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The demand for high-quality freshwater is increasing due to global population growth, intensifying agricultural practices and expanding industrial development. Additionally, many global regions have low levels of rainfall which makes them arid and incapable of supporting large human populations or agriculture. Currently, large quantities of fossil fuels are used to generate the power needed to drive energy intensive desalination processes that deliver high-quality freshwater to many of these regions. However, the use of fossil fuels has led to high greenhouse gas emissions, environmental degradation and global warming. Solar-thermal desalination is a low-cost, sustainable and eco-friendly strategy for producing high-quality freshwater without using energy derived from fossil fuels. However, in spite of recent developments to advance solar-thermal desalination, the most effective strategies for achieving higher performance levels still remains elusive. To tackle this problem, the present article reviews several solar-thermal still configurations, including materials, system design parameters, influencing factors and operational parameters. Moreover, recent material advances in plasmonic nanoparticle-based volumetric systems, nanomaterial enhanced phase change materials and interfacial solar evaporators are discussed. These new material advances can have the potential to significantly improve the conversion of light-to-heat, enhance vapor generation and promote greater water production rates.

Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection.

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.

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.

Community question and answer (CQA) sites mostly involve knowledge-bases feeding into their automated question answering systems. This paper focuses on Stack Overflow which is an online community for developers to share knowledge in computer programming. The proposed framework consists of composing of a paired Q&A corpus, followed by building of a document model with the use of paragraph vector in distributed representation via the doc2vec method, then similarity ranking to fetch a matched answer to a given question. The model pairs the two so as to represent the semantic relevance between the questions and answers generated by the proposed method. The initial experimental results have shown the system is able to provide answers automatically and with a performance of 50% accuracy when compared to expert opinions.

An Adaptive Crossover Memetic Differential Harmony Search (ACMDHS) method was developed for optimizing document clustering in this paper. Due to the complexity of the documents available today, the allocation of the centroid of the document clusters and finding the optimum clusters in the search space are more complex to deal with. One of the possible enhancements on the document clustering is the use of Harmony Search (HS) algorithm to optimize the search. As HS is highly dependent on its control parameters, a differential version of HS was introduced. In the modified version of HS, the Band Width parameter (BW) has been replaced by another pitch adjustment technique due to the sensitivity of the BW parameter. Thus, the Differential Evolution (DE) mutation was used instead. In this paper the DE crossover was also used with the Differential HS for further search space exploitation, the produced global search is named Crossover DHS (CDHS). Moreover, DE crossover (Cr) and mutation (F) probabilities are dynamically tuned through generations. The Memetic optimization was used to enhance the local search capability of CDHS. The proposed ACMDHS was compared to other document clustering techniques using HS, DHS, and K-means methods. It was also compared to its other two variants which are the Memetic DHS (MDHS) and the Crossover Memetic Differential Harmony Search (CMDHS). Moreover, two state-of-the-art clustering methods were also considered in comparisons, the Chaotic Gradient Artificial Bee Colony (CGABC) and the Differential Evolution Memetic Clustering (DEMC). From the experimental results, it was shown that CMDHS variant (the non-adaptive version of ACMDHS) and ACMDHS were highly competitive while both CMDHS and ACMDHS were superior to all other methods.

Challenges for text processing in ancient document images are mainly due to the high degree of variations in foreground and background. Image binarization is an image segmentation technique used to separate the image into text and background components. Although several techniques for binarizing text documents have been proposed, the performance of these techniques varies and depends on the image characteristics. Therefore, selecting binarization techniques can be a key idea to achieve improved results. This paper proposes a framework for selecting binarizing techniques of palm leaf manuscripts using Support Vector Machines (SVMs). The overall process is divided into three steps: (i) feature extraction: feature patterns are extracted from grayscale images based on global intensity, local contrast, and intensity; (ii) treatment of imbalanced data: imbalanced dataset is balanced by using Synthetic Minority Oversampling Technique as to improve the performance of prediction; and (iii) selection: SVM is applied in order to select the appropriate binarization techniques. The proposed framework has been evaluated with palm leaf manuscript images and benchmarking dataset from DIBCO series and compared the performance of prediction between imbalanced and balanced datasets. Experimental results showed that the proposed framework can be used as an integral part of an automatic selection process.

