Browsing by Author "Le Hoang, Son"

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    ARM-AMO: An Efficient Association Rule Mining Algorithm Based on Animal Migration Optimization
    (Elsevier, 2018-05-10) Le Hoang, Son; Chiclana, Francisco; Kumar, Raghavendra; Mittal, Mamta; Khari, Manju; Chatterjee, Jyotir Moy; Baik, Sung Wook
    Association rule mining (ARM) aims to find out association rules that satisfy predefined minimum support and confidence from a given database. However, in many cases ARM generates extremely large number of association rules, which are impossible for end users to comprehend or validate, thereby limiting the usefulness of data mining results. In this paper, we propose a new mining algorithm based on Animal Migration Optimization (AMO), called ARM-AMO, to reduce the number of association rules. It is based on the idea that rules which are not of high support and unnecessary are deleted from the data. Firstly, Apriori algorithm is applied to generate frequent itemsets and association rules. Then, AMO is used to reduce the number of association rules with a new fitness function that incorporates frequent rules. It is observed from the experiments that, in comparison with the other relevant techniques, ARM-AMO greatly reduces the computational time for frequent item set generation, memory for association rule generation, and the number of rules generated.
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    Dynamic Structural Neural Network
    (IOS Press, 2017-12) Chiclana, Francisco; Cu Nguyen, Giap; Le Hoang, Son
    Artificial neural network (ANN) has been well applied in pattern recognition, classification and machine learning thanks to its high performance. Most ANNs are designed by a static structure whose weights are trained during a learning process by supervised or unsupervised methods. These training methods require a set of initial weights values, which are normally randomly generated, with different initial sets of weight values leading to different convergent ANNs for the same training set. Dealing with these drawbacks, a trend of dynamic ANN was invoked in the past year. However, they are either too complex or far from practical applications such as in the pathology predictor in binary multi-input multi-output (MIMO) problems, when the role of a symptom is considered as an agent, a pathology predictor’s outcome is formed by action of active agents while other agents’ activities seem to be ignored or have mirror effects. In this paper, we propose a new dynamic structural ANN for MIMO problems based on the dependency graph, which gives clear cause and result relationships between inputs and outputs. The new ANN has the dynamic structure of hidden layer as a directed graph showing the relation between input, hidden and output nodes. The properties of the new dynamic structural ANN are experienced with a pathology problem and its learning methods’ performances are compared on a real well known dataset. The result shows that both approaches for structural learning process improve the quality of ANNs during learning iteration.
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    Neutrosophic approach for enhancing quality of signals
    (Springer, 2019-03-07) Jha, S.; Kumar, Raghavendra; Le Hoang, Son; Chiclana, Francisco; Puri, V.; Priyadarshini, I.
    Information in a signal is often followed by undesirable disturbance which is termed as noise. Preventing noise in the signal leads to signal integrity, which also leads to better signal quality. The previous related works have the major issues while reducing noise in signals regarding assumptions, frequency and time domain, etc. This paper proposes a new Neutrosophic approach to reduce noises and errors in signal transmission. In the proposed method, confidence function is used as the truth membership function, which is associated with sampled time intervals. Then, we define a Dependency function at each time interval for the frequency of transmitted signal. Finally, a Falsehood function, which indicates the loss in information due to amplitude distortion, is defined. This function shows how much information has been lost. Our objective is to minimize the falsehood function using several neutrosophic systems. Experimental results show 1% decrease in loss compared to the original signal without PAM. It is shown the decrease of 0.1% if the frequency is shifted to a higher range.