Knowledge Management

Bioinformatics — The Machine Learning Approach

/pdf/network-intrusion-detection-an-analyst-s-handbook-56wcp68j2p.pdf

Network Intrusion Detection: An Analyst’s Handbook

The general model

Neural networks demonstrate great potential for discovering non-linear relationships
in time-series and extrapolating from them. Results of forecasting using financial data are
particularly good [LapFar87, Schone90, ChaMeh92]. In contrast, traditional statistical
methods are restrictive as they try to express these non-linear relationships as linear models.
This thesis investigates the use of the Backpropagation neural model for time-series
forecasting. In general, neural forecasting research [Hinton87] can be approached in three
ways: research into, the weight space, into the physical representation of inputs, and into the
learning algorithms. A new method to enhance input representations to a neural network,
referred to as model sNx, has been developed. It has been studied alongside a traditional
method in model N. The two methods reduce the unprocessed network inputs to a value
between 0 and 1. Unlike the method in model N, the variants of model sNx, sN1 and sN2,
accentuate the contracted input value by different magnitudes. This different approach to
data reduction exploits the characteristics of neural extrapolation to achieve better forecasts.
The feasibility of the principle of model sNx has been shown in forecasting the direction of
the FFSE-100 Index.
The experimental strategy involved optimisation procedures using one data set and
the application of the optimal network from each model to make forecasts on different data
sets with similar and dissimilar patterns to the first.
A Neural Forecasting System (NFS) has been developed as a vehicle for the research.
The NFS offers historical and live simulations, and supports: a data alignment facility for
standardising data files with non-uniform sampling times and volumes, and merging them
into a spreadsheet; a parameter specification table for specifications of neural and system
control parameter values; a pattern specification language for specification of input pattern
formation using one or more time-series, and loading to a configured network; a snapshot
facility for re-construction of a partially trained network to continue or extend a training
session, or re-construction of a trained network to forecast for live tests; and a log facility for
recording experimental results.
Using the NFS, specific pattern features selected from major market trends have been
investigated [Pring8O]: triple-top ('three peaks'), double-top ('two peaks'), narrow
band ('modulating'), bull ('rising') and recovery ('U-turn'). Initially, the triple-top pattern
was used in the N model to select between the logarithmic or linear data form for presenting
raw input data. The selected linear method was then used in models sN1, sN2 and N for
network optimisations. Experiments undertaken used networks of permutations of sizes of
input nodes (I), hidden nodes (H), and tolerance value. Selections were made for: the best
method, by value, direction, or value and direction, for measuring prediction accuracy; the best configuration function, H - I 4), with 4) equal to 0.9, 2 or 3; and the better of sN1 and
sN2. The evaluation parameters were, among others, the prediction accuracy (%), the
weighted return (%), the Relative Threshold Prediction Index (RTPI) indicator, the forecast
error margins. The RTPI was developed to filter out networks forecasting above a minimum
prediction accuracy with a credit in the weighted return (%). Two optimal networks, one
representing model sNx and one N were selected and then tested on the double-top, narrow
band, bull and recovery patterns.
This thesis made the following research conthbutions.
• A new method in model sNx capable of more consistent and accurate predictions.
• The new RTPI neural forecasting indicator.
• A method to forecast during the consolidation ('non-diversifying') trend which most
traditional methods are not good at.
• A set of improvements for more effective neural forecasting systems.

Neural networks for financial forecasting

The present statistical models used for forecasting cannot effectively handle uncertainty and instability nature of foreign exchange data. In this work, an artificial neural network foreign exchange rate forecasting model (AFERFM) was designed for foreign exchange rate forecasting to correct some of these problems. The design was divided into two phases, namely: training and forecasting. In the training phase, back propagation algorithm was used to train the foreign exchange rates and learn how to approximate input. Sigmoid Activation Function (SAF) was used to transform the input into a standard range [0, 1]. The learning weights were randomly assigned in the range [-0.1, 0.1] to obtain the output consistent with the training. SAF was depicted using a hyperbolic tangent in order to increase the learning rate and make learning efficient. Feed forward Network was used to improve the efficiency of the back propagation. Multilayer Perceptron Network was designed for forecasting. The datasets from oanda website were used as input in the back propagation for the evaluation and forecasting of foreign exchange rates. The design was implemented using matlab7.6 and visual studio because of their supports for implementing forecasting system. The system was tested using mean square error and standard deviation with learning rate of 0.10, an input layer, 3 hidden layers and an output layer. The best known related work, Hidden Markov foreign exchange rate forecasting model (HFERFM) showed an accuracy of 69.9% as against 81.2% accuracy of AFERFM. This shows that the new approach provided an improved technique for carrying out foreign exchange rate forecasting. KeywordsArtificial Neural Network; Back propagation Algorithm; Hidden Markov Model; BaumWeld Algorithm; Sigmoid Activation Function and Foreign Exchange Rate.

http://www.datascienceassn.org/sites/default/files/Artificial%20Neural%20Network%20Model%20for%20Forecasting%20Foreign%20Exchange%20Rate.pdf

Artificial neural network model for forecasting foreign exchange rate

A Predictive Model for Phishing Detection

Researchers have used many techniques in designing intrusion detection systems (IDS) and yet we still do not have an effective IDS. The interest in this work is to combine techniques of data mining and expert systems in designing an effective anomaly‐based IDS. Combining methods may give better coverage, and make the detection more effective. The idea is to mine system audit data for consistent and useful patterns of user behaviour, and then keep these normal behaviours in profiles. An expert system is used as the detection system that recognizes anomalies and raises an alarm. The evaluation of the intrusion detection system design was carried out to justify the importance of the work.

A new two‐tiered strategy to intrusion detection

Intercropping, which is the agricultural practice of growing two or more crops in the same land area, is not currently yielding adequate results in Africa. Despite the advantages of intercropping like improved soil fertility, protection against pests and diseases and eventual increase in farm yield, this farming practice is faced with challenges-inadequate planning, bad crop management and lack of required intercropping expertise. Consequently, this has resulted in inadequate reward for farmers and a general decline in crop production. In this regard, the authors present an Intelligent and Integrated Intercropping Decision Support System for Intercropping IDSS-I for improved crop production. The design adopts a forecasting component that provides farmers with the estimated yield and income depending on the size of land, soil type and weather condition. Although the implementation was carried out using JAVA and SQL, usability testing revealed 85% acceptance of the tool among the contacted 10 large scale farmers. It was also confirmed that the system provided 95% diagnosis information for 90% common Africa crop diseases.

An Integrated Decision Support System for Intercropping

Recently, the use of mobile applications in knowledge management systems has been a majordiscussion in some literatures. In the past, the author’s conceptualized and designs a knowledge management system for “Ifa”. In this paper, a mobile application is designed which applies mobile knowledge management (mKM) to the “ifa” oracle consultation process implemented with the Netbeans 6.7.1 JavaME IDE and is compatible with MIDP 2.0 and CLDC 1.1 mobile devices. However, we address the potential suitability of mKM as a prototype model for mobile, quick information retrieval in the “ifa” oracle consultation process. This approach promotes the use of mobile technology and mobile knowledge management in African tradition.

	 

	Key words: Mobile knowledge management, “ifa”, African tradition, expert system, Nigeria.

https://www.academicjournals.org/article/article1379677191_Folorunso et al.pdf

A. T. Akinwale

Papers

Artificial neural network model for forecasting foreign exchange rate

A Predictive Model for Phishing Detection

A new two‐tiered strategy to intrusion detection

An Integrated Decision Support System for Intercropping

Population prediction using artificial neural network