Search results for: Multi-criteria decision making

Authors: Rajeev Kaula

Abstract:

Business rules and data warehouse are concepts and technologies that impact a wide variety of organizational tasks. In general, each area has evolved independently, impacting application development and decision-making. Generating knowledge from data warehouse is a complex process. This paper outlines an approach to ease import of information and knowledge from a data warehouse star schema through an inference class of business rules. The paper utilizes the Oracle database for illustrating the working of the concepts. The star schema structure and the business rules are stored within a relational database. The approach is explained through a prototype in Oracle-s PL/SQL Server Pages.

Keywords: Business Rules, Data warehouse, PL/SQL ServerPages, Relational model, Web Application.

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Authors: Peter Clutterbuck, Terry Rowlands, Owen Seamons

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In this paper we describe one critical research program within a complex, ongoing multi-year project (2010 to 2014 inclusive) with the overall goal to improve the learning outcomes for first year undergraduate commerce/business students within an Information Systems (IS) subject with very large enrolment. The single research program described in this paper is the analysis of student attitudes and decision making in relation to the availability of formative assessment feedback via Web-based real time conferencing and document exchange software (Adobe Connect). The formative assessment feedback between teaching staff and students is in respect of an authentic problem-based, team-completed assignment. The analysis of student attitudes and decision making is investigated via both qualitative (firstly) and quantitative (secondly) application of the Theory of Planned Behavior (TPB) with a two statistically-significant and separate trial samples of the enrolled students. The initial qualitative TPB investigation revealed that perceived self-efficacy, improved time-management, and lecturer-student relationship building were the major factors in shaping an overall favorable student attitude to online feedback, whilst some students expressed valid concerns with perceived control limitations identified within the online feedback protocols. The subsequent quantitative TPB investigation then confirmed that attitude towards usage, subjective norms surrounding usage, and perceived behavioral control of usage were all significant in shaping student intention to use the online feedback protocol, with these three variables explaining 63 percent of the variance in the behavioral intention to use the online feedback protocol. The identification in this research of perceived behavioral control as a significant determinant in student usage of a specific technology component within a virtual learning environment (VLE) suggests that VLEs could now be viewed not as a single, atomic entity, but as a spectrum of technology offerings ranging from the mature and simple (e.g., email, Web downloads) to the cutting-edge and challenging (e.g., Web conferencing and real-time document exchange). That is, that all VLEs should not be considered the same. The results of this research suggest that tertiary students have the technological sophistication to assess a VLE in this more selective manner.

Keywords: Formative assessment feedback, virtual learning environment, theory of planned behavior, perceived behavioral control.

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Authors: Yanshuang Zhang, Byungho Jeong

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There exists some time lag between the consumption of inputs and the production of outputs. This time lag effect should be considered in calculating efficiency of decision making units (DMU). Recently, a couple of DEA models were developed for considering time lag effect in efficiency evaluation of research activities. However, these models can’t discriminate efficient DMUs because of the nature of basic DEA model in which efficiency scores are limited to ‘1’. This problem can be resolved a super-efficiency model. However, a super efficiency model sometimes causes infeasibility problem. This paper suggests an output oriented super-efficiency model for efficiency evaluation under the consideration of time lag effect. A case example using a long term research project is given to compare the suggested model with the MpO model.

Keywords: DEA, Super-efficiency, Time Lag.

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Authors: Viliam Makis, Farnoosh Naderkhani, Leila Jafari

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In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example.

Keywords: Bayesian control chart, semi-Markov decision process, quality control, partially observable process.

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Authors: Taimur Qureshi, Djamel A Zighed

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Many supervised induction algorithms require discrete data, even while real data often comes in a discrete and continuous formats. Quality discretization of continuous attributes is an important problem that has effects on speed, accuracy and understandability of the induction models. Usually, discretization and other types of statistical processes are applied to subsets of the population as the entire population is practically inaccessible. For this reason we argue that the discretization performed on a sample of the population is only an estimate of the entire population. Most of the existing discretization methods, partition the attribute range into two or several intervals using a single or a set of cut points. In this paper, we introduce a technique by using resampling (such as bootstrap) to generate a set of candidate discretization points and thus, improving the discretization quality by providing a better estimation towards the entire population. Thus, the goal of this paper is to observe whether the resampling technique can lead to better discretization points, which opens up a new paradigm to construction of soft decision trees.

Keywords: Bootstrap, discretization, resampling, soft decision trees.

