Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

The higher life quality standards, the changes of habits and new well‐being requirements have led to an increase in the demand for housing. Decision‐making problems in construction management often involve a complex decision‐making process in which multiple requirements and conditions have to be taken into consideration simultaneously. However, not every attribute used in multidimensional scaling is equally and precisely weighted in the real world. Thus quantitative and qualitative assessments are often required to deal with uncertainty, subjective and imprecise data. The accuracy of performance measures in common multi-attribute methods is usually assumed to be accurate. Grey theory is a new technique for performing prediction, relational analysis and decision‐making in many areas. This paper considers the application of grey relations methodology for defining the utility of an alternative and is proposed as a method of multiple criteria COmplex PRoportional Assessment of alternatives with Grey relations (COPRAS‐G). In this model parameters of the alternatives are determined by the grey relational grade and are expressed in intervals. A case study of assessing external walls of four alternatives was used to demonstrate the applicability and the effectiveness of the proposed approach. The results show that this method can be implemented as an effective decision aid in multi‐attribute selection. Santrauka Straipsnio tikslas – pasiūlyti alternatyvią daugelio rodiklių vertinimo metodiką. Šios metodikos išskirtinumas – rodiklių reikšmės, aprašomos intervalais. Tai labiau atitinka realias aplinkos sąlygas ir galimas reikšmes. Modelis su intervaluose pateiktomis efektyvumo rodiklių reikšmėmis pritaikytas E. K. Zavadsko ir A. Kaklausko sukurtam metodui COPRAS (Complex Proportional Evaluation). Taip gautas naujas metodas COPRAS-G. Šis metodas pritaikytas daugiabučių namų išorinių sienų efektyvumo vertinimo uždaviniui spręsti. Sudaryta efektyvumo rodiklių sistema, nustatyta efektyvumo rodiklių reikšmė, rodiklių rangas. Pagal sudarytą modelį pritaikę COPRAS-G metodą, galime spręsti daugelį statybos, vadybos alternatyvų vertinimo uždavinių.

Multi-attribute analysis is a useful tool in many economical, managerial, constructional, etc. problems. The accuracy of performance measures in COPRAS (The multi-attribute COmplex PRoportional ASsessment of alternatives) method is usually assumed to be accurate. This method assumes direct and proportional dependence of the weight and utility degree of investigated versions on a system of attributes adequately describing the alternatives and on values and weights of the attributes. However, there is usually some uncertainty involved in all multi-attribute model inputs. The objective of this research is to demonstrate how simulation can be used to reflect fuzzy inputs, which allows more complete interpretation of model results. A case study is used to demonstrate the concept of general contractor choice of on the basis of multiple attributes of efficiency with fuzzy inputs applying COPRAS-G method. The research has concluded that the COPRAS-G method is appropriate to use.

The construction industry is an important industry because of its effects on different aspects of human life experiences and circumstances. Environmental concerns have been considered in designing and planning processes of construction supply chains in the recent past. One of the most crucial problems in managing supply chains is the process of evaluation and selection of green suppliers. This process can be categorized as a multi-criteria decision-making (MCDM) problem. The aim of this study is to propose a novel and efficient methodology for evaluation of green construction suppliers with uncertain information. The framework of the proposed methodology is based on weighted aggregated sum product assessment (WASPAS) and the simple multi-attribute rating technique (SMART), and Fermatean fuzzy sets (FFSs) are used to deal with uncertainty of information. The methodology was applied to a green supplier evaluation and selection in the construction industry. Fifteen suppliers were chosen to be evaluated with respect to seven criteria including “estimated cost”, “delivery efficiency”, “product flexibility”, “reputation and management level”, “eco-design”, and “green image pollution”. Sensitivity and comparative analyses were also conducted to assess the efficiency and validity of the proposed methodology. The analyses showed that the results of the proposed methodology were stable and also congruent with those of some existing methods.