Development of a neural network based model for predicting the occurrence of spread F within the Brazilian sector
- Authors: Paradza, Masimba Wellington
- Date: 2009
- Subjects: Neural networks (Computer science) , Ionosphere , F region
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5460 , http://hdl.handle.net/10962/d1005245 , Neural networks (Computer science) , Ionosphere , F region
- Description: Spread F is a phenomenon of the ionosphere in which the pulses returned from the ionosphere are of a much greater duration than the transmitted ones. The occurrence of spread F can be predicted using the technique of Neural Networks (NNs). This thesis presents the development and evaluation of NN based models (two single station models and a regional model) for predicting the occurrence of spread F over selected stations within the Brazilian sector. The input space for the NNs included the day number (seasonal variation), hour (diurnal variation), sunspot number (measure of the solar activity), magnetic index (measure of the magnetic activity) and magnetic position (latitude, magnetic declination and inclination). Twelve years of spread F data measured during 1978 to 1989 inclusively at the equatorial site Fortaleza and low latitude site Cachoeira Paulista are used in the development of an input space and NN architecture for the NN models. Spread F data that is believed to be related to plasma bubble developments (range spread F) were used in the development of the models while those associated with narrow spectrum irregularities that occur near the F layer (frequency spread F) were excluded. The results of the models show the dependency of the probability of spread F as a function of local time, season and latitude. The models also illustrate some characteristics of spread F such as the onset and peak occurrence of spread F as a function of distance from the equator. Results from these models are presented in this thesis and compared to measured data and to modelled data obtained with an empirical model developed for the same purpose.
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- Date Issued: 2009
Forecasting solar cycle 24 using neural networks
- Authors: Uwamahoro, Jean
- Date: 2009
- Subjects: Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5468 , http://hdl.handle.net/10962/d1005253 , Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Description: The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.
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- Date Issued: 2009
Predictability of Geomagnetically Induced Currents using neural networks
- Authors: Lotz, Stefanus Ignatius
- Date: 2009
- Subjects: Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5483 , http://hdl.handle.net/10962/d1005269 , Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Description: It is a well documented fact that Geomagnetically Induced Currents (GIC’s) poses a significant threat to ground-based electric conductor networks like oil pipelines, railways and powerline networks. A study is undertaken to determine the feasibility of using artificial neural network models to predict GIC occurrence in the Southern African power grid. The magnitude of an induced current at a specific location on the Earth’s surface is directly related to the temporal derivative of the geomagnetic field (specifically its horizontal components) at that point. Hence, the focus of the problem is on the prediction of the temporal variations in the horizontal geomagnetic field (@Bx/@t and @By/@t). Artificial neural networks are used to predict @Bx/@t and @By/@t measured at Hermanus, South Africa (34.27◦ S, 19.12◦ E) with a 30 minute prediction lead time. As input parameters to the neural networks, insitu solar wind measurements made by the Advanced Composition Explorer (ACE) satellite are used. The results presented here compare well with similar models developed at high-latitude locations (e.g. Sweden, Finland, Canada) where extensive GIC research has been undertaken. It is concluded that it would indeed be feasible to use a neural network model to predict GIC occurrence in the Southern African power grid, provided that GIC measurements, powerline configuration and network parameters are made available.
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- Date Issued: 2009
Protein secondary structure prediction using neural networks and support vector machines
- Authors: Tsilo, Lipontseng Cecilia
- Date: 2009
- Subjects: Neural networks (Computer science) , Support vector machines , Proteins -- Structure -- Mathematical models
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5569 , http://hdl.handle.net/10962/d1002809 , Neural networks (Computer science) , Support vector machines , Proteins -- Structure -- Mathematical models
- Description: Predicting the secondary structure of proteins is important in biochemistry because the 3D structure can be determined from the local folds that are found in secondary structures. Moreover, knowing the tertiary structure of proteins can assist in determining their functions. The objective of this thesis is to compare the performance of Neural Networks (NN) and Support Vector Machines (SVM) in predicting the secondary structure of 62 globular proteins from their primary sequence. For each NN and SVM, we created six binary classifiers to distinguish between the classes’ helices (H) strand (E), and coil (C). For NN we use Resilient Backpropagation training with and without early stopping. We use NN with either no hidden layer or with one hidden layer with 1,2,...,40 hidden neurons. For SVM we use a Gaussian kernel with parameter fixed at = 0.1 and varying cost parameters C in the range [0.1,5]. 10- fold cross-validation is used to obtain overall estimates for the probability of making a correct prediction. Our experiments indicate for NN and SVM that the different binary classifiers have varying accuracies: from 69% correct predictions for coils vs. non-coil up to 80% correct predictions for stand vs. non-strand. It is further demonstrated that NN with no hidden layer or not more than 2 hidden neurons in the hidden layer are sufficient for better predictions. For SVM we show that the estimated accuracies do not depend on the value of the cost parameter. As a major result, we will demonstrate that the accuracy estimates of NN and SVM binary classifiers cannot distinguish. This contradicts a modern belief in bioinformatics that SVM outperforms other predictors.
