Search results for: Decision Making of New Government R&D Program

Authors: Ezeni Brzovska, Durdana Ozretic-Dosen

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The purpose of this paper is to deepen the understanding of the product cues that influence purchase decision for a specific product category – chocolate, and to identify demographic differences in the buying behavior. ANOVA was employed for analyzing the significance level for nine product cues, and the survey showed statistically significant differences among different age and gender groups, and between respondents with different levels of education. From the theoretical perspective, the study adds to the existing knowledge by contributing with the research results from the new environment (Southeast Europe, Macedonia), which has been neglected so far. Establishing the level of significance for the product cues that affect buying behavior in the chocolate consumption context might help managers to improve marketing decision-making, and better meet consumer needs through identifying opportunities for packaging innovations and/or personalization toward different target groups.

Keywords: Chocolate consumption context, chocolate selection, demographic characteristics, product cues.

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Authors: R. Malisa, E. Schwella, K. I. Theletsane

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Due to climate change, population growth and rapid urbanization, the demand for water in South Africa is inevitably surpassing supply. To address similar challenges globally, there has been a paradigm shift from conventional urban waste water management “government” to a “governance” paradigm. From the governance paradigm, Integrated Urban Water Management (IUWM) principle emerged. This principle emphasizes efficient urban waste water treatment and production of high-quality recyclable effluent. In so doing mimicking natural water systems, in their processes of recycling water efficiently, and averting depletion of natural water resources.  The objective of this study was to investigate drivers of shifting the current urban waste water management approach from a “government” paradigm towards “governance”. The study was conducted through Interactive Management soft systems research methodology which follows a qualitative research design. A case study methodology was employed, guided by realism research philosophy. Qualitative data gathered were analyzed through interpretative structural modelling using Concept Star for Professionals Decision-Making tools (CSPDM) version 3.64.  The constructed model deduced that the main drivers in shifting the Stellenbosch municipal urban waste water management towards IUWM “governance” principles are mainly social elements characterized by overambitious expectations of the public on municipal water service delivery, mis-interpretation of the constitution on access to adequate clean water and sanitation as a human right and perceptions on recycling water by different communities. Inadequate public participation also emerged as a strong driver. However, disruptive events such as draught may play a positive role in raising an awareness on the value of water, resulting in a shift on the perceptions on recycled water. Once the social elements are addressed, the alignment of governance and administration elements towards IUWM are achievable. Hence, the point of departure for the desired paradigm shift is the change of water service authorities and serviced communities’ perceptions and behaviors towards shifting urban waste water management approaches from “government” to “governance” paradigm.

Keywords: Integrated urban water management, urban water system, waste water governance, waste water treatment works.

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Authors: Michael Grigutsch, Marco Kennemann, Peter Nyhuis

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Due to the recovering global economy, enterprises are increasingly focusing on logistics. Investing in logistic measures for a production generates a large potential for achieving a good starting point within a competitive field. Unlike during the global economic crisis, enterprises are now challenged with investing available capital to maximize profits. In order to be able to create an informed and quantifiably comprehensible basis for a decision, enterprises need an adequate model for logistically and monetarily evaluating measures in production. The Collaborate Research Centre 489 (SFB 489) at the Institute for Production Systems (IFA) developed a Logistic Information System which provides support in making decisions and is designed specifically for the forging industry. The aim of a project that has been applied for is to now transfer this process in order to develop a universal approach to logistically and monetarily evaluate measures in production.

Keywords: Measures in Production, Logistic Operating Curves, Transfer Functions, Production Logistics

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Authors: P. Beraldi, M. E. Bruni

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Data Envelopment Analysis (DEA) is one of the most widely used technique for evaluating the relative efficiency of a set of homogeneous decision making units. Traditionally, it assumes that input and output variables are known in advance, ignoring the critical issue of data uncertainty. In this paper, we deal with the problem of efficiency evaluation under uncertain conditions by adopting the general framework of the stochastic programming. We assume that output parameters are represented by discretely distributed random variables and we propose two different models defined according to a neutral and risk-averse perspective. The models have been validated by considering a real case study concerning the evaluation of the technical efficiency of a sample of individual firms operating in the Italian leather manufacturing industry. Our findings show the validity of the proposed approach as ex-ante evaluation technique by providing the decision maker with useful insights depending on his risk aversion degree.

