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Extreme learning machines (ELMs) has been theoretically and experimentally proved to achieve promising performance at a fast learning speed for supervised classification tasks. However, it does not perform well on imbalanced binary classification tasks and tends to get biased toward the majority class. Besides, since a large amount of training data with labels are not always available in the real world, there is an urgent demand to develop an efficient semi-supervised version of ELM for imbalanced binary classification tasks. In this article, owing to the distinct insensitivity of area under the ROC curve (AUC) to both class skews and changes of class distributions, we focus the study on integrating AUC maximization into the ELM framework to tackle with imbalanced binary classification tasks well. By demystifying the AUC metric with the ELM framework, we develop a new AUC-based ELM called AUC-ELM for imbalanced binary classification, which essentially is revealed to be equivalent to an ELM on another transformed data space. Accordingly, its semi-supervised version called SAUC-ELM is also developed. Both AUC-ELM and SAUC-ELM have the distinctive merits: 1) they share the advantage of ELM in both generalization capability and training efficiency, and further uniquely tailored for imbalanced binary classification tasks and 2) in contrast to the existing imbalanced variants of ELM, such as class-specific cost regulation ELM and semi-supervised ELM, they have fewer parameters to tune, thereby reducing the computational cost for model selection. Experiments on a heap of datasets show that both AUC-ELM and SAUC-ELM outperform the other comparative methods in terms of both classification performance and training speed.

The use of sensors for monitoring livestock has opened up new possibilities for the management of livestock in extensive grazing systems. The work presented in this paper aimed to develop a model for predicting the metabolisable energy intake (MEI) of sheep by using temperature, pitch angle, roll angle, distance, speed, and grazing time data obtained directly from wearable sensors on the sheep. A Deep Belief Network (DBN) algorithm was used to predict MEI, which to our knowledge, has not been attempted previously. The results demonstrated that the DBN method could predict the MEI for sheep using sensor data alone. The mean square error (MSE) values of 4.46 and 20.65 have been achieved using the DBN model for training and testing datasets, respectively. We also evaluated the influential sensor data variables, i.e., distance and pitch angle, for predicting the MEI. Our study demonstrates that the application of machine learning techniques directly to on-animal sensor data presents a substantial opportunity to interpret biological interactions in grazing systems directly from sensor data. We expect that further development and refinement of this technology will catalyse a step-change in extensive livestock management, as wearable sensors become widely used by livestock producers.

Deep learning (DL) has achieved superior classification in many applications due to its capability of extracting features from the data. However, the success of DL comes with the tradeoff of possible overfitting. The bias towards the data it has seen during the training process leads to poor generalisation. One way of solving this issue is by having enough training data so that the classifier is invariant to many data patterns. In the literature, data augmentation has been used as a type of regularisation method to reduce the chance for the model to overfit. However, most of the relevant works focus on image, sound or text data. There is not much work on numerical data augmentation, although many real-world problems deal with numerical data. In this paper, we propose using a technique based on Fuzzy C-Means clustering and fuzzy membership grades. Fuzzy-related techniques are used to address the variance problem by generating new data items based on fuzzy numbers and each data item's belongings to different fuzzy clusters. This data augmentation technique is used to improve the generalisation of a Deep Neural Network that is suitable for numerical data. By combining the proposed fuzzy data augmentation technique with the Dropout regularisation technique, we manage to balance the classification model's bias-variance tradeoff. Our proposed technique is evaluated using four popular data sets and is shown to provide better regularisation and higher classification accuracy compared with popular regularisation approaches.

Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor systems as well as the continued use of unstructured manual data recording mechanisms. Irregular data and the resulting missing values severely limit the data's ability to be analysed and modelled for classification and forecasting tasks. Often, conventional methods used for handling time series data introduce bias and make strong assumptions on the underlying data generation process, which can lead to poor model predictions. Traditional machine learning and deep learning methods, although at the forefront of data modelling, are at best compromised by irregular time series data sets and fail to model the temporal irregularity of incomplete time series. Gated recurrent neural networks (RNN), such as LSTM and GRU, have had outstanding success in sequential modelling, and have been applied in many application fields, including natural language processing. These models have become an obvious choice for time series modelling and a promising tool for handling irregular time series data. RNNs have a unique ability to be adapted to make effective use of missing value patterns, time intervals and complex temporal dependencies in irregular univariate and multivariate time series data. In this paper, we provide a systematic review of recent studies in which gated recurrent neural networks have been successfully applied to irregular time series data for prediction tasks within several fields, including medical, human activity recognition, traffic monitoring and environmental monitoring. The review highlights the two common approaches for handling irregular time series data: missing value imputation at the data pre-processing stage and modification of algorithms to directly handle missing values in the learning process. Reviewed models are confined to those that can address issues with irregular time series data and does not cover the broader range of models that deal more generally with sequences and regular time series. This paper aims to present the most effective techniques emerging within this branch of research as well as to identify remaining challenges, so that researchers may build upon this platform of work towards further novel techniques for handling irregular time series data.

