Open Science Research Excellence

Open Science Index

Commenced in January 2007 Frequency: Monthly Edition: International Paper Count: 11

11
10009981
Multicriteria Decision Analysis for Development Ranking of Balkan Countries
Authors:
Abstract:

In this research, the Balkan peninsula countries' developmental integration into European Union represents the strategic economic development objectives of the countries in the region. In order to objectively analyze the level of economic development competition of Balkan Peninsula countries, the mathematical compromise programming technique of multicriteria evaluation is used in this ranking problem. The primary aim of this research is to explain the role and significance of the multicriteria method evaluation using a real example of compromise solutions. Using the mathematical compromise programming technique, twelve countries of the Balkan Peninsula are economically evaluated and mutually compared. The economic development evaluation of the countries is performed according to five evaluation criteria forming the basis for economic development evaluation. The multiattribute model is solved using the mathematical compromise programming technique for producing different Pareto solutions. The results obtained by the multicriteria evaluation gives the possibility of identification and evaluation of the most eminent economic development indicators for each country separately. Finally, in this way, the proposed method has proved to be a successful model for the evaluation of the Balkan peninsula countries' economic development competition.

10
10009298
Multidimensional Compromise Programming Evaluation of Digital Commerce Websites
Authors:
Abstract:

Multidimensional compromise programming evaluation of digital commerce websites is essential not only to have recommendations for improvement, but also to make comparisons with global business competitors. This research provides a multidimensional decision making model that prioritizes the objective criteria weights of various commerce websites using multidimensional compromise solution. Evaluation of digital commerce website quality can be considered as a complex information system structure including qualitative and quantitative factors for a multicriteria decision making problem. The proposed multicriteria decision making approach mainly consists of three sequential steps for the selection problem. In the first step, three major different evaluation criteria are characterized for website ranking problem. In the second step, identified critical criteria are weighted using the standard deviation procedure. In the third step, the multidimensional compromise programming is applied to rank the digital commerce websites.

9
10009198
Multidimensional Compromise Optimization for Development Ranking of the Gulf Cooperation Council Countries and Turkey
Authors:
Abstract:

In this research, a multidimensional  compromise optimization method is proposed for multidimensional decision making analysis in the development ranking of the Gulf Cooperation Council Countries and Turkey. The proposed approach presents ranking solutions resulting from different multicriteria decision analyses, which yield different ranking orders for the same ranking problem, consisting of a set of alternatives in terms of numerous competing criteria when they are applied with the same numerical data. The multiobjective optimization decision making problem is considered in three sequential steps. In the first step, five different criteria related to the development ranking are gathered from the research field. In the second step, identified evaluation criteria are, objectively, weighted using standard deviation procedure. In the third step, a country selection problem is illustrated with a numerical example as an application of the proposed multidimensional  compromise optimization model. Finally, multidimensional  compromise optimization approach is applied to rank the Gulf Cooperation Council Countries and Turkey. 

8
10009013
Multidimensional Performance Tracking
Authors:
Abstract:
In this study, a model, together with a software tool that implements it, has been developed to determine the performance ratings of employees in an organization operating in the information technology sector using the indicators obtained from employees' online study data. Weighted Sum (WS) Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method based on multidimensional decision making approach were used in the study. WS and TOPSIS methods provide multidimensional decision making (MDDM) methods that allow all dimensions to be evaluated together considering specific weights, allowing employees to objectively evaluate the problem of online performance tracking. The application of WS and TOPSIS mathematical methods, which can combine alternatives with a large number of dimensions and reach simultaneous solution, has been implemented through an online performance tracking software. In the application of WS and TOPSIS methods, objective dimension weights were calculated by using entropy information (EI) and standard deviation (SD) methods from the data obtained by employees' online performance tracking method, decision matrix was formed by using performance scores for each employee, and a single performance score was calculated for each employee. Based on the calculated performance score, employees were given a performance evaluation decision. The results of Pareto set evidence and comparative mathematical analysis validate that employees' performance preference rankings in WS and TOPSIS methods are closely related. This suggests the compatibility, applicability, and validity of the proposed method to the MDDM problems in which a large number of alternative and dimension types are taken into account. With this study, an objective, realistic, feasible and understandable mathematical method, together with a software tool that implements it has been demonstrated. This is considered to be preferable because of the subjectivity, limitations and high cost of the methods traditionally used in the measurement and performance appraisal in the information technology sector.
7
10002615
Poincaré Plot for Heart Rate Variability
Abstract:

Heart is the most important part in the body of living organisms. It affects and is affected by any factor in the body. Therefore, it is a good detector for all conditions in the body. Heart signal is a non-stationary signal; thus, it is utmost important to study the variability of heart signal. The Heart Rate Variability (HRV) has attracted considerable attention in psychology, medicine and has become important dependent measure in psychophysiology and behavioral medicine. The standards of measurements, physiological interpretation and clinical use for HRV that are most often used were described in many researcher papers, however, remain complex issues are fraught with pitfalls. This paper presents one of the nonlinear techniques to analyze HRV. It discusses many points like, what Poincaré plot is and how Poincaré plot works; also, Poincaré plot's merits especially in HRV. Besides, it discusses the limitation of Poincaré cause of standard deviation SD1, SD2 and how to overcome this limitation by using complex correlation measure (CCM). The CCM is most sensitive to changes in temporal structure of the Poincaré plot as compared toSD1 and SD2.