Keywords: DEA, Stochastic Programming, Ex-ante evaluation technique, Conditional Value at Risk.

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Authors: Mazziotti Gomez de Teran C., Fiore D., Cola B., Fardelli A.

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The present paper summarizes the analysis of the request for consultation of information and data on industrial emissions made publicly available on the web site of the Ministry of Environment, Land and Sea on integrated pollution prevention and control from large industrial installations, the so called “AIA Portal”. As a matter of fact, a huge amount of information on national industrial plants is already available on internet, although it is usually proposed as textual documentation or images. Thus, it is not possible to access all the relevant information through interoperability systems and also to retrieval relevant information for decision making purposes as well as rising of awareness on environmental issue. Moreover, since in Italy the number of institutional and private subjects involved in the management of the public information on industrial emissions is substantial, the access to the information is provided on internet web sites according to different criteria; thus, at present it is not structurally homogeneous and comparable. To overcome the mentioned difficulties in the case of the Coordinating Committee for the implementation of the Agreement for the industrial area in Taranto and Statte, operating before the IPPC permit granting procedures of the relevant installation located in the area, a big effort was devoted to elaborate and to validate data and information on characterization of soil, ground water aquifer and coastal sea at disposal of different subjects to derive a global perspective for decision making purposes. Thus, the present paper also focuses on main outcomes matured during such experience.

Keywords: Public information, emissions into atmosphere, IPPC permits, territorial information systems.

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: Autonomous surveillance, Bayesian reasoning, decision-support, interventions, patterns-of-life, predictive analytics, predictive insights.

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Authors: Alfonso Bastías, Álvaro González

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The actual grow of the infrastructure in develop country require sophisticate ways manage the operation and control the quality served. This research wants to concentrate in the operation of this infrastructure beyond the construction. The infrastructure-s operation involves an uncertain environment, where unexpected variables are present every day and everywhere. Decision makers need to make right decisions with right information/data analyzed most in real time. To adequately support their decisions and decrease any negative impact and collateral effect, they need to use computational tools called decision support systems (DSS), but now the main source of information came from common users thought an extensive crowdsourcing

Keywords: Crowdsourcing, Learning Systems, Decision Support Systems, Infrastructure, Construction.

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Authors: György Á. Horváth, Piroska Harazin

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The transition to sustainable development requires considerable investments from stakeholders, both financial and immaterial. However, accounting for such investments often poses a challenge, as ventures with intangible or non-financial returns remain oblivious to conventional accounting techniques and risk assessment. That such investments may significantly contribute to the welfare of those affected may act as a driving force behind attempting to bridge this gap. This gains crucial importance as investments must be also backed by governments and administrations; entities whose budget depends on taxpayers- contributions and whose tasks are based on securing the welfare of their citizens. Besides economic welfare, citizens also require social and environmental wellbeing too. However, administrations must also safeguard that welfare is guaranteed not only to present, but to future generations too. With already strained budgets and the requirement of sustainable development, governments on all levels face the double challenge of making both of these ends meet.

Keywords: Accounting, Administration and Government, RiskAssessment, Sustainable Development

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Authors: Mehdi Kheradmand, Mehdi Azhdary Moghaddam, Abdolreza Zahiri, Khalil Ghorbani

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In modeling phenomena, the presence of local conditions may cause the use of a general relation not to produce good results and thus fail to demonstrate local changes. If possible, identifying homogenous limits and providing simple linear relations for each of these limits will increase the accuracy of models. Accordingly, the models are divided into simpler and smaller problems to solve complicated problems, and the obtained answers will be combined. This simple idea can be applied to decision tree models. For this aim, the input data values are divided into several sub-intervals or sub-regions, and an appropriate model is extracted for an appropriate model or equation. This research proposes the M5 decision tree method as a solution to accurately compute the flow discharge in meandering compound channels.

