Search results for: Decision analysis

Authors: Mary L. G. S. Senna, Veruska C. Dutra, Afonso R. Aquino

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The study is in application and analysis of two tourism management tools that can contribute to making public managers decision: the Barometer of Tourism Sustainability (BTS) and the Ecological Footprint (EF). The results have shown that BTS allows you to have an integrated view of the tourism system, awakening to the need for planning of appropriate actions so that it can achieve the positive scale proposed (potentially sustainable). Already the methodology of ecological tourism footprint is an important tool to measure potential impacts generated by tourism to tourist reality.

Keywords: Barometer of tourism sustainability, ecological footprint of tourism, Jalapão/Brazil, sustainable tourism.

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Authors: M. I. Abdelhamid, A. O. Ghallab, R. S. Ettouney, M. A. El-Rifai

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An optimization scheme based on COM server is suggested for communication between Genetic Algorithm (GA) toolbox of MATLAB and Aspen HYSYS. The structure and details of the proposed framework are discussed. The power of the developed scheme is illustrated by its application to the optimization of a recently developed natural gas liquefaction process in which Aspen HYSYS was used for minimization of the power consumption by optimizing the values of five operating variables. In this work, optimization by coupling between the GA in MATLAB and Aspen HYSYS model of the same process using the same five decision variables enabled improvements in power consumption by 3.3%, when 77% of the natural gas feed is liquefied. Also on inclusion of the flow rates of both nitrogen and carbon dioxide refrigerants as two additional decision variables, the power consumption decreased by 6.5% for a 78% liquefaction of the natural gas feed.

Keywords: Stranded gas liquefaction, genetic algorithm, COM server, single nitrogen expansion, carbon dioxide pre-cooling.

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Authors: Ali Payan

Abstract:

The aim of this paper is to evaluate the performance of the faculties of Islamic Azad University of Zahedan Branch based on two-component (teaching and research) decision making units (DMUs) in data envelopment analysis (DEA). Nowadays it is obvious that most of the systems as DMUs do not act as a simple inputoutput structure. Instead, if they have been studied more delicately, they include network structure. University is such a network in which different sections i.e. teaching, research, students and office work as a parallel structure. They consume some inputs of university commonly and some others individually. Then, they produce both dependent and independent outputs. These DMUs are called two-component DMUs with network structure. In this paper, performance of the faculties of Zahedan branch is calculated by using relative efficiency model and also, a formula to compute relative efficiencies teaching and research components based on DEA are offered.

Keywords: Data envelopment analysis, faculties of Islamic Azad University of Zahedan branch, two-component DMUs.

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Authors: Georges Bou Ghantous

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This research delves into the transformative impact of Artificial Intelligence (AI) on Infrastructure as Code (IaC) practices, specifically focusing on the redefinition of infrastructure orchestration. By harnessing AI technologies such as machine learning algorithms and predictive analytics, organizations can achieve unprecedented levels of efficiency and optimization in managing their infrastructure resources. AI-driven IaC introduces proactive decision-making through predictive insights, enabling organizations to anticipate and address potential issues before they arise. Dynamic resource scaling, facilitated by AI, ensures that infrastructure resources can seamlessly adapt to fluctuating workloads and changing business requirements. Through case studies and best practices, this paper sheds light on the tangible benefits and challenges associated with AI-driven IaC transformation, providing valuable insights for organizations navigating the evolving landscape of digital infrastructure management.

Keywords: Artificial intelligence, AI, infrastructure as code, IaC, efficiency optimization, predictive insights, dynamic resource scaling, proactive decision-making.

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Authors: P. V. S. Mascarenhas, B. C. P. Albuquerque, D. J. F. Campos, L. L. Almeida, V. R. Domingues, L. C. S. M. Ozelim

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Retaining slope structures are increasingly considered in geotechnical engineering projects due to extensive urban cities growth. These kinds of engineering constructions may present instabilities over the time and may require reinforcement or even rebuilding of the structure. In this context, statistical analysis is an important tool for decision making regarding retaining structures. This study approaches the failure probability of the construction of a retaining wall over the debris of an old and collapsed one. The new solution’s extension length will be of approximately 350 m and will be located over the margins of the Lake Paranoá, Brasilia, in the capital of Brazil. The building process must also account for the utilization of the ruins as a caisson. A series of in situ and laboratory experiments defined local soil strength parameters. A Standard Penetration Test (SPT) defined the in situ soil stratigraphy. Also, the parameters obtained were verified using soil data from a collection of masters and doctoral works from the University of Brasília, which is similar to the local soil. Initial studies show that the concrete wall is the proper solution for this case, taking into account the technical, economic and deterministic analysis. On the other hand, in order to better analyze the statistical significance of the factor-of-safety factors obtained, a Monte Carlo analysis was performed for the concrete wall and two more initial solutions. A comparison between the statistical and risk results generated for the different solutions indicated that a Gabion solution would better fit the financial and technical feasibility of the project.