E-Learning in higher education has changed the way of how teaching and learning are conducted, and this has also created new learning opportunities for both on and off campus education. To implement e-learning in higher education institutions successfully, many factors need to be examined. The scope of the subject of e-learning discussed in this paper is limited to internet-based learning, which includes networked learning, distance learning, and online learning. The main purpose of this paper is to review past research in the domain of e-learning in order to identify the Critical Success Factors (CSF) that can affect the successful implementation of e-learning in higher education. This paper will summarize and present eight important CSFs commonly found which are educational technology, computing experience, attitude, social influence, course development, language, teaching and learning styles and demography of the students.

This paper proposes algorithms for the extraction of features from candlestick patterns for technical analysis of share indices. The significant features consist of: the direction of candlestick, the gap between CLOSE and OPEN price of two candlesticks, the body level of current and previous candlesticks, and the length of the candlesticks. K-Means clustering approach is applied for solving the unclearly defined length of Upper Shadow, Body and Lower Shadow. The Thai SET index OHLC data from 1990 to 2017 are used as the experimental dataset. The results show the similarity between the candlestick chart from raw data and decoding data, which is applied by the proposed algorithms. The output result from the approach can be used as the input to other machine learning methods such as Artificial Neuron Networks, Reinforcement Learning, or Content Based Image Retrieval (CBIR).

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.

The global demand for energy is increasing and the detrimental consequences of rising greenhouse gas emissions, global warming and environmental degradation present major challenges. Solar energy offers a clean and viable renewable energy source with the potential to alleviate the detrimental consequences normally associated with fossil fuel-based energy generation. However, there are two inherent problems associated with conventional solar thermal energy conversion systems. The first involves low thermal conductivity values of heat transfer fluids, and the second involves the poor optical properties of many absorbers and their coating. Hence, there is an imperative need to improve both thermal and optical properties of current solar conversion systems. Direct solar thermal absorption collectors incorporating a nanofluid offers the opportunity to achieve significant improvements in both optical and thermal performance. Since nanofluids offer much greater heat absorbing and heat transfer properties compared to traditional working fluids. The review summarizes current research in this innovative field. It discusses direct solar absorber collectors and methods for improving their performance. This is followed by a discussion of the various types of nanofluids available and the synthesis techniques used to manufacture them. In closing, a brief discussion of nanofluid property modelling is also presented.

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.

The issues of accessibility, management, storage and organization of Learning Objects (LOs) in education systems are a high priority of the Thai Government. Incorporating personalized learning or learning styles in a learning object management system to improve the accessibility of LOs has been addressed continuously in the Thai education system. A proposed Learning Object Management Model (LOMM) is discussed in this paper which aims to adapt and optimize the learning process based on characteristics of the individual learners. This study aims to find the correlation between learning styles and LOs characteristics in the LOMM. Decision Tree and Apriori algorithms were used to generate a predictive model for the classification of learners. Development of the predictive model was based on survey results from 1,586 high school students in Nakhon Ratchasima province, Thailand. The diverse LOs characteristics were analyzed in order to find the correlation with learning styles of the learners. The classification model consists of 24 sub-models used to predict a learner’s class based on 8 groups of LOs characteristics. The best accuracy obtained in the study was 80.23%. Finally, for the next phase this approach has been designed to support the proposed LOMM and it is expected that it could be readily applied to other e-learning systems and digital repositories.

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.