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Authors: Nam Lethanh, Bryan T. Adey

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In this paper, the potential use of an exponential hidden Markov model to model a hidden pavement deterioration process, i.e. one that is not directly measurable, is investigated. It is assumed that the evolution of the physical condition, which is the hidden process, and the evolution of the values of pavement distress indicators, can be adequately described using discrete condition states and modeled as a Markov processes. It is also assumed that condition data can be collected by visual inspections over time and represented continuously using an exponential distribution. The advantage of using such a model in decision making process is illustrated through an empirical study using real world data.

Keywords: Deterioration modeling, Exponential distribution, Hidden Markov model, Pavement management

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Authors: N. Southiseng, J. Walsh

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Prior to 1975, women in Laos suffered from having reduced levels of power over decision-making in their families and in their communities. This has had a negative impact on their ability to develop their own identities. Their roles were identified as being responsible for household activities and making preparations for their marriage. Many women lost opportunities to get educated and access the outdoor work that might have empowered them to improve their situations. So far, no accurate figures of either emigrants or return migrants have been compiled but it appears that most of them were women, and it was women who most and more frequently remitted money home. However, very few recent studies have addressed the relationship between remittances and the roles of women in Laos. This study, therefore, aims at redressing to some extent the deficiencies in knowledge. Qualitative techniques were used to gather data, including individual in-depth interviews and direct observation in combination with the content analysis method. Forty women in Vientiane Municipality and Savannakhet province were individually interviewed. It was found that the monetary remittance was typically used for family security and well-being; on fungible activities; on economic and business activities; and on community development, especially concerning hospitality and providing daily household necessities. Remittances played important roles in improving many respondents- livelihoods and positively changed their identities in families and communities. Women became empowered as they were able to start commercial businesses, rather than taking care of (just) housework, children and elders. Interviews indicated that 92.5% of the respondents their quality of lives improved, 90% felt happier in their families and 82.5% felt conflicts in their families were reduced.

Keywords: Laos, Monetary Remittances, Social Remittance, Women's Empowerment.

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Authors: S. Thirumurugan, D. R. Prince Williams

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Software Reliability is one of the key factors in the software development process. Software Reliability is estimated using reliability models based on Non Homogenous Poisson Process. In most of the literature the Software Reliability is predicted only in testing phase. So it leads to wrong decision-making concept. In this paper, two Software Reliability concepts, testing and operational phase are studied in detail. Using S-Shaped Software Reliability Growth Model (SRGM) and Exponential SRGM, the testing and operational reliability values are obtained. Finally two reliability values are compared and optimal release time is investigated.

Keywords: Error Detection Rate, Estimation of Parameters, Instantaneous Failure Rate, Mean Value Function, Non Homogenous Poisson Process (NHPP), Software Reliability.

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Authors: Taner Ersoz, Filiz Ersoz, Seyma Ozbilge

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In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.

Keywords: Client classification, loan suitability, risk rating, CART analysis, decision tree.

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Authors: Defu Zhang, Qingshan Jiang, Xin Li

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This paper deals with the application of a well-known neural network technique, multilayer back-propagation (BP) neural network, in financial data mining. A modified neural network forecasting model is presented, and an intelligent mining system is developed. The system can forecast the buying and selling signs according to the prediction of future trends to stock market, and provide decision-making for stock investors. The simulation result of seven years to Shanghai Composite Index shows that the return achieved by this mining system is about three times as large as that achieved by the buy and hold strategy, so it is advantageous to apply neural networks to forecast financial time series, the different investors could benefit from it.

Keywords: Data mining, neural network, stock forecasting.

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Authors: M. Arja, G. Sauce, B. Souyri

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Overall cost is a significant consideration in any decision-making process. Although many studies were carried out on overall cost in construction, little has treated the uncertainties of real life cycle development. On the basis of several case studies, a feedback process was performed on the historical data of studied buildings. This process enabled to identify some factors causing uncertainty during the operational period. As a result, the research proposes a new method for assessing the overall cost during a part of the building-s life cycle taking account of the building actual value, its end-of-life value and the influence of the identified life cycle uncertainty factors. The findings are a step towards a higher level of reliability in overall cost evaluation taking account of some usually unexpected uncertainty factors.

Keywords: Asset management, building life cycle uncertainty, building value, overall cost.