Keywords: Economical analysis, probability of failure, retaining walls, statistical analysis.

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Authors: Yongsheng Guo, Xiaoxian Zhu, Mandella Osei-Assibey Bonsu

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This study explores the effects of national culture on consumer behaviour by identifying the characteristics of Chinese consumers’ decision making in Europe. It offers a better understanding of how cultural factors affect consumers’ behaviour, and how consumers make decisions in other nations with different culture. It adopted a grounded theory approach and conducted 24 in-depth interviews. Grounded theory models are developed to link the causal conditions, process, and consequences. Results reveal that some cultural factors including conservatism, emotionality, acquaintance community, long-term orientation and principles affect Chinese consumers when making purchase decisions in Europe. Most Chinese consumers plan and prepare their expenditure and stay in Europe as cultural learners, and purchase durable products or assets as investment, and share their experiences within a community. This study identified potential problems such as political and social environment, complex procedures, and restrictions. This study found that external factors influence internal factors and then internal characters determine consumer behaviour. This study proposes that cultural traits developed in convergence evolution through social selection and Chinese consumers persist most characters but adapt some perceptions and actions overtime in other countries. This study suggests that cultural marketing could be adopted by companies to reflect consumers’ preferences. Agencies, shops, and the authorities could take actions to reduce the complexity and restrictions.

Keywords: National culture, consumer behaviour, cultural marketing, decision making.

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Authors: M. Haghighi, M. Zowghi, B. Zohouri

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Quality Function Deployment (QFD) is an expounded, multi-step planning method for delivering commodity, services, and processes to customers, both external and internal to an organization. It is a way to convert between the diverse customer languages expressing demands (Voice of the Customer), and the organization-s languages expressing results that sate those demands. The policy is to establish one or more matrices that inter-relate producer and consumer reciprocal expectations. Due to its visual presence is called the “House of Quality" (HOQ). In this paper, we assumed HOQ in multi attribute decision making (MADM) pattern and through a proposed MADM method, rank technical specifications. Thereafter compute satisfaction degree of customer requirements and for it, we apply vagueness and uncertainty conditions in decision making by fuzzy set theory. This approach would propound supervised neural network (perceptron) for MADM problem solving.

Keywords: MADM, fuzzy set, QFD, supervised neural network (perceptron).

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Authors: Arati Siddharth Petkar, Joel E. M. Macwan

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Preferences for residential location are of a diverse nature. Primarily they are based on the socio-economic, socio-cultural, socio-demographic characteristics of the household. It also depends on character, and the growth potential of different areas in a city. In the present study, various criteria affecting residential location preferences from the Urban Dwellers’ perspective have been analyzed. The household survey has been conducted in two parts: Existing Buyers’ survey and Future Buyers’ survey. The analysis reveals that workplace location is the most governing criterion in deciding residential location from the majority of the urban dwellers perspective. For analyzing the importance of varied criteria, Analytical Hierarchy Process approach has been explored. The suggested approach will be helpful for urban planners, decision makers and developers, while designating a new residential area or redeveloping an existing one.

Keywords: Analytical hierarchy process, household, preferences, residential location preferences, residential land use, urban dwellers.

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Authors: Jiri Barta

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This paper deals with a simulation programs and technologies using in the educational process for members of the crisis management. Risk analysis, simulation, preparation and planning are among the main activities of workers of crisis management. Made correctly simulation of emergency defines the extent of the danger. On this basis, it is possible to effectively prepare and plan measures to minimize damage. The paper is focused on simulation programs that are trained at the University of Defence. Implementation of the outputs from simulation programs in decision-making processes of crisis staffs is one of the main tasks of the research project.

Keywords: Crisis Management, Continuity, Critical Infrastructure, Dangerous substance, Education, Flood, Simulation Programs.

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Authors: J. K. Alhassan, S. A. Lawal

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This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: Decision tree, classification, data mining, scholarship.

