Search results for: multi attribute analysis

Authors: Serhat Tuzun, Tufan Demirel

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Multi-Criteria Decision Making (MCDM) is the modelling of real-life to solve problems we encounter. It is a discipline that aids decision makers who are faced with conflicting alternatives to make an optimal decision. MCDM problems can be classified into two main categories: Multi-Attribute Decision Making (MADM) and Multi-Objective Decision Making (MODM), based on the different purposes and different data types. Although various MADM techniques were developed for the problems encountered, their methodology is limited in modelling real-life. Moreover, objective results are hard to obtain, and the findings are generally derived from subjective data. Although, new and modified techniques are developed by presenting new approaches such as fuzzy logic; comprehensive techniques, even though they are better in modelling real-life, could not find a place in real world applications for being hard to apply due to its complex structure. These constraints restrict the development of MADM. This study aims to conduct a comprehensive analysis of preference methods in MADM and propose an approach based on information. For this purpose, a detailed literature review has been conducted, current approaches with their advantages and disadvantages have been analyzed. Then, the approach has been introduced. In this approach, performance values of the criteria are calculated in two steps: first by determining the distribution of each attribute and standardizing them, then calculating the information of each attribute as informational energy.

Keywords: literature review, multi-attribute decision making, operations research, preference method, informational energy

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Authors: Sonia Rebai, Mohamed Naceur Azaiez, Dhafer Saidane

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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, financial rating

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Authors: B. Vikram, Y. Anu Leena, Y. Anu Neena, M. V. Krishna Rao, V. S. S. Kumar

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The industry is often criticized for inefficiencies in outcomes such as time and cost overruns, low productivity, poor quality and inadequate customer satisfaction. To enhance the chances for construction projects to be successful, selecting an able contractor is one of the fundamental decisions to be made by clients. The selection of the most appropriate contractor is a multi-criteria decision making (MCDM) process. In this paper, multi-attribute utility theory (MAUT) is employed utilizing the multiplicative form of utility function for ranking the prequalified contractors. Performance assessment criteria covering contracting company attributes, experience record, past performance, performance potential, financial stability and project specific criteria are considered for contractor evaluation. A case study of multistoried building for which four contractors submitted bids is considered to illustrate the applicability of multiplicative approach of MAUT to rank the prequalified contractors. The proposed MAUT decision making methodology can also be employed to other decision making situations.

Keywords: multi-attribute utility theory, construction industry, prequalification, contractor

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Authors: Triveni Gogoi, Rima Chatterjee

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Shear wave velocity (Vs) estimation is an important approach in the seismic exploration and characterization of a hydrocarbon reservoir. There are varying methods for prediction of S-wave velocity, if recorded S-wave log is not available. But all the available methods for Vs prediction are empirical mathematical models. Shear wave velocity can be estimated using P-wave velocity by applying Castagna’s equation, which is the most common approach. The constants used in Castagna’s equation vary for different lithologies and geological set-ups. In this study, multiple regression analysis has been used for estimation of S-wave velocity. The EMERGE module from Hampson-Russel software has been used here for generation of S-wave log. Both single attribute and multi attributes analysis have been carried out for generation of synthetic S-wave log in Upper Assam basin. Upper Assam basin situated in North Eastern India is one of the most important petroleum provinces of India. The present study was carried out using four wells of the study area. Out of these wells, S-wave velocity was available for three wells. The main objective of the present study is a prediction of shear wave velocities for wells where S-wave velocity information is not available. The three wells having S-wave velocity were first used to test the reliability of the method and the generated S-wave log was compared with actual S-wave log. Single attribute analysis has been carried out for these three wells within the depth range 1700-2100m, which corresponds to Barail group of Oligocene age. The Barail Group is the main target zone in this study, which is the primary producing reservoir of the basin. A system generated list of attributes with varying degrees of correlation appeared and the attribute with the highest correlation was concerned for the single attribute analysis. Crossplot between the attributes shows the variation of points from line of best fit. The final result of the analysis was compared with the available S-wave log, which shows a good visual fit with a correlation of 72%. Next multi-attribute analysis has been carried out for the same data using all the wells within the same analysis window. A high correlation of 85% has been observed between the output log from the analysis and the recorded S-wave. The almost perfect fit between the synthetic S-wave and the recorded S-wave log validates the reliability of the method. For further authentication, the generated S-wave data from the wells have been tied to the seismic and correlated them. Synthetic share wave log has been generated for the well M2 where S-wave is not available and it shows a good correlation with the seismic. Neutron porosity, density, AI and P-wave velocity are proved to be the most significant variables in this statistical method for S-wave generation. Multilinear regression method thus can be considered as a reliable technique for generation of shear wave velocity log in this study.