6
10002015
Influence of Replacement Used Reference Coordinate System for Georeferencing of the Old Map of Europe
Abstract:
The article describes the effect of the replacement of the used reference coordinate system in the georeferencing of an old map of Europe. The map was georeferenced into three types of projection – the equal-area conic (original cartographic projection), cylindrical Plate Carrée and cylindrical Mercator map projection. The map was georeferenced by means of the affine and the second-order polynomial transformation. The resulting georeferenced raster datasets from the Plate Carrée and Mercator projection were projected into the equal-area conic projection by means of projection equations. The output is the comparison of drawn graphics, the magnitude of standard deviations for individual projections and types of transformation.
5
14027
Detecting Abnormal ECG Signals Utilising Wavelet Transform and Standard Deviation
Abstract:

ECG contains very important clinical information about the cardiac activities of the heart. Often the ECG signal needs to be captured for a long period of time in order to identify abnormalities in certain situations. Such signal apart of a large volume often is characterised by low quality due to the noise and other influences. In order to extract features in the ECG signal with time-varying characteristics at first need to be preprocessed with the best parameters. Also, it is useful to identify specific parts of the long lasting signal which have certain abnormalities and to direct the practitioner to those parts of the signal. In this work we present a method based on wavelet transform, standard deviation and variable threshold which achieves 100% accuracy in identifying the ECG signal peaks and heartbeat as well as identifying the standard deviation, providing a quick reference to abnormalities.

4
10480
Simulation and Design of the Geometric Characteristics of the Oscillatory Thermal Cycler
Abstract:
Since polymerase chain reaction (PCR) has been invented, it has emerged as a powerful tool in genetic analysis. The PCR products are closely linked with thermal cycles. Therefore, to reduce the reaction time and make temperature distribution uniform in the reaction chamber, a novel oscillatory thermal cycler is designed. The sample is placed in a fixed chamber, and three constant isothermal zones are established and lined in the system. The sample is oscillated and contacted with three different isothermal zones to complete thermal cycles. This study presents the design of the geometric characteristics of the chamber. The commercial software CFD-ACE+TM is utilized to investigate the influences of various materials, heating times, chamber volumes, and moving speed of the chamber on the temperature distributions inside the chamber. The chamber moves at a specific velocity and the boundary conditions with time variations are related to the moving speed. Whereas the chamber moves, the boundary is specified at the conditions of the convection or the uniform temperature. The user subroutines compiled by the FORTRAN language are used to make the numerical results realistically. Results show that the reaction chamber with a rectangular prism is heated on six faces; the effects of various moving speeds of the chamber on the temperature distributions are examined. Regarding to the temperature profiles and the standard deviation of the temperature at the Y-cut cross section, the non-uniform temperature inside chamber is found as the moving speed is larger than 0.01 m/s. By reducing the heating faces to four, the standard deviation of the temperature of the reaction chamber is under 1.4×10-3K with the range of velocities between 0.0001 m/s and 1 m/s. The nature convective boundary conditions are set at all boundaries while the chamber moves between two heaters, the effects of various moving velocities of the chamber on the temperature distributions are negligible at the assigned time duration.
3
6125
Standard Deviation of Mean and Variance of Rows and Columns of Images for CBIR
Abstract:

This paper describes a novel and effective approach to content-based image retrieval (CBIR) that represents each image in the database by a vector of feature values called “Standard deviation of mean vectors of color distribution of rows and columns of images for CBIR". In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. This paper describes the approach that uses contents as feature vector for retrieval of similar images. There are several classes of features that are used to specify queries: colour, texture, shape, spatial layout. Colour features are often easily obtained directly from the pixel intensities. In this paper feature extraction is done for the texture descriptor that is 'variance' and 'Variance of Variances'. First standard deviation of each row and column mean is calculated for R, G, and B planes. These six values are obtained for one image which acts as a feature vector. Secondly we calculate variance of the row and column of R, G and B planes of an image. Then six standard deviations of these variance sequences are calculated to form a feature vector of dimension six. We applied our approach to a database of 300 BMP images. We have determined the capability of automatic indexing by analyzing image content: color and texture as features and by applying a similarity measure Euclidean distance.

2
9517
A Real-Time Specific Weed Recognition System Using Statistical Methods
Abstract:
The identification and classification of weeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in shape, color and texture, weed control system is feasible. The goal of this paper is to build a real-time, machine vision weed control system that can detect weed locations. In order to accomplish this objective, a real-time robotic system is developed to identify and locate outdoor plants using machine vision technology and pattern recognition. The algorithm is developed to classify images into broad and narrow class for real-time selective herbicide application. The developed algorithm has been tested on weeds at various locations, which have shown that the algorithm to be very effectiveness in weed identification. Further the results show a very reliable performance on weeds under varying field conditions. The analysis of the results shows over 90 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.
1
14364
Arabic Character Recognition using Artificial Neural Networks and Statistical Analysis
Abstract:
In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input matrix, are fed to an ANN. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions.
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