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Authors: Derya Eren Akyol, Gonca Tuncel, G. Mirac Bayhan

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Activity-Based Costing (ABC) represents an alternative paradigm to traditional cost accounting system and it often provides more accurate cost information for decision making such as product pricing, product mix, and make-orbuy decisions. ABC models the causal relationships between products and the resources used in their production and traces the cost of products according to the activities through the use of appropriate cost drivers. In this paper, the implementation of the ABC in a manufacturing system is analyzed and a comparison with the traditional cost based system in terms of the effects on the product costs are carried out to highlight the difference between two costing methodologies. By using this methodology, a valuable insight into the factors that cause the cost is provided, helping to better manage the activities of the company.

Keywords: Activity-based costing, manufacturing systems, product costs, traditional costing.

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Authors: D. Pinto, L. Castro, M.L. Cruzat, S. Barros, J. Gironás, C. Oberli, M. Torres, C. Escauriaza, A. Cipriano

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Flash Floods, together with landslides, are a common natural threat for people living in mountainous regions and foothills. One way to deal with this constant menace is the use of Early Warning Systems, which have become a very important mitigation strategy for natural disasters. In this work we present our proposal for a pilot Flash Flood Early Warning System for Santiago, Chile, the first stage of a more ambitious project that in a future stage shall also include early warning of landslides. To give a context for our approach, we first analyze three existing Flash Flood Early Warning Systems, focusing on their general architectures. We then present our proposed system, with main focus on the decision support system, a system that integrates empirical models and fuzzy expert systems to achieve reliable risk estimations.

Keywords: Decision Support System, Early Warning Systems, Flash Flood, Natural Hazard.

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Authors: Jolly Puri, Shiv Prasad Yadav

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Multi-component data envelopment analysis (MC-DEA) is a popular technique for measuring aggregate performance of the decision making units (DMUs) along with their components. However, the conventional MC-DEA is limited to crisp input and output data which may not always be available in exact form. In real life problems, data may be imprecise or fuzzy. Therefore, in this paper, we propose (i) a fuzzy MC-DEA (FMC-DEA) model in which shared and undesirable fuzzy resources are incorporated, (ii) the proposed FMC-DEA model is transformed into a pair of crisp models using α cut approach, (iii) fuzzy aggregate performance of a DMU and fuzzy efficiencies of components are defined to be fuzzy numbers, and (iv) a numerical example is illustrated to validate the proposed approach.

Keywords: Multi-component DEA, fuzzy multi-component DEA, fuzzy resources.

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Authors: Nermin Ozgulbas, Ali Serhan Koyuncugil

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One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an Early Warning System (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7,853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.

Keywords: Early Warning Systems, Data Mining, Financial Risk, SMEs.

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Authors: H. Dujmic, V. Papic, H. Turic

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The article presents a new method for detection of artificial objects and materials from images of the environmental (non-urban) terrain. Our approach uses the hue and saturation (or Cb and Cr) components of the image as the input to the segmentation module that uses the mean shift method. The clusters obtained as the output of this stage have been processed by the decision-making module in order to find the regions of the image with the significant possibility of representing human. Although this method will detect various non-natural objects, it is primarily intended and optimized for detection of humans; i.e. for search and rescue purposes in non-urban terrain where, in normal circumstances, non-natural objects shouldn-t be present. Real world images are used for the evaluation of the method.

Keywords: Landscape surveillance, mean shift algorithm, image segmentation, target detection.

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Authors: Susmita Ghosh, Bhaskar Bhowmick

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Market is an important factor for start-ups to look into during decision-making in product development and related areas. Emerging country markets are more uncertain in terms of information availability and institutional supports. The literature review of market uncertainty reveals the need for identifying factors representing the market uncertainty. This paper identifies factors for market uncertainty using Exploratory Factor Analysis (EFA) and confirmed the number of factor retention using an alternative factor retention criterion ‘Parallel Analysis’. 500 entrepreneurs, engaged in start-ups from all over India participated in the study. This paper concludes with the factor structure of ‘market uncertainty’ having dimensions of uncertainty in industry orientation, uncertainty in customer orientation and uncertainty in marketing orientation.

Keywords: Uncertainty, market, orientation, competitor, demand.