Background: Globally, large quantities (tonnes) of diverse sources of food wastes derived from horticulture are produced and offer a valuable renewable source of biochemical compounds. Developing new recycling and food waste utilisation strategies creates unique opportunities for producing gold (Au) nanoparticles with desirable antibacterial properties. The present study used an eco-friendly procedure for biologically synthesizing gold (Au) nanoparticle shapes from waste Citrullis lanatus var (watermelon).Methods: The green chemistry-based procedure used in this study was straightforward and used both red and green parts of waste watermelon. The generated Au nanoparticles were subsequently evaluated using several advanced characterization techniques. The antibacterial properties of the various extracts and synthesised nanoparticles were evaluated using the Kirby-Bauer sensitivity method.Results: The advanced characterization techniques revealed the Au particles ranged in size from nano (100 nm) up micron (2.5 µm) and had a variety of shapes. The red watermelon extract tended to produce spheres and hexagonal plates, while the green watermelon extract tended to generate triangular shaped nanoparticles. Both red and green watermelon extracts produced nanoparticles with similar antibacterial properties. The most favourable response was achieved using a 5:1 green watermelon-based mixture for Staphylococcus epidermidis, which produced a maximum inhibition zone of 12 mm. While gram-negative bacteria Escherichia coli produced a maximum inhibition zone of 10 mm for the same mixture.Conclusions: The study has shown both red and green parts of waste watermelon can be used to produce Au nanoparticles with antibacterial activity towards both Escherichia coli and Staphylococcus epidermidis. The study has also demonstrated an alternative method for producing high-value Au nanoparticles with potential pharmaceutical applications.

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).

In this paper, we propose a Memetic-based clustering method that improves the partitioning of document clustering. Our proposed method is named as Differential Evolution Memetic Clustering (DEMC). Differential Evolution (DE) is used for the selection of the best set of cluster centres (centroids) while the Chaotic Logistic Search (CLS) is used to enhance the best set of solutions found by DE. For the purpose of comparison, the DEMC is compared with the basic DE, Differential Evolution Simulated Annealing (DESA) and the Differential Evolution K-Means (DEKM) methods as well as the traditional partitioning clustering using the K-means. The DEMC is also compared with the recently proposed Chaotic Gradient Artificial Bee Colony (CGABC) document clustering method. The reuters-21578, a pair of the 20-news group, classic 3 and TDT benchmark collection (TDT5) along with real-world six-event-crimes datasets are used in the experiments in this paper. The results showed that the proposed DEMC outperformed the other methods in terms of the convergence rate measured by the fitness function (ADDC) and the compactness of the resulted clusters measured by the F-macro and F-micro measures.

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.

A hybrid sampling technique is proposed by combining Fuzzy C-Mean Clustering and Synthetic Minority Oversampling Technique (FCMSMT) for tackling the imbalanced multiclass classification problem. The mean number of classes is used as the number of instances for applying undersampling and oversampling. Using the mean as the fixed number of the required instances for each class can prevent the within-class imbalance data from being eliminated erroneously during undersampling. This technique can decrease both within-class and between-class errors, and thus can increase the classification performance. The study was conducted using eight benchmark datasets from KEEL and UCI repositories and the results were compared against three major classifiers based on G-mean and AUC measurements. The results reveal that the proposed technique could handle most of the multiclass imbalanced datasets used in the experiments for all classifiers and retain the integrity of the original data.

Emotion classification using electroencephalogram (EEG) has been actively researched in many Human-Computer Interaction (HCI) applications such as healthcare, recreation and education. In the last decade, studies in affective Brain-Computer Interfaces (aBCI) have increased especially in the discipline of affective computing. One of the topics is fuzzy classification of human emotions. Fuzzy set theory has been applied due to its potential benefit of human comprehensibility. Furthermore, the approach exhibits shorter computation time and it is faster than other machine learning techniques if it has only a number of manageable rules. It is also suitable for online applications. However, it is important to have an efficient means for extracting appropriate rules. In this study, it is proposed to use Fuzzy C-Mean (FCM) for extracting fuzzy rules to be used in the fuzzy inference system (FIS) for classification purpose. This technique uses the EEG data for learning and to produce rules. Three advantages are suggested: accuracy, flexibility and computation time. The results show that the algorithm has an improved accuracy in comparison to fuzzy classification using fixed rules and Support Vector Machine (SVM), with values of 55.77%, 49.62% and 54% respectively. In addition the proposed technique also required less execution time.

