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Subject = artificial neural network;
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Displaying Results 1 - 4 of 4 on page 1 of 1
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Application of Artificial Intelligence for Detecting Computing Derived Viruses
(2017)
Blackledge, Jonathan; Asiru, Omotayo; Dlamini, Moses
Application of Artificial Intelligence for Detecting Computing Derived Viruses
(2017)
Blackledge, Jonathan; Asiru, Omotayo; Dlamini, Moses
Abstract:
<p>Computer viruses have become complex and operates in a stealth mode to avoid detection. New viruses are argued to be created each and every day. However, most of these supposedly ‘new’ viruses are not completely new. Most of the supposedly ‘new’ viruses are not necessarily created from scratch with completely new (something novel that has never been seen before) mechanisms. For example, most of these viruses just change their form and signatures to avoid detection. But their operation and the way they infect files and systems is still the same. Hence, such viruses cannot be argued to be new. In this paper, the authors refer to such viruses as derived viruses. Just like new viruses, derived viruses are hard to detect with current scanning-detection methods. Therefore, this paper proposes a virus detection system that detects derived viruses better than existing methods. The proposed system integrates a mutating engine together with neural network to improve the detection rat...
http://arrow.dit.ie/engscheleart/259
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Application of Artificial Intelligence for Detecting Derived Viruses
(2017)
Asiru, Omotayo; Dlamini, Moses; Blackledge, Jonathan
Application of Artificial Intelligence for Detecting Derived Viruses
(2017)
Asiru, Omotayo; Dlamini, Moses; Blackledge, Jonathan
Abstract:
<p>Computer viruses have become complex and operates in a stealth mode to avoid detection. New viruses are argued to be created each and every day. However, most of these supposedly ‘new’ viruses are not completely new. Most of the supposedly ‘new’ viruses are not necessarily created from scratch with completely new (something novel that has never been seen before) mechanisms. For example, most of these viruses just change their form and signatures to avoid detection. But their operation and the way they infect files and systems is still the same. Hence, such viruses cannot be argued to be new. In this paper, the authors refer to such viruses as derived viruses. Just like new viruses, derived viruses are hard to detect with current scanning-detection methods. Therefore, this paper proposes a virus detection system that detects derived viruses better than existing methods. The proposed system integrates a mutating engine together with neural network to improve the detection rat...
http://arrow.dit.ie/engscheleart/257
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Artificial neural network application in short-term prediction in an oscillating water column
(2010)
Sheng, Wanan; Lewis, Anthony
Artificial neural network application in short-term prediction in an oscillating water column
(2010)
Sheng, Wanan; Lewis, Anthony
Abstract:
Oscillating Water Column (OWC) is one type of promising wave energy devices due to its obvious advantage over many other wave energy converters: no moving component in sea water. Two types of OWCs (bottom-fixed and floating) have been widely investigated, and the bottom-fixed OWCs have been very successful in several practical applications. Recently, the proposal of massive wave energy production and the availability of wave energy have pushed OWC applications from near-shore to deeper water regions where floating OWCs are a better choice. For an OWC under sea waves, the air flow driving air turbine to generate electricity is a random process. In such a working condition, single design/operation point is nonexistent. To improve energy extraction, and to optimise the performance of the device, a system capable of controlling the air turbine rotation speed is desirable. To achieve that, this paper presents a short-term prediction of the random, process by an artificial neural network ...
http://hdl.handle.net/10468/2891
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Reducing errors of wind speed forecasts by an optimal combination of post-processing methods
(2011)
Sweeney, Conor; Lynch, Peter; Nolan, Paul
Reducing errors of wind speed forecasts by an optimal combination of post-processing methods
(2011)
Sweeney, Conor; Lynch, Peter; Nolan, Paul
Abstract:
Seven adaptive approaches to post-processing wind speed forecasts are discussed and compared. 48-hour forecasts are run at horizontal resolutions of 7 km and 3 km for a domain centred over Ireland. Forecast wind speeds over a two year period are compared to observed wind speeds at seven synoptic stations around Ireland and skill scores calculated. Two automatic methods for combining forecast streams are applied. The forecasts produced by the combined methods give bias and root mean squared errors that are better than the numerical weather prediction forecasts at all station locations. One of the combined forecast methods results in skill scores that are equal to or better than all of its component forecast streams. This method is straightforward to apply and should prove beneficial in operational wind forecasting.
http://hdl.handle.net/10197/3403
Displaying Results 1 - 4 of 4 on page 1 of 1
Bibtex
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RefWorks
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Institution
Dublin Institute of Technology (2)
University College Cork (1)
University College Dublin (1)
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Conference item (3)
Journal article (1)
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Peer-reviewed (3)
Non-peer-reviewed (1)
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2017 (2)
2011 (1)
2010 (1)
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