Analysis of time series data has been a challenging research subject for decades. Email traffic has recently been modelled as a time series function using a Recurrent Neural Network (RNN) and RNNs were shown to provide higher prediction accuracy than previous probabilistic models from the literature. Given the exponential rise of email workloads which need to be handled by email servers, in this paper we first present and discuss the literature on modelling email traffic. We then explain the advantages and limitations of different approaches as well as their points of agreement and disagreement. Finally, we present a comprehensive comparison between the performance of RNN and Long Short Term Memory (LSTM) models. Our experimental results demonstrate that both approaches can achieve high accuracy over four large datasets acquired from different universities’ servers, outperforming existing work, and show that the use of LSTM and RNN is very promising for modelling email traffic.

The effectiveness of Gesture-Based Learning System (GBLS) has been reported in some recent studies. However, not many of those studies have investigated on how GBLS mode influences the learning outcomes. The aim of this study therefore focuses on investigating how GBLS mode impacts the learning outcomes. The findings of this study revealed that GBLS's features positively affect the students' intrinsic motivation. Consequently, the increase in the intrinsic motivation leads to improving the learning outcomes; this study also showed that GBLS's features indirectly influence the learning outcomes via intrinsic motivation. In other words, this study found that the GBLS's features (interactivity and multimodality) create an instructional learning environment that positively influences the students' intrinsic motivation. The increase of the students' positive intrinsic motivation led to enhancing the learning achievements of students.

Nave Bayes, k-nearest neighbors, Adaboost, support vector machines and neural networks are five among others commonly used text classifiers. Evaluation of these classifiers involves a variety of factors to be considered including benchmark used, feature selections, parameter settings of algorithms, and the measurement criteria employed. Researchers have demonstrated that some algorithms outperform others on some corpus, however, inconsistency of human labeling and high dimensionality of feature spaces are two issues to be addressed in text categorization. This paper focuses on evaluating the five commonly used text classifiers by using an automatically generated text document collection which is labeled by a group of experts to alleviate subjectivity of human category assignments, and at the same time to examine the influence of the number of features on the performance of the algorithms.

The performance of classification algorithms with imbalanced streaming data depends upon efficient re-balancing strategy for learning tasks. The difficulty becomes more elevated with multi-class highly imbalanced streaming data. In this paper, we investigate the multi-class imbalance problem in data streams and develop an adaptive framework to cope with imbalanced data scenarios. The proposed One-Vs-All Adaptive Window re-Balancing with Retain Knowledge (OVA-AWBReK) classification framework will combine OVA binarization with Automated Re-balancing Strategy (ARS) using Racing Algorithm (RA). We conducted experiments on highly imbalanced datasets to demonstrate the use of the proposed OVA-AWBReK framework. The results show that OVA-AWBReK framework can enhance the classification performance of the multi-class highly imbalanced data.

Recently, extensive use of digital music has led to an increase in songs in online music applications and personal music libraries. In large music libraries, songs which are not listened to regularly, most probably will go unnoticed. There are many ways of browsing songs in an online music library. In the field of Music Information Retrieval (MIR), some type of visual forms such as colour, avatar, mood picture and album cover to visualise music, have been introduced. However, there is no research focusing explicitly on textures. To create new method of browsing music, we proposed a framework to visualise music mood using visual texture. In order to determine how well people can interact with the visual texture to browse songs in music library, usability testing was conducted. In this paper, we will present the results of the usability testing.

Word representations are created using analogy context-based statistics and lexical relations on words. Word representations are inputs for the learning models in Natural Language Understanding (NLU) tasks. However, to understand language, knowing only the context is not sufficient. Reading between the lines is a key component of NLU. Embedding deeper word relationships which are not represented in the context enhances the word representation. This paper presents a word embedding which combines an analogy, context-based statistics using Word2Vec, and deeper word relationships using Conceptnet, to create an expanded word representation. In order to fine-tune the word representation, Self-Organizing Map is used to optimize it. The proposed word representation is compared with semantic word representations using Simlex 999. Furthermore, the use of 3D visual representations has shown to be capable of representing the similarity and association between words. The proposed word representation shows a Spearman correlation score of 0.886 and provided the best results when compared to the current state-of-the-art methods, and exceed the human performance of 0.78.

Data mining is the analytics and knowledge discovery process of analyzing large volumes of data from various sources and transforming the data into useful information. Various disciplines have contributed to its development and is becoming increasingly important in the scientific and industrial world. This article presents a review of data mining techniques and applications from 1996 to 2016. Techniques are divided into two main categories: predictive methods and descriptive methods. Due to the huge number of publications available on this topic, only a selected number are used in this review to highlight the developments of the past 20 years. Applications are included to provide some insights into how each data mining technique has evolved over the last two decades. Recent research trends focus more on large data sets and big data. Recently there have also been more applications in area of health informatics with the advent of newer algorithms.

One of the fundamental tasks in the management of livestock is to understand their behaviour and use this information to increase livestock productivity and welfare. Developing new and improved methods to classify livestock behaviour based on their daily activities can greatly improve livestock management. In this paper, we propose the use of a hierarchical machine learning method to classify livestock behaviours. We first classify the livestock behaviours into two main behavioural categories. Each of the two categories is then broken down at the next level into more specific behavioural categories. We have tested the proposed methodology using two commonly used classifiers, Random Forest, Support Vector Machine and a newer approach involving Deep Belief Networks. Our results show that the proposed hierarchical classification technique works better than the conventional approach. The experimental studies also show that Deep Belief Networks perform better than the Random Forest and Support Vector Machine for most cases.

