Linguistic Terms Research Articles

Prioritization of Personal Protective Equipment Plans for Construction Projects Based on an Integrated Analytic Network Process and Fuzzy VIKOR Method

The risk of both fatal accidents and non-fatal injuries in the construction industry is significantly high in most countries. To reduce this construction safety risk, the proper use of personal protective equipment (PPE) is one of the major measures on the jobsite. In this research, in order to comprehensively assess the PPE plans, a three-phase framework was proposed to identify the optimal solution for PPE planning from a set of alternatives. As a result, four main criteria and fifteen sub-criteria were identified based on a systematic literature review, and a decision-making model integrating the analytic network process (ANP) and VIekriterijumsko KOmpromisno Rangiranje was developed. As the assessment information in the survey was incomplete and vague, the fuzzy sets theory was adopted to transform the linguistic terms into fuzzy numbers for evaluation. The model further calculated the weight of each criterion and prioritized the potential PPE plan alternatives. Finally, the presented model was implemented in a case study to verify its feasibility and applicability for practical construction management. The proposed method enables the selection of the most compromising solution as the optimal PPE plan. This research assists decision-makers and safety planners at construction workplaces to improve the overall safety performance and reduce accident risks, which significantly contributes to construction safety management and practice.

Quasirung orthopair fuzzy linguistic sets and their application to multi criteria decision making

Linguistic term fuzzy sets provide an intuitive way to express preferences, enhancing understanding and communication among decision-makers. In this article, we introduce the novel concept of p,q-quasirung orthopair fuzzy linguistic sets (p,q-QOFLSs), which merge the principles of p,q-quasirung orthopair fuzzy sets (p,q-QOFSs) with linguistic fuzzy sets. This new framework offers a more robust approach to handle uncertain and imprecise information in decision-making processes, characterized by linguistic membership and non-membership degrees. We establish several fundamental operational laws, alongside score and accuracy functions, to facilitate the comparison of p,q-quasirung orthopair fuzzy linguistic numbers. Leveraging these operational laws, we propose a series of weighted averaging and geometric operators under p,q-QOFLSs. Furthermore, we formulate a multi-attribute decision-making methodology using these operators. The significance of the proposed method lies in its ability to model complex decision-making scenarios with enhanced precision. A numerical example validates the practicality and adaptability of the methodology, supported by sensitivity analyses and comparative evaluations, highlighting the innovation and efficiency of the approach.

Langue littéraire – langue standard: une nécessaire révision terminologique de la langue roumaine

The purpose of this paper is to analyse two linguistic terms, limbă literară (literary language) and limbă standard (standard language), which illustrate a particular type of terminological instability in Romanian linguistics: throughout times, they have been used either in a hyponymy/hypernymy relation, or in an equivalence one. We first provide a short history of the usage of the two terms from the ’50s onwards, and then we concentrate on the situation encountered today in linguistic papers, but also in school curricula and textbooks. As a result of our analysis, we advocate for a terminological revision of the two terms: while limbă literară is the only appropriate term for Old Romanian (early 16th – late 18th centuries), limbă standard is now used (and has been for a while) as its synonym, highlighting the standardization process that Romanian has undergone (starting from the 19th century), and should be registered as such in dictionaries.

Stakeholder requirement-oriented prioritization method of quality characteristics based on probabilistic linguistic term set

Stakeholder requirement-oriented prioritization method of quality characteristics based on probabilistic linguistic term set

Evaluation of Supplier Sustainable Development Programs Using the HFLTS-QFD Method

Objective: This study proposes a decision model for evaluating sustainable supplier development programs (SSDPs) using the HFLTS-QFD technique, which is a combination of Hesitant Fuzzy Linguistic Term Sets (HFLTS) with the Quality Function Deployment (QFD) method. Theoretical Framework: The implementation of supplier development programs is important to improve supplier performance in aspects related to economic, environmental and social aspects. The evaluation of these programs is necessary to decide on their continuation or discontinuation, as well as to direct the allocation of resources. Method: The bibliographical research supported the definition of the requirements and criteria used for the evaluation of SDPs. The implementation of the model was done using MS Excel® software. The application in an illustrative case involving the evaluation of five SDPs allowed us to demonstrate the applicability of the model. Results and Discussion: The most relevant criteria were organization strategy, social responsibility, employee training and environmental management. The programs with the best classification were SDP4 and SDP5. Research Implications: The proposed model supports the selection of criteria considering their relative weights and the degree of difficulty in collecting data from suppliers when evaluating each PDFS. It also has the capacity to assist group decision-making, enabling the assignment of weights to decision-makers. Originality/Value: Unlike previous models for evaluating PDFS, which use economic or environmental criteria, the proposed model considers criteria related to the three dimensions of the Triple Bottom Line, in addition to allowing the use of linguistic expressions to deal with decisions under hesitation and uncertainty.

