Search results for: Attribute selection

Authors: Norah Alshahrani, Abdulaziz Almaleh

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Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD: Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by SVM, achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: Autism Spectrum Disorder, ASD, Machine Learning, ML, Feature Selection, Support Vector Machine, SVM.

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Authors: Yeon-kug Moon, Kang-Yoon Lee, Yun-Jae Won, Seung-Ok Lim

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This paper describes a low-voltage and low-power channel selection analog front end with continuous-time low pass filters and highly linear programmable gain amplifier (PGA). The filters were realized as balanced Gm-C biquadratic filters to achieve a low current consumption. High linearity and a constant wide bandwidth are achieved by using a new transconductance (Gm) cell. The PGA has a voltage gain varying from 0 to 65dB, while maintaining a constant bandwidth. A filter tuning circuit that requires an accurate time base but no external components is presented. With a 1-Vrms differential input and output, the filter achieves -85dB THD and a 78dB signal-to-noise ratio. Both the filter and PGA were implemented in a 0.18um 1P6M n-well CMOS process. They consume 3.2mW from a 1.8V power supply and occupy an area of 0.19mm2.

Keywords: component ; Channel selection filters, DC offset, programmable gain amplifier, tuning circuit

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Authors: Cigdem Safak Saglam

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Most thermal power plants use steam as working fluid in their power cycle. Therefore, in addition to fuel, water is the other main input for thermal plants. Water and steam must be highly pure in order to protect the systems from corrosion, scaling and biofouling. Pure process water is produced in water treatment plants having many several treatment methods. Treatment plant design is selected depending on raw water source and required water quality. Although working principle of fossil-fuel fired thermal power plants are same, there is no standard design and equipment arrangement valid for all thermal power plant utility systems. Besides that, there are many other technology evaluation and selection criteria for designing the most optimal water systems meeting the requirements such as local conditions, environmental restrictions, electricity and other consumables availability and transport, process water sources and scarcity, land use constraints etc. Aim of this study is explaining the adopted methodology for technology selection for process water preparation and industrial waste water treatment plant in a thermal power plant project located in Tufanbeyli, Adana Province in Turkey. Thermal power plant is fired with indigenous lignite coal extracted from adjacent lignite reserves. This paper addresses all above-mentioned factors affecting the thermal power plant water treatment facilities (demineralization + waste water treatment) design and describes the ultimate design of Tufanbeyli Thermal Power Plant Water Treatment Plant.

Keywords: Thermal power plant, lignite coal, pre-treatment, demineralization, electrodialysis, recycling, waste water, process water.

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Authors: Seyed Nima Tayarani Bathaie

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In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.

Keywords: Face detection algorithm, Haar features, Security of ATM.

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Authors: I. Falconett, K. Nagasaka

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This study investigates the performance of radial basis function networks (RBFN) in forecasting the monthly CO2 emissions of an electric power utility. We also propose a method for input variable selection. This method is based on identifying the general relationships between groups of input candidates and the output. The effect that each input has on the forecasting error is examined by removing all inputs except the variable to be investigated from its group, calculating the networks parameter and performing the forecast. Finally, the new forecasting error is compared with the reference model. Eight input variables were identified as the most relevant, which is significantly less than our reference model with 30 input variables. The simulation results demonstrate that the model with the 8 inputs selected using the method introduced in this study performs as accurate as the reference model, while also being the most parsimonious.

Keywords: Correlation analysis, CO2 emissions forecasting, electric power utility, radial basis function networks.

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Authors: S. Rathilal

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Numeracy, like Literacy is considered to be a core value of modern societies. Most higher education institutions in South Africa include being numerate as an important graduate attribute. It is argued that a suitability numerate society contributes to social justice, empowerment, financial and environmental sustainability and a lack of numeracy practices can contribute to disempowerment. Numeracy is commonly misconstrued as a basic and simple practice, similar in nature to basic arithmetic. This study highlights the complexities of higher education numeracy practices by analyzing a programme in a higher education institution in South Africa using the New Literacies Studies perspective.

Keywords: Higher Education, New Literacy Studies, Numeracy Practices.

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Authors: Jie Liu, Xudong Luo, Pingping Lin, Yifan Fan

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Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially the fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, ma-chine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.

