters for each
feature representation. It is further investigated whether the use of Stacked Autoencoder
as a pre-processing step for multilayer perceptron (MLP) will lead to improved
classi cation results. / MT 2018
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LAN based Remote Access Multiple Users (RAMU) Voice Information Retrieval System.January 1992 (has links)
Sum Wai Chun. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references. / Acknowledgement / Abstract / Thesis / Chapter I --- Introduction --- p.P.1 - P.3 / Chapter II --- System Overview of RAMU --- p.P.3 - P.5 / Chapter III --- Hardware Implementation of VRU --- p.P.5 - P.8 / Chapter IV --- Software Design of RAMU --- p.P.8 - P.15 / Chapter V --- Software Support of RAMU --- p.P.15 / Chapter VI --- Potential Applications of RAMU --- p.P.16 - P.17 / Chapter VII --- Suggested Further Works --- p.P.17 - P.18 / Chapter VIII --- Demonstrated Applications Developed on RAMU --- p.P.18 - P.19 / Chapter IX --- Conclusion --- p.P.19 / Bibliography / Chapter Appendix 1 -- --- TIU / Chapter * --- Circuit layout / Chapter * --- Circuit Operation of TIU / Chapter * --- DIP Switch Settings of TIU / Chapter * --- Data Sheets of8255 / Chapter * --- Data Sheets of MC145436 / Chapter Appendix 2 -- --- Application Generator / Chapter * --- User Guide of the Application Generator Program / Chapter * --- Program Listing of AppGen. Pas and its supporting units / Chapter - --- M__AppGen.Pas / Chapter - --- DataStru. Pas / Chapter - --- ColorDef. Pas / Chapter Appendix 3 -- --- AEM / Chapter * --- Parameters of Running the AEM / Chapter * --- Program Listing of LAppExec.Pas and its supporting units / Chapter - --- ToneCard. Pas / Chapter - --- VrpSupp. Pas / Chapter - --- MiscUtil. Pas / Chapter - --- DataStru. Pas / Chapter - --- LanMStru. Pas / Appendix 4-- Manufacturer Manual of VRP-70 Voice Card
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