Search results for: Selection of investments.

Authors: Ayse Gecer, Nihal Erginel

Abstract:

Quality control is the crucial step for ISO 9001 Quality System Management Standard for companies. While measuring the quality level of both raw material and semi product/product, the calibration of the measuring device is an essential requirement. Calibration suppliers are in the service sector and therefore the calibration supplier selection is becoming a worthy topic for improving service quality. This study presents the results of a questionnaire about the selection criteria of a calibration supplier. The questionnaire was applied to 103 companies and the results are discussed in this paper. The analysis was made with MINITAB 14.0 statistical programs. “Competence of documentations" and “technical capability" are defined as the prerequisites because of the ISO/IEC17025:2005 standard. Also “warranties and complaint policy", “communication", “service features", “quality" and “performance history" are defined as very important criteria for calibration supplier selection.

Keywords: Calibration, criteria of calibration supplier selection, calibration supplier selection, questionnaire

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Authors: M. Naserian, M. Jooshaki, M. Fotuhi-Firuzabad, M. Hossein Mohammadi Sanjani, A. Oraee

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In recent years, the development of communication infrastructure and smart meters have facilitated the utilization of demand-side resources which can enhance stability and economic efficiency of power systems. Direct load control programs can play an important role in the utilization of demand-side resources in the residential sector. However, investments required for installing control equipment can be a limiting factor in the development of such demand response programs. Thus, selection of consumers with higher potentials is crucial to the success of a direct load control program. Heating, ventilation, and air conditioning (HVAC) systems, which due to the heat capacity of buildings feature relatively high flexibility, make up a major part of household consumption. Considering that the consumption of HVAC systems depends highly on the ambient temperature and bearing in mind the high investments required for control systems enabling direct load control demand response programs, in this paper, a solution is presented to uncover consumers with high air conditioner demand among a large number of consumers and to measure the demand response potential of such consumers. This can pave the way for estimating the investments needed for the implementation of direct load control programs for residential HVAC systems and for estimating the demand response potentials in a distribution system. In doing so, we first cluster consumers into several groups based on the correlation coefficients between hourly consumption data and hourly temperature data using K-means algorithm. Then, by applying a recent algorithm to the hourly consumption and temperature data, consumers with high air conditioner consumption are identified. Finally, demand response potential of such consumers is estimated based on the equivalent desired temperature setpoint changes.

Keywords: Data-driven analysis, demand response, direct load control, HVAC system.

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Authors: Mohammad H. Almomani, Rosmanjawati Abdul Rahman

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Statistical selection procedures are used to select the best simulated system from a finite set of alternatives. In this paper, we present a procedure that can be used to select the best system when the number of alternatives is large. The proposed procedure consists a combination between Ranking and Selection, and Ordinal Optimization procedures. In order to improve the performance of Ordinal Optimization, Optimal Computing Budget Allocation technique is used to determine the best simulation lengths for all simulation systems and to reduce the total computation time. We also argue the effect of increment in simulation samples for the combined procedure. The results of numerical illustration show clearly the effect of increment in simulation samples on the proposed combination of selection procedure.

Keywords: Indifference-Zone, Optimal Computing Budget Allocation, Ordinal Optimization, Ranking and Selection, Subset Selection.

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Authors: Daniel I. Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, we present three feature selection methods: Information Gain, Support Vector Machine feature selection called (SVM_FS) and Genetic Algorithm with SVM (called GA_SVM). We show that the best results were obtained with GA_SVM method for a relatively small dimension of the feature vector.

Keywords: Feature Selection, Learning with Kernels, Support Vector Machine, Genetic Algorithm, and Classification.

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Authors: Carlos Huertas, Reyes Juarez-Ramirez

Abstract:

Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.

Keywords: Feature selection, mass spectrometry, biomarker discovery, cancer.