Information overloading leads to the need for an efficient search tool to eliminate a considerable amount of irrelevant or unimportant data and present the contents in an easy-browsing form. Personalized faceted search has been one of the potential tools to provide a hierarchical list of facets or categories that helps searchers to organize the information of the search results. Facet selection is one of the important steps to pursue a good faceted search. Collaborative-based personalization was introduced to facet selection. Previous studies have been performed on the use of Collaborative Filtering techniques for personalized facet selection. However, none of the study has investigated Artificial neural network techniques on personalized facet selection. Therefore, this study aims to investigate the possible use of deep Autoencoder on the prediction of facet interests. Autoencoder model was applied to address the association of collaborative interest in facets. The experiments were conducted on 100K and 1M rating records of Movielen dataset. Rating score was used to represent the explicit feedback on facet interests. The performance was reported by comparing the proposed technique and the state-of-the-art model-based Collaborative Filtering techniques in terms of prediction accuracy and computational time. The results showed that the proposed Autoencoder-based model achieved better performance and it was able to significantly improve the prediction of personal facet interests.

Text clustering is an effective way that helps crime investigation through grouping of crime-related documents. This paper proposes a Memetic Algorithm Feature Selection (MAFS) approach to enhance the performance of document clustering algorithms used to partition crime reports and criminal news as well as some benchmark text datasets. Two clustering algorithms have been selected to demonstrate the effectiveness of the proposed MAFS method; they are the k-means and Spherical k-means (Spk). The reason behind using these clustering methods is to observe the performance of these algorithms before and after applying a hybrid FS that uses a Memetic scheme. The proposed MAFS method combines a Genetic Algorithm-based wrapper FS with the Relief-F filter. The performance evaluation was based on the clustering outcomes before and after applying the proposed MAFS method. The test results showed that the performance of both k-means and spk improved after the MAFS.

Dyslexia is a disability that causes difficulties in reading and writing despite average intelligence. This hidden disability often goes undetected since dyslexics are normal and healthy in every other way. Electroencephalography (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. The aims of this paper are to examine pros and cons of existing EEG-based pattern classification frameworks for dyslexia and recommend optimisations through the findings to assist future research. A critical analysis of the literature is conducted focusing on each framework’s (1) data collection, (2) pre-processing, (3) analysis and (4) classification methods. A wide range of inputs as well as classification approaches has been experimented for the improvement in EEG-based pattern classification frameworks. It was uncovered that incorporating reading- and writing-related tasks to experiments used in data collection may help improve these frameworks instead of using only simple tasks, and those unwanted artefacts caused by body movements in the EEG signals during reading and writing activities could be minimised using artefact subspace reconstruction. Further, support vector machine is identified as a promising classifier to be used in EEG-based pattern classification frameworks for dyslexia.

Augmented Reality (AR) is a technology that augments reality with either two or three dimensional computer generated imagery (CGI), objects and/or information, and allows users to interact with them. AR on mobile devices are evolving and offer a great deal of potential in terms of learning and training. This paper discusses the development process of a mobile prototype learning environment that utilises mobile-Augmented Reality (mAR). The prototype is called the Human Anatomy in Mobile Augmented Reality or HuMAR, and the selected learning topic is the anatomy of the human skeletal structure. The main objective of HuMAR is to aid students and it could potentially enhance their learning process. There has been a report stating that there is a decline in retaining and generating long lasting information longer when learning the abovementioned topic. This paper describes the theory, concept, prototype development and results of HuMAR taken from a pilot test. The pilot test used the experimental method with science's students from three different universities. The objectives of the pilot test were to consolidate users’ experience from a didactic and technical point of view. Based on the results of the pilot test, it is concluded that students were satisfied with HuMAR in terms of its usability and features; which in turn could have a positive impact in their learning process.

In the real world of credit card fraud detection, due to a minority of fraud related transactions, has created a class imbalance problem. With the increase of transactions at massive scale, the imbalanced data is immense and has created a challenging issue on how well Machine Learning (ML) techniques can scale up to efficiently learn to detect fraud from the massive incoming data and to respond faster with high prediction accuracy and reduced misclassification costs. This paper is based on experiments that compared several popular ML techniques and investigated their suitability as a “scalable algorithm” when working with highly imbalanced massive or “Big” datasets. The experiments were conducted on two highly imbalanced datasets using Random Forest, Balanced Bagging Ensemble, and Gaussian Naive Bayes. We observed that many detection algorithms performed well with medium-sized dataset but struggled to maintain similar predictions when it is massive.

A hybrid sampling technique is proposed by combining Complementary Fuzzy Support Vector Machine (CMTFSVM) and Synthetic Minority Oversampling Technique (SMOTE) for handling the imbalanced classification problem. The proposed technique uses an optimised membership function to enhance the classification performance and it is compared with three different classifiers. The experiments consisted of four standard benchmark datasets and one real world data of plant cells. The results revealed that implementing CMTFSVM followed by SMOTE provided better result over other FSVM classifiers for the benchmark datasets. Furthermore, it presented the best result on real world dataset with 0.9589 of G-mean and 0.9598 of AUC. It can be concluded that the proposed techniques work well with imbalanced benchmark and real world data.