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Authors: K. Allaw, J. Adjizian Gerard, M. Chehayeb, A. Raad, W. Fahs, A. Badran, A. Fakherdin, H. Madi, N. Badaro Saliba

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Population estimation using Geographic Information System (GIS) and remote sensing faces many obstacles such as the determination of permanent residents. A permanent resident is an individual who stays and works during all four seasons in his village. So, all those who move towards other cities or villages are excluded from this category. The aim of this study is to identify the factors affecting the percentage of permanent residents in a village and to determine the attributed weight to each factor. To do so, six factors have been chosen (slope, precipitation, temperature, number of services, time to Central Business District (CBD) and the proximity to conflict zones) and each one of those factors has been evaluated using one of the following data: the contour lines map of 50 m, the precipitation map, four temperature maps and data collected through surveys. The weighting procedure has been done using decision tree method. As a result of this procedure, temperature (50.8%) and percentage of precipitation (46.5%) are the most influencing factors.

Keywords: Remote sensing and GIS, permanent residence, decision tree, Lebanon.

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Authors: Yurii Bloshko, Oksana Olar

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This paper presents the analysis of six different classes of Petri nets: fuzzy Petri nets (FPN), generalized fuzzy Petri nets (GFPN), parameterized fuzzy Petri nets (PFPN), T2GFPN, flexible generalized fuzzy Petri nets (FGFPN), binary Petri nets (BPN). These classes were simulated in the special software PNeS® for the analysis of its pros and cons on the example of models which are dedicated to the decision-making process of passenger transport logistics. The paper includes the analysis of two approaches: when input values are filled with the experts’ knowledge; when fuzzy expectations represented by output values are added to the point. These approaches fulfill the possibilities of triples of functions which are replaced with different combinations of t-/s-norms.

Keywords: Fuzzy petri net, intelligent computational techniques, knowledge representation, triangular norms.

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Authors: Enas M. F. El Houby, Marwa S. Hassan

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Combined therapy using Interferon and Ribavirin is the standard treatment in patients with chronic hepatitis C. However, the number of responders to this treatment is low, whereas its cost and side effects are high. Therefore, there is a clear need to predict patient’s response to the treatment based on clinical information to protect the patients from the bad drawbacks, Intolerable side effects and waste of money. Different machine learning techniques have been developed to fulfill this purpose. From these techniques are Associative Classification (AC) and Decision Tree (DT). The aim of this research is to compare the performance of these two techniques in the prediction of virological response to the standard treatment of HCV from clinical information. 200 patients treated with Interferon and Ribavirin; were analyzed using AC and DT. 150 cases had been used to train the classifiers and 50 cases had been used to test the classifiers. The experiment results showed that the two techniques had given acceptable results however the best accuracy for the AC reached 92% whereas for DT reached 80%.

Keywords: Associative Classification, Data mining, Decision tree, HCV, interferon.

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Authors: Megumi Nishida, Naoya Motegi, Takurou Kikuchi, Tomoko Tokumura

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In recent years, the power system has been changed and a flexible power pricing system such as demand response has been sought in Japan. The demand response system works simply in the household sector and the owner as the decision-maker, can benefit from power saving. On the other hand, the execution of demand response in the office building is more complex than in the household because various people such as owners, building administrators and occupants are involved in the decision-making process. While the owners benefit from demand saving, the occupants are exposed to restricted benefits of a demand-saved environment. One of the reasons is that building systems are usually under centralized management and each occupant cannot choose freely whether to participate in demand response or not. In addition, it is unclear whether incentives give occupants the motivation to participate. However, the recent development of IT and building systems enables the personalized control of the office environment where each occupant can control the lighting level or temperature individually. Therefore, it can be possible to have a system which each occupant can make a decision of whether or not to participate in demand response in the office building. This study investigates personal responses to demand response requests, under the condition where each occupant can adjust their brightness individually in their workspace. Once workers participate in the demand response, their desk-lights are automatically turned off. The participation rates in the demand response events are compared among four groups, which are divided by different motivation, the presence, or absence of incentives and the method of participation. The result shows that there are significant differences of participation rates in demand response event between four groups. The method of participation has a large effect on the participation rate. The “Opt-out” groups where the occupants are automatically enrolled in a demand response event if they do not express non-participation have the highest participation rate in the four groups. Incentives also have an effect on the participation rate. This study also reports on the impact of low illumination office environment on the occupants, such as stress or fatigue. The electrocardiogram and the questionnaire are used to investigate the autonomic nervous activity and subjective fatigue symptoms of the occupants. There is no big difference between dim workspace during demand response event and bright workspace in autonomic nervous activity and fatigue.

Keywords: Demand response, illumination, questionnaire, electrocardiograph.