Keywords: Stage-discharge relationship, decision tree, M5 decision tree model, meandering compound channels.

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Authors: Richa Grover, Rahul Grover, V. Balaji Rao, Kavish Kejriwal

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Selection of suppliers is a crucial problem in the supply chain management. On top of that, sustainable supplier selection is the biggest challenge for the organizations. Environment protection and social problems have been of concern to society in recent years, and the traditional supplier selection does not consider about this factor; therefore, this research work focuses on introducing sustainable criteria into the structure of supplier selection criteria. Sustainable Supply Chain Management (SSCM) is the management and administration of material, information, and money flows, as well as coordination among business along the supply chain. All three dimensions - economic, environmental, and social - of sustainable development needs to be taken care of. Purpose of this research is to maximize supply chain profitability, maximize social wellbeing of supply chain and minimize environmental impacts. Problem statement is selection of suppliers in a sustainable supply chain network by ranking the suppliers against sustainable criteria identified. The aim of this research is twofold: To find out what are the sustainable parameters that can be applied to the supply chain, and to determine how these parameters can effectively be used in supplier selection. Multicriteria decision making tools will be used to rank both criteria and suppliers. AHP Analysis will be used to find out ratings for the criteria identified. It is a technique used for efficient decision making. TOPSIS will be used to find out rating for suppliers and then ranking them. TOPSIS is a MCDM problem solving method which is based on the principle that the chosen option should have the maximum distance from the negative ideal solution (NIS) and the minimum distance from the ideal solution.

Keywords: Sustainable supply chain management, supplier selection, MCDM tools, AHP analysis, TOPSIS method.

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Authors: M. Yosofi, S. Ezeddini, A. Ollivier, V. Lavaste, C. Mayousse

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Additive manufacturing processes have changed significantly in a wide range of industries and their application progressed from rapid prototyping to production of end-use products. However, their environmental impact is still a rather open question. In order to support the growth of this technology in the industrial sector, environmental aspects should be considered and predictive models may help monitor and reduce the environmental footprint of the processes. This work presents predictive models based on a previously developed methodology for the environmental impact evaluation combined with a technical and economical assessment. Here we applied the methodology to the Fused Deposition Modeling process. First, we present the predictive models relative to different types of machines. Then, we present a decision-making tool designed to identify the optimum manufacturing strategy regarding technical, economic, and environmental criteria.

Keywords: Additive manufacturing, decision-makings, environmental impact, predictive models.

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Authors: Nadezda Pizika

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The paper discusses possible approaches of embedding the development of employability skills in the program curriculum. This paper contains analysis of the problem areas raised by employers regarding new graduates’ readiness to join workforce, the ways of possible improvements, and the actions required from different stakeholders. The case discussed in the paper is related to Computer and Information Science (CIS) Program offered at Higher Colleges of Technology (UAE).

Keywords: Curriculum Design, Employability Skills, Employers, Graduates.

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Authors: Nalinee Sophatsathit

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This research focuses on the use of a recommender system in decision support by means of a used car dealer case study in Bangkok Metropolitan. The goal is to develop an effective used car purchasing system for dealers based on the above premise. The underlying principle rests on content-based recommendation from a set of usability surveys. A prototype was developed to conduct buyers- survey selected from 5 experts and 95 general public. The responses were analyzed to determine the mean and standard deviation of buyers- preference. The results revealed that both groups were in favor of using the proposed system to assist their buying decision. This indicates that the proposed system is meritorious to used car dealers.

Keywords: Recommender Systems, Decision Support, Content- Based Recommendation, used car dealer.