It has been established that it is possible to reveal human emotions using electroencephalogram (EEG) signals. Most studies used a wide variety of data sets and methods, therefore a comparison between the performances of their approaches is difficult. This paper reports a study on the effects of the number of electrode channels and frequency bands for emotion classification based on a database for emotion analysis using physiological signals (DEAP). Discrete wavelet transform (DWT) was used for feature extraction and support vector machine (SVM) was applied as the classifier. From experimental results, it is found that (a) using more electrodes channels did not guarantee better accuracy, (b) comparing with all frequency bands, using a few of them did not reduce the accuracy dramatically and some results revealed that only two bands produced better results, and, (c) the more emotions to be classified, the lower accuracy was achieved based on the same method.

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.

This paper presents an ensemble neural network and interval neutrosophic sets approach to the problem of binary classification. A bagging technique is applied to an ensemble of pairs of neural networks created to predict degree of truth membership, indeterminacy membership, and false membership values in the interval neutrosophic sets. In our approach, the error and vagueness are quantified in the classification process as well. A number of aggregation techniques are proposed in this paper. We applied our techniques to the classical benchmark problems including ionosphere, pima-Indians diabetes, and liver-disorders from the UCI machine learning repository. Our approaches improve the classification performance as compared to the existing techniques which applied only to the truth membership values. Furthermore, the proposed ensemble techniques also provide better results than those obtained from only a single pair of neural networks.

In this paper, we examine the use of knowledge query technology as applied to conversation bots in the instant messaging environment. Hence, we designed an artificial intelligent conversation robot or bots called Artificial Intelligence Natural language Identity (hereafter, AINI) to mimic human conversation. Our goal is to introduce a Domain Matrix Knowledge Model and an Automated Knowledge Extraction Agent (AKEA) to create AINI's knowledge bases, and in turn provide intelligent query mechanisms. We report an evaluation on the collection and analysis of a corpus containing over 3280 utterances in a series of real instant messages exchanged between the AINI conversation bot and 65 online ''buddies''. About 1721 utterances were produced by AINI, 88.03% were from open-domain knowledge, 2.15% from domain-specific knowledge base and 9.82% were inappropriate and amusing responses. These results show that domain knowledge plays significant roles in conversations between two or more human users and in human-machine conversation.

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.

Use of relevance feedback (RF) in the feature vector model has been one of the most widely used approaches to fine tuning queries for content-based image retrieval (CBIR). We propose a framework that extends RF to capturing the inter-query relationship between current and previous queries. Using the feature vector model, this avoids the need to “memorize” actual retrieval relationships between actual image indexes and the previous queries. This approach is suited to image database applications in which images are frequently added and removed. In the previous work [1], we developed a feature vector framework for inter-query learning using statistical discriminant analysis. One weakness of the previous framework is that the criteria for exploring and merging with an existing visual group are based on two constant thresholds, which are selected through trial and error. Another weakness is that it is not suited to mutually interrelated data clusters. Instead of using constant values, we have further extended the framework using positive feedback sample size as a factor for determining thresholds. Experiments demonstrated that our proposed framework outperforms the previous framework.

Cyber security has become a serious challenge for organizations due to growing use of the Internet and increasing values of information that are stored in organizations' information systems. Because of the complexity and the number of different variables involved with information security, a special analytical tool is required to address the problem of how to balance the investment in different parts of the system in order to reduce the losses due to cyber attacks. Using the System Dynamics approach as an effective analytical tool is proposed to demonstrate dealing of complex situations due to cyber attacks involving different variables such as attractiveness of targets to losses, and their inter-relationships. Using this model, a casual loop diagram can be used to present an overview of the model and its associated variables. The model is based on quantitative measurements of security and the time that attackers may need to compromise a system. The model will assist in achieving the balance between investment on security and the resultant reduction of losses due to cyber attacks.

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.

While consumers' adoption and uptake of Renewable Energy (RE) in Australia have increased in recent years, feedback and surveys show the barriers for RE adoption are largely due to cost and perceived reliability of the technologies. Lessening the effects of these barriers is necessary towards influencing consumers' RE adoption behavior. RE regulations and policies based on respective governments' commitment, community demands and environmental requirements have been created and aim at promoting and supporting RE adoption. This paper reports findings on the barriers of consumers' adoption behavior, and a collection of primary data through a survey in the City of Melville, Western Australia. It then outlines and compares key strategies and policies between four countries: Australia, the USA, UK and Singapore. Information on the incentives, subsidies, pricing and technological supports will provide valuable insights for policy makers so as to enhance the policies and strategies in order to reduce the barriers.