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Authors: Mohammad H. Almomani, Rosmanjawati Abdul Rahman

Abstract:

In this paper, we consider the effect of the initial sample size on the performance of a sequential approach that used in selecting a good enough simulated system, when the number of alternatives is very large. We implement a sequential approach on M=M=1 queuing system under some parameter settings, with a different choice of the initial sample sizes to explore the impacts on the performance of this approach. The results show that the choice of the initial sample size does affect the performance of our selection approach.

Keywords: Ranking and Selection, Ordinal Optimization, Optimal Computing Budget Allocation, Subset Selection, Indifference-Zone, Initial Sample Size.

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Authors: Jyoti Dua

Abstract:

An algorithm is a well-defined procedure that takes some input in the form of some values, processes them and gives the desired output. It forms the basis of many other algorithms such as searching, pattern matching, digital filters etc., and other applications have been found in database systems, data statistics and processing, data communications and pattern matching. This paper introduces algorithmic “Enhanced Bidirectional Selection” sort which is bidirectional, stable. It is said to be bidirectional as it selects two values smallest from the front and largest from the rear and assigns them to their appropriate locations thus reducing the number of passes by half the total number of elements as compared to selection sort.

Keywords: Bubble sort, cocktail sort, selection sort, heap sort.

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Authors: Adtha Lawanna

Abstract:

Software maintenance is one of the essential processes of Software-Development Life Cycle. The main philosophies of retaining software concern the improvement of errors, the revision of codes, the inhibition of future errors, and the development in piece and capacity. While the adjustment has been employing, the software structure has to be retested to an upsurge a level of assurance that it will be prepared due to the requirements. According to this state, the test cases must be considered for challenging the revised modules and the whole software. A concept of resolving this problem is ongoing by regression test selection such as the retest-all selections, random/ad-hoc selection and the safe regression test selection. Particularly, the traditional techniques concern a mapping between the test cases in a test suite and the lines of code it executes. However, there are not only the lines of code as one of the requirements that can affect the size of test suite but including the number of functions and faulty versions. Therefore, a model for test case selection is developed to cover those three requirements by the integral technique which can produce the smaller size of the test cases when compared with the traditional regression selection techniques.

Keywords: Software maintenance, regression test selection, test case.

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Authors: Adtha Lawanna

Abstract:

Software maintenance is one of the essential processes of Software-Development Life Cycle. The main philosophies of retaining software concern the improvement of errors, the revision of codes, the inhibition of future errors, and the development in piece and capacity. While the adjustment has been employing, the software structure has to be retested to an upsurge a level of assurance that it will be prepared due to the requirements. According to this state, the test cases must be considered for challenging the revised modules and the whole software. A concept of resolving this problem is ongoing by regression test selection such as the retest-all selections, random/ad-hoc selection and the safe regression test selection. Particularly, the traditional techniques concern a mapping between the test cases in a test suite and the lines of code it executes. However, there are not only the lines of code as one of the requirements that can affect the size of test suite but including the number of functions and faulty versions. Therefore, a model for test case selection is developed to cover those three requirements by the integral technique which can produce the smaller size of the test cases when compared with the traditional regression selection techniques.

Keywords: Software maintenance, regression test selection, test case.

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Authors: Hassan Haghighi, Omid Mafi

Abstract:

Existing experiences indicate that one of the most prominent reasons that some ERP implementations fail is related to selecting an improper ERP package. Among those important factors resulting in inappropriate ERP selections, one is to ignore preliminary activities that should be done before the evaluation of ERP packages. Another factor yielding these unsuitable selections is that usually organizations employ prolonged and costly selection processes in such extent that sometimes the process would never be finalized or sometimes the evaluation team might perform many key final activities in an incomplete or inaccurate way due to exhaustion, lack of interest or out-of-date data. In this paper, a systematic approach that recommends some activities to be done before and after the main selection phase is introduced for choosing an ERP package. On the other hand, the proposed approach has utilized some ideas that accelerates the selection process at the same time that reduces the probability of an erroneous final selection.