Keywords: Castagna's equation, multi linear regression, multi attribute analysis, shear wave logs

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Authors: Jolanta Tamošaitienė

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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: Amirhossein Mahlouji

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In current business market, the concept of integrating environmental sustainability into long-term as well as routine operations is becoming a prevailing trend. Therefore, several stimuli are helping organization to move toward environmental sustainability. The concept of green supply chain management can help provide a strategic framework to develop a customized sustainability roadmap for each organization. In this regard, this paper is mainly focused on presenting a strategic decision making framework that will assist top level decision-making issues. This decision-making tool is based on literature and practice in the area of environmentally conscious business practices. The goal of this paper will be on the components and parameters of green supply chain management and how they serve as a baseline for the decision framework. Later, the applicability of a multi-input multi-output decision model (MIMO), will be analyzed as the analytical network process, within the green supply chain.

Keywords: Multi-attribute, Green Supply Chain, Environmental, Sustainability

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Authors: Mohanad Alhabo, Naveed Nawaz

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The ongoing call or data session must be maintained to ensure a good quality of service. This can be accomplished by performing the handover procedure while the user is on the move. However, the dense deployment of small cells in 5G networks is a challenging issue due to the extensive number of handovers. In this paper, a neighbour cell list method is proposed to reduce the number of target small cells and hence minimizing the number of handovers. The neighbour cell list is built by omitting cells that could cause an unnecessary handover and handover failure because of short time of stay of the user in these cells. A multi-attribute decision making technique, simple additive weighting, is then applied to the optimized neighbour cell list. Multi-tier small cells network is considered in this work. The performance of the proposed method is analysed and compared with that of the existing methods. Results disclose that our method has decreased the candidate small cell list, unnecessary handovers, handover failure, and short time of stay cells compared to the competitive method.

Keywords: handover, HetNets, multi-attribute decision making, small cells

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Authors: Prashanth Pillai, Purnaprajna Mangsuli

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Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports.

Keywords: natural language processing, deep learning, transformers, information retrieval

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Authors: Kazuko Sakamoto, Keiichiro Kawarabayashi, Yoji Kitani

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Japanese sweets are one of the important elements of the Chur Japan strategy. In this research, we investigated what kind of sweets are liked to the Chinese tourist. What is generally eaten is influenced by culture, a sense of values, and business practice. Therefore, what was adapted there is sold. However, when traveling, what its country does not have is called for. Then, how far should we take in Chinese people's taste in a design?　This time, the design attribute (a color and a form) which leads to sweets "being delicious" was clarified by rough aggregate theory.As a result, the difference in the taste of Chinese people and Japanese people became clear.

Keywords: design attribute, international comparison, taste by appearance, design attribute

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Authors: Jamil Ahmed

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Access control models aim to decide whether a user should be denied or granted access to the user‟s requested activity. Various access control models have been established and proposed. The most prominent of these models include role-based, attribute-based, policy based access control models as well as role-centric attribute based access control model. In this paper, a novel access control model is presented called “Role centric Attribute based Policy Driven Access Control (RAPDAC) model”. RAPDAC incorporates the concept of “policy” in the “role centric attribute based access control model”. It leverages the concept of "policy‟ by precisely combining the evaluation of conditions, attributes, permissions and roles in order to allow authorization access. This approach allows capturing the "access control policy‟ of a real time application in a well defined manner. RAPDAC model allows making access decision at much finer granularity as illustrated by the case study of a real time library information system.

