Multicriteria optimization to select images as passwords in recognition based graphical authentication systems

Usability and guessability are two conflicting criteria in assessing the suitability of an image to be used as password in the recognition based graph -ical authentication systems (RGBSs). We present the first work in this area that uses a new approach, which effectively integrates a series of techniques in order to rank images taking into account the values obtained for each of the dimen -sions of usability and guessability, from two user studies. Our approach uses fuzzy numbers to deal with non commensurable criteria and compares two multicriteria optimization methods namely, TOPSIS and VIKOR. The results suggest that VIKOR method is the most applicable to make an objective state-ment about which image type is better suited to be used as password. The paper also discusses some improvements that could be done to improve the ranking assessment

Indicators and Evaluation Tools for the Assessment of Urban Sustainability

This paper attempts to provide an explanation of why reductionistic approaches are not adequate to tackle the urban sustainability issue in a consistent way. Concepts such as urban environmental carrying capacity and ecological footprint are discussed. Multicriteria evaluation is proposed as a general multidimensional framework for the assessment of urban sustainability. This paper deals with the following main topics: · definition of the concept of urban sustainability, · discussion of relevant sustainability indicators, · multicriteria evaluation as a framework for the assessment of urban sustainability, · an illustrative example.Urban environmental carrying capacity, ecological footprint, multicriteria evaluation, NAIADE method

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works

Multiobjective optimization for multiproduct batch plant design under economic and environmental considerations

This work deals with the multicriteria cost–environment design of multiproduct batch plants, where the design variables are the size of the equipment items as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a genetic algorithm (GA) with a discrete-event simulator (DES). Another incentive to use this kind of optimization method is that, there is no easy way of calculating derivatives of the objective functions, which then discards gradient optimization methods. To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a multiobjective genetic algorithm (MOGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design

Requirement Analysis and Implementation of Multicriteria Analysis in the NEEDS Project

This report specifies the requirements for and implementation of the multicriteria analysis of future energy technologies performed by a large number of stakeholders within the EU-funded integrated projct NEEDS. The report is composed of two main parts and the appendix. The first part starts with a summary of the objectives of the analysis followed by a detailed specifiation of the analyzed problem, in particular the analysis context, discussion of the sets of criteria and alternatives, and the participation of the stakeholders. Next, the planned problem analysis process is first outlined, and then discussed in more detail. Finally, the requirements for the multicritria analysis are specified. The second part deals with the implementation of the dedicated Web-site developed for this analysis, and later extended to support analysis of any multicriteria choice between discrete alternatives. It starts with an overview of the problem analysis process and the corresponding basic assumptions. Te architecture of the application and its features are then presented. Lessons learned from the development and use of this application conclude this part of the report. The appendix contains a review of the state-of-the-art of applying multicriteria analysis to energy problems, as well as characteristics of three applications that exploit the multicriteria analysis methods for energy problems considered relevant to the analysis reported in this paper

Managing Interacting Criteria: Application to Environmental Evaluation Practices

The need for organizations to evaluate their environmental practices has been recently increasing. This fact has led to the development of many approaches to appraise such practices. In this paper, a novel decision model to evaluate company’s environmental practices is proposed to improve traditional evaluation process in different facets. Firstly, different reviewers’ collectives related to the company’s activity are taken into account in the process to increase company internal efficiency and external legitimacy. Secondly, following the standard ISO 14031, two general categories of environmental performance indicators, management and operational, are considered. Thirdly, since the assumption of independence among environmental indicators is rarely verified in environmental context, an aggregation operator to bear in mind the relationship among such indicators in the evaluation results is proposed. Finally, this new model integrates quantitative and qualitative information with different scales using a multi-granular linguistic model that allows to adapt diverse evaluation scales according to appraisers’ knowledge

A holistic multi-methodology for sustainable renovation

A review of the barriers for building renovation has revealed a lack of methodologies, which can promote sustainability objectives and assist various stakeholders during the design stage of building renovation/retrofitting projects. The purpose of this paper is to develop a Holistic Multi-methodology for Sustainable Renovation, which aims to deal with complexity of renovation projects. It provides a framework through which to involve the different stakeholders in the design process to improve group learning and group decision-making, and hence make the building renovation design process more robust and efficient. Therefore, the paper discusses the essence of multifaceted barriers in building renovation regarding cultural changes and technological/physical changes. The outcome is a proposal for a multi-methodology framework, which is developed by introducing, evaluating and mixing methods from Soft Systems Methodologies (SSM) with Multiple Criteria Decision Making (MCDM). The potential of applying the proposed methodology in renovation projects is demonstrated through a case study

A novel sorting method topsis-sort: an applicaiton for tehran environmental quality evaluation

Many real-life problems are multi-objective by nature that requires evaluation of more than one criterion, therefore MCDM has become an important issue. In recent years, many MCDM methods have been developed; the existing approaches have been improved and extended. Multi criteria decision analysis has been regarded as a suitable set of methods to perform sustainability evaluations. Among numerous MCDM methods developed to solve real-life decision problems, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily in diverse application areas. In this paper, a novel sorting method (TOPSIS-Sort) based on the classic TOPSIS method is presented. In the TOPSIS-Sort approach an outranking relation is used for sorting purposes. The proposed approach uses characteristic profiles for defining the classes and outranking relation as the preference model. Application of the proposed approach is demonstrated by classifying 22 districts of Tehran into five classes (but none of the districts fits into Classes 4 and 5), representing areas with different levels of environmental quality. An analysis and assessment of the environmental conditions in Tehran helps to identify the districts with the poor environmental quality. Priority should be given to these areas to maintain and improve the quality of environment. The results obtained by the TOPSIS-Sort give credence to its success, because the results of sorting con firm our and specialists’ evaluation of the districts. This research provides appropriate results with respect to the development of sorting models in the form of outranking relations. The model, proposed by this study, is applicable to the other outranking methods such as ELECTRE, PROMETHEE, etc

PROMETHEE-ROC Model for Assessing the Readiness of Technology for Generating Energy

This paper puts forward a proposal for a multicriteria decision model for prioritizing technologies that are critical for power generation in the energy sector. It deals with the context of imprecise information regarding importance of criteria; then an integration of surrogate weights with the PROMETHEE method is undertaken in order to approach this context. In this type of strategic decision problem, how to deal with imprecise information is always a challenge. The use of surrogate weights presents a significant contribution and it can facilitate the assignment of weights in a decision ranking problem, which requires the decision-maker (DM) to order the criteria by their importance for the decision problem. Thus for this situation of assessing the readiness of technology for generating energy where the DM is able and feels comfortable to order all criteria by their relative importance, the proposed approach of surrogate weights in the PROMETHEE II method, the PROMETHEE-ROC model, is shown to be an adequate approach