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Authors: Abdallah Al-Shammari

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This study discusses the effect of uncertainty on production levels of a petrochemical complex. Uncertainly or variations in some model parameters, such as prices, supply and demand of materials, can affect the optimality or the efficiency of any chemical process. For any petrochemical complex with many plants, there are many sources of uncertainty and frequent variations which require more attention. Many optimization approaches are proposed in the literature to incorporate uncertainty within the model in order to obtain a robust solution. In this work, a stability analysis approach is applied to a deterministic LP model of a petrochemical complex consists of ten plants to investigate the effect of such variations on the obtained optimal production levels. The proposed approach can determinate the allowable variation ranges of some parameters, mainly objective or RHS coefficients, before the system lose its optimality. Parameters with relatively narrow range of variations, i.e. stability limits, are classified as sensitive parameters or constraints that need accurate estimate or intensive monitoring. These stability limits offer easy-to-use information to the decision maker and help in understanding the interaction between some model parameters and deciding when the system need to be re-optimize. The study shows that maximum production of ethylene and the prices of intermediate products are the most sensitive factors that affect the stability of the optimum solution

Keywords: Linear programming, Petrochemicals, stability analysis, uncertainty

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Authors: D. Garg, S. Luthra, A. Haleem

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Reverse logistics (RL) is supposed to be a systematic procedure that helps in improving the environmental hazards and maintain business sustainability for industries. Industries in Indian are now opting for adoption of RL techniques in business. But, RL practices are not popular in Indian industries because of many barriers for its successful implementation. Therefore, need arises to identify and evaluate the barriers to implement RL practices by taking an Indian industries perspective. Literature review approach and case study approach have been adapted to identify relevant barriers to implement RL practices. Further, Fuzzy Decision Making Trial and Evaluation Laboratory methodology has been brought into use for evaluating causal relationships among the barriers to implement RL practices. Seven barriers out of ten barriers have been categorized into the cause group and remaining into effect group. This research will help Indian industries to manage these barriers towards effective implementing RL practices.

Keywords: Barriers, decision making trial and evaluation laboratory, fuzzy set theory, Indian industries, reverse logistics.

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Authors: Ahmad Aljaafreh

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This paper proposes a hierarchical hidden Markov model (HHMM) to model the detection of M vehicles in a wireless sensor network (WSN). The HHMM model contains an extra level of hidden Markov model to model the temporal transitions of each state of the first HMM. By modeling the temporal transitions, only those hypothesis with nonzero transition probabilities needs to be tested. Thus, this method efficiently reduces the computation load, which is preferable in WSN applications.This paper integrates several techniques to optimize the detection performance. The output of the states of the first HMM is modeled as Gaussian Mixture Model (GMM), where the number of states and the number of Gaussians are experimentally determined, while the other parameters are estimated using Expectation Maximization (EM). HHMM is used to model the sequence of the local decisions which are based on multiple hypothesis testing with maximum likelihood approach. The states in the HHMM represent various combinations of vehicles of different types. Due to the statistical advantages of multisensor data fusion, we propose a heuristic based on fuzzy weighted majority voting to enhance cooperative classification of moving vehicles within a region that is monitored by a wireless sensor network. A fuzzy inference system weighs each local decision based on the signal to noise ratio of the acoustic signal for target detection and the signal to noise ratio of the radio signal for sensor communication. The spatial correlation among the observations of neighboring sensor nodes is efficiently utilized as well as the temporal correlation. Simulation results demonstrate the efficiency of this scheme.

Keywords: Classification, decision fusion, fuzzy logic, hidden Markov model

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Authors: Xiaohua Liu, Juan F. Beltran, Nishant Mohanchandra, Godfried T. Toussaint

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Support vector machines (SVMs) are considered to be the best machine learning algorithms for minimizing the predictive probability of misclassification. However, their drawback is that for large data sets the computation of the optimal decision boundary is a time consuming function of the size of the training set. Hence several methods have been proposed to speed up the SVM algorithm. Here three methods used to speed up the computation of the SVM classifiers are compared experimentally using a musical genre classification problem. The simplest method pre-selects a random sample of the data before the application of the SVM algorithm. Two additional methods use proximity graphs to pre-select data that are near the decision boundary. One uses k-Nearest Neighbor graphs and the other Relative Neighborhood Graphs to accomplish the task.

Keywords: Machine learning, data mining, support vector machines, proximity graphs, relative-neighborhood graphs, k-nearestneighbor graphs, random sampling, training data condensation.