Character segmentation of handwriting of ancient manuscript is a crucial step in Optical Character Recognition (OCR) system. The segmentation of connected components is one of the factors that affects the performance of the recognition system. In order to improve the efficiency of OCR systems, segmentation of touching characters is a significant task. This paper proposes a combined method for the handling of connected handwritten segmentation of Thai-Noi script on palm leaf manuscripts. This combined process of handwritten segmentation estimates points of connected positions from two methods: (1) points of foreground detection: foreground skeleton is established first, the path and junction are then detected using Hough transform and histogram of projection profile; and (2) points of background detection: background skeleton is extracted, the top and bottom paths are then detected by using Hough transform. The proposed method has been implemented and experimental results show that the combined method obtained better segmentation of handwritten documents on palm leaves.

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.

Recently, recommendation systems have become an important tool to support and improve decision making for educational purposes. However, developing recommendation systems is far from trivial and there are specific issues associated with individual problems. Low-correlated input features is a problem that influences the overall accuracy of decision tree models. Weak relationship between input features can cause decision trees work inefficiently. This paper reports the use of features grouping method to improve the classification accuracy of decision trees. Such method groups related input features together based on their ontologies. The new inherited features are then used instead as new features to the decision trees. The proposed method was tested with five decision tree models. The dataset used in this study were collected from schools in Nakhonratchasima province, Thailand. The experimental results indicated that the proposed method can improve the classification accuracy of all decision tree models. Furthermore, such method can significantly decrease the computational time in the training period.

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.

The accuracy in the estimation of hydrocyclone parameter, d50/sub c/, can substantially be improved by application of artificial neural networks (ANN). With ANN, many nonconventional operational variables such as water and solid split ratios, overflow and underflow densities, apex and spigot flowrates can easily be incorporated as the input parameters in the prediction of d50/sub c/. The ANN yields high correlation of data, hence it can be used in automatic control and multiphase operations of hydrocyclones.

A system based on coded infrared signal transmission for the estimation of position of mobile robots in a structured environment is reported. Particular emphasis is placed on the polar coordinate arrangement in which signals are sent from the transmitters situated at the corners of the boundaries of operation. A multisensor system, strategically situated onboard the robot, has been found to improve the accuracy of the position estimation substantially. The information detected by the sensors is suitably processed to calculate the central position of the robot geometrically. The algorithms for the position calculations and the operational strategy are presented. This system forms the basis for the coordination and cooperation philosophy of multiple mobile robots sharing the same environment and performing cooperative or competitive tasks.

Malware has become a major problem to organizations and they are becoming more sophisticated in many ways. They have abilities to penetrate through deployed defensive measures and stay active while resisting containment responses. Malware are also evading and attacking the defenses put up by organizations. The classical containment techniques to contain a successful infiltration of Malware have limited effectiveness against the determined and resilient malice. This paper advocates using the offensive techniques typically used by Malware to disable them as part of the containment response. In this paper, two experiments involving the application of offensive techniques on different Malware are presented. One of which involves a smartphone Malware. The result of this experiments demonstrate applicability of such techniques as part of containment response.

Recently, several binarisation techniques have been proposed to process different kinds of ancient document images. While many well-known binarisation techniques are particularly suitable for certain types of document images, there is no specific guidelines on the determination of the appropriate type of image degradation, or characteristics of the image. In this paper, a novel method has been proposed to generate the optimal binary image from different binarised outputs from a document image. This approach is based on weight majority vote, and uncertain pixels are then determined based on local areas of the binarised images, by applying iteration of weight majority vote. Experiment over benchmark data set of the Document Image Binarization Contest (DIBCO) 2011 shows that the proposed method provided better performance than most well-known techniques. The proposed method has also been applied to ancient manuscripts on palm leaves from Thailand and this approach provided better results than binarised outputs from original binarisation techniques.