Keywords: authorization, access control model, role based access control, attribute based access control

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Authors: Gang Liu, Huimin Song, Can Wang, Runnan Zhang, Lu Fang

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Currently, resource sharing and system security are critical issues. This paper proposes a POL module composed of PRIV ILEGE attribute (PA), obligation and log which improves attribute based access control (ABAC) model in dynamically granting authorizations and revoking authorizations. The following describes the new model termed PABAC in terms of the POL module structure, attribute definitions, policy formulation and authorization architecture, which demonstrate the advantages of it. The POL module addresses the problems which are not predicted before and not described by access control policy. It can be one of the subject attributes or resource attributes according to the practical application, which enhances the flexibility of the model compared with ABAC. A scenario that illustrates how this model is applied to the real world is provided.

Keywords: access control, attribute based access control, granting authorizations, privilege, revoking authorizations, system security

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Authors: S. Suman, G. N. Singh

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The present manuscript addresses the estimation procedure of population parameter using Poisson probability distribution when characteristic under study possesses a rare sensitive attribute. The generalized form of unrelated randomized response model is suggested in order to acquire the truthful responses from respondents. The resultant estimators have been proposed for two situations when the information on an unrelated rare non-sensitive characteristic is known as well as unknown. The properties of the proposed estimators are derived, and the measure of confidentiality of respondent is also suggested for respondents. Empirical studies are carried out in the support of discussed theory.

Keywords: Poisson distribution, randomized response model, rare sensitive attribute, non-sensitive attribute

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Authors: Mokoena Oratilwe Penwell, Seeletse Solly Matshonisa

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The demand for quality products is a vital factor determining the success of a producing company, and the reality of this demand influences customer satisfaction. In Sefako Makgatho Health Sciences University (SMU), concerns over the quality of food being sold have been raised by mostly students and staff who are primary consumers of food being sold by the cafeteria. Suspicions of food poisoning and the occurrence of diarrhea-related to food from the cafeteria, amongst others, have been raised. However, minimal measures have been taken to resolve the issue of food quality. New service providers have been appointed, and still, the same trends are being observed, the quality of food seems to depreciate continuously. This paper uses multi-attribute failure mode analysis (MAFMA) for failure detection and minimization on the machines used for food production by SMU catering company before being sold to both staff, and students so as to improve production plant reliability, and performance. Analytical Hierarchy Process (AHP) will be used for the severity ranking of the weight criterions and development of the hierarchical structure for the cafeteria company. Amongst other potential issues detected, maintenance of the machines and equipment used for food preparations was of concern. Also, the staff lacked sufficient hospitality skills, supervision, and management in the cafeteria needed greater attention to mitigate some of the failures occurring in the food production plant.

Keywords: MAFMA, food quality, maintenance, supervision

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Authors: Alokananda Ghosh, Shraban Sarkar

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The study aims to identify suitable sites for new hospitals with critical obstetric care facilities in Birbhum, one of the vulnerable and underserved districts of Eastern India, considering six main and 14 sub-criteria, using GIS-based Analytic Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT) approach. The criteria were identified through field surveys and previous literature. After collecting expert decisions, a pairwise comparison matrix was prepared using the Saaty scale to calculate the weights through AHP. On the contrary, objective weighting methods, i.e., Entropy and Criteria Importance through Interaction Correlation (CRITIC), were used to perform the MAUT. Finally, suitability maps were prepared by weighted sum analysis. Sensitivity analyses of AHP were performed to explore the effect of dominant criteria. Results from AHP reveal that ‘maternal death in transit’ followed by ‘accessibility and connectivity’, ‘maternal health care service (MHCS) coverage gap’ were three important criteria with comparatively higher weighted values. Whereas ‘accessibility and connectivity’ and ‘maternal death in transit’ were observed to have more imprint in entropy and CRITIC, respectively. While comparing the predictive suitable classes of these three models with the layer of existing hospitals, except Entropy-MAUT, the other two are pointing towards the left-over underserved areas of existing facilities. Only 43%-67% of existing hospitals were in the moderate to lower suitable class. Therefore, the results of the predictive models might bring valuable input in future planning.