Keywords: enterprise resource planning, evaluation and selectionof ERP packages, organizational readiness for employing ERP, evaluationlists.

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Authors: Ahmed Al-Ani

Abstract:

Feature selection is an important step in many pattern classification problems. It is applied to select a subset of features, from a much larger set, such that the selected subset is sufficient to perform the classification task. Due to its importance, the problem of feature selection has been investigated by many researchers. In this paper, a novel feature subset search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results.

Keywords: Ant Colony Optimization, ant systems, feature selection, pattern recognition.

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Authors: Younies Mahmoud, Mai Mabrouk, Elsayed Sallam

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Analyzing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection.

Keywords: DNA microarray, feature selection, missing data, bioinformatics.

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Authors: Sarra Ben Hariz, Zied Elouedi, Khaled Mellouli

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The belief K-modes method (BKM) approach is a new clustering technique handling uncertainty in the attribute values of objects in both the cluster construction task and the classification one. Like the standard version of this method, the BKM results depend on the chosen initial modes. So, one selection method of initial modes is developed, in this paper, aiming at improving the performances of the BKM approach. Experiments with several sets of real data show that by considered the developed selection initial modes method, the clustering algorithm produces more accurate results.

Keywords: Clustering, Uncertainty, Belief function theory, Belief K-modes Method, Initial modes selection.

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Authors: Omid Jadidi, Fatemeh Firouzi, Enzo Bagliery

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Supplier selection, in real situation, is affected by several qualitative and quantitative factors and is one of the most important activities of purchasing department. Since at the time of evaluating suppliers against the criteria or factors, decision makers (DMS) do not have precise, exact and complete information, supplier selection becomes more difficult. In this case, Grey theory helps us to deal with this problem of uncertainty. Here, we apply Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to evaluate and select the best supplier by using interval fuzzy numbers. Through this article, we compare TOPSIS with some other approaches and afterward demonstrate that the concept of TOPSIS is very important for ranking and selecting right supplier.

Keywords: TOPSIS, fuzzy number, MADM, Supplier selection

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Authors: C. Ardil

Abstract:

In this paper, a new MCDMA approach, the permanent analytics process is proposed to assess the immovable valuation criteria and their significance in the placement of the healthcare facility. Five decision factors are considered for the value and selection of immovables. In the multiple factor selection problems, the priority vector of the criteria used to compare several immovables is first determined using the permanent analytics method, a mathematical model for the multiple criteria decisionmaking process. Then, to demonstrate the viability and efficacy of the suggested approach, twenty potential candidate locations were evaluated using the hospital site selection problem's decision criteria. The ranking accuracy of estimation was evaluated using composite programming, which took into account both the permanent analytics process and the weighted multiplicative model. 

Keywords: Hospital Facility Location Selection, Permanent Analytics Process, Multiple Criteria Decision Making (MCDM)

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Authors: Edikan E. Akpanibah, Okwigbedi Oghen’Oro

Abstract:

In this paper, we studied the optimal portfolio selection in a defined contribution (DC) pension scheme with multiple contributors under constant elasticity of variance (CEV) model and the impact of stochastic additional voluntary contribution on the investment strategies. We assume that the voluntary contributions are stochastic and also consider investments in a risk free asset and a risky asset to increase the expected returns of the contributing members. We derived a stochastic differential equation which consists of the members’ monthly contributions and the invested fund and obtained an optimized problem with the help of Hamilton Jacobi Bellman equation. Furthermore, we find an explicit solution for the optimal investment strategy with stochastic voluntary contribution using power transformation and change of variables method and the corresponding optimal fund size was obtained. We discussed the impact of the voluntary contribution on the optimal investment strategy with numerical simulations and observed that the voluntary contribution reduces the optimal investment strategy of the risky asset.

Keywords: DC pension fund, Hamilton-Jacobi-Bellman, optimal investment strategies, power transformation method, stochastic, voluntary contribution.