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Authors: Y. Rathiranee, D. M. Semasinghe

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This study attempts to identify the factors influencing on women empowerment of rural area in Sri Lanka through micro finance services. Data were collected from one hundred (100) rural women involving self-employment activities through a questionnaire using direct personal interviews. Judgment and Convenience Random sampling technique was used to select the sample size from three Divisional Secretariat divisions of Kandawalai, Poonakari and Karachchi in Kilinochchi District. The factor analysis was performed on fourteen (14) variables for screening and reducing the variables to identify the influencing factors on empowerment. Multiple regression analysis was used to identify the relationship between the three empowerment factors and the impact of micro finance on overall empowerment of rural women. The result of this study summarized the variables into three factors namely decision making, freedom to mobility and family support and which are positively associated with empowerment. In addition to this the value of adjusted R2 is 0.248 indicates that all the variables extracted can be explained 24.8% of the variation in the women empowerment through microfinance. Independent variables of these three factors have positive correlation with women empowerment as well as significant values at 5 percent level.

Keywords: Influencing factors, Micro finance, rural women and women empowerment.

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Authors: Gizem Intepe, Tufan Koc

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S-Curves are commonly used in technology forecasting. They show the paths of product performance in relation to time or investment in R&D. It is a useful tool to describe the inflection points and the limit of improvement of a technology. Companies use this information to base their innovation strategies. However inadequate use and some limitations of this technique lead to problems in decision making. In this paper first technology forecasting and its importance for company level strategies will be discussed. Secondly the S-Curve and its place among other forecasting techniques will be introduced. Thirdly its use in technology forecasting will be discussed based on its advantages, disadvantages and limitations. Finally an application of S-curve on 3D TV technology using patent data will also be presented and the results will be discussed.

Keywords: Patent analysis, Technological forecasting. S curves, 3D TV

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Authors: Ingu Kim, Shangmun Shin, Yongsun Choi, Nguyen Manh Thang, Edwin R. Ramos, Won-Joo Hwang

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Project selection problems on management information system (MIS) are often considered a multi-criteria decision-making (MCDM) for a solving method. These problems contain two aspects, such as interdependencies among criteria and candidate projects and qualitative and quantitative factors of projects. However, most existing methods reported in literature consider these aspects separately even though these two aspects are simultaneously incorporated. For this reason, we proposed a hybrid method using analytic network process (ANP) and fuzzy logic in order to represent both aspects. We then propose a goal programming model to conduct an optimization for the project selection problems interpreted by a hybrid concept. Finally, a numerical example is conducted as verification purposes.

Keywords: Analytic Network Process (ANP), Multi-Criteria Decision-Making (MCDM), Fuzzy Logic, Information System Project Selection, Goal Programming.

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Authors: Ian Kree, Davina Chin Lee Yien

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This paper presents Cost per Equivalent Wafer Out, which we find useful in wafer fab operational cost monitoring and controlling. It removes the loading and product mix effect in the cost variance analysis. The operation heads, therefore, could immediately focus on identifying areas for cost improvement. Without this, they would have to measure the impact of the loading variance and product mix variance between actual and budgeted prior to make any decision on cost improvement. Cost per Equivalent Wafer Out, thereby, increases efficiency in wafer fab operational cost monitoring and controlling.

Keywords: Cost Control, Cost Variance, Operational Expenditure, Semiconductor.

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Authors: Moses F. Oduori, Enoch K. Musyoka, Thomas O. Mbuya

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The selection of materials is an essential task in mechanical design processes. This paper sets out to demonstrate the application of analytical decision making during mechanical design and, particularly, in selecting a suitable material for a given application. Equations for the mechanical design of a manual winch rope drum are used to derive quantitative material performance indicators, which are then used in a multiple attribute decision making (MADM) model to rank the candidate materials. Thus, the processing of mechanical design considerations and material properties data into information that is suitable for use in a quantitative materials selection process is demonstrated for the case of a rope drum design. Moreover, Microsoft Excel^®, a commonly available computer package, is used in the selection process. The results of the materials selection process are in agreement with current industry practice in rope drum design. The procedure that is demonstrated here should be adaptable to other design situations in which a need arises for the selection of engineering materials, and other engineering entities.

Keywords: Design Decisions, Materials Selection, Mechanical Design, Rope Drum Design.

