Search results for: decision attribute

Authors: Dr. L. Arockiam, A. Aloysius

Abstract:

In general, class complexity is measured based on any one of these factors such as Line of Codes (LOC), Functional points (FP), Number of Methods (NOM), Number of Attributes (NOA) and so on. There are several new techniques, methods and metrics with the different factors that are to be developed by the researchers for calculating the complexity of the class in Object Oriented (OO) software. Earlier, Arockiam et.al has proposed a new complexity measure namely Extended Weighted Class Complexity (EWCC) which is an extension of Weighted Class Complexity which is proposed by Mishra et.al. EWCC is the sum of cognitive weights of attributes and methods of the class and that of the classes derived. In EWCC, a cognitive weight of each attribute is considered to be 1. The main problem in EWCC metric is that, every attribute holds the same value but in general, cognitive load in understanding the different types of attributes cannot be the same. So here, we are proposing a new metric namely Attribute Weighted Class Complexity (AWCC). In AWCC, the cognitive weights have to be assigned for the attributes which are derived from the effort needed to understand their data types. The proposed metric has been proved to be a better measure of complexity of class with attributes through the case studies and experiments

Keywords: Software Complexity, Attribute Weighted Class Complexity, Weighted Class Complexity, Data Type

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Authors: Chia-Yu Hsu, Julaimin Goh, Pei-Chann Chang

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Home is important for Chinese people. Because the information regarding the house attributes and surrounding environments is incomplete in most real estate agency, most house buyers are difficult to consider the overall factors effectively and only can search candidates by sorting-based approach. This study aims to develop a decision support system for housing purchasing, in which surrounding facilities of each house are quantified. Then, all considered house factors and customer preferences are incorporated into Simple Multi-Attribute Ranking Technique (SMART) to support the housing evaluation. To evaluate the validity of proposed approach, an empirical study was conducted from a real estate agency. Based on the customer requirement and preferences, the proposed approach can identify better candidate house with consider the overall house attributes and surrounding facilities.

Keywords: decision support system, real estate, decision analysis, housing evaluation, SMART

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Authors: Jolanta Tamošaitienė

Abstract:

The article focuses on a developed comprehensive model to be used in an agile environment for the risk assessment and selection based on multi-attribute methods. The model is based on a multi-attribute evaluation of risk in construction, and the determination of their optimality criterion values are calculated using complex Multiple Criteria Decision-Making methods. The model may be further applied to risk assessment in an agile construction environment. The attributes of risk in a construction project are selected by applying the risk assessment condition to the construction sector, and the construction process efficiency in the construction industry accounts for the agile environment. The paper presents the comprehensive risk assessment model in an agile construction environment. It provides a background and a description of the proposed model and the developed analysis of the comprehensive risk assessment model in an agile construction environment with the criteria.

Keywords: Assessment, environment, agile, model, risk.

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Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi

Abstract:

One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm, to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: Rough Set Theory, Attribute Reduction, Fuzzy Logic, Memetic Algorithms, Record to Record Algorithm, Great Deluge Algorithm.

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Authors: H. Rafiei, M. Rabbani

Abstract:

A novel concept to balance and tradeoff between make-to-stock and make-to-order has been hybrid MTS/MTO production context. One of the most important decisions involved in the hybrid MTS/MTO environment is determining whether a product is manufactured to stock, to order, or hybrid MTS/MTO strategy. In this paper, a model based on analytic network process is developed to tackle the addressed decision. Since the regarded decision deals with the uncertainty and ambiguity of data as well as experts- and managers- linguistic judgments, the proposed model is equipped with fuzzy sets theory. An important attribute of the model is its generality due to diverse decision factors which are elicited from the literature and developed by the authors. Finally, the model is validated by applying to a real case study to reveal how the proposed model can actually be implemented.

Keywords: Fuzzy analytic network process, Hybrid make-tostock/ make-to-order, Order partitioning, Production planning.

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Authors: Songlin Yang, Ying Ge

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This paper shows that some properties of the decision rules in the literature do not hold by presenting a counterexample. We give sufficient and necessary conditions under which these properties are valid. These results will be helpful when one tries to choose the right decision rules in the research of rough set theory.

Keywords: set, Decision table, Decision rule, coverage factor.

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Authors: G. Shruthi, Purohit Shrinivasacharya

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The biggest problem in cloud is securing an outsourcing data. A cloud environment cannot be considered to be trusted. It becomes more challenging when outsourced data sources are managed by multiple outsourcers with different access rights. Several methods have been proposed to protect data confidentiality against the cloud service provider to support fine-grained data access control. We propose a method with combined Cipher Text Policy Attribute-based Encryption (CP-ABE) and Timed-release encryption (TRE) secure method to control medical data storage in public cloud.

Keywords: Attribute, encryption, security, trapdoor.