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Authors: Gülcin Büyüközkan, Mehmet Sakir Ersoy

Abstract:

The decision of information technology (IT) outsourcing requires close attention to the evaluation of supplier selection process because the selection decision involves conflicting multiple criteria and is replete with complex decision making problems. Selecting the most appropriate suppliers is considered an important strategic decision that may impact the performance of outsourcing engagements. The objective of this paper is to aid decision makers to evaluate and assess possible IT outsourcing suppliers. An axiomatic design based fuzzy group decision making is adopted to evaluate supplier alternatives. Finally, a case study is given to demonstrate the potential of the methodology. KeywordsIT outsourcing, Supplier selection, Multi-criteria decision making, Axiomatic design, Fuzzy logic.

Keywords: IT outsourcing, Supplier selection, Multi-criteria decision making, Axiomatic design, Fuzzy logic

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Authors: F. Iscan, C. Yagci

Abstract:

Rapid population growth, urbanization and industrialization are known as the most important factors of environment problems. Elimination and management of solid wastes are also within the most important environment problems. One of the main problems in solid waste management is the selection of the best site for elimination of solid wastes. Lately, Geographical Information System (GIS) has been used for easing selection of landfill area. GIS has the ability of imitating necessary economic, environmental and political limitations. They play an important role for the site selection of landfill area as a decision support tool. In this study; map layers will be studied for minimum effect of environmental, social and cultural factors and maximum effect for engineering/economic factors for site selection of landfill areas and using GIS for a decision support mechanism in solid waste landfill areas site selection will be presented in Aksaray/Turkey city, Güzelyurt district practice.

Keywords: GIS, landfill, solid waste, spatial analysis.

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Authors: Ebere U. Okolo, Eunice C. Okpalaojiego, Chimaobi V. Okolo

Abstract:

Some investors prefer to keep their money in the bank rather than invest in Small and Medium Enterprise (SME) due to the high cost of running small and medium scale enterprise in Enugu State. This cost primarily concerns multiple-taxation, enormous tax burdens, levies and charges. This study examines the effect of multiple-taxation on the investments in SMEs. The study used survey design with SME population of 80. Questionnaire was used to collect data. Simple percentages/frequencies were used to analyze the data and the research hypotheses were tested with ANOVA. It was found that multiple taxation has negative effect on SMEs investment. Furthermore, the relationship between SMEs investment and its ability to pay tax is significant. The researcher recommends that government should develop a tax policy that considers the enhancement of SMEs’ capital allowance when imposing taxes. Government should also consider a tax policy that encourages investment in SMEs by consolidating all taxes in one slot and latter disseminate to various government purses rather than having many closely related but different taxes at the same time.

Keywords: Investments, multiple taxation, small and medium enterprises.

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Authors: Anouch Mkhitarian

Abstract:

The article examines an opportunity of corruption restriction exercised by international business community in Russia. Integration of Russian economy into the international business does not reduce corruption inside the country. Foreign actors investing in Russia under the condition of obtaining their required rates of returns will be reluctant to harm their investments by involving into anticorruption activities. Furthermore, many Russian firms- competitive advantage could be directly related to their corruption connections. In this case, foreign investments would only accentuate corrupt companies- success by supporting them financially

Keywords: Corrution, FDI, Russian Federation

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Authors: G. Erkomaishvili, M. Kobalava, T. Lazariashvili, N. Damenia

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Current state of foreign direct investment (FDI) in Georgia is analyzed and evaluated in the paper, the existing legislative background for regulating investments and stimulating policies to attract investments are shown. It is noted that in developing countries encouragement of investment activity, support and implementation are of the most important tasks, implying a consistent investment policy, investor-friendly tax regime and the legal system, reducing administrative barriers and restrictions, fare competitive conditions and business development infrastructure. The work deals with the determining factor of FDIs and the main directions of stimulation, as well as prospective industries where new investments are needed. Contributing and hindering factors and stimulating measures are analyzed. As a result of the research, the direct and indirect factors attracting FDI have been identified. Facilitating factors to FDI inflow are as follows: simplicity of starting business, geopolitical location, low taxes, access to credit, ease of ownership registration, natural resources, low burden of regulations, low level of corruption and low crime rates. Hindering factors to FDI inflow are as follows: small market, lack of policy for attracting investments, low qualification of the workforce (despite the large number of unemployed people it is difficult to find workers with necessary special skills and qualifications), high interest rates, instability of national currency exchange rate, presence of conflict zones within the country and so forth.