Keywords: sentiment analysis, fine-grained, machine learning, deep learning

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Authors: M. Padmavathi, R. M. Suresh

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Every machine plays roles of client and server simultaneously in a peer-to-peer (P2P) network. Though a P2P network has many advantages over traditional client-server models regarding efficiency and fault-tolerance, it also faces additional security threats. Users/IT administrators should be aware of risks from malicious code propagation, downloaded content legality, and P2P software’s vulnerabilities. Security and preventative measures are a must to protect networks from potential sensitive information leakage and security breaches. Bit Torrent is a popular and scalable P2P file distribution mechanism which successfully distributes large files quickly and efficiently without problems for origin server. Bit Torrent achieved excellent upload utilization according to measurement studies, but it also raised many questions as regards utilization in settings, than those measuring, fairness, and Bit Torrent’s mechanisms choice. This work proposed a block selection technique using Fuzzy ACO with optimal rules selected using ACO.

Keywords: Ant Colony Optimization (ACO), Bit Torrent, Download time, Peer-to-Peer (P2P) network, Performance.

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial part of maintaining a customer-oriented business in the telecommunications industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years, which has made it more important to understand customers’ needs in this strong market. For those who are looking to turn over their service providers, understanding their needs is especially important. Predictive churn is now a mandatory requirement for retaining customers in the telecommunications industry. Machine learning can be used to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: Machine Learning, Gradient Boosting, Logistic Regression, Churn, Random Forest, Decision Tree, ROC, AUC, F1-score.

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Authors: Y. N. Phang, E. F. Loh

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The zero inflated models are usually used in modeling count data with excess zeros where the existence of the excess zeros could be structural zeros or zeros which occur by chance. These type of data are commonly found in various disciplines such as finance, insurance, biomedical, econometrical, ecology, and health sciences which involve sex and health dental epidemiology. The most popular zero inflated models used by many researchers are zero inflated Poisson and zero inflated negative binomial models. In addition, zero inflated generalized Poisson and zero inflated double Poisson models are also discussed and found in some literature. Recently zero inflated inverse trinomial model and zero inflated strict arcsine models are advocated and proven to serve as alternative models in modeling overdispersed count data caused by excessive zeros and unobserved heterogeneity. The purpose of this paper is to review some related literature and provide a variety of examples from different disciplines in the application of zero inflated models. Different model selection methods used in model comparison are discussed.

Keywords: Overdispersed count data, model selection methods, likelihood ratio, AIC, BIC.

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Authors: Girish Kumar Singh, Sonajharia Minz

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Granular computing deals with representation of information in the form of some aggregates and related methods for transformation and analysis for problem solving. A granulation scheme based on clustering and Rough Set Theory is presented with focus on structured conceptualization of information has been presented in this paper. Experiments for the proposed method on four labeled data exhibit good result with reference to classification problem. The proposed granulation technique is semi-supervised imbibing global as well as local information granulation. To represent the results of the attribute oriented granulation a tree structure is proposed in this paper.

Keywords: Granular computing, clustering, Rough sets, datamining.

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Authors: Cha-Hwa Lin, Je-Wei Hu

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The conventional GA combined with a local search algorithm, such as the 2-OPT, forms a hybrid genetic algorithm(HGA) for the traveling salesman problem (TSP). However, the geometric properties which are problem specific knowledge can be used to improve the search process of the HGA. Some tour segments (edges) of TSPs are fine while some maybe too long to appear in a short tour. This knowledge could constrain GAs to work out with fine tour segments without considering long tour segments as often. Consequently, a new algorithm is proposed, called intelligent-OPT hybrid genetic algorithm (IOHGA), to improve the GA and the 2-OPT algorithm in order to reduce the search time for the optimal solution. Based on the geometric properties, all the tour segments are assigned 2-level priorities to distinguish between good and bad genes. A simulation study was conducted to evaluate the performance of the IOHGA. The experimental results indicate that in general the IOHGA could obtain near-optimal solutions with less time and better accuracy than the hybrid genetic algorithm with simulated annealing algorithm (HGA(SA)).

Keywords: Traveling salesman problem, hybrid geneticalgorithm, priority selection, 2-OPT.