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Authors: Taimur Qureshi, Djamel A Zighed

Abstract:

Many supervised induction algorithms require discrete data, even while real data often comes in a discrete and continuous formats. Quality discretization of continuous attributes is an important problem that has effects on speed, accuracy and understandability of the induction models. Usually, discretization and other types of statistical processes are applied to subsets of the population as the entire population is practically inaccessible. For this reason we argue that the discretization performed on a sample of the population is only an estimate of the entire population. Most of the existing discretization methods, partition the attribute range into two or several intervals using a single or a set of cut points. In this paper, we introduce a technique by using resampling (such as bootstrap) to generate a set of candidate discretization points and thus, improving the discretization quality by providing a better estimation towards the entire population. Thus, the goal of this paper is to observe whether the resampling technique can lead to better discretization points, which opens up a new paradigm to construction of soft decision trees.

Keywords: Bootstrap, discretization, resampling, soft decision trees.

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Authors: C. Ardil

Abstract:

Aircraft selection decisions can be challenging due to their multidimensional and interdisciplinary nature. They involve multiple stakeholders with conflicting objectives and numerous alternative options with uncertain outcomes. This study focuses on the analysis of aerial firefighting aircraft that can be chosen for the Air Fire Service to extinguish forest fires. To make such a selection, the characteristics of the fire zones must be considered, and the capability to manage the logistics involved in such operations, as well as the purchase and maintenance of the aircraft, must be determined. The selection of firefighting aircraft is particularly complex because they have longer fleet lives and require more demanding operation and maintenance than scheduled passenger air service. This paper aims to use the fuzzy proximity measure method to select the most appropriate aerial firefighting aircraft based on decision criteria using multiple attribute decision making analysis. Following fuzzy decision analysis, the most suitable aerial firefighting aircraft is ranked and determined for the Air Fire Service.

Keywords: Aerial firefighting aircraft selection, multiple criteria decision making, fuzzy sets, standard fuzzy sets, determinate fuzzy sets, indeterminate fuzzy sets, proximity measure method, Minkowski distance family function, Hausdorff distance function, MCDM, PMM, PMM-F

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Authors: Vicky Min-How Lim, Siew Fan Wong, Tong Ming Lim

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Automatic keyphrase extraction is useful in efficiently locating specific documents in online databases. While several techniques have been introduced over the years, improvement on accuracy rate is minimal. This research examines attribute scores for author-supplied keyphrases to better understand how the scores affect the accuracy rate of automatic keyphrase extraction. Five attributes are chosen for examination: Term Frequency, First Occurrence, Last Occurrence, Phrase Position in Sentences, and Term Cohesion Degree. The results show that First Occurrence is the most reliable attribute. Term Frequency, Last Occurrence and Term Cohesion Degree display a wide range of variation but are still usable with suggested tweaks. Only Phrase Position in Sentences shows a totally unpredictable pattern. The results imply that the commonly used ranking approach which directly extracts top ranked potential phrases from candidate keyphrase list as the keyphrases may not be reliable.

Keywords: Accuracy, Attribute Score, Author-supplied keyphrases, Automatic keyphrase extraction.

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Authors: Kemal Polat

Abstract:

In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.

Keywords: Fuzzy C-means clustering, Fuzzy C-means clustering based attribute weighting, Pima Indians diabetes dataset, SVM.

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Authors: Sonia Rebai, Mohamed Naceur Azaiez, Dhafer Saidane

Abstract:

In this study, we develop a performance evaluation model based on a multi-attribute utility approach aiming at reaching the sustainable banking (SB) status. This model is built accounting for various banks’ stakeholders in a win-win paradigm. In addition, it offers the opportunity for adopting a global measure of performance as an indication of a bank’s sustainability degree. This measure is referred to as banking sustainability performance index (BSPI). This index may constitute a basis for ranking banks. Moreover, it may constitute a bridge between the assessment types of financial and extra-financial rating agencies. A real application is performed on three French banks.

Keywords: Multi-attribute utility theory, Performance, Sustainable banking.

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Authors: M. Mazhar Manzoor, Nasar.A, A. Sattar

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In the open space of decision support system the mental impression of a manager-s decision has been the subject of large importance than the ordinary famous one, when helped by decision support system. Much of this study is an attempt to realize the relation of decision support system usage and decision outcomes that governs the system. For example, several researchers have proposed so many different models to analyze the linkage between decision support system processes and results of decision making. This study draws the important relation of manager-s mental approach with the use of decision support system. The findings of this paper are theoretical attempts to provide Decision Support System (DSS) in a way to exhibit and promote the learning in semi structured area. The proposed model shows the points of one-s learning improvements and maintains a theoretical approach in order to explore the DSS contribution in enhancing the decision forming and governing the system.