Keywords: hospital site suitability, analytic hierarchy process, multi-attribute utility theory, entropy, criteria importance through interaction correlation, multi-criteria decision analysis

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Authors: Zixi You

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With the demand of multilingual speakers increasing in the job market, multi-language learning programs have become more and more popular among undergraduate students. A study on multi-language learning strategies is therefore highly demanded on both practical and theoretical levels. Based on previous classification of learning strategies in SLA, and an investigation of BA Modern Language program students (with post-A level L2 and ab initio L3 learning experience from year one), this study explores and compares different types of learning strategies used by multi-language speakers and learners, transferable learning strategies between L2 and L3, and factors affecting the transfer. The results indicate that all the 23 types of learning strategies of L2 are employed when learning L3 from ab initio level, yet with different tendencies. Learning strategy transfer from L2 to L3 (i.e., the learners attribute the applying of these L3 learning strategies to be a direct result of their L2 learning experience) are observed in all 23 types of learning strategies. Comparatively, six types of “cognitive strategies” have higher transfer tendency than others. With regard to the failure of the transfer of some particular L2 strategies and the development of independent L3 strategies of individual learners, factors such as language proficiency, language typology and learning environment have played important roles among others. The presentation of this study will provide audiences with detailed data, insightful analysis and discussion on both theoretical and practical aspects of multi-language learning that will benefit both students and educators.

Keywords: learning strategy, multi-language acquisition, second language acquisition, strategy transfer

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Authors: Bita Payami-Shabestari, Dariush Eslami

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The purpose of this paper is to develop a multi-product economic production quantity model under vendor management inventory policy and restrictions including limited warehouse space, budget, and number of orders, average shortage time and maximum permissible shortage. Since the “costs” cannot be predicted with certainty, it is assumed that data behave under uncertain environment. The problem is first formulated into the framework of a bi-objective of multi-product economic production quantity model. Then, the problem is solved with three multi-objective decision-making (MODM) methods. Then following this, three methods had been compared on information on the optimal value of the two objective functions and the central processing unit (CPU) time with the statistical analysis method and the multi-attribute decision-making (MADM). The results are compared with statistical analysis method and the MADM. The results of the study demonstrate that augmented-constraint in terms of optimal value of the two objective functions and the CPU time perform better than global criteria, and goal programming. Sensitivity analysis is done to illustrate the effect of parameter variations on the optimal solution. The contribution of this research is the use of random costs data in developing a multi-product economic production quantity model under vendor management inventory policy with several constraints.

Keywords: economic production quantity, random cost, supply chain management, vendor-managed inventory

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Authors: Mansir Abubakar, Hazlina Hamdan, Norwati Mustapha, Teh Noranis Mohd Aris

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In order to build a matching pattern for each class correspondences of ontology, it is required to specify a set of attribute correspondences across two corresponding classes by clustering. Clustering reduces the size of potential attribute correspondences considered in the matching activity, which will significantly reduce the computation workload; otherwise, all attributes of a class should be compared with all attributes of the corresponding class. Most existing ontology matching approaches lack scalable attributes discovery methods, such as cluster-based attribute searching. This problem makes ontology matching activity computationally expensive. It is therefore vital in ontology matching to design a scalable element or attribute correspondence discovery method that would reduce the size of potential elements correspondences during mapping thereby reduce the computational workload in a matching process as a whole. The objective of this work is 1) to design a clustering method for discovering similar attributes correspondences and relationships between ontologies, 2) to discover element correspondences by classifying elements of each class based on element’s value features using K-medoids clustering technique. Discovering attribute correspondence is highly required for comparing instances when matching two ontologies. During the matching process, any two instances across two different data sets should be compared to their attribute values, so that they can be regarded to be the same or not. Intuitively, any two instances that come from classes across which there is a class correspondence are likely to be identical to each other. Besides, any two instances that hold more similar attribute values are more likely to be matched than the ones with less similar attribute values. Most of the time, similar attribute values exist in the two instances across which there is an attribute correspondence. This work will present how to classify attributes of each class with K-medoids clustering, then, clustered groups to be mapped by their statistical value features. We will also show how to map attributes of a clustered group to attributes of the mapped clustered group, generating a set of potential attribute correspondences that would be applied to generate a matching pattern. The K-medoids clustering phase would largely reduce the number of attribute pairs that are not corresponding for comparing instances as only the coverage probability of attributes pairs that reaches 100% and attributes above the specified threshold can be considered as potential attributes for a matching. Using clustering will reduce the size of potential elements correspondences to be considered during mapping activity, which will in turn reduce the computational workload significantly. Otherwise, all element of the class in source ontology have to be compared with all elements of the corresponding classes in target ontology. K-medoids can ably cluster attributes of each class, so that a proportion of attribute pairs that are not corresponding would not be considered when constructing the matching pattern.