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Authors: Wei-Chieh Yeh, Yung-Tang Shen

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The concept of community building started in 1994 in Taiwan. After years of development, it fostered the notion of active local resident participation in community issues as co-operators, instead of minions. Participatory design gives participants more control in the decision-making process, helps to reduce the friction caused by arguments and assists in bringing different parties to consensus. This results in an increase in the efficiency of projects run in the community. Therefore, the participation of local residents is key to the success of community building. This study applied participatory design to develop plans for the reuse of deserted spaces in the community from the first stage of brainstorming for design ideas, making creative models to be employed later, through to the final stage of construction. After conducting a series of participatory designed activities, it aimed to integrate the different opinions of residents, develop a sense of belonging and reach a consensus. Besides this, it also aimed at building the residents’ awareness of their responsibilities for the environment and related issues of sustainable development. By reviewing relevant literature and understanding the history of related studies, the study formulated a theory. It took the “2012-2014 Changhua County Community Planner Counseling Program” as a case study to investigate the implementation process of participatory design. Research data are collected by document analysis, participants’ observation and in-depth interviews. After examining the three elements of “Design Participation”, “Construction Participation”, and” Follow–up Maintenance Participation” in the case, the study emerged with a promising conclusion: Maintenance works were carried out better compared to common public works. Besides this, maintenance costs were lower. Moreover, the works that residents were involved in were more creative. Most importantly, the community characteristics could be easy be recognized.

Keywords: Participatory design, Deserted spaces, Community building, Reuse.

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Authors: S. Ghorbani, N. I. Polushin

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The machinability of workpieces (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron) in turning operation has been carried out using different types of cutting tool (conventional, cutting tool with holes in toolholder and cutting tool filled up with composite material) under dry conditions on a turning machine at different stages of spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). Experimentation was performed as per Taguchi’s orthogonal array. To evaluate the relative importance of factors affecting surface roughness the single decision tree (SDT), Decision tree forest (DTF) and Group method of data handling (GMDH) were applied.

Keywords: Decision Tree Forest, GMDH, surface roughness, taguchi method, turning process.

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Authors: Daniela I. Borissova, Ivan C. Mustakerov

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The paper describes an approach for defining of k-best night vision devices based on multi-criteria mixed-integer optimization modeling. The parameters of night vision devices are considered as criteria that have to be optimized. Using different user preferences for the relative importance between parameters different choice of k-best devices can be defined. An ideal device with all of its parameters at their optimum is used to determine how far the particular device from the ideal one is. A procedure for evaluation of deviation between ideal solution and k-best solutions is presented. The applicability of the proposed approach is numerically illustrated using real night vision devices data. The proposed approach contributes to quality of decisions about choice of night vision devices by making the decision making process more certain, rational and efficient. 

Keywords: K-best devices, mixed-integer model, multi-criteria problem, night vision devices.

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Authors: P. H. Tee, S. M. Zamri, K. M. Kasim, S. K. Tiew

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This study evaluates the management of osteoporosis in a tertiary care government hospital in Malaysia. As the number of admitted patients having osteoporotic fractures is on the rise, osteoporotic medications are an increasing financial burden to government hospitals because they account for half of the orthopedic budget and expenditure. Comprehensive knowledge among practitioners is important to detect early and avoid this preventable disease and its serious complications. The purpose of this study is to evaluate the awareness, knowledge, and practices in managing osteoporosis among practitioners in Hospital Tengku Ampuan Rahimah (HTAR), Klang. A questionnaire from an overseas study in managing osteoporosis among primary care physicians is adapted to Malaysia’s Clinical Practice Guideline of Osteoporosis 2012 (revised 2015) and international guidelines were distributed to all orthopedic practitioners in HTAR Klang (including surgeons, orthopedic medical officers), endocrinologists, rheumatologists and geriatricians. The participants were evaluated on their expertise in the diagnosis, prevention, treatment decision and medications for osteoporosis. Collected data were analyzed for all descriptive and statistical analyses as appropriate. All 45 participants responded to the questionnaire. Participants scored highest on expertise in prevention, followed by diagnosis, treatment decision and lastly, medication. Most practitioners stated that own-initiated continuing professional education from articles and books was the most effective way to update their knowledge, followed by attendance in conferences on osteoporosis. This study confirms the importance of comprehensive training and education regarding osteoporosis among tertiary care physicians and surgeons, predominantly in pharmacotherapy, to deliver wholesome care for osteoporotic patients.