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- Date Issued: 2009
A feasibility study into total electron content prediction using neural networks
- Authors: Habarulema, John Bosco
- Date: 2008
- Subjects: Electrons , Neural networks (Computer science) , Global Positioning System , Ionosphere , Ionospheric electron density
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5466 , http://hdl.handle.net/10962/d1005251 , Electrons , Neural networks (Computer science) , Global Positioning System , Ionosphere , Ionospheric electron density
- Description: Global Positioning System (GPS) networks provide an opportunity to study the dynamics and continuous changes in the ionosphere by supplementing ionospheric measurements which are usually obtained by various techniques such as ionosondes, incoherent scatter radars and satellites. Total electron content (TEC) is one of the physical quantities that can be derived from GPS data, and provides an indication of ionospheric variability. This thesis presents a feasibility study for the development of a Neural Network (NN) based model for the prediction of South African GPS derived TEC. The South African GPS receiver network is operated and maintained by the Chief Directorate Surveys and Mapping (CDSM) in Cape Town, South Africa. Three South African locations were identified and used in the development of an input space and NN architecture for the model. The input space includes the day number (seasonal variation), hour (diurnal variation), sunspot number (measure of the solar activity), and magnetic index(measure of the magnetic activity). An attempt to study the effects of solar wind on TEC variability was carried out using the Advanced Composition Explorer (ACE) data and it is recommended that more study be done using low altitude satellite data. An analysis was done by comparing predicted NN TEC with TEC values from the IRI2001 version of the International Reference Ionosphere (IRI), validating GPS TEC with ionosonde TEC (ITEC) and assessing the performance of the NN model during equinoxes and solstices. Results show that NNs predict GPS TEC more accurately than the IRI at South African GPS locations, but that more good quality GPS data is required before a truly representative empirical GPS TEC model can be released.
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- Date Issued: 2008
An analysis of neural networks and time series techniques for demand forecasting
- Authors: Winn, David
- Date: 2007
- Subjects: Time-series analysis , Neural networks (Computer science) , Artificial intelligence , Marketing -- Management , Marketing -- Data processing , Marketing -- Statistical methods , Consumer behaviour
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5572 , http://hdl.handle.net/10962/d1004362 , Time-series analysis , Neural networks (Computer science) , Artificial intelligence , Marketing -- Management , Marketing -- Data processing , Marketing -- Statistical methods , Consumer behaviour
- Description: This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasting targets as well as Time Series forecasts, suggesting that producers could reduce costs by adopting this more effective method.
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- Date Issued: 2007
The effective combating of intrusion attacks through fuzzy logic and neural networks
- Authors: Goss, Robert Melvin
- Date: 2007
- Subjects: Computer security , Fuzzy logic , Neural networks (Computer science)
- Language: English
- Type: Thesis , Masters , MTech
- Identifier: vital:9794 , http://hdl.handle.net/10948/512 , http://hdl.handle.net/10948/d1011917 , Computer security , Fuzzy logic , Neural networks (Computer science)
- Description: The importance of properly securing an organization’s information and computing resources has become paramount in modern business. Since the advent of the Internet, securing this organizational information has become increasingly difficult. Organizations deploy many security mechanisms in the protection of their data, intrusion detection systems in particular have an increasingly valuable role to play, and as networks grow, administrators need better ways to monitor their systems. Currently, many intrusion detection systems lack the means to accurately monitor and report on wireless segments within the corporate network. This dissertation proposes an extension to the NeGPAIM model, known as NeGPAIM-W, which allows for the accurate detection of attacks originating on wireless network segments. The NeGPAIM-W model is able to detect both wired and wireless based attacks, and with the extensions to the original model mentioned previously, also provide for correlation of intrusion attacks sourced on both wired and wireless network segments. This provides for a holistic detection strategy for an organization. This has been accomplished with the use of Fuzzy logic and neural networks utilized in the detection of attacks. The model works on the assumption that each user has, and leaves, a unique footprint on a computer system. Thus, all intrusive behaviour on the system and networks which support it, can be traced back to the user account which was used to perform the intrusive behavior.