Keywords: Foreign direct investment, investment attracting policies, investor, reinvestment.

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Authors: O. Kadri, M. D. Mouss, L.H. Mouss, F. Merah

Abstract:

This paper proposes a novel hybrid algorithm for feature selection based on a binary ant colony and SVM. The final subset selection is attained through the elimination of the features that produce noise or, are strictly correlated with other already selected features. Our algorithm can improve classification accuracy with a small and appropriate feature subset. Proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through a real Rotary Cement kiln dataset. The results show that our algorithm outperforms existing algorithms.

Keywords: Binary Ant Colony algorithm, Support VectorMachine, feature selection, classification.

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Authors: I. Rodionov, A. Semenov, E. Gosteva, O. Sokolova

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The venture capital becomes more and more advanced and effective source of the innovation project financing, connected with a high-risk level. In the developed countries, it plays a key role in transforming innovation projects into successful businesses and creating the prosperity of the modern economy. In Russia, there are many necessary preconditions for creation of the effective venture investment system: the network of the public institutes for innovation financing operates; there is a significant number of the small and medium-sized enterprises, capable to sell production with good market potential. However, the current system does not confirm the necessary level of efficiency in practice that can be substantially explained by the absence of the accurate plan of action to form the national venture model and by the lack of experience of successful venture deals with profitable exits in Russian economy. This paper studies the influence of various factors on the venture industry development by the example of the IT-sector in Russia. The choice of the sector is based on the fact, that this segment is the main driver of the venture capital market growth in Russia, and the necessary set of data exists. The size of investment of the second round is used as the dependent variable. To analyse the influence of the previous round, such determinant as the volume of the previous (first) round investments is used. There is also used a dummy variable in regression to examine that the participation of an investor with high reputation and experience in the previous round can influence the size of the next investment round. The regression analysis of short-term interrelations between studied variables reveals prevailing influence of the volume of the first round investments on the venture investments volume of the second round. The most important determinant of the value of the second-round investment is the value of first–round investment, so it means that the most competitive on the Russian market are the start-up teams that can attract more money on the start, and the target market growth is not the factor of crucial importance. This supports the point of view that VC in Russia is driven by endogenous factors and not by exogenous ones that are based on global market growth.

Keywords: Venture industry, venture investment, determinants of the venture sector development, IT-sector.

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Authors: Ahmed Al-Ani

Abstract:

The Ant Colony Optimization (ACO) is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It has recently attracted a lot of attention and has been successfully applied to a number of different optimization problems. Due to the importance of the feature selection problem and the potential of ACO, this paper presents a novel method that utilizes the ACO algorithm to implement a feature subset search procedure. Initial results obtained using the classification of speech segments are very promising.

Keywords: Ant Colony Optimization, ant systems, feature selection, pattern recognition.

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Authors: Paulo Fernando Silva Filho, Elcio Hideiti Shiguemori

Abstract:

The selection of specific landmarks for an Unmanned Aerial Vehicles’ Visual Navigation systems based on Automatic Landmark Recognition has significant influence on the precision of the system’s estimated position. At the same time, manual selection of the landmarks does not guarantee a high recognition rate, which would also result on a poor precision. This work aims to develop an automatic landmark selection that will take the image of the flight area and identify the best landmarks to be recognized by the Visual Navigation Landmark Recognition System. The criterion to select a landmark is based on features detected by ORB or AKAZE and edges information on each possible landmark. Results have shown that disposition of possible landmarks is quite different from the human perception.