Emotion classification and recognition from electroencephalogram (EEG) signals have been studied extensively due to its potential benefits such as entertainment and health care. Concerning classification, various techniques have been developed and applied. Support Vector Machines (SVMs) has been reported as the most used because of its accuracy. Nevertheless, although SVMs has satisfactory performance, it is unable to provide explanation of the relationships between a model's inputs and outputs. Specifically, it is desirable for a medical application for diagnosis to provide comprehensible rules. Consequently, SVM might not be suitable. In this study, SVM is treated as a black-box and then rules are extracted using the Classification And Regression Trees (CART) approach. A dataset from the Database for Emotion Analysis using Physiological Signals (DEAP) is used in this study. The experimental results show that although a classic SVM model has provided the best accuracy, a rule extraction model from SVM output by CART (SVM-CART) is better than a basic CART model. Therefore, the proposed SVM-CART approach is suitable for applications which need explanations and comprehensibility, such as medical applications.

Spatial interpolation methods are normally used to create aerial rainfall maps from remote measuring data collected by raingauge network. However, most spatial interpolation methods are not in the form of interpretable data models. This could make further analysis on the spatial data difficult. This paper proposes a methodology to analyze and establish an interpretable fuzzy model for monthly rainfall spatial interpolation. The proposed methodology integrates the benefits of various soft computing techniques. The final outcome is the proposal of an interpretable fuzzy model that allows human analysts to gain insight into the spatial data to be modeled. The accuracy of the model is evaluated by eight monthly rainfall data in the northeast region of Thailand. The interpretability of the model is assessed by the interpretable fuzzy modeling criteria. The experimental results showed that the proposed methodology could be an alternative technique to create rainfall maps and to understand the characteristics of the spatial data.

Effective system development to support higher education institutions is important. There are two steps in developing a good system and they are system architecture analysis and design. The first task of this research is to design the use case diagrams, system overview and the system architecture. The second is to evaluate the system architecture of a proposed Student Relationship Management (SRM) system using Internet of Things (IoT) to collect digital footprint of higher education institutions. The outcome of this research include the system architecture of the proposed student relationship management system (SRMS)-IoT consists of six main parts: 1) service stations, 2) system identification, 3) system integration API, 4) SRM internal system, 5) report analytic and 6) web server and database server. Evaluation of the results shows an overall appropriateness at a very high level: the overall appropriateness of usability result was also at a very high level, which shows that this research is appropriate to be used as a guideline for further system development to support student services, and to promote learning and analysis of student behaviours in higher education institutions.

Content filtering in a mobile recommendation system plays a vital role in providing solution to help mobile device users obtain their desire content. However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. These problems can be viewed as a user’s first time connection to a mobile recommendation system and initial rating of the content in an early stage of the system. Hence, to obtain personalized content for mobile user, mobile content filtering is needed. This paper proposes a framework for integrated mobile content recommendation. The framework makes use of classification and adaptive association rule techniques to build an integrated model. The results demonstrate that the proposed framework outperforms related techniques. This can address the problem of sparsity for mobile content recommendation systems. To cite this document: Worapat Paireekreng, Kok Wai Wong, and Chun Che Fung, "A framework for integrated mobile content recommendation", International Journal of Electronic Commerce Studies, Vol.4, No.2, pp. 185-202, 2013. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.1038

Educational robotics involves using robots as an educational tool to provide a long term, and progressive learning activity, to cater to different age group. The current concern is that, using robots in education should not be an instance of a one-off project for the sole purpose of participating in a competitive event. Instead, it should be a sustainable long-term progression spanning the primary school to pre-university level. This article presents a framework for robotics using a multiphase approach to enhance user learning in a competitive arena.

Deep learning techniques have shown to perform well in Question Answering (QA) tasks. We present a framework that combines Memory Network (MN) and Reinforcement Learning (Q-learning) to perform QA, termed Reinforced MN (R-MN). We investigate the proposed framework by the use of Long Short Term Memory Network (LSTM) and Dynamic Memory Network (DMN). We call them Reinforced LSTM (R-LSTM) and Reinforced DMN (R-DMN), respectively. The input text sequence and question are passed to both MN and Q-Learning. The output of the MN is then fed to Q-Learning as a second input for refinement. The R-MN is trained end-to-end. We evaluated R-MNs on the bAbI 1 K QA dataset for all of the 20 tasks. We achieve superior performance when compared to conventional method of RL, LSTM and the state of the art technique, DMN. Using only half of the training data, both R-LSTM and R-DMN achieved all of the bAbI tasks with high accuracies. The experimental results demonstrated that the proposed framework of combining MN and Q-learning enhances the QA tasks while using less training data.