Keywords: Awareness, knowledge, osteoporosis, practices.

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Authors: Sohrab Khanmohammadi, Raana Soltani Zarrin

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In this paper, a heuristic method for simultaneous rescue robot path-planning and mission scheduling is introduced based on project management techniques, multi criteria decision making and artificial potential fields path-planning. Groups of injured people are trapped in a disastrous situation. These people are categorized into several groups based on the severity of their situation. A rescue robot, whose ultimate objective is reaching injured groups and providing preliminary aid for them through a path with minimum risk, has to perform certain tasks on its way towards targets before the arrival of rescue team. A decision value is assigned to each target based on the whole degree of satisfaction of the criteria and duties of the robot toward the target and the importance of rescuing each target based on their category and the number of injured people. The resulted decision value defines the strength of the attractive potential field of each target. Dangerous environmental parameters are defined as obstacles whose risk determines the strength of the repulsive potential field of each obstacle. Moreover, negative and positive energies are assigned to the targets and obstacles, which are variable with respects to the factors involved. The simulation results show that the generated path for two cases studies with certain differences in environmental conditions and other risk factors differ considerably.

Keywords: Artificial potential field, GERT, path planning

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Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, G. Corkery, E. Broderick, J. Walsh

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Farm management and operations will drastically change due to access to real-time data, real-time forecasting and tracking of physical items in combination with Internet of Things (IoT) developments to further automate farm operations. Dairy farms have embraced technological innovations and procured vast amounts of permanent data streams during the past decade; however, the integration of this information to improve the whole farm decision-making process does not exist. It is now imperative to develop a system that can collect, integrate, manage, and analyze on-farm and off-farm data in real-time for practical and relevant environmental and economic actions. The developed systems, based on machine learning and artificial intelligence, need to be connected for useful output, a better understanding of the whole farming issue and environmental impact. Evolutionary Computing (EC) can be very effective in finding the optimal combination of sets of some objects and finally, in strategy determination. The system of the future should be able to manage the dairy farm as well as an experienced dairy farm manager with a team of the best agricultural advisors. All these changes should bring resilience and sustainability to dairy farming as well as improving and maintaining good animal welfare and the quality of dairy products. This review aims to provide an insight into the state-of-the-art of big data applications and EC in relation to smart dairy farming and identify the most important research and development challenges to be addressed in the future. Smart dairy farming influences every area of management and its uptake has become a continuing trend.

Keywords: Big data, evolutionary computing, cloud, precision technologies

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Authors: Chi-Fang Huang, Yun-Shiow Chen, Yun-Kung Chung

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In the past decade, artificial neural networks (ANNs) have been regarded as an instrument for problem-solving and decision-making; indeed, they have already done with a substantial efficiency and effectiveness improvement in industries and businesses. In this paper, the Back-Propagation neural Networks (BPNs) will be modulated to demonstrate the performance of the collaborative forecasting (CF) function of a Collaborative Planning, Forecasting and Replenishment (CPFR®) system. CPFR functions the balance between the sufficient product supply and the necessary customer demand in a Supply and Demand Chain (SDC). Several classical standard BPN will be grouped, collaborated and exploited for the easy implementation of the proposed modular ANN framework based on the topology of a SDC. Each individual BPN is applied as a modular tool to perform the task of forecasting SKUs (Stock-Keeping Units) levels that are managed and supervised at a POS (point of sale), a wholesaler, and a manufacturer in an SDC. The proposed modular BPN-based CF system will be exemplified and experimentally verified using lots of datasets of the simulated SDC. The experimental results showed that a complex CF problem can be divided into a group of simpler sub-problems based on the single independent trading partners distributed over SDC, and its SKU forecasting accuracy was satisfied when the system forecasted values compared to the original simulated SDC data. The primary task of implementing an autonomous CF involves the study of supervised ANN learning methodology which aims at making “knowledgeable" decision for the best SKU sales plan and stocks management.