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Authors: K. Bouchbout, Z. Alimazighi

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The importance of inter-organizational system (IOS) has been increasingly recognized by organizations. However, IOS adoption has proved to be difficult and, at this stage, why this is so is not fully uncovered. In practice, benefits have often remained concentrated, primarily accruing to the dominant party, resulting in low rates of adoption and usage, and often culminating in the failure of the IOS. The main research question is why organizations initiate or join IOS and what factors influence their adoption and use levels. This paper reviews the literature on IOS adoption and proposes a theoretical framework in order to identify the critical factors to capture a complete picture of IOS adoption. With our proposed critical factors, we are able to investigate their relative contributions to IOS adoption decisions. We obtain findings that suggested that there are five groups of factors that significantly affect the adoption and use decision of IOS in the Supply Chain Management (SCM) context: 1) interorganizational context, 2) organizational context, 3) technological context, 4) perceived costs, and 5) perceived benefits.

Keywords: Business-to-Business relationships, buyer-supplier relationships, Critical factors, Interorganizational Information Systems, IOS adoption and use.

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Authors: Mayar A. Omar, Ismail Gomaa, Heba Badawy, Hosam Moubarak

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Enterprise resource planning (ERP) is an integrated system that helps companies in managing their resources. There are two types of ERP systems, the traditional ERP systems, and the cloud ERP systems. Cloud ERP systems were introduced after the development of cloud computing technology. This research aims to identify the factors that affect the adoption of cloud ERP in Egyptian companies. Moreover, the aim of our study is to provide guidance to Egyptian companies in the cloud ERP adoption decision and to participate in increasing the number of the cloud ERP studies that are conducted in the Middle East and in developing countries. There are many factors influencing the adoption of cloud ERP in Egyptian organizations which are discussed and explained in the research. Those factors are examined through combining the Diffusion of Innovation theory (DOI) and technology-organization-environment framework (TOE). Data were collected through a survey that was developed using constructs from the existing studies of cloud computing and cloud ERP technologies and was then modified to fit our research. The analysis of the data was based on Structural Equation Modeling (SEM) using Smart PLS software that was used for the empirical analysis of the research model.

Keywords: cloud computing, cloud ERP systems, DOI, Egypt, SEM, TOE

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Authors: Asim N., M. Mazhar Manzoor, Shariq A.

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Knowledge is renowned as a significant component for sustaining competitive advantage and gives leading edge in business. This study emphasizes towards proper and effectuate utilization of internal and external (both either explicit or tacit) knowledge comes from stakeholders, highly supportive to combat with the challenges and enhance organizational productivity. Furthermore, it proposed a model under context of IRSA framework which facilitates the organization including flow of knowledge and experience sharing among employees. In discussion section an innovative model which indulges all functionality as mentioned in analysis section.

Keywords: Effective Decision-Making, Internal & ExternalKnowledge, Knowledge Management, Tacit & Explicit Knowledge.

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Authors: Mehdi Seifbarghy, Keyvan Roshan

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Main goal of preventive healthcare problems are at decreasing the likelihood and severity of potentially life-threatening illnesses by protection and early detection. The levels of establishment and staffing costs along with summation of the travel and waiting time that clients spent are considered as objectives functions of the proposed nonlinear integer programming model. In this paper, we have proposed a bi-objective mathematical model for designing a network of preventive healthcare facilities so as to minimize aforementioned objectives, simultaneously. Moreover, each facility acts as M/M/1 queuing system. The number of facilities to be established, the location of each facility, and the level of technology for each facility to be chosen are provided as the main determinants of a healthcare facility network. Finally, to demonstrate performance of the proposed model, four multi-objective decision making techniques are presented to solve the model.

Keywords: Preventive healthcare problems, Non-linear integer programming models, Multi-objective decision making techniques

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Authors: H. N. Suresh, Dr. V. Udaya Shankara

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A technique proposed for the automatic detection of spikes in electroencephalograms (EEG). A multi-resolution approach and a non-linear energy operator are exploited. The signal on each EEG channel is decomposed into three sub bands using a non-decimated wavelet transform (WT). The WT is a powerful tool for multi-resolution analysis of non-stationary signal as well as for signal compression, recognition and restoration. Each sub band is analyzed by using a non-linear energy operator, in order to detect spikes. A decision rule detects the presence of spikes in the EEG, relying upon the energy of the three sub-bands. The effectiveness of the proposed technique was confirmed by analyzing both test signals and EEG layouts.

Keywords: EEG, Spike, SNEO, Wavelet Transform

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Authors: Chih-Hsiang Chang, Fang-Jyun Su

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This paper explores whether stock characteristics influence the herding formation among investors in the US equity market. To extend the research scope of the existing literature, this paper further examines the role that stock risk characteristics play in the US equity market, and the way they influence investors’ decision-making. First, empirical results show that whether general stocks or high-risk stocks, there are no herding behaviors among the investors in the US equity market during the whole research period or during four great events. Moreover, stock characteristics have great influence on investors’ trading decisions. Finally, there is a bidirectional lead-lag relationship of the herding formation between high-risk stocks and low-risk stocks, but the influence of high-risk stocks on the low-risk stocks is stronger than that of low-risk stocks on the high-risk stocks.

