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Some special properties of G A- and LS-based neural learning method

- Ghosh, Ranadhir

  • Authors: Ghosh, Ranadhir
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Journal of Intelligent Systems Vol. 14, no. 4 (2005), p. 289-319
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  • Description: Many works in the area of hybrid neural learning algorithms combine global and local based method for artificial neural network. In this paper, we discuss some special properties of a hybrid neural learning algorithm that combines the GA based method with least square based methods such as QR factorization. We look at different types of learning properties of this new hybrid algorithm, such as time complexity, convergence property, and the stability of the algorithm.
  • Description: C1
  • Description: 2003001361

The preliminary investigation of the factors that influence the E-learning adoption in higher education institutes: Jordan case study

- Al-Hawari, Maen, Al-Halabi, S.

  • Authors: Al-Hawari, Maen , Al-Halabi, S.
  • Date: 2010
  • Type: Text , Journal article
  • Relation: International Journal of Distance Education Technologies Vol. 8, no. 4 (2010), p. 1-11
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  • Description: Creativity and high performance in learning processes are the main concerns of educational institutions. E-learning contributes to the creativity and performance of these institutions and reproduces a traditional learning model based primarily on knowledge transfer into more innovative models based on collaborative learning. In this paper, the authors focus on the preliminary investigation of factors that influence e-learning adoption in Jordan. As a pioneer country for e-learning systems in the Middle East, an investigation has been completed for one of Jordan's universities that has implemented e-learning. Factors are defined through the analysis of unstructured interviews with developers and users of the e-learning systems, and Leximancer content analysis software is used to analyze the interview's content. Main factors include Internet, legislations, human factors, and Web content. Copyright © 2010, IGI Global.

Connection topologies for combining genetic and least square methods for neural learning

- Ghosh, Ranadhir

  • Authors: Ghosh, Ranadhir
  • Date: 2004
  • Type: Text , Journal article
  • Relation: Journal of Intelligent Systems Vol. 13, no. 3 (2004), p. 199-232
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  • Description: In the last few years, there have been many works in the area of hybrid neural learning algorithms combining a global and local based method for training artificial neural networks. In this paper, we discuss various connection strategies that can be applied to a special kind of a hybrid neural learning algorithm group, one that combines a genetic algorithm-based method with various least square-based methods like QR factorization. The relative advantages and disadvantages of the different connection types are studied to find a suitable connection topology for combining the two different learning methods. The methodology also finds the optimum number of hidden neurons using a hierarchical combination methodology structure for weights and architecture. We have tested our proposed approach on XOR, 10 bit odd parity, and some other real-world benchmark data sets, such as the hand-writing character dataset from CEDAR, Breast cancer, and Heart Disease from the UCI machine learning repository.
  • Description: C1

A Grid-based neural network framework for multimodal biometrics

- Venkatraman, Sitalakshmi

  • Authors: Venkatraman, Sitalakshmi
  • Date: 2010
  • Type: Text , Journal article
  • Relation: Proceedings of World Academy of Science, Engineering and Technology Vol. 72, no. (2010), p. 298-303
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  • Description: Recent scientific investigations indicate that multimodal biometrics overcome the technical limitations of unimodal biometrics, making them ideally suited for everyday life applications that require a reliable authentication system. However, for a successful adoption of multimodal biometrics, such systems would require large heterogeneous datasets with complex multimodal fusion and privacy schemes spanning various distributed environments. From experimental investigations of current multimodal systems, this paper reports the various issues related to speed, error-recovery and privacy that impede the diffusion of such systems in real-life. This calls for a robust mechanism that caters to the desired real-time performance, robust fusion schemes, interoperability and adaptable privacy policies. The main objective of this paper is to present a framework that addresses the abovementioned issues by leveraging on the heterogeneous resource sharing capacities of Grid services and the efficient machine learning capabilities of artificial neural networks (ANN). Hence, this paper proposes a Grid-based neural network framework for adopting multimodal biometrics with the view of overcoming the barriers of performance, privacy and risk issues that are associated with shared heterogeneous multimodal data centres. The framework combines the concept of Grid services for reliable brokering and privacy policy management of shared biometric resources along with a momentum back propagation ANN (MBPANN) model of machine learning for efficient multimodal fusion and authentication schemes. Real-life applications would be able to adopt the proposed framework to cater to the varying business requirements and user privacies for a successful diffusion of multimodal biometrics in various day-to-day transactions.