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Authors: Li-Yeh Chuang, Yu-Jen Hou, Jr., Cheng-Hong Yang

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Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, research is limited by the cost of genotyping the tremendous number of SNPs. Therefore, it is important to identify a small subset of informative SNPs, the so-called tag SNPs. This subset consists of selected SNPs of the genotypes, and accurately represents the rest of the SNPs. Furthermore, an effective evaluation method is needed to evaluate prediction accuracy of a set of tag SNPs. In this paper, a genetic algorithm (GA) is applied to tag SNP problems, and the K-nearest neighbor (K-NN) serves as a prediction method of tag SNP selection. The experimental data used was taken from the HapMap project; it consists of genotype data rather than haplotype data. The proposed method consistently identified tag SNPs with considerably better prediction accuracy than methods from the literature. At the same time, the number of tag SNPs identified was smaller than the number of tag SNPs in the other methods. The run time of the proposed method was much shorter than the run time of the SVM/STSA method when the same accuracy was reached.

Keywords: Genetic Algorithm (GA), Genotype, Single nucleotide polymorphism (SNP), tag SNPs.

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Authors: Maarit Valo, Pertti Hurme

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This study focuses on teamwork in Finnish working life. Through a wide cross-section of teams the study examines the causes to which team members attribute the outcomes of their teams. Qualitative data was collected from 314 respondents. They wrote 616 stories to describe memorable experiences of success and failure in teamwork. The stories revealed 1930 explanations. The findings indicate that both favorable and unfavorable team outcomes are perceived as being caused by the characteristics of team members, relationships between members, team communication, team structure, team goals, team leadership, and external forces. The types represent different attribution levels in the context of organizational teamwork.

Keywords: Team, teamwork, team outcomes, workplace, working life.

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Authors: Jung-Hyun Kim, Jihyung Kim, Kwangjae Lim, Dong Seung Kwon

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Network coding has recently attracted attention as an efficient technique in multicast/broadcast services. The problem of finding the optimal network coding mechanism maximizing the bandwidth efficiency is hard to solve and hard to approximate. Lots of network coding-based schemes have been suggested in the literature to improve the bandwidth efficiency, especially network coding-based automatic repeat request (NCARQ) schemes. However, existing schemes have several limitations which cause the performance degradation in resource limited systems. To improve the performance in resource limited systems, we propose NCARQ with overlapping selection (OS-NCARQ) scheme. The advantages of OS-NCARQ scheme over the traditional ARQ scheme and existing NCARQ schemes are shown through the analysis and simulations.

Keywords: ARQ, Network coding, Multicast/Broadcast services, Packet-based systems.

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Authors: Azeem Ahmad, Magnus Goransson, Aamir Shahzad

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The selection of appropriate requirements for product releases can make a big difference in a product success. The selection of requirements is done by different requirements prioritization techniques. These techniques are based on pre-defined and systematic steps to calculate the requirements relative weight. Prioritization is complicated by new development settings, shifting from traditional co-located development to geographically distributed development. Stakeholders, connected to a project, are distributed all over the world. These geographically distributions of stakeholders make it hard to prioritize requirements as each stakeholder have their own perception and expectations of the requirements in a software project. This paper discusses limitations of the Analytical Hierarchy Process with respect to geographically distributed stakeholders- (GDS) prioritization of requirements. This paper also provides a solution, in the form of a modified AHP, in order to prioritize requirements for GDS. We will conduct two experiments in this paper and will analyze the results in order to discuss AHP limitations with respect to GDS. The modified AHP variant is also validated in this paper.

Keywords: Requirements Prioritization, GeographicallyDistributed Stakeholders, AHP, Modified AHP.

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Authors: N. Moghadam, H. Sadeghi

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In this paper a novel scheme for watermarking digital audio during its compression to MPEG-1 Layer III format is proposed. For this purpose we slightly modify some of the selected MDCT coefficients, which are used during MPEG audio compression procedure. Due to the possibility of modifying different MDCT coefficients, there will be different choices for embedding the watermark into audio data, considering robustness and transparency factors. Our proposed method uses a genetic algorithm to select the best coefficients to embed the watermark. This genetic selection is done according to the parameters that are extracted from the perceptual content of the audio to optimize the robustness and transparency of the watermark. On the other hand the watermark security is increased due to the random nature of the genetic selection. The information of the selected MDCT coefficients that carry the watermark bits, are saves in a database for future extraction of the watermark. The proposed method is suitable for online MP3 stores to pursue illegal copies of musical artworks. Experimental results show that the detection ratio of the watermarks at the bitrate of 128kbps remains above 90% while the inaudibility of the watermark is preserved.

