Understanding Customers' Affective Needs with Linguistic Summarization

Abstract: To increase the chance of launching a successful product into market, it is essential to satisfy customers’ affective needs during the product design stage. However, understanding customers’ affective needs is very difficult task and product designers might misunderstand the customers’ affective needs. In this study, linguistic summarization with fuzzy set is used to present customers’ affective needs with natural language statements which could be easily understood by human beings. The relations between customers’ affective needs and product design elements are represented by type-I and type-II fuzzy quantified sentences. To illustrate the applicability of the linguistic summarization with fuzzy set in translating customers’ affective needs to natural language statements, a case study is conducted on mobile phone design. The results indicate that the linguistic summarization with fuzzy set can be a useful tool to assist designers to create products satisfying affective needs of customer

Developing a Labeled Affective Magnitude scale and Fuzzy Linguistic scale for tactile feeling

Affective design is the inclusion or representation of human emotions and subjective impressions in product design processes. In affective design, a number of different scales are commonly used to reveal and measure subjective emotions related to the design features of products. Osgood's Semantic Differential Scale (SDS) is one of the scales that has often been used for this purpose. However, there are some drawbacks in the SDS due to the ordinal nature of the scale that leads to losses or distortions of a significant amount of information and this makes it difficult to justify parametric statistical analysis. In this study, two scales, namely a Labeled Affective Magnitude (LAM) scale and a Fuzzy Linguistic scale, are developed. The LAM scale is an alternative scale based on magnitude estimation and has ratio properties. The Fuzzy Linguistic scale is an interval scale for which responses are linguistic descriptors that are identified with fuzzy numbers or intervals. The scales were developed for tactile feelings because they are an important factor in product evaluation. Statistical analysis was conducted to compare the scales. There was no significant difference between the newly constructed fuzzy scale and 11 point SDS, whereas there was a significant difference between the newly constructed LAM scale and 11 point SDS

Reinterpreting Interpretability for Fuzzy Linguistic Descriptions of Data

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Efficacy of AZM therapy in patients with gingival overgrowth induced by Cyclosporine A: a systematic review

<p>Abstract</p> <p>Background</p> <p>In daily clinical practice of a dental department it's common to find gingival overgrowth (GO) in periodontal patients under treatment with Cyclosporine A (CsA). The pathogenesis of GO and the mechanism of action of Azithromycin (AZM) are unclear. A systematic review was conducted in order to evaluate the efficacy of Azithromycin in patients with gingival overgrowth induced by assumption of Cyclosporine A.</p> <p>Methods</p> <p>A bibliographic search was performed using the online databases MEDLINE, EMBASE and Cochrane Central of Register Controlled Trials (CENTRAL) in the time period between 1966 and September 2008.</p> <p>Results</p> <p>The literature search retrieved 24 articles; only 5 were Randomised Controlled Trials (RCTs), published in English, fulfilled the inclusion criteria. A great heterogeneity between proposed treatments and outcomes was found, and this did not allow to conduct a quantitative meta-analysis. The systematic review revealed that a 5-day course of Azithromycin with Scaling and Root Planing reduces the degree of gingival overgrowth, while a 7-day course of metronidazole is only effective on concomitant bacterial over-infection.</p> <p>Conclusion</p> <p>Few RCTs on the efficacy of systemic antibiotic therapy in case of GO were found in the literature review. A systemic antibiotic therapy without plaque and calculus removal is not able to reduce gingival overgrowth. The great heterogeneity of diagnostic data and outcomes is due to the lack of precise diagnostic methods and protocols about GO. Future studies need to improve both diagnostic methods and tools and adequate classification aimed to determine a correct prognosis and an appropriate therapy for gingival overgrowth.</p

Fit between humanitarian professionals and project requirements: hybrid group decision procedure to reduce uncertainty in decision-making

Choosing the right professional that has to meet indeterminate requirements is a critical aspect in humanitarian development and implementation projects. This paper proposes a hybrid evaluation methodology for some non-governmental organizations enabling them to select the most competent expert who can properly and adequately develop and implement humanitarian projects. This methodology accommodates various stakeholders’ perspectives in satisfying the unique requirements of humanitarian projects that are capable of handling a range of uncertain issues from both stakeholders and project requirements. The criteria weights are calculated using a two-step multi-criteria decision-making method: (1) Fuzzy Analytical Hierarchy Process for the evaluation of the decision maker weights coupled with (2) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the alternatives which provide the ability to take into account both quantitative and qualitative evaluations. Sensitivity analysis have been developed and discussed by means of a real case of expert selection problem for a non-profit organisation. The results show that the approach allows a decrease in the uncertainty associated with decision-making, which proves that the approach provides robust solutions in terms of sensitivity analysis

IVIFCM-TOPSIS pro hodnocení úvěrového rizika bank

Bank credit risk assessment is performed by credit rating agencies in order to reduce information asymmetry in financial markets. This costly process has been automated in earlier studies by using systems based on machine learning methods. However, such systems suffer from interpretability issues and do not utilize expert knowledge effectively. To overcome those problems, multi-criteria group decision-making (MCGDM) methods have recently been used to simulate the assessment process performed by the committee of multiple credit risk experts. However, standard MCGDM methods fail to consider high uncertainty inherently associated with the assessment and do not work effectively when the assessed credit risk criteria interact with each other. To address these issues, we propose MCGDM model for bank credit risk assessment that has two advantages: (1) The imprecise assessment criteria are represented by interval-valued intuitionistic fuzzy sets, and (2) the interactions among the criteria are modeled using fuzzy cognitive maps. When combined with traditional TOPSIS approach to ranking alternatives, we show that the proposed model can be effectively applied to assess bank credit risk.Hodnocení úvěrového rizika bank provádí ratingové agentury, aby se snížila informační asymetrie na finančních trzích. Tento nákladný proces byl v dřívějších studiích automatizován pomocí systémů založených na metodách strojového učení. Takové systémy však trpí problémy s interpretovatelností a nevyužívají efektivně expertní znalosti. K překonání těchto problémů byly v poslední době použity metody vícekriteriálního skupinového rozhodování (MCGDM), které simulují proces hodnocení prováděný skupinou odborníků na úvěrové riziko. Standardní metody MCGDM však nezohledňují vysokou nejistotu neodmyslitelně spojenou s hodnocením a nepracují účinně, když se posuzovaná kritéria úvěrového rizika vzájemně ovlivňují. K vyřešení těchto problémů navrhujeme model MCGDM pro hodnocení bankovního úvěrového rizika, který má dvě výhody: (1) Nepřesná hodnotící kritéria jsou reprezentována intervalovými intuicionistickými fuzzy množinami a (2) interakce mezi kritérii jsou modelovány pomocí fuzzy kognitivní mapy. V kombinaci s tradičním přístupem TOPSIS k klasifikačním alternativám ukazujeme, že navrhovaný model lze efektivně uplatnit při hodnocení bankovního úvěrového rizika