Keywords: attribute correspondence, clustering, computational workload, k-medoids clustering, ontology matching

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

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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: Luís Marques, João Rocha, Andreia Fernandes, Maria Moura, Cláudia Caseiro, Filipa Figueiredo, João Nunes

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The implementation of circularity strategies other than recycling, such as reducing the amount of raw material, as well as reusing or sharing existing products, remains marginal. The European Commission announced that the transition towards a more circular economy could lead to the net creation of about 700,000 jobs in Europe by 2030, through additional labour demand from recycling plants, repair services and other circular activities. Efforts to create new circular business models in accordance with completely circular processes, as opposed to linear ones, have increased considerably in recent years. In order to create a societal Circular Economy transition model, it is necessary to include innovative solutions, where startups play a key role. Early-stage startups based on new business models according to circular processes often face difficulties in creating enough impact. The StartUp Zero Program designs a model and approach to increase the flow of startups in the Circular Economy field, focusing on a systemic decision analysis and multi-generational approach, considering Multi-Criteria Decision Analysis to support a decision-making tool, which is also supported by the use of a combination of an Analytical Hierarchy Process and Multi-Attribute Value Theory methods. We define principles, criteria and indicators for evaluating startup prerogatives, quantifying the evaluation process in a unique result. Additionally, this entrepreneurship program spanning 16 months involved more than 2400 young people, from ages 14 to 23, in more than 200 interaction activities.

Keywords: circular economy, entrepreneurship, startups;, multi-criteria decision analysis

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Authors: Jaishree Ranganathan, Poonam Rajurkar, Angelina A. Tzacheva, Zbigniew W. Ras

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In today’s world, business often depends on the customer feedback and reviews. Sentiment analysis helps identify and extract information about the sentiment or emotion of the of the topic or document. Attribute selection is a challenging problem, especially with large datasets in actionable pattern mining algorithms. Action Rule Mining is one of the methods to discover actionable patterns from data. Action Rules are rules that help describe specific actions to be made in the form of conditions that help achieve the desired outcome. The rules help to change from any undesirable or negative state to a more desirable or positive state. In this paper, we present a Lexicon based weighted scheme approach to identify emotions from customer feedback data in the area of manufacturing business. Also, we use Rough sets and explore the attribute selection method for large scale datasets. Then we apply Actionable pattern mining to extract possible emotion change recommendations. This kind of recommendations help business analyst to improve their customer service which leads to customer satisfaction and increase sales revenue.

Keywords: actionable pattern discovery, attribute selection, business data, data mining, emotion

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Authors: Bidzina Matsaberidze

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It is known that, in emergency situations, multi-attribute group decision-making (MAGDM) models are characterized by insufficient objective data and a lack of time to respond to the task. Evidence theory is an effective tool for describing such incomplete information in decision-making models when the expert and his knowledge are involved in the estimations of the MAGDM parameters. We consider an emergency decision-making model, where expert assessments on humanitarian aid from distribution centers (HADC) are represented in q-rung ortho-pair fuzzy numbers, and the data structure is described within the data body theory. Based on focal probability construction and experts’ evaluations, an objective function-distribution centers’ selection ranking index is constructed. Our approach for solving the constructed bicriteria partitioning problem consists of two phases. In the first phase, based on the covering’s matrix, we generate a matrix, the columns of which allow us to find all possible partitionings of the HADCs with the service centers. Some constraints are also taken into consideration while generating the matrix. In the second phase, based on the matrix and using our exact algorithm, we find the partitionings -allocations of the HADCs to the centers- which correspond to the Pareto-optimal solutions. For an illustration of the obtained results, a numerical example is given for the facility location-selection problem.