Keywords: Content-Based Audio Watermarking, Genetic AudioWatermarking.

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Authors: M. F. Yilmaz, B. Ö. Çağlayan

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Fragility curve is an effective common used tool to determine the earthquake performance of structural and nonstructural components. Also, it is used to determine the nonlinear behavior of bridges. There are many historical bridges in the Turkish railway network; the earthquake performances of these bridges are needed to be investigated. To derive fragility curve Intensity measures (IMs) and Engineering demand parameters (EDP) are needed to be determined. And the relation between IMs and EDP are needed to be derived. In this study, a typical simply supported steel girder riveted railway bridge is studied. Fragility curves of this bridge are derived by two parameters lognormal distribution. Time history analyses are done for selected 60 real earthquake data to determine the relation between IMs and EDP. Moreover, efficiency, practicality, and sufficiency of three different IMs are discussed. PGA, Sa(0.2s) and Sa(1s), the most common used IMs parameters for fragility curve in the literature, are taken into consideration in terms of efficiency, practicality and sufficiency.

Keywords: Railway bridges, earthquake performance, fragility analyses, selection of intensity measures.

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Authors: Shigeaki Sakurai, Youichi Kitahata, Ryohei Orihara

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This paper proposes a method that discovers sequential patterns corresponding to user-s interests from sequential data. This method expresses the interests as constraint patterns. The constraint patterns can define relationships among attributes of the items composing the data. The method recursively decomposes the constraint patterns into constraint subpatterns. The method evaluates the constraint subpatterns in order to efficiently discover sequential patterns satisfying the constraint patterns. Also, this paper applies the method to the sequential data composed of stock price indexes and verifies its effectiveness through comparing it with a method without using the constraint patterns.

Keywords: Sequential pattern mining, Constraint pattern, Attribute constraint, Stock price indexes

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Authors: Holger Keitel

Abstract:

The prediction of long-term deformations of concrete and reinforced concrete structures has been a field of extensive research and several different creep models have been developed so far. Most of the models were developed for constant concrete stresses, thus, in case of varying stresses a specific superposition principle or time-integration, respectively, is necessary. Nowadays, when modeling concrete creep the engineering focus is rather on the application of sophisticated time-integration methods than choosing the more appropriate creep model. For this reason, this paper presents a method to quantify the uncertainties of creep prediction originating from the selection of creep models or from the time-integration methods. By adapting variance based global sensitivity analysis, a methodology is developed to quantify the influence of creep model selection or choice of time-integration method. Applying the developed method, general recommendations how to model creep behavior for varying stresses are given.

Keywords: Concrete creep models, time-integration methods, sensitivity analysis, prediction uncertainty.

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Authors: Mohammad Taghi Taghavifard, Danial Mirheydari

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This paper suggests an algorithm for the evaluation and selection of suppliers. At the beginning, all the needed materials and services used by the organization were identified and categorized with regard to their nature by ABC method. Afterwards, in order to reduce risk factors and maximize the organization's profit, purchase strategies were determined. Then, appropriate criteria were identified for primary evaluation of suppliers applying to the organization. The output of this stage was a list of suppliers qualified by the organization to participate in its tenders. Subsequently, considering a material in particular, appropriate criteria on the ordering of the mentioned material were determined, taking into account the particular materials' specifications as well as the organization's needs. Finally, for the purpose of validation and verification of the proposed model, it was applied to Mobarakeh Steel Company (MSC), the qualified suppliers of this Company are ranked by the means of a Hierarchical Fuzzy TOPSIS method. The obtained results show that the proposed algorithm is quite effective, efficient and easy to apply.