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Authors: M. Raseen, A.Assi, P.W. C. Prasad, A. Harb

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Binary Decision Diagrams (BDDs) are useful data structures for symbolic Boolean manipulations. BDDs are used in many tasks in VLSI/CAD, such as equivalence checking, property checking, logic synthesis, and false paths. In this paper we describe a new approach for the realization of a BDD package. To perform manipulations of Boolean functions, the proposed approach does not depend on the recursive synthesis operation of the IF-Then-Else (ITE). Instead of using the ITE operation, the basic synthesis algorithm is done using Boolean NOR operation.

Keywords: Binary Decision Diagram (BDD), ITE Operation, Boolean Function, NOR operation.

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Authors: Ernesto A. Lagarda-Leyva, Manuel A. Valenzuela L., José G. Oshima C., Arnulfo A. Naranjo-Flores

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From August to December of 2016, a diagnostic and strategic planning study was carried out on the supply chain of the company Agropecuaria GABO S.A. de C.V. The final product of the study was the development of the strategic plan and a project portfolio to meet the demands of the three links in the supply chain of the Roma tomato exported annually to the United States of America. In this project, the strategic objective of ensuring the proper handling of the product was selected and one of the goals associated with this was the employment of quantitative methods to support decision making. Considering the antecedents, the objective of this case study was to develop a model to analyze the behavioral dynamics in the distribution chain, from the logistics of storage and shipment of Roma tomato in 81-case pallets (11.5 kg per case), to the two pre-cooling rooms and eventual loading onto transports, seeking to reduce the bottleneck and the associated costs by means of a dynamic interface. The methodology used was that of system dynamics, considering four phases that were adapted to the purpose of the study: 1) the conceptualization phase; 2) the formulation phase; 3) the evaluation phase; and 4) the communication phase. The main practical conclusions lead to the possibility of reducing both the bottlenecks in the cooling rooms and the costs by simulating scenarios and modifying certain policies. Furthermore, the creation of the dynamic interface between the model and the stakeholders was achieved by generating interaction with buttons and simple instructions that allow making modifications and observing diverse behaviors.

Keywords: Agrilogistics, distribution, scenarios, system dynamics.

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Authors: Arthur Yan

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Recent theorizations on the cognitive process of moral judgment have focused on the role of intuitions and emotions, marking a departure from previous emphasis on conscious, step-by-step reasoning. My study investigated how being in a disgusted mood state affects moral judgment. Participants were induced to enter a disgusted mood state through listening to disgusting sounds and reading disgusting descriptions. Results shows that they, when compared to control who have not been induced to feel disgust, are more likely to endorse actions that are emotionally aversive but maximizes utilitarian return The result is analyzed using the 'emotion-as-information' approach to decision making. The result is consistent with the view that emotions play an important role in determining moral judgment.

Keywords: Disgust, mood induction, moral judgment, emotion-as-information.

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Authors: Supunmali Ahangama, Danny Chiang Choon Poo

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Health analytics (HA) is used in healthcare systems for effective decision making, management and planning of healthcare and related activities. However, user resistances, unique position of medical data content and structure (including heterogeneous and unstructured data) and impromptu HA projects have held up the progress in HA applications. Notably, the accuracy of outcomes depends on the skills and the domain knowledge of the data analyst working on the healthcare data. Success of HA depends on having a sound process model, effective project management and availability of supporting tools. Thus, to overcome these challenges through an effective process model, we propose a HA process model with features from rational unified process (RUP) model and agile methodology.

Keywords: Agile methodology, health analytics, unified process model, UML.

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Authors: Sarah O. Alhumoud, Mawaheb I. Altuwaijri, Tarfa M. Albuhairi, Wejdan M. Alohaideb

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Large-scale data stream analysis has become one of the important business and research priorities lately. Social networks like Twitter and other micro-blogging platforms hold an enormous amount of data that is large in volume, velocity and variety. Extracting valuable information and trends out of these data would aid in a better understanding and decision-making. Multiple analysis techniques are deployed for English content. Moreover, one of the languages that produce a large amount of data over social networks and is least analyzed is the Arabic language. The proposed paper is a survey on the research efforts to analyze the Arabic content in Twitter focusing on the tools and methods used to extract the sentiments for the Arabic content on Twitter.

Keywords: Big Data, Social Networks, Sentiment Analysis.