Keywords: Clustering, edges, feature points, landmark selection, X-Means.

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Authors: Walid S. Abd El-hamid, Sherif S. El-Etriby, Mohiy M. Hadhoud

Abstract:

Regression testing is a maintenance activity applied to modified software to provide confidence that the changed parts are correct and that the unchanged parts have not been adversely affected by the modifications. Regression test selection techniques reduce the cost of regression testing, by selecting a subset of an existing test suite to use in retesting modified programs. This paper presents the first general regression-test-selection technique, which based on code and allows selecting test cases for any programs written in any programming language. Then it handles incomplete program. We also describe RTSDiff, a regression-test-selection system that implements the proposed technique. The results of the empirical studied that performed in four programming languages java, C#, Cµ and Visual basic show that the efficiency and effective in reducing the size of test suit.

Keywords: Regression testing, testing, test selection, softwareevolution, software maintenance.

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Authors: José M. Merigó, Anna M. Gil-Lafuente

Abstract:

We study the possibility of using geometric operators in the selection of human resources. We develop three new methods that use the ordered weighted geometric (OWG) operator in different indexes used for the selection of human resources. The objective of these models is to manipulate the neutrality of the old methods so the decision maker is able to select human resources according to his particular attitude. In order to develop these models, first a short revision of the OWG operator is developed. Second, we briefly explain the general process for the selection of human resources. Then, we develop the three new indexes. They will use the OWG operator in the Hamming distance, in the adequacy coefficient and in the index of maximum and minimum level. Finally, an illustrative example about the new approach is given.

Keywords: OWG operator, decision making, human resources, Hamming distance.

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Authors: S. Mahmoodzadeh, J. Shahrabi, M. Pariazar, M. S. Zaeri

Abstract:

In this article, by using fuzzy AHP and TOPSIS technique we propose a new method for project selection problem. After reviewing four common methods of comparing alternatives investment (net present value, rate of return, benefit cost analysis and payback period) we use them as criteria in AHP tree. In this methodology by utilizing improved Analytical Hierarchy Process by Fuzzy set theory, first we try to calculate weight of each criterion. Then by implementing TOPSIS algorithm, assessment of projects has been done. Obtained results have been tested in a numerical example.

Keywords: Fuzzy AHP, Project Selection, TOPSIS Technique.

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Authors: C. Ardil

Abstract:

Fighter aircraft selection is one of the most critical strategies for defense multiple criteria decision-making analysis to increase the decisive power of air defense and its superior power in the defense strategy. Vague set theory is an adequate approach for modeling vagueness, uncertainty, and imprecision in decision-making problems. This study integrates vague set theory and the technique for order of preference by similarity to ideal solution (TOPSIS) to support fighter aircraft selection. The proposed method is applied in the selection of fighter aircraft for the Air Force. In the proposed approach, the ratings of alternatives and the importance weights of criteria for fighter aircraft selection are represented by the vague set theory. Finally, an illustrative example for fighter aircraft selection is given to demonstrate the applicability and effectiveness of the proposed approach. The fighter aircraft candidates were selected under six criteria including costability, payloadability, maneuverability, speedability, stealthility, and survivability. Analysis results show that the best fighter aircraft is selected with the highest closeness coefficient value. The proposed method can also be applied to solve other multiple criteria decision analysis problems. 

Keywords: fighter aircraft selection, vague set theory, fuzzy set theory, neutrosophic set theory, multiple criteria decision making analysis, MCDMA, TOPSIS

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Authors: Mina Alibeigi, Sattar Hashemi, Ali Hamzeh

Abstract:

Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original features. In this paper, we propose a new unsupervised feature selection method which will remove redundant features from the original feature space by the use of probability density functions of various features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several datasets derived from UCI repository database, illustrate the effectiveness of our proposed methods in comparison with the other compared methods in terms of both classification accuracy and the number of selected features.

Keywords: Feature, Feature Selection, Filter, Probability Density Function

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