Keywords: emergency MAGDM, q-rung orthopair fuzzy sets, evidence theory, HADC, facility location problem, multi-objective combinatorial optimization problem, Pareto-optimal solutions

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Authors: Joon-Ho Lee, Jeong-Hwan Yoon, Jung-Hwan Yoon, Sangmo Kang, Su-Yeon Hong, Hyun-Woo Jeong, Jaeick Chae

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There are several dedicated individual chambers for sports that are supplied and used, but none of them are multi-pressured all-in-one chambers that can provide a sports multi-environment simultaneously. In this study, we design a multi-pressure (positive/atmospheric/negative pressure) integrated vessel that can be used for the sport-multi-artificial environment system. We presented additional vessel designs with enlarged space for the tall users; with reinforcement pads added to reduce the maximum stress in the joints of its shells, and then carried out numerical analysis for the structural analysis with maximum stress and structural safety. Under the targeted allowable pressure conditions, maximum stresses occurred at the joint of the shell, and the entrance, the safety of the structure was checked with the allowable stress of its material.

Keywords: structural analysis, multi-pressure, integrated vessel, sport-multi-artificial environment

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Matthew Yeager, Christopher Willy, John Bischoff

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The conceptualization and design phases of a system lifecycle consume a significant amount of the lifecycle budget in the form of direct tasking and capital, as well as the implicit costs associated with unforeseeable design errors that are only realized during downstream phases. Ad hoc or iterative approaches to generating system requirements oftentimes fail to consider the full array of feasible systems or product designs for a variety of reasons, including, but not limited to: initial conceptualization that oftentimes incorporates a priori or legacy features; the inability to capture, communicate and accommodate stakeholder preferences; inadequate technical designs and/or feasibility studies; and locally-, but not globally-, optimized subsystems and components. These design pitfalls can beget unanticipated developmental or system alterations with added costs, risks and support activities, heightening the risk for suboptimal system performance, premature obsolescence or forgone development. Supported by rapid advances in learning algorithms and hardware technology, sensors and sensor systems have become commonplace in both commercial and industrial products. The evolving array of hardware components (i.e. sensors, CPUs, modular / auxiliary access, etc…) as well as recognition, data fusion and communication protocols have all become increasingly complex and critical for design engineers during both concpetualization and implementation. This work seeks to develop and utilize a non-deterministic approach for sensor system design within the multi-attribute tradespace exploration (MATE) paradigm, a technique that incorporates decision theory into model-based techniques in order to explore complex design environments and discover better system designs. Developed to address the inherent design constraints in complex aerospace systems, MATE techniques enable project engineers to examine all viable system designs, assess attribute utility and system performance, and better align with stakeholder requirements. Whereas such previous work has been focused on aerospace systems and conducted in a deterministic fashion, this study addresses a wider array of system design elements by incorporating both traditional tradespace elements (e.g. hardware components) as well as popular multi-sensor data fusion models and techniques. Furthermore, statistical performance features to this model-based MATE approach will enable non-deterministic techniques for various commercial systems that range in application, complexity and system behavior, demonstrating a significant utility within the realm of formal systems decision-making.

Keywords: multi-attribute tradespace exploration, data fusion, sensors, systems engineering, system design

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Authors: Assem I. El-Ansary

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This paper illustrates the event-oriented Behavioral Pattern Analysis (BPA) modeling approach in developing an Multi-Agent Railway Control System (MARCS). The Event defined in BPA is a real-life conceptual entity that is unrelated to any implementation. The major contributions of this research are the Behavioral Pattern Analysis (BPA) modeling methodology, and the development of an interactive software tool (DECISION), which is based on a combination of the Analytic Hierarchy Process (AHP) and the ELECTRE Multi-Criteria Decision Making (MCDM) methods.