Managing perceptions on the linguistic terms of qualitative scales

In this paper, we consider the problem of how individuals perceive the ordinal proximities between the linguistic terms of an ordered qualitative scale. We address the challenge of accurately measuring these ordinal proximities in ordered qualitative scales used in surveys. To tackle this issue, we have devised a visual procedure, managed through sliders, and an appropriate software that generates the metrizable ordinal measure which represents the ordinal arrangement of the proximities between the linguistic terms of an ordered qualitative scale. The procedure has been applied to real-world survey data to validate its effectiveness. The results indicate that the new proposal significantly outperforms traditional techniques in terms of accuracy and reliability, providing a robust tool for researchers in fields requiring precise ordinal data analysis.

The Development of the Lean Six Sigma Readiness Assessment Model for the Hotel Sector

ABSTRACT Lean Six Sigma (LSS) implementation still shows a high failure rate. Lack of readiness is one of the causes. Readiness assessment requires special exploration of each sector, including the hotel sector, where there is still no development of an LSS readiness assessment model. This study aims to develop a readiness assessment model for LSS implementation in the hotel sector. Data processing in the readiness assessment model uses the fuzzy logic method, is translated again into linguistic terms using Euclidean distance, and is ranked using the TOPSIS approach. The literature review produced 34 Readiness Factors (RF) that were validated by eight credible hotel practitioners and used as guidelines in conducting LSS readiness assessments in the hotel sector. Case studies were conducted on Hotel A and Hotel B, which showed that Hotel A was “Very Ready” and Hotel B was “Ready” to implement LSS. This research is perhaps the first of its kind in the hotel sector. This research also uses the Delphi method to collect data on the importance level, which may not have been done in collecting data on LSS readiness in any sector.

Enhancing group decision-making: Maximum consensus aggregation for fuzzy cross-efficiency under hesitant fuzzy linguistic information

Group decision-making (GDM) is essential as it recognizes the inherent complexity of many decision scenarios, which frequently require the collective wisdom and knowledge of multiple decision-makers (DMs) to be effectively resolved. The proposed method aims to develop fuzzy data envelopment analysis (DEA) cross-efficiency models tailored to address GDM challenges, wherein attribute values are provided by DMs using hesitant fuzzy linguistic term sets (HFLTSs). For this purpose, we initially transform HFLTSs into their corresponding fuzzy envelopes, defined as trapezoidal fuzzy numbers (TrFNs). This conversion strategy effectively minimizes the loss in assessments based on HFLTSs while retaining the inherent ambiguity of the original information. Building upon this foundation, we develop fuzzy cross-efficiency models by leveraging the α-level sets of fuzzy envelopes. These models are designed to handle fuzzy input and output variables under various α-level sets, which are capable of considering all possible attribute values for each alternative. Following this, we implement a maximum consensus model using fuzzy cross-efficiency to assign weights to DMs. These weights facilitate the aggregation of individual fuzzy cross-efficiency intervals obtained from DMs’ assessments into collective ones, which serve to rank alternatives. Finally, we showcase the effectiveness and superiority of our proposal through numerical validation and comparative analysis.

Intercultural communication skills and student mobility in Morocco

This study examines intercultural communication competence among international students in Morocco, taking as a sample the Ecole Nationale des Sciences Appliquées (ENSA) in Tétouan. The survey of 26 students assessed six key aspects: intercultural communication skills, culture shock, general satisfaction, cultural awareness, intercultural sensitivity and language proficiency. The results show that 58% of students have a high level of intercultural competence. Culture shock is evenly distributed between high, moderate and low levels. Overall satisfaction is high, with 80% of students satisfied with their experience. Cultural awareness and intercultural sensitivity are also high among the majority of participants. In linguistic terms, there was significant progress in Arabic, while the level of French was generally high. The study highlights the crucial importance of intercultural communication skills in the success of international student mobility in Morocco. It highlights areas requiring particular attention, such as support for students experiencing significant culture shock and the continuous improvement of language skills. Despite certain limitations, in particular the size of the sample and the focus on a single institution, this research provides insight into the role of intercultural communication in the success of international student mobility in Morocco.

Exploring the categorical nature of colour perception: Insights from artificial networks

The electromagnetic spectrum of light from a rainbow is a continuous signal, yet we perceive it vividly in several distinct colour categories. The origins and underlying mechanisms of this phenomenon remain partly unexplained. We investigate categorical colour perception in artificial neural networks (ANNs) using the odd-one-out paradigm. In the first experiment, we compared unimodal vision networks (e.g., ImageNet object recognition) to multimodal vision-language models (e.g., CLIP text-image matching). Our results show that vision networks predict a significant portion of human data (approximately 80%), while vision-language models account for the remaining unexplained data, even in non-linguistic experiments. These findings suggest that categorical colour perception is a language-independent representation, though it is partly shaped by linguistic colour terms during its development. In the second experiment, we explored how the visual task influences the colour categories of an ANN by examining twenty-four Taskonomy networks. Our results indicate that human-like colour categories are task-dependent, predominantly emerging in semantic and 3D tasks, with a notable absence in low-level tasks. To explain this difference, we analysed kernel responses before the winner-takes-all stage, observing that networks with mismatching colour categories may still align in underlying continuous representations. Our findings quantify the dual influence of visual signals and linguistic factors in categorical colour perception and demonstrate the task-dependent nature of this phenomenon, suggesting that categorical colour perception emerges to facilitate certain visual tasks.

AI-powered decision making for road