Rainfall time series forecasting is a crucial task in water resource planning and management. Conventional time series prediction models and intelligent models have been applied to this task. Attempt to develop better models is an ongoing endeavor. Besides accuracy, the transparency and practicality of the model are the other important issues that need to be considered. To address these issues, this study proposes the use of a modular technique to a monthly rainfall time series prediction model. The proposed model consists of two main layers, namely, a prediction layer and an aggregation layer. In the prediction layer, Mamdani-type fuzzy inference system is used to capture the input-output relationship of the rainfall pattern. In the aggregation layer, Bayesian learning and nonlinear programming are used to capture the uncertainty in the time dimension. Eight monthly rainfall time series collected from the northeast region of Thailand are used to evaluate the proposed model. The experimental results showed that the proposed model could improve the prediction accuracy from the single model. Furthermore, human analysts can interpret such model as it contains set of fuzzy rules.

Information Technology (IT) Management Frameworks are a fundamental tool used by IT professionals to efficiently manage IT resources and are globally applied to IT service delivery and management. Sustainability is a recent notion that describes the need for economic, environmental and social development without compromising the ability of future generations to meet their own needs; this applies to businesses as well as society in general. Unfortunately, IT Management Frameworks do not take sustainability into account. To the practitioner this chapter demonstrates sustainability integration which allows CIOs and IT managers to improve the sustainability of their organisation. To the researcher this chapter argues that sustainability concerns need to be provided through its integration into the mainstream of IT Management Frameworks. This is demonstrated through the high-level integration of sustainability in Six Sigma, CobiT, ITIL and PRINCE2.

Although consumers' adoption rate of Renewable Energy (RE) have increased in recent years, yet feedback and surveys have shown the barriers for RE adoption are largely due to factors of initial investment cost, lack of RE awareness, and perceived lack of reliability of the technologies. Therefore, overcoming the effects of these barriers is becoming important in order to influence consumers' RE adoption behavior and attitudes. RE policies and regulations in many countries around the world have been established in order to promote adoption of RE. This paper first outlines and compares the key strategies and policies between four developed countries: Australia, the United States of America, United Kingdom and Singapore. It then presents the development of Renewable Energy Intelligence System, a knowledge portal tool which aims to provide knowledge and increase awareness of RE. Hence, it is expected that the portal will influence consumers' decision towards the adoption of RE.

Smart Grid has a tightly coupled relationship between the generation, transmission and distribution of electrical power, and the digital information and communication technologies (ICT) that control or manage the system. Due to the heavy reliance on ICT in Smart Grid, cyber security and economic impacts of cyber-attacks are challenging issues. This paper presents a conceptual model of Smart Grid taking consideration of different kinds of attacks in the communication layer, and impacts of the attacks on valuable assets and services in each layer of the model. A mathematic model is then proposed for risk assessment by focusing on economics impacts based on their costs and benefits.

Advances in information technology depend on the availability of telecommunication, network and mobile technologies. With the rapid increasing number of mobile devices being used as essential terminals or platforms for communication, security threats now target the whole telecommunication infrastructure that includes mobile devices, radio access network, and the core network operated by the mobile operators. In particular, the mobile core network is the most important part of the mobile communication system because different access networks are consolidated at the core network. Therefore, any risks associated with the core network would have a significant impact on the mobile network regardless of technologies of access networks are in use. This paper reviews the security risks in the mobile core network for data services by considering the confidentiality, integrity and availability (CIA) aspects, and then relates them to the ITU-T X.805 reference framework. Finally, this paper provides a recommendation on how to address these risks using the ITU-T X.805 reference framework. This paper will benefit mobile operators and network designers looking to secure the mobile packet core system.

This paper presents an integrated approach to automatically provide an overview of content on Thai websites based on tag cloud. This approach is intended to address the information overload issue by presenting the overview to users in order that they could assess whether the information meets their needs. The approach has incorporated Web content extraction, Thai word segmentation, and information presentation to generate a tag cloud in Thai language as an overview of the key content in the webpage. From the experimental study, the generated Thai Tag clouds are able to provide an overview of the tags which frequently appear in the title and body of the content. Moreover, the first few lines in the tag cloud offer an improved readability.