Keywords: CPFR, artificial neural networks, global logistics, supply and demand chain.

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Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri

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This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well. 

Keywords: Data mining technique, the decision support system, knowledge and decision rules.

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Authors: C. Oprean, C. V. Kifor, S. C. Negulescu, C. Candea, L. Oprean, C. Oprean, S. Kifor

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This paper presents an innovative approach within the area of Group Decision Support System (GDSS) by using tools based on intelligent agents. It introduces iGDSS, a software platform for decision support and collaboration and an application of this platform - eCollaborative Decisions - for academic environment, all these developed within a framework of a research project.

Keywords: Group Decision Support System, Managerial Academic Decisions, Computer Interaction.

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Authors: Abdoullah Fath-Allah, Laila Cheikhi, Rafa E. Al-Qutaish, Ali Idri

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The e-government emerging concept transforms the way in which the citizens are dealing with their governments. Thus, the citizens can execute the intended services online anytime and anywhere. This results in great benefits for both the governments (reduces the number of officers) and the citizens (more flexibility and time saving). Therefore, building a maturity model to assess the egovernment portals becomes desired to help in the improvement process of such portals. This paper aims at proposing an egovernment maturity model based on the measurement of the best practices’ presence. The main benefit of such maturity model is to provide a way to rank an e-government portal based on the used best practices, and also giving a set of recommendations to go to the higher stage in the maturity model.

Keywords: Best practices, e-government portal, maturity model, quality model.

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Authors: Ayed Salman, Fawaz Al-Anzi, Aseel Al-Minayes

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Heuristics-based search methodologies normally work on searching a problem space of possible solutions toward finding a “satisfactory" solution based on “hints" estimated from the problem-specific knowledge. Research communities use different types of methodologies. Unfortunately, most of the times, these hints are immature and can lead toward hindering these methodologies by a premature convergence. This is due to a decrease of diversity in search space that leads to a total implosion and ultimately fitness stagnation of the population. In this paper, a novel Decision Maturity framework (DMF) is introduced as a solution to this problem. The framework simply improves the decision on the direction of the search by materializing hints enough before using them. Ideas from this framework are injected into the particle swarm optimization methodology. Results were obtained under both static and dynamic environment. The results show that decision maturity prevents premature converges to a high degree.

Keywords: Heuristic Search, hints, Particle Swarm Optimization, Decision Maturity Framework.

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Authors: N. A. Ibrahim, A. Kudus, I. Daud, M. R. Abu Bakar

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Competing risks survival data that comprises of more than one type of event has been used in many applications, and one of these is in clinical study (e.g. in breast cancer study). The decision tree method can be extended to competing risks survival data by modifying the split function so as to accommodate two or more risks which might be dependent on each other. Recently, researchers have constructed some decision trees for recurrent survival time data using frailty and marginal modelling. We further extended the method for the case of competing risks. In this paper, we developed the decision tree method for competing risks survival time data based on proportional hazards for subdistribution of competing risks. In particular, we grow a tree by using deviance statistic. The application of breast cancer data is presented. Finally, to investigate the performance of the proposed method, simulation studies on identification of true group of observations were executed.

Keywords: Competing risks, Decision tree, Simulation, Subdistribution Proportional Hazard.