Keywords: Decision Support System , Learning Organization,

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Authors: H. Rafiei, M. Rabbani

Abstract:

This paper proposes a new decision making structure to determine the appropriate product delivery strategy for different products in a manufacturing system among make-to-stock, make-toorder, and hybrid strategy. Given product delivery strategies for all products in the manufacturing system, the position of the Order Penetrating Point (OPP) can be located regarding the delivery strategies among which location of OPP in hybrid strategy is a cumbersome task. In this regard, we employ analytic network process, because there are varieties of interrelated driving factors involved in choosing the right location. Moreover, the proposed structure is augmented with fuzzy sets theory in order to cope with the uncertainty of judgments. Finally, applicability of the proposed structure is proven in practice through a real industrial case company. The numerical results demonstrate the efficiency of the proposed decision making structure in order partitioning and OPP location.

Keywords: Hybrid make-to-stock/make-to-order, Multi-attribute decision making, Order partitioning, Order penetration point.

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Authors: Ali E. Abbas

Abstract:

Industrial Engineering is a broad multidisciplinary field with intersections and applications in numerous areas. When designing a product, it is important to determine the appropriate attributes of value and the preference function for which the product is optimized. This paper provides some guidelines on appropriate selection of attributes for preference and value functions for engineering design.

Keywords: Decision analysis, engineering design, direct vs. indirect values.

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Authors: Wen-Hsiang Lai, Nguyen Quang Vinh

Abstract:

In order to explore the relationship of promotion activities, destination attribute and destination image of Vietnam and find possible solutions, this study uses decision system analysis (DSA) method to develop flowcharts based on three rounds of expert interviews. The interviews were conducted with the experts who were confirmed to directly participate or influence on the decision making that drives the promotion of Vietnam tourism process. This study identifies three models and describes specific decisions on promotion activities, destination attributes and destination images. This study finally derives a general model for promoting the Tourism Industrial Service Network (TISN) in Vietnam. This study finds that the coordination with all sectors and industries of tourism to facilitate favorable condition and improving destination attributes in linking with the efficient promotion activities is highly recommended in order to make visitors satisfied and improve the destination image.

Keywords: Destination attributes, Destination image, Decision system analysis, Tourism promotion

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Authors: R. Weinzierl, R. Estes, N. Tinsley, M. Keshlaf

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The corn earworm, Helicoverpa zea Boddie, is a serious pest of corn. Larval feeding in ear tips destroys kernels and allows growth of fungi and production of mycotoxins. Infested sweet corn is not marketable. Development of improved transgenic hybrids expressing insecticidal toxins from Bacillus thuringiensis (Bt) may limit or prevent crop losses. The effectiveness of Attribute^® II Bt resistance and applications of Voliam Xpress insecticide were evaluated for effectiveness in controlling corn earworm in plots near Urbana, IL, USA, in 2013. Where no insecticides were applied, ear infestations and kernel damage in Attribute^® II ‘Protector’ plots were consistently lower (near zero) than in plots of the non-Bt isoline ‘Garrison.’ Multiple applications of Voliam Xpress significantly reduced the number of corn earworm larvae and kernel damage in the Garrison plots, but infestations and damage in these plots were greater than in Protectorplots that did not receive insecticide applications. Our results indicate that Attribute^® II Bt resistance is more effective than multiple applications of an insecticide for preventing losses caused by corn earworm in sweet corn.

Keywords: Bacillus thuringiensis, Helicoverpa zea, insect pest management, transgenic sweet corn.

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Authors: P. Ghann, J. Shiguang, C. Zhou

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Access control is one of the most challenging issues facing information security. Access control is defined as, the ability to permit or deny access to a particular computational resource or digital information by an unauthorized user or subject. The concept of usage control (UCON) has been introduced as a unified approach to capture a number of extensions for access control models and systems. In UCON, an access decision is determined by three factors: authorizations, obligations and conditions. Attribute mutability and decision continuity are two distinct characteristics introduced by UCON for the first time. An observation of UCON components indicates that, the components are predefined and static. In this paper, we propose a new and flexible model of usage control for the creation and elimination of some of these components; for example new objects, subjects, attributes and integrate these with the original UCON model. We also propose a model for concurrent usage scenarios in UCON.

Keywords: Access Control, Concurrency, Digital container, Usage control.

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Authors: Chengming Li, Yong Yin, Peipei Guo, Xiaoli Liu

Abstract:

Without taking account of the attribute richness of POI (point of interest) data and spatial distribution limited by roads, a POI generalization method considering both attribute information and spatial distribution has been proposed against the existing point generalization algorithm merely focusing on overall information of point groups. Hierarchical characteristic of urban POI information expression has been firstly analyzed to point out the measurement feature of the corresponding hierarchy. On this basis, an urban POI generalizing strategy has been put forward: POIs urban road network have been divided into three distribution pattern; corresponding generalization methods have been proposed according to the characteristic of POI data in different distribution patterns. Experimental results showed that the method taking into account both attribute information and spatial distribution characteristics of POI can better implement urban POI generalization in the mapping presentation.