Determining the influence of visual training on EEG activity patterns using association rule mining

- Yan, Fangang, Watters, Paul, Wang, Wei

  • Authors: Yan, Fangang , Watters, Paul , Wang, Wei
  • Date: 2011
  • Type: Text , Conference proceedings
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  • Description: To confirm that visual training can change EEG patterns by association rule mining method, firstly, we collected the EEG of people who are under a long-term visual professional training (visual training group) and novice people (control group) during a specific mental tasks. Secondly, we determined the difference of brain electrical activity between the two groups using machine learning methods. Thirdly, we discovered distinct patterns using association rule algorithm, finding that the two groups were separable based on their completion of visual professional cognitive tasks. In the beta band, visual training group showed a specific and significant association pattern which included FP1 and C4. The results indicate that the EEG patterns were modified because of visual professional training. We further discuss the impact of long-term visual professional training on the EEG. © 2011 IEEE.

Improving Naive Bayes classifier using conditional probabilities

- Taheri, Sona, Mammadov, Musa, Bagirov, Adil

  • Authors: Taheri, Sona , Mammadov, Musa , Bagirov, Adil
  • Date: 2010
  • Type: Text , Conference proceedings
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  • Description: Naive Bayes classifier is the simplest among Bayesian Network classifiers. It has shown to be very efficient on a variety of data classification problems. However, the strong assumption that all features are conditionally independent given the class is often violated on many real world applications. Therefore, improvement of the Naive Bayes classifier by alleviating the feature independence assumption has attracted much attention. In this paper, we develop a new version of the Naive Bayes classifier without assuming independence of features. The proposed algorithm approximates the interactions between features by using conditional probabilities. We present results of numerical experiments on several real world data sets, where continuous features are discretized by applying two different methods. These results demonstrate that the proposed algorithm significantly improve the performance of the Naive Bayes classifier, yet at the same time maintains its robustness. © 2011, Australian Computer Society, Inc.
  • Description: 2003009505

The ballarat incremental knowledge engine

- Dazeley, Richard, Warner, Philip, Johnson, Scott, Vamplew, Peter

  • Authors: Dazeley, Richard , Warner, Philip , Johnson, Scott , Vamplew, Peter
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: Paper pressented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 195-207
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  • Description: Ripple Down Rules (RDR) is a maturing collection of methodologies for the incremental development and maintenance of medium to large rule-based knowledge systems. While earlier knowledge based systems relied on extensive modeling and knowledge engineering, RDR instead takes a simple no-model approach that merges the development and maintenance stages. Over the last twenty years RDR has been significantly expanded and applied in numerous domains. Until now researchers have generally implemented their own version of the methodologies, while commercial implementations are not made available. This has resulted in much duplicated code and the advantages of RDR not being available to a wider audience. The aim of this project is to develop a comprehensive and extensible platform that supports current and future RDR technologies, thereby allowing researchers and developers access to the power and versatility of RDR. This paper is a report on the current status of the project and marks the first release of the software. © 2010 Springer-Verlag Berlin Heidelberg.