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- Date Issued: 2007
NeGPAIM : a model for the proactive detection of information security intrusions, utilizing fuzzy logic and neural network techniques
- Authors: Botha, Martin
- Date: 2003
- Subjects: Computer security , Fuzzy logic , Neural networks (Computer science)
- Language: English
- Type: Thesis , Doctoral , DTech (Computer Studies)
- Identifier: vital:10792 , http://hdl.handle.net/10948/142 , Computer security , Fuzzy logic , Neural networks (Computer science)
- Description: “Information is the lifeblood of any organisation and everything an organisation does involves using information in some way” (Peppard, 1993, p.5). Therefore, it can be argued that information is an organisation’s most precious asset and as with all other assets, like equipment, money, personnel, and so on, this asset needs to be protected properly at all times (Whitman & Mattord, 2003, pp.1-14). The introduction of modern technologies, such as e-commerce, will not only increase the value of information, but will also increase security requirements of those organizations that are intending to utilize such technologies. Evidence of these requirements can be observed in the 2001 CSI/FBI Computer Crime and Security Survey (Power, 2001). According to this source, the annual financial losses caused through security breaches in 2001 have increased by 277% when compared to the results from 1997. The 2002 and 2003 Computer Crime and Security Survey confirms this by stating that the threat of computer crime and other related information security breaches continues unabated and that the financial toll is mounting (Richardson, 2003). Information is normally protected by means of a process of identifying, implementing, managing and maintaining a set of information security controls, countermeasures or safeguards (GMITS, 1998). In the rest of this thesis, the term security controls will be utilized when referring to information protection mechanisms or procedures. These security controls can be of a physical (for example, door locks), a technical (for example, passwords) and/or a procedural nature (for example, to make back-up copies of critical files)(Pfleeger, 2003, pp.22-23; Stallings, 1995, p.1). The effective identification, implementation, management and maintenance of this set of security controls are usually integrated into an Information Security Management Program, the objective of which is to ensure an acceptable level of information confidentiality, integrity and availability within the organisation at all times (Pfleeger, 2003, pp.10-12; Whitman & Mattord, 2003, pp.1-14; Von Solms, 1993). Once the most effective security controls have been identified and implemented, it is important that this level of security be maintained through a process of continued control. For this reason, it is important that proper change management, measurement, audit, monitoring and detection be implemented (Bruce & Dempsey, 1997). Monitoring and detection are important functions and refer to the ability to identify and detect situations where information security policies have been compromised and/or breached or security violations have taken place (BS 7799, 1999; GMITS, 1998; Von Solms, 1993). The Information Security Officer is usually the person responsible for most of the operational tasks in the control process within an Information Security Management Program (Von Solms, 1993). In practice, these tasks could also be performed by a system administrator, network administrator, etc. In the rest of the thesis the person responsible for these tasks will be referred to as system administrator. These tasks have proved to be very challenging and demanding. The main reason for this is the rapid advancement of technology in the discipline of Information Technology, for example, the modern distributed computing environment, the Internet, the “freedom” of end-users, the introduction of e-commerce, and etc. (Whitman & Mattord, 2003, p.9; Sundaram, 2000, p.1; Moses, 2001, p.6; Allen, 2001, p.1). As a result of the importance of this control process, and especially the monitoring and detection tasks, it is vital that the system administrator has proper tools at his/her disposal to perform this task effectively. Many of the tools that are currently available to the system administrator, utilize technical controls, such as, audit logs and user profiles. Audit logs are normally used to record all events executed on a system. These logs are simply files that record security and non-security related events that take place on a computer system within an organisation. For this reason, these logs can be used by these tools to gain valuable information on security violations, such as intrusions and, therefore, are able to monitor the current actions of each user (Microsoft, 2002; Smith, 1989, pp. 116-117). User profiles are files that contain information about users` desktop operating environments and are used by the operating system to structure each user environment so that it is the same each time a user logs onto the system (Microsoft, 2002; Block, 1994, p.54). Thus, a user profile is used to indicate which actions the user is allowed to perform on the system. Both technical controls (audit logs and user profiles) are frequently available in most computer environments (such as, UNIX, Firewalls, Windows, etc.) (Cooper et al, 1995, p.129). Therefore, seeing that the audit logs record most events taking place on an information system and the user profile indicates the authorized actions of each user, the system administrator could most probably utilise these controls in a more proactive manner.
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- Date Issued: 2003