Keywords: POI, Road network, spatial information expression, selection method, distribution pattern.

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Authors: Gülfem Alptekin, S. Emre Alptekin

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Global competitiveness has recently become the biggest concern of both manufacturing and service companies. Electronic commerce, as a key technology enables the firms to reach all the potential consumers from all over the world. In this study, we have presented commonly used electronic payment systems, and then we have shown the evaluation of these systems in respect to different criteria. The payment systems which are included in this research are the credit card, the virtual credit card, the electronic money, the mobile payment, the credit transfer and the debit instruments. We have realized a systematic comparison of these systems in respect to three main criteria: Technical, economical and social. We have conducted a fuzzy multi-criteria decision making procedure to deal with the multi-attribute nature of the problem. The subjectiveness and imprecision of the evaluation process are modeled using triangular fuzzy numbers.

Keywords: Electronic payment systems, fuzzy multi-criteriadecision making, analytical hierarchy process.

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Authors: Zhen Wu, David Lupien St-Pierre, Georges Abdul-Nour

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In decision making under strict uncertainty, decision makers have to choose a decision without any information about the states of nature. The classic criteria of Laplace, Wald, Savage, Hurwicz and Starr are introduced and compared in a case study of sewer network planning. Furthermore, results from different criteria are discussed and analyzed. Moreover, this paper discusses the idea that decision making under strict uncertainty (DMUSU) can be viewed as a two-player game and thus be solved by a solution concept in game theory: Nash equilibrium.

Keywords: Decision criteria, decision making, sewer network planning, strict uncertainty.

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to prevent deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network. 

Keywords: Accident risks estimation, artificial neural network, deep learning, K-mean, road safety.

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Authors: Dewan Md. Farid, Jerome Darmont, Nouria Harbi, Nguyen Huu Hoa, Mohammad Zahidur Rahman

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In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources.

Keywords: Attributes selection, Conditional probabilities, information gain, network intrusion detection.

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Authors: Gülcin Büyüközkan, Mehmet Sakir Ersoy

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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: Chih-Cheng Chen, Yung-Tan Lee, Shih-Yang Lee, Shih-Kuei Huang, Tien-Tze Chen, Qiu-Jun Chen

Abstract:

The evaluation of the contribution of professional baseball starting pitchers is a complex decision-making problem that includes several quantitative attributes. It is considered a type of multi-attribute or multi-criteria decision making (MADM/MCDM) problem. This study proposes a model using the Grey Relational Analysis (GRA) to evaluate the starting pitcher contribution for teams of the Chinese Professional Baseball League. The GRA calculates the individual grey relational degree of each alternative to the positive ideal alternative. An empirical analysis was conducted to show the use of the model for the starting pitcher contribution problem. The results demonstrate the effectiveness and feasibility of the proposed model.

Keywords: Starting pitchers, Grey Relational Analysis, Chinese Professional Baseball

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

Abstract:

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: Yang Bao, Shi Wei Deng, Wang Qun Lin

Abstract:

This paper introduces the concept and principle of data cleaning, analyzes the types and causes of dirty data, and proposes several key steps of typical cleaning process, puts forward a well scalability and versatility data cleaning framework, in view of data with attribute dependency relation, designs several of violation data discovery algorithms by formal formula, which can obtain inconsistent data to all target columns with condition attribute dependent no matter data is structured (SQL) or unstructured (NoSql), and gives 6 data cleaning methods based on these algorithms.

Keywords: Data cleaning, dependency rules, violation data discovery, data repair.

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Authors: B. Mikulskienė

Abstract:

Public administration institutions in cooperation with politicians are not the sole policy decision makers in full meaning any longer. Meanwhile, a special role, namely steering the decision making process, could be delegated to them. Despite the wide scientific discussion on different aspects what has direct impact on policy creation, there is a lack of holistic practical managerial advice, which could integrate infrastructure of policy decision making with intellectual capital and with interconnection of partnership. The proposed harmonized decision making model of process, people and partnership entitled by acronym HM-3P is analyzed as a framework for implementation of public administration steering role seeking the coherent social involvement in policy decision making.

Keywords: participatory decision making, partnership, stakeholders.

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

Abstract:

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: Leehter Yao, Chin-Chin Lin

Abstract:

A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n-dimensional fuzzy decision region is approximated by union of hyperellipsoids. By explicitly parameterizing these hyperellipsoids, the decision regions are determined by estimating the parameters of each hyperellipsoid.Genetic Algorithm is applied to estimate the parameters of each region component. With the global optimization ability of GA, the learned decision region can be arbitrarily complex.

Keywords: Ellipsoid, genetic algorithm, decision regions, classification.

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