Skype Traffic Classification Using Cost Sensitive Algorithms

- Azab, Azab, Layton, Robert, Alazab, Mamoun, Watters, Paul

  • Authors: Azab, Azab , Layton, Robert , Alazab, Mamoun , Watters, Paul
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: Proceedings - 4th Cybercrime and Trustworthy Computing Workshop, CTC 2013 p. 14-21
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  • Description: Voice over IP (VoIP) technologies such as Skype are becoming increasingly popular and widely used in different organisations, and therefore identifying the usage of this service at the network level becomes very important. Reasons for this include applying Quality of Service (QoS), network planning, prohibiting its use in some networks and lawful interception of communications. Researchers have addressed VoIP traffic classification from different viewpoints, such as classifier accuracy, building time, classification time and online classification. This previous research tested their models using the same version of a VoIP product they used for training the model, giving generalizability only to that version of the product. This means that as new VoIP versions are released, these classifiers become obsolete. In this paper, we address if this approach is applicable to detecting new, untrained, versions of Skype. We suggest that using cost-sensitive classifiers can help to improve the accuracy of detecting untrained versions, by testing compared to other algorithms. Our experiment demonstrates promising preliminary results to detect Skype version 4, by building a cost sensitive classifier on Skype version 3, achieving an F-measure score of 0.57. This is a drastic improvement from not using cost sensitivity, which scores an F-measure of 0. This approach may be enhanced to improve the detection results and extended to improve detection for other applications that change protocols from version to version.

Structure learning of Bayesian Networks using global optimization with applications in data classification

- Taheri, Sona, Mammadov, Musa

  • Authors: Taheri, Sona , Mammadov, Musa
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Optimization Letters Vol. 9, no. 5 (2014), p. 931-948
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  • Description: Bayesian Networks are increasingly popular methods of modeling uncertainty in artificial intelligence and machine learning. A Bayesian Network consists of a directed acyclic graph in which each node represents a variable and each arc represents probabilistic dependency between two variables. Constructing a Bayesian Network from data is a learning process that consists of two steps: learning structure and learning parameter. Learning a network structure from data is the most difficult task in this process. This paper presents a new algorithm for constructing an optimal structure for Bayesian Networks based on optimization. The algorithm has two major parts. First, we define an optimization model to find the better network graphs. Then, we apply an optimization approach for removing possible cycles from the directed graphs obtained in the first part which is the first of its kind in the literature. The main advantage of the proposed method is that the maximal number of parents for variables is not fixed a priory and it is defined during the optimization procedure. It also considers all networks including cyclic ones and then choose a best structure by applying a global optimization method. To show the efficiency of the algorithm, several closely related algorithms including unrestricted dependency Bayesian Network algorithm, as well as, benchmarks algorithms SVM and C4.5 are employed for comparison. We apply these algorithms on data classification; data sets are taken from the UCI machine learning repository and the LIBSVM. © 2014, Springer-Verlag Berlin Heidelberg.

Commentary : A decomposition of the outlier detection problem into a set of supervised learning problems

- Zhu, Ye, Ting, Kaiming

  • Authors: Zhu, Ye , Ting, Kaiming
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Machine Learning Vol. 105, no. 2 (2016), p. 301-304
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  • Description: This article discusses the material in relation to iForest (Liu et al. in ACM Trans Knowl Discov Data 6(1):3, 2012) reported in a recent Machine Learning Journal paper by Paulheim and Meusel (Mach Learn 100(2–3):509–531, 2015). It presents an empirical comparison result of iForest using the default parameter settings suggested by its creator (Liu et al. 2012) and iForest using the settings employed by Paulheim and Meusel (2015). This comparison has an impact on the conclusion made by Paulheim and Meusel (2015). © 2016, The Author(s).

Categorical features transformation with compact one-hot encoder for fraud detection in distributed environment