Keywords: ABC analysis, Hierarchical Fuzzy TOPSIS, Primary supplier evaluation, Purchasing strategy, supplier selection

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Authors: G.Kavitha, V.Sankaranarayanan

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Grid computing provides a virtual framework for controlled sharing of resources across institutional boundaries. Recently, trust has been recognised as an important factor for selection of optimal resources in a grid. We introduce a new method that provides a quantitative trust value, based on the past interactions and present environment characteristics. This quantitative trust value is used to select a suitable resource for a job and eliminates run time failures arising from incompatible user-resource pairs. The proposed work will act as a tool to calculate the trust values of the various components of the grid and there by improves the success rate of the jobs submitted to the resource on the grid. The access to a resource not only depend on the identity and behaviour of the resource but also upon its context of transaction, time of transaction, connectivity bandwidth, availability of the resource and load on the resource. The quality of the recommender is also evaluated based on the accuracy of the feedback provided about a resource. The jobs are submitted for execution to the selected resource after finding the overall trust value of the resource. The overall trust value is computed with respect to the subjective and objective parameters.

Keywords: access control, feedback, grid computing, reputation, security, trust, trust parameter.

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Authors: R. A. Salam, M.A. Rodrigues

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The number of features required to represent an image can be very huge. Using all available features to recognize objects can suffer from curse dimensionality. Feature selection and extraction is the pre-processing step of image mining. Main issues in analyzing images is the effective identification of features and another one is extracting them. The mining problem that has been focused is the grouping of features for different shapes. Experiments have been conducted by using shape outline as the features. Shape outline readings are put through normalization and dimensionality reduction process using an eigenvector based method to produce a new set of readings. After this pre-processing step data will be grouped through their shapes. Through statistical analysis, these readings together with peak measures a robust classification and recognition process is achieved. Tests showed that the suggested methods are able to automatically recognize objects through their shapes. Finally, experiments also demonstrate the system invariance to rotation, translation, scale, reflection and to a small degree of distortion.

Keywords: Image mining, feature selection, shape recognition, peak measures.

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Authors: Pankaj Dutta, Debjani Chakraborty

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This paper investigates the effect of product substitution in the single-period 'newsboy-type' problem in a fuzzy environment. It is supposed that the single-period problem operates under uncertainty in customer demand, which is described by imprecise terms and modelled by fuzzy sets. To perform this analysis, we consider the fuzzy model for two-item with upward substitution. This upward substitutability is reasonable when the products can be stored according to certain attribute levels such as quality, brand or package size. We show that the explicit consideration of this substitution opportunity increase the average expected profit. Computational study is performed to observe the benefits of product's substitution.

Keywords: Fuzzy demand, Newsboy, Single-period problem, Substitution.

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Authors: Amudha Kadirvelu, Sunil Gurtu, Sivalal Sadasivan

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Pharmacology curriculum plays an integral role in medical education. Learning pharmacology to choose and prescribe drugs is a major challenge encountered by students. We developed pharmacology applied learning activities for first year medical students that included realistic clinical situations with escalating complications which required the students to analyze the situation and think critically to choose a safe drug. Tutor feedback was provided at the end of session. Evaluation was done to assess the students- level of interest and usefulness of the sessions in rational selection of drugs. Majority (98 %) of the students agreed that the session was an extremely useful learning exercise and agreed that similar sessions would help in rational selection of drugs. Applied learning sessions in the early years of medical program may promote deep learning and bridge the gap between pharmacology theory and clinical practice. Besides, it may also enhance safe prescribing skills.

Keywords: Medical education, pharmacology curriculum, applied learning, safe prescribing.

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Authors: O. Siriporn, S. Benjawan

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This paper represents four unsupervised clustering algorithms namely sIB, RandomFlatClustering, FarthestFirst, and FilteredClusterer that previously works have not been used for network traffic classification. The methodology, the result, the products of the cluster and evaluation of these algorithms with efficiency of each algorithm from accuracy are shown. Otherwise, the efficiency of these algorithms considering form the time that it use to generate the cluster quickly and correctly. Our work study and test the best algorithm by using classify traffic anomaly in network traffic with different attribute that have not been used before. We analyses the algorithm that have the best efficiency or the best learning and compare it to the previously used (K-Means). Our research will be use to develop anomaly detection system to more efficiency and more require in the future.

Keywords: Unsupervised, clustering, anomaly, machine learning.