Keywords: analysis, multi-agent, railway control, modeling methodology, software modeling, event-oriented, behavioral pattern, use cases

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Authors: Gopinath Rathod, Vinod Puranik

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Sustainability is regarded as a key concept for survival in the competitive scenario. Industrial risk and diversification of risk type’s increases with industrial developments. In the context of sustainable manufacturing, the evaluation of risk is difficult because of the incomplete information and multiple indicators. Fuzzy Multiple Attribute Decision Method (FMADM) has been used with a three level hierarchical decision making model to evaluate aggregate risk for sustainable manufacturing projects. A case study has been presented to reflect the risk characteristics in sustainable manufacturing projects.

Keywords: sustainable manufacturing, decision making, aggregate risk, fuzzy logic, fuzzy multiple attribute decision method

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Authors: Chang-Bin Ha, Yong-Jun Kim, Dong-Hyun Ha, Hyoung-Kyu Song

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The conventional Wi-Fi back scatter system can only process one-to-one communication between the Wi-Fi reader and the Wi-Fi tag. For improvement of throughput of the conventional system, this paper proposes the multi-to-multi communication system. In the proposed system, the interference by the multi-to-multi communication is effectively cancelled by the orthogonal multiple access based on the identification code of the tag. Although the overhead is generated by the procedure for the multi-to-multi communication, because the procedure is processed by the Wi-Fi protocol, the overhead is insignificant for the entire communication procedure. From the numerical results, it is confirmed that the proposed system has nearly proportional increased throughput in according to the number of the tag that simultaneously participates in communication.

Keywords: backscatter, multi-to-multi communication, orthogonality, Wi-Fi

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Authors: Mohammed Gamil Montasser, Angelo Battaglia

Abstract:

Tourism is making its presence felt across the Sultanate of Oman. The story is one of the most recognized phenomena as a sustainable solid growth and is considered a remarkable outcome for any destination. The competitive situation and challenges within the tourism industry worldwide entail a better understanding of the destination position and its image to achieve Oman’s aspiration to retain its international reputation as one of the most desirable destinations in the Middle East. To access general perceptions of Oman’s attributes, their importance and their influences among U.S. tourists, an online survey was conducted with 522 American travelers who have traveled internationally, including non-visitors, virtual-visitors and visitors to Oman. This research involved a total of 36 attributes in the survey. Participants were asked to rate their agreement on how each attribute represented Oman and how important each attribute was for selecting destinations on 5- point Likert Scale. They also indicated if each attribute has a positive, neutral or negative influence on their destination selection. Descriptive statistics and importance performance analysis (IPA) were conducted. IPA illustrated U.S. tourists’ perceptions of Oman’s destination attributes and their importance in destination selection on a matrix with four quadrants, divided by actual mean value in each grid for importance (M=3.51) and performance (M=3.57). Oman tourism organizations and destination managers may use these research findings for future marketing and management efforts toward the U.S. travel market.

Keywords: analysis of importance, performance, destination attributes, Oman's position, U.S. tourists

Procedia PDF Downloads 306

Authors: Assem El-Ansary

Abstract:

This paper illustrates the event-oriented Behavioral Pattern Analysis (BPA) modeling approach in developing an Multi-Agent TeleRobotic Security Control System (MTSCS). The event defined in BPA is a real-life conceptual entity that is unrelated to any implementation. The major contributions of this research are the Behavioral Pattern Analysis (BPA) modeling methodology, and the development of an interactive software tool (DECISION), which is based on a combination of the Analytic Hierarchy Process (AHP) and the ELECTRE Multi-Criteria Decision Making (MCDM) methods.

Keywords: analysis, multi-agent, TeleRobotics control, security, modeling methodology, software modeling, event-oriented, behavioral pattern, use cases

Procedia PDF Downloads 438

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, SVM

Procedia PDF Downloads 413