- Ul Haq, Ikram, Gondal, Iqbal, Vamplew, Peter, Brown, Simon

  • Authors: Ul Haq, Ikram , Gondal, Iqbal , Vamplew, Peter , Brown, Simon
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2019 16th Australasian Conference on Data Mining, AusDM 2018; Bathurst, NSW; 28 November 2018 through 30 November 2018 Vol. 996, p. 69-80
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  • Description: Fraud detection for online banking is an important research area, but one of the challenges is the heterogeneous nature of transactions data i.e. a combination of numeric as well as mixed attributes. Usually, numeric format data gives better performance for classification, regression and clustering algorithms. However, many machine learning problems have categorical, or nominal features, rather than numeric features only. In addition, some machine learning platforms such as Apache Spark accept numeric data only. One-hot Encoding (OHE) is a widely used approach for transforming categorical features to numerical features in traditional data mining tasks. The one-hot approach has some challenges as well: the sparseness of the transformed data and that the distinct values of an attribute are not always known in advance. Other than the model accuracy, compactness of machine learning models is equally important due to growing memory and storage needs. This paper presents an innovative technique to transform categorical features to numeric features by compacting sparse data even if all the distinct values are not known. The transformed data can be used for the development of fraud detection systems. The accuracy of the results has been validated on synthetic and real bank fraud data and a publicly available anomaly detection (KDD-99) dataset on a multi-node data cluster. © Springer Nature Singapore Pte Ltd. 2019.

Protein complexes detection based on global network representation learning

- Xu, Bo, Li, Kun, Liu, Xiaoxia, Liu, Delong, Zhang, Yijia, Lin, Hongfei, Yang, Zhihao, Wang, Jian, Xia, Feng

  • Authors: Xu, Bo , Li, Kun , Liu, Xiaoxia , Liu, Delong , Zhang, Yijia , Lin, Hongfei , Yang, Zhihao , Wang, Jian , Xia, Feng
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); Madrid, Spain; 3-6 Dec. 2018 p. 210-213
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  • Description: Detecting protein complexes from protein-protein interaction (PPI) networks allows biologists reveal the principle of cellular organization and functions. Existing computational methods try to incorporate biological evidence to enhance the quality of predicted complexes. However, it is still a challenge to integrate biological information into complexes discovery process under a unified framework. Recently, network embedding methods showed their effectiveness in graph data analysis tasks. It provides a framework for incorporating both network structure and additional node attribute information. This salient feature is particularly desirable in the context of protein complexes identification. However, none of the existing network embedding methods take node attribute proximity and high-order structure proximity into account at the same time. In this paper, we propose a novel global network embedding method, which preserves global network structure and biological information. We utilize this global representation learning method to learn vector representation for proteins. Then, we use a seed-extension clustering method to discover overlapping protein complexes with the embedding results. This novel protein complexes detection method we called GLONE. Evaluated on five real yeast PPI networks, our method outperforms the competing algorithms in terms of different evaluation metrics.

A prioritized objective actor-critic method for deep reinforcement learning

- Nguyen, Ngoc, Nguyen, Thanh, Vamplew, Peter, Dazeley, Richard, Nahavandi, Saeid

  • Authors: Nguyen, Ngoc , Nguyen, Thanh , Vamplew, Peter , Dazeley, Richard , Nahavandi, Saeid
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 33, no. 16 (2021), p. 10335-10349
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  • Description: An increasing number of complex problems have naturally posed significant challenges in decision-making theory and reinforcement learning practices. These problems often involve multiple conflicting reward signals that inherently cause agents’ poor exploration in seeking a specific goal. In extreme cases, the agent gets stuck in a sub-optimal solution and starts behaving harmfully. To overcome such obstacles, we introduce two actor-critic deep reinforcement learning methods, namely Multi-Critic Single Policy (MCSP) and Single Critic Multi-Policy (SCMP), which can adjust agent behaviors to efficiently achieve a designated goal by adopting a weighted-sum scalarization of different objective functions. In particular, MCSP creates a human-centric policy that corresponds to a predefined priority weight of different objectives. Whereas, SCMP is capable of generating a mixed policy based on a set of priority weights, i.e., the generated policy uses the knowledge of different policies (each policy corresponds to a priority weight) to dynamically prioritize objectives in real time. We examine our methods by using the Asynchronous Advantage Actor-Critic (A3C) algorithm to utilize the multithreading mechanism for dynamically balancing training intensity of different policies into a single network. Finally, simulation results show that MCSP and SCMP significantly outperform A3C with respect to the mean of total rewards in two complex problems: Food Collector and Seaquest. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
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