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Authors: Sanjeev Kumar Appicharla

Abstract:

This paper presents the results of the modelling and analysis of the European Railway Traffic Management (ERTMS) safety critical incident to raise awareness of biases in systems engineering process on the Cambrian Railway in the UK using the RAIB 17/2019 as a primary input. The RAIB, the UK independent accident investigator, published the Report- RAIB 17/2019 giving the details of their investigation of the focal event in the form of immediate cause, causal factors and underlying factors and recommendations to prevent a repeat of the safety-critical incident on the Cambrian Line. The Systems for Investigation of Railway Interfaces (SIRI) is the Methodology used to model and analyse the safety-critical incident. The SIRI Methodology uses the Swiss Cheese Model to model the incident and identify latent failure conditions (potentially less than adequate conditions) by means of the Management Oversight and Risk Tree technique. The benefits of the SIRI Methodology are threefold: first is that it incorporates “Heuristics and Biases” approach, in the Management Oversight and Risk Tree technique to identify systematic errors. Civil engineering and programme management railway professionals are aware of role “optimism bias” plays in programme cost overruns and are aware of bow tie (fault and event tree) model-based safety risk modelling technique. However, the role of systematic errors due to “Heuristics and Biases” is not appreciated as yet. This overcomes the problems of omission of human and organisational factors from accident analysis. Second, the scope of the investigation includes all levels of the socio-technical system, including government, regulatory, railway safety bodies, duty holders, signalling firms and transport planners, and front-line staff such that lessons learned at the decision making and implementation level as well. Third, the author’s past accident case studies are supplemented with research pieces of evidence drawn from the practitioner’s and academic researchers’ publications as well. This is to discuss the role of system thinking to improve the decision making and risk management processes and practices in the IEC 15288 Systems Engineering standard, and in the industrial context such as the GB railways and Artificial Intelligence (AI) contexts as well.

Keywords: Accident analysis, AI algorithm internal audit, bounded rationality, Byzantine failures, heuristics and biases approach.

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Authors: Vishnu Pratap Singh Kirar

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The main aim of a communication system is to achieve maximum performance. In Cognitive Radio any user or transceiver has ability to sense best suitable channel, while channel is not in use. It means an unlicensed user can share the spectrum of a licensed user without any interference. Though, the spectrum sensing consumes a large amount of energy and it can reduce by applying various artificial intelligent methods for determining proper spectrum holes. It also increases the efficiency of Cognitive Radio Network (CRN). In this survey paper we discuss the use of different learning models and implementation of Artificial Neural Network (ANN) to increase the learning and decision making capacity of CRN without affecting bandwidth, cost and signal rate.

Keywords: Artificial Neural Network, Cognitive Radio, Cognitive Radio Networks, Back Propagation, Spectrum Sensing.

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Authors: Tristan O’Neill, Trina Myers, Jarrod Trevathan

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The Tropical Data Hub (TDH) is a virtual research environment that provides researchers with an e-research infrastructure to congregate significant tropical data sets for data reuse, integration, searching, and correlation. However, researchers often require data and metadata synthesis across disciplines for cross-domain analyses and knowledge discovery. A triplestore offers a semantic layer to achieve a more intelligent method of search to support the synthesis requirements by automating latent linkages in the data and metadata. Presently, the benchmarks to aid the decision of which triplestore is best suited for use in an application environment like the TDH are limited to performance. This paper describes a new evaluation tool developed to analyze both features and performance. The tool comprises a weighted decision matrix to evaluate the interoperability, functionality, performance, and support availability of a range of integrated and native triplestores to rank them according to requirements of the TDH.

Keywords: Virtual research environment, Semantic Web, performance analysis, tropical data hub.

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Authors: Xiantong Li, Jianzhong Li

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Graph has become increasingly important in modeling complicated structures and schemaless data such as proteins, chemical compounds, and XML documents. Given a graph query, it is desirable to retrieve graphs quickly from a large database via graph-based indices. Different from the existing methods, our approach, called VFM (Vertex to Frequent Feature Mapping), makes use of vertices and decision features as the basic indexing feature. VFM constructs two mappings between vertices and frequent features to answer graph queries. The VFM approach not only provides an elegant solution to the graph indexing problem, but also demonstrates how database indexing and query processing can benefit from data mining, especially frequent pattern mining. The results show that the proposed method not only avoids the enumeration method of getting subgraphs of query graph, but also effectively reduces the subgraph isomorphism tests between the query graph and graphs in candidate answer set in verification stage.

Keywords: Decision Feature, Frequent Feature, Graph Dataset, Graph Query

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