Keywords: Stock characteristics, herding formation, investment decision, US equity market, lead-lag relationship.

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Authors: Cristina Vatamanu, Doina Cosovan, Dragoş Gavriluţ, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through (semi)-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: Detection Rate, False Positives, Perceptron, One Side Class, Ensembles, Decision Tree, Hybrid methods, Feature Selection.

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Authors: Amer M. Momani, Abdulaziz A. Ahmed

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The many feasible alternatives and conflicting objectives make equipment selection in materials handling a complicated task. This paper presents utilizing Monte Carlo (MC) simulation combined with the Analytic Hierarchy Process (AHP) to evaluate and select the most appropriate Material Handling Equipment (MHE). The proposed hybrid model was built on the base of material handling equation to identify main and sub criteria critical to MHE selection. The criteria illustrate the properties of the material to be moved, characteristics of the move, and the means by which the materials will be moved. The use of MC simulation beside the AHP is very powerful where it allows the decision maker to represent his/her possible preference judgments as random variables. This will reduce the uncertainty of single point judgment at conventional AHP, and provide more confidence in the decision problem results. A small business pharmaceutical company is used as an example to illustrate the development and application of the proposed model.

Keywords: Analytic Hierarchy Process (AHP), Materialhandling equipment selection, Monte Carlo simulation, Multi-criteriadecision making

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Authors: Torsti Rantapuska, Sariseelia Sore

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Acquiring commercial off-the-shelf (COTS) software applications is becoming routine in organizations. However, eliciting user requirements, finding the candidate COTS products and making the decision is a complex task, especially for SMEs who do not have the time and knowledge needed to do the task properly. The existing models intended to help the decision makers are originally designed for professional use. SMEs are obligated to rely on the software vendor’s ability to solve the problem with the systems provided.  In this paper, we develop a model for SMEs for the acquisition of Commercial Off-The-Shelf (COTS) software products. A leading idea of the model is that the ICT investment is basically a change initiative and therefore it should also be taken as a process of organizational learning. The model is designed bearing three objectives in mind: 1) business orientation, 2) agility, and 3) Learning and knowledge management orientation. The model can be applied to ICT investments in SMEs which have a professional team leader with basic business and IT knowledge. 

 

Keywords: COTS acquisition, ICT investment, organizational learning, ICT adoption.

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Authors: S. Bouharati, I. Bouharati, C. Benzidane, F. Alleg, M. Belmahdi

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Sensors possess several properties of physical measures. Whether devices that convert a sensed signal into an electrical signal, chemical sensors and biosensors, thus all these sensors can be considered as an interface between the physical and electrical equipment. The problem is the analysis of the multitudes of saved settings as input variables. However, they do not all have the same level of influence on the outputs. In order to identify the most sensitive parameters, those that can guide users in gathering information on the ground and in the process of model calibration and sensitivity analysis for the effect of each change made. Mathematical models used for processing become very complex. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available signals information from sensors. Moreover, the system allows the study of the influence of the various factors that take part in the decision system. Since its inception fuzzy set theory has been regarded as a formalism suitable to deal with the imprecision intrinsic to many problems. At the same time, fuzzy sets allow to use symbolic models. In this study an example was applied for resolving variety of physiological parameters that define human health state. The application system was done for medical diagnosis help. The inputs are the signals expressed the cardiovascular system parameters, blood pressure, Respiratory system paramsystem was done, it will be able to predict the state of patient according any input values.

Keywords: Sensors, Sensivity, fuzzy logic, analysis, physiological parameters, medical diagnosis.

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Authors: I. Kontorovich, R. Zazkis

Abstract:

We present a framework of researcher knowledge development in conducting a study in mathematics education. The key components of the framework are: knowledge germane to conducting a particular study, processes of knowledge accumulation, and catalyzing filters that influence a researcher decision making. The components of the framework originated from a confluence between constructs and theories in Mathematics Education, Higher Education and Sociology. Drawing on a self-reflective interview with a leading researcher in mathematics education, Professor Michèle Artigue, we illustrate how the framework can be utilized in data analysis. Criteria for framework evaluation are discussed.

Keywords: Community of practice, knowledge development, mathematics education research, researcher knowledge.

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