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Authors: H. Zarei, M. H. Fazel Zarandi, M. Karbasian

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A Decision Support System/Expert System for stock portfolio selection presented where at first step, both technical and fundamental data used to estimate technical and fundamental return and risk (1st phase); Then, the estimated values are aggregated with the investor preferences (2nd phase) to produce convenient stock portfolio. In the 1st phase, there are two expert systems, each of which is responsible for technical or fundamental estimation. In the technical expert system, for each stock, twenty seven candidates are identified and with using rough sets-based clustering method (RC) the effective variables have been selected. Next, for each stock two fuzzy rulebases are developed with fuzzy C-Mean method and Takai-Sugeno- Kang (TSK) approach; one for return estimation and the other for risk. Thereafter, the parameters of the rule-bases are tuned with backpropagation method. In parallel, for fundamental expert systems, fuzzy rule-bases have been identified in the form of “IF-THEN" rules through brainstorming with the stock market experts and the input data have been derived from financial statements; as a result two fuzzy rule-bases have been generated for all the stocks, one for return and the other for risk. In the 2nd phase, user preferences represented by four criteria and are obtained by questionnaire. Using an expert system, four estimated values of return and risk have been aggregated with the respective values of user preference. At last, a fuzzy rule base having four rules, treats these values and produce a ranking score for each stock which will lead to a satisfactory portfolio for the user. The stocks of six manufacturing companies and the period of 2003-2006 selected for data gathering.

Keywords: Stock Portfolio Selection, Fuzzy Rule-Base ExpertSystems, Financial Decision Support Systems, Technical Analysis, Fundamental Analysis.

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Authors: C. Ardil

Abstract:

Aircraft have different capabilities and specifications according to the required strategic goals and objectives in operations. With various types on the market with different aircraft characteristics, it becomes difficult to select a suitable aircraft for certain operations and requirements. The entropy weighting method (EWM) is a useful, highly consistent, and reliable method for obtaining the weights of the criteria and is worth integrating with the decision uncertainty distance (DUD) method, which is more applicable and requires less computation than other methods. An illustrative example is presented to demonstrate the validity and usability of the proposed methodology. Comparing the ranking results matches the distance-based approach, which is the technique for order preference by similarity to ideal solution (TOPSIS) method, which shows the robustness of the entropy DUD hybrid method. Validity analysis shows that the proposed hybrid multiple criteria decision-making analysis (MCDMA) methodology is quantitatively stable and reliable.

Keywords: aircraft selection, decision uncertainty distance (DUD), multiple criteria decision making analysis, MCDMA, TOPSIS

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Authors: D.Venkatesan, K.Kannan, S. Raja Balachandar

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In this paper a new Genetic Algorithm based on a heuristic operator and Centre of Mass selection operator (CMGA) is designed for the unbounded knapsack problem(UKP), which is NP-Hard combinatorial optimization problem. The proposed genetic algorithm is based on a heuristic operator, which utilizes problem specific knowledge. This center of mass operator when combined with other Genetic Operators forms a competitive algorithm to the existing ones. Computational results show that the proposed algorithm is capable of obtaining high quality solutions for problems of standard randomly generated knapsack instances. Comparative study of CMGA with simple GA in terms of results for unbounded knapsack instances of size up to 200 show the superiority of CMGA. Thus CMGA is an efficient tool of solving UKP and this algorithm is competitive with other Genetic Algorithms also.

Keywords: Genetic Algorithm, Unbounded Knapsack Problem, Combinatorial Optimization, Meta-Heuristic, Center of Mass

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Authors: C. Ardil

Abstract:

The decision to select an unmanned combat aircraft is complicated since several options and conflicting criteria must be considered at simultaneously. When making multiple criteria decision, it is important to consider the selected evaluation criteria, including priceability, payloadability, stealthability, speedability , and survivability. The fundamental goal of the study is to select the best unmanned combat aircraft by taking these evaluation criteria into account. The optimal aircraft was chosen using the fuzzy proximity measure method, which enables decision-makers to designate preferences as standard fuzzy set numbers during the multiple criteria decision-making process. To assess the applicability of the proposed approach, a numerical example is provided. Finally, by comparing determined unmanned combat aircraft, the proposed method produced a successful application, and the best aircraft was selected.

Keywords: standard fuzzy sets (SFS), unmanned combat aircraft selection, multiple criteria decision making (MCDM), multiple criteria group decision making (MCGDM), proximity measure method (PMM)

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