In this paper, we present a semantic parser using a knowledgebase. Instead of relying on filtering the concepts extracted from the knowledgebase, we use all the concepts to create the parser. A simple search is conducted on ConceptNet for the words in the input sentence. In this paper, two proposed techniques are used to extract concepts from the ConceptNet 5. The reason for proposing two techniques in this paper is to address the issue of removing the supervision and training process. The first approach extracts all concepts from ConceptNet 5 for each input word. The extracted concepts are used to search again in ConceptNet 5, which creates multiple levels of search results. This deep concept structure creates a multi-level search to create the semantic parse result. The second approach follows the same first step of extracting concepts using the input text. However, the extracted concepts are passed through a relationship check and then used for the second level search. Concepts are drawn from 2 levels of searching in ConceptNet. The extracted concepts are used to create the parser. Furthermore, we use the initial concepts extracted to search again in ConceptNet. The parser we created is tested on Free917, Stanford Sentiment dataset and the WebQ. We achieve recall of 93.82%, 94.91% for Stanford Sentiment dataset, accuracy of 77.1%, 79.2% for Free917 and 26.5%, 38.2% for WebQ respectively for the two approaches. This shows state-of-the-art results compared to other methods for each datasets.

As email workloads keep rising, email servers need to handle this explosive growth while offering good quality of service to users. In this work, we focus on modeling the workload of the email servers of four universities (2 from Greece, 1 from the UK, 1 from Australia). We model all types of email traffic, including user and system emails, as well as spam. We initially tested some of the most popular distributions for workload characterization and used statistical tests to evaluate our findings. The significant differences in the prediction accuracy results for the four datasets led us to investigate the use of a Recurrent Neural Network (RNN) as time series modeling to model the server workload, which is a first for such a problem. Our results show that the use of RNN modeling leads in most cases to high modeling accuracy for all four campus email traffic datasets.

In this paper, a document clustering method with a hybrid feature selection method is proposed. The proposed hybrid feature selection method integrates a Genetic-based wrapper method with ranking filter. The method is named Memetic Algorithm-Feature Selection (MA-FS). In this paper, MA-FS is combined with K-means and Spherical K-means (SK-means) clustering methods to perform document clustering. For the purpose of comparison, another unsupervised feature selection method, Feature Selection Genetic Text Clustering (FSGATC), is used. Two real-world criminal report document sets were used along with two popular benchmark datasets which are Reuters and 20newsgroup, were used in the comparisons. F-Micro, F-Macro and Average Distance of Document to Cluster (ADDC) measures were used for evaluation. The test results showed that the MA-FS method has outperformed the FSGATC method. It has also outperformed the results after using the entire feature space (ALL).

Realization of human emotion classification from Electroencephalogram (EEG) has great potential. Various methods in machine learning have been applied for EEG emotion classification and among these techniques, Support Vector Machines (SVMs) has demonstrated that it can provide good classification results. Therefore, SVM has been used widely in Affective Brain-Computer Interfaces (aBCI). However, EEG signals are non-stationary and they normally associate with outliers and uncertainties, and these issues could affect the performance of SVM. This study proposes the use of Fuzzy Support Vector Machine (FSVM) to deal with these issues. A benchmark dataset, Database for Emotion Analysis using Physiological Signals (DEAP), was used for subject-dependence classification. The experimental results showed that FSVM could deal with uncertainties and outliers, and enhanced the accuracies of arousal, valence and dominance classifications when compared to the SVM. Moreover, it was found that when gamma band was used as a feature from the two channels gave the best performance in comparison to other bands.

This study examined how desktop virtual reality (VR) enhances learning and not merely does desktop VR influence learning. Various relevant constructs and their measurement factors were identified to examine how desktop VR enhances learning and the fit of the hypothesized model was analyzed using structural equation modeling. The results supported the indirect effect of VR features to the learning outcomes, which was mediated by the interaction experience and the learning experience. Learning experience which was individually measured by the psychological factors, that is, presence, motivation, cognitive benefits, control and active learning, and reflective thinking took central stage in affecting the learning outcomes in the desktop VR-based learning environment. The moderating effect of student characteristics such as spatial ability and learning style was also examined. The results show instructional designers and VR software developers how to improve the learning effectiveness and further strengthen their desktop VR-based learning implementation. Through this research, an initial theoretical model of the determinants of learning effectiveness in a desktop VR-based learning environment is contributed.

In most classification problems, sometimes in order to achieve better results, data cleaning is used as a preprocessing technique. The purpose of data cleaning is to remove noise, inconsistent data and errors in the training data. This should enable the use of a better and representative data set to develop a reliable classification model. In most classification models, unclean data could sometime affect the classification accuracies of a model. In this paper, we investigate the use of misclassification analysis for data cleaning. In order to demonstrate our concept, we have used Artificial Neural Network (ANN) as the core computational intelligence technique. We use four benchmark data sets obtained from the University of California Irvine (UCI) machine learning repository to investigate the results from our proposed data cleaning technique. The experimental data sets used in our experiment are binary classification problems, which are German credit data, BUPA liver disorders, Johns Hopkins Ionosphere and Pima Indians Diabetes. The results show that the proposed cleaning technique could be a good alternative to provide some confidence when constructing a classification model.