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Authors: Saif Obaid Alkaabi, Nabil Ayad

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Governments constantly seek to offer faster, more secure, efficient and effective services for their citizens. Recent changes and developments to communication services and technologies, mainly due the Internet, have led to immense improvements in the way governments of advanced countries carry out their interior operations Therefore, advances in e-government services have been broadly adopted and used in various developed countries, as well as being adapted to developing countries. The implementation of advances depends on the utilization of the most innovative structures of data techniques, mainly in web dependent applications, to enhance the main functions of governments. These functions, in turn, have spread to mobile and wireless techniques, generating a new advanced direction called m-government. This paper discusses a selection of available m-government applications and several business modules and frameworks in various fields. Practically, the m-government models, techniques and methods have become the improved version of e-government. M-government offers the potential for applications which will work better, providing citizens with services utilizing mobile communication and data models incorporating several government entities. Developing countries can benefit greatly from this innovation due to the fact that a large percentage of their population is young and can adapt to new technology and to the fact that mobile computing devices are more affordable. The use of models of mobile transactions encourages effective participation through the use of mobile portals by businesses, various organizations, and individual citizens. Although the application of m-government has great potential, it does have major limitations. The limitations include: the implementation of wireless networks and relative communications, the encouragement of mobile diffusion, the administration of complicated tasks concerning the protection of security (including the ability to offer privacy for information), and the management of the legal issues concerning mobile applications and the utilization of services.

Keywords: E-government, m-government, system dependability, system security, trust.

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Authors: Afrooz Moatari Kazerouni

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In a competitive production environment, critical decision making are based on data resulted by random sampling of product units. Efficiency of these decisions depends on data quality and also their reliability scale. This point leads to the necessity of a reliable measurement system. Therefore, the conjecture process and analysing the errors contributes to a measurement system known as Measurement System Analysis (MSA). The aim of this research is on determining the necessity and assurance of extensive development in analysing measurement systems, particularly with the use of Repeatability and Reproducibility Gages (GR&R) to improve physical measurements. Nowadays in productive industries, repeatability and reproducibility gages released so well but they are not applicable as well as other measurement system analysis methods. To get familiar with this method and gain a feedback in improving measurement systems, this survey would be on “ANOVA" method as the most widespread way of calculating Repeatability and Reproducibility (R&R).

Keywords: Analysis of Variance (ANOVA), MeasurementSystem Analysis (MSA), Part-Operator interaction effect, Repeatability and Reproducibility.

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Authors: Daniel Stamate

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We introduce a logic-based framework for database updating under constraints. In our framework, the constraints are represented as an instantiated extended logic program. When performing an update, database consistency may be violated. We provide an approach of maintaining database consistency, and study the conditions under which the maintenance process is deterministic. We show that the complexity of the computations and decision problems presented in our framework is in each case polynomial time.

Keywords: Databases, knowledge bases, constraints, updates, minimal change, consistency.

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Authors: Tony Austin, Shanghua Sun, Nathan Lea, Steve Iliffe, Dipak Kalra, David Ingram, David Patterson

Abstract:

Computer-based decision support (CDSS) systems can deliver real patient care and increase chances of long-term survival in areas of chronic disease management prone to poor control. One such CDSS, for the management of warfarin, is described in this paper and the outcomes shown. Data is derived from the running system and show a performance consistently around 20% better than the applicable guidelines.

Keywords: "Decision Support", "Anticoagulant Control"

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Authors: Gizelle K. Vianna, Gustavo S. Oliveira, Gabriel V. Cunha

Abstract:

The use of decision support systems in agriculture may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. In our work, we designed and implemented a decision support system for small tomatoes producers. This work investigates ways to recognize the late blight disease from the analysis of digital images of tomatoes, using a pair of multilayer perceptron neural networks. The networks outputs are used to generate repainted tomato images in which the injuries on the plant are highlighted, and to calculate the damage level of each plant. Those levels are then used to construct a situation map of a farm where a cellular automata simulates the outbreak evolution over the fields. The simulator can test different pesticides actions, helping in the decision on when to start the spraying and in the analysis of losses and gains of each choice of action.

Keywords: Artificial neural networks, cellular automata, decision support system, pattern recognition.

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