Games technology has undergone tremendous development. In this article, the authors report the rapid advancement that has been observed in the way games software is being developed, as well as in the development of games content using game engines. One area that has gained special attention is modeling the game environment such as terrain and buildings. This article presents the continuous level of detail terrain modeling techniques that can help generate and render realistic terrain in real time. Deployment of characters in the environment is increasingly common. This requires strategies to map scalable behavior characteristics for characters as well. The authors present two important aspects of crowd simulation: the realism of the crowd behavior and the computational overhead involved. A good simulation of crowd behavior requires delicate balance between these aspects. The focus in this article is on human behavior representation for crowd simulation. To enhance the player experience, the authors present the concept of player adaptive entertainment computing, which provides a personalized experience for each individual when interacting with the game. The current state of game development involves using very small percentage (typically 4% to 12%) of CPU time for game artificial intelligence (AI). Future game AI requires developing computational strategies that have little involvement of CPU for online play, while using CPU’s idle capacity when the game is not being played, thereby emphasizing the construction of complex game AI models offline. A framework of such nonconventional game AI models is introduced.

When designing educational computer games, designers usually consider target age, interactivity, interface and other related issues. They rarely explore the genres which should employ into one type of educational game. Recently some digital game-based researchers attempt to combine game genre with learning theory. Different researcher use different pedagogy conceptions. The purpose of this paper is to investigate the appropriate computer genre in designing effective educational computer games that can be used as ;learning tools in Thai undergraduate classrooms by integrating the learning conceptions from the previous work of those researchers. The following steps are used to conduct this study: summarize the survey of Thai students’ opinions toward game genre; analyse the characteristics of genre of computer game; match those characteristics of genre with learning theories; and propose the conceptual model from the comparison. The results of this analysis can be used as a guideline for educational computer game designers in considering appropriate genres of game for educational purpose.

In this paper, a Complementary Fuzzy Support Vector Machine CMTFSVM technique is proposed to handle outlier and noise in classification problems. Fuzzy membership values are applied for each input point to reflect the degree of importance of the instances. Datasets from the UCI and KEEL are used for the comparison. In order to confirm the proposed methodology, 40i¾?% random noise is added to the datasets. The experiment results of CMTFSVM are analysed and compared with the Complementary Neural Network CMTNN. The outcome indicated that the combined CMTFSVM outperformed the CMTNN approach.

This paper describes an integrated system based on open-domain and domain-specific knowledge for the purpose of providing query-based intelligent web interaction. It is understood that general purpose conversational agents are not able to answer questions on specific domain subject. On the other hand, domain specific systems lack the flexibility to handle common sense questions. To overcome the above limitations, this paper proposed an integrated system comprises of an artificial intelligent conversation software robot or chatterbot, called Artificial Intelligence Natural-language Identity (hereafter, AINI), and an Automated Knowledge Extraction Agent (AKEA) for the acquisition of real world knowledge from the Internet. The objective of AKEA is to retrieve real world knowledge or information from trustworthy websites. AINI is the mechanism used to manage the knowledge and to provide appropriate answer to the user. In this paper, we compare the performance of the proposed system against two popular search engines, two question answering systems and two other conversational systems.

Improvement of classification accuracy is importance in data analysis problems. Enhancement of techniques have been proposed previously to address the problems as regard to classification performance, however, the issues of misclassification and noise elimination in the early stage of processing have been ignored by many researchers. If these problems were addressed, the performance of the classification may be improved. In this paper, a framework for misclassification analysis is proposed. Feature selection using Fuzzy C-means can be implemented in the early stage of model building. Then, ensemble techniques using majority vote algorithm could be incorporated in order to reduce misclassification. The proposed technique has shown an improved classification performance in terms of accuracy rate. The performance was improved for both cases of binary and multiclass data sets at 14.36% on average. In addition, the performance of the classification model for multiclass data sets improved more in comparison to the binary data sets.

Information and Communications Technologies (ICTs), especially the Internet, have become a key enabler for government organisations, businesses and individuals. With increasing growth in the adoption and use of ICT devices such as smart phones, personal computers and the Internet, Cybersecurity is one of the key concerns facing modern organisations in both developed and developing countries. This paper presents an overview of cybersecurity challenges in Bhutan, within the context that the nation is emerging as an ICT developing country. This study examines the cybersecurity incidents reported both in national media and government reports, identification and analysis of different types of cyber threats, understanding of the characteristics and motives behind cyber-attacks, and their frequency of occurrence since 1999. A discussion on an ongoing research study to investigate cybersecurity management and practices for Bhutan's government organisations is also highlighted.

The purpose of this paper is to impart the development of a Process-Knowledge Management System (P-KMS) designed to investigate drilling in gas hydrate environments, identify potential well risks (well control, borehole stability and/or well integrity), and assess mitigation of them due to alteration of drilling parameters and/or strategies. Since successful drilling in gas hydrates environments is influenced strongly by numerous parameters and relations that are sometimes ill-defined, or not understood properly, the P-KMS developed needs to permit users the ability to update the knowledge utilized, as well as handle uncertainty as multidimensional phenomena. For this, traditional engineering simulations and uncertainty handling by soft computing have been combined in a novel approach with knowledge management paradigms. This allows the inclusion of knowledge from multiple field experts, and its combination with approved company best practices and guidelines. In order to consider the uncertainties associated with knowledge, approximate reasoning by type-2 fuzzy logic is combined with relevant numerical simulations (pressure and temperature fields, thermodynamic and kinetic gas hydrate behavior). This combination of knowledge is established by the P-KMS through the construction of individual reasoning blocks and a coherent reasoning lattice. Because expert knowledge (expressed in rules and equations) and standard engineering calculations are combined in a modular form, updating of knowledge, as well as well situation-specific applications of it, are assisted. The P-KMS can be applied in two reasoning modes, either for dynamic process assessment (simulation mode) or static well situation analysis (parameter inference mode). During reasoning, uncertainties employed are monitored constantly in such a way that knowledge gap identification and system performance evaluation is supported. Consequently, it is anticipated that using such a P-KMS approach will lead to (1) increase in well-site security, (2) reduction of overall well costs, and (3) creation of experience in development, implementation and maintenance of a P-KMS for similar problem areas.

Fuzzy rule based systems have been very popular in many engineering applications. However, when generating fuzzy rules from the available information, this may result in a sparse fuzzy rule base. Fuzzy rule interpolation techniques have been established to solve the problems encountered in processing sparse fuzzy rule bases. In most engineering applications, the use of more than one input variable is common, however, the majority of the fuzzy rule interpolation techniques only present detailed analysis to one input variable case. This paper investigates characteristics of two selected fuzzy rule interpolation techniques for multidimensional input spaces and proposes an improved fuzzy rule interpolation technique to handle multidimensional input spaces. The three methods are compared by means of application examples in the field of petroleum engineering and mineral processing. The results show that the proposed fuzzy rule interpolation technique for multidimensional input spaces can be used in engineering applications.

This paper proposes an extended version of a previously developed low cost parallel computation platform called Para Worker. The new system is termed Para Worker 2 which differentiates from the early system. The new proposed system adds enhanced features of improved dynamic object reallocation, adaptive consistency protocols, and location transparency as compared to the original system. The proposal is particularly useful for the implementation and execution of computational intelligence techniques such as evolutionary computing for engineering applications.

Rainfall prediction in this paper is a spatial interpolation problem that makes use of the daily rainfall information to predict volume of rainfall at unknown locations within area covered by existing observations. This paper proposed the use of self-organising map (SOM), backpropagation neural networks (BPNN) and fuzzy rule systems to perform rainfall spatial interpolation based on local method. The SOM is first used to separate the whole data space into some local surface automatically without any knowledge from the analyst. In each sub-surface, the complexity of the whole data space is reduced to something more homogeneous. After classification, BPNNs are then use to learn the generalization characteristics from the data within each cluster. Fuzzy rules for each cluster are then extracted. The fuzzy rule base is then used for rainfall prediction. This method is used to compare with an established method, which uses radial basis function networks and orographic effect. Results show that this method could provide similar results from the established method. However, this method has the advantage of allowing analyst to understand and interact with the model using fuzzy rules.

Spatial interpolation is an important feature of a Geographic Information System, which is the procedure used to estimate values at unknown locations within the area covered by existing observations. In this paper, we describe a conservative spatial interpolation technique that incorporates the advantages of local interpolation, Euclidean interpolation, and conservative fuzzy reasoning, and a dynamic fuzzy-reasoning-based function estimator with parameters optimised by a genetic algorithm. The main objective of this paper is to formulate a computationally efficient spatial interpolation technique similar to the IDWA technique that can be used in real time application. The main feature of our spatial interpolation technique is a capability for spatial interpolation and extrapolation in a higher-dimensional space. Examples from a rainfall spatial interpolation problem are used to illustrate the applicability of the proposed technique.

In this paper, a classifier based on a combination of Rough Set and 1-v-1 (one-versus-one) Support Vector Machine for Content Based Image Retrieval system is presented. Some problems of 1-v-1 Support Vector Machine can be reduced using Rough Set. With Rough Set, a 1-v-1 Support Vector Machine can provide good results when dealing with incomplete and uncertain data and features. In addition, boundary region in Rough Set can reduce the error rate. Storage requirements are reduced when compared to the conventional 1-v-1 Support Vector Machine. This classifier has better semantic interpretation of the classification process. We compare our Content Based Image Retrieval system with other image retrieval systems that uses neural network, K-nearest neighbour and Support Vector Machine as the classifier in their methodology. Experiments are carried out using a standard Corel dataset to test the accuracy and robustness of the proposed system. The experiment results show the proposed method can retrieve images more efficiently than other methods in comparison.

Content-Based Image Retrieval (CBIR) system has become a focus of research in the area of image processing and machine vision. General CBIR system automatically index and retrieve images with visual features such as colour, texture and shape. However, current research found that there is a significant gap between visual features and semantic features used by humans to describe images. In order to bridge the semantic gap, some researchers have proposed methods for managing and decreasing image features, and extract useful features from a feature vector. This paper presents an image retrieval system utilising fuzzy rough set based on mutual information decreasing method and the Support Vector Machine (SVM) classifier. The system has training and testing phases. In order to reduce the semantic gap, the propose retrieval system used relevance feedback to improve the retrieval performance. This paper also compared the proposed method with other traditional retrieval systems that use PCA, kernel PCA, Isomap and MVU for their feature reduction method. Experiments are carried out using a standard Corel dataset to test the accuracy and robustness of the proposed system. The experiment results show the propose method can retrieve images more efficiently than the traditional methods. The use of fuzzy rough set based on mutual information decreasing method, SVM and relevance feedback ensures that the propose image retrieval system produces results which are highly relevant to the content of an image query.

This paper reviews the types of mobile Augmented Reality (mAR) interface being utilized in various applications such as education, advertisement, production, tourism and other applications. The objective of this paper is to examine the limitations on the types of mAR when they are used in higher education in terms of interaction between learning, teaching and instructional design. Based on the review, it can be concluded that there is an insufficient mAR interface being used in viewing the augmented images for classroom learning. For example, there are only two interfaces found that is being applied in the current mAR education applications. A comparative review presented in this paper suggests the appropriate mAR interface that can be implemented in education that could possibly enhance the learning outcomes.

This paper proposes a methodology to create an interpretable fuzzy model for monthly rainfall time series prediction. The proposed methodology incorporates the advantages of artificial neural network, fuzzy logic and genetic algorithm. In the first step, the differences between the time series data are calculated and they are used to define the interval between the membership functions of a Mamdani-type fuzzy inference system. Next, artificial neural network is used to develop the model from input-output data and the established model is then used to extract the fuzzy rules. The parameters of the created fuzzy model are then optimized by using genetic algorithm. The proposed model was applied to eight monthly rainfall time series data in the northeast region of Thailand. The experimental results showed that the proposed model provided satisfactory prediction accuracy when compared to other commonly-used prediction models. Due to the interpretability nature of the model, human analysts can gain insight knowledge of the data to be modeled.

Naive Bayes(NB), kNN and Adaboost are three commonly used text classifiers. Evaluation of these classifiers involves a variety of factors to be considered including benchmark used, feature selections, parameter settings of algorithms, and the measurement criteria employed. Researchers have demonstrated that some algorithms outperform others on some corpus, however, labeling and corpus bias are two concerns in text categorization. This paper focuses on evaluating the three commonly used text classifiers by using an automatically generated text document set which is labelled by a group of experts to alleviate subjectiveness of labelling, and at the same time to examine how the performance of the algorithms is influenced by feature selection algorithms and the number of features selected.

Monthly rainfall spatial interpolation is an important task in hydrological study to comprehensively observe the spatial distribution of the monthly rainfall variable in the study area. A number of spatial interpolation methods have been successfully applied to perform this task. However, those methods mainly aim at achieving satisfactory interpolation accuracy and they disregard the interpolation interpretability. Without interpretability, human analysts will not be able to gain insight of the model of the spatial data. This paper proposes an alternative approach to achieve both accuracy and interpretability of the monthly rainfall spatial interpolation solution. A combination of fuzzy clustering, fuzzy inference system, genetic algorithm and modular technique has been used. The accuracy of the proposed method has been compared to the most commonly-used methods in geographic information systems as well as previously proposed method. The experimental results showed that the proposed model provided satisfactory interpolation accuracy in comparison with other methods. Besides, the interpretability of the proposed model could be achieved in both global and local perspectives. Human analysts may therefore understand the model from the derived model’s parameters and fuzzy rules.

This article presents findings from a Content Analysis of verbal and audio protocol of Mental Model study. When using touch screen display with audio feedback, it was found that the users' mental model is in one dimensional as using screen-reader program. Previous experience using screen-reader program may affected the users' experience using a touch screen with audio feedback. In addition, investigation of blind users' spatial ability on users' performance was also conducted. This study revealed that there was a significant interaction effect between the user's spatial ability and the time taken to answer a question using touch display with audio feedback.

Fairly recently, extensive use of digital music has led to an increase of songs in online music applications and personal music libraries. In large libraries, songs which are not listened to regularly will go unnoticed. There are many ways of browsing and discovering new music. In the field of Music Information Retrieval (MIR), some type of visual form such as colour, avatar, mood picture and album cover to visualise music, have been introduced. In this paper, we propose a framework for visualising music mood using texture images to assist listeners in discovering new songs and artists that might not have been discovered otherwise using conventional means.

Feature selection is an important step in processing the images especially for applications such as content based image retrieval. In large multimedia databases, it may not be practical to search through the entire database in order to retrieve similar images from a query. Good data structures for similarity search and indexing are needed, and the existing data structures do not scale well for the high dimensional multimedia descriptors. Thus feature selection is an important step. Fuzzy rough feature selection method has many advantages in determining the relevant features. In this paper, five feature selection methods are compared with the fuzzy rough method. These five feature selection methods are Relief-F, Information Gain, Gain Ratio, OneR and the statistical measure χ2. The main purpose of the comparison is to rank the image features and see which method provides better results. An image retrieval dataset (COREL dataset) was used in the comparison. In order to evaluate the performance of the six methods, ranking of the important features is defined. This is then used to compare with the automated ranking produced by the aforesaid feature selection methods. Results show that the retrieval system using fuzzy rough feature selection has better retrieval accuracy and provide good Precision Recall performance. The advantages of the use of fuzzy rough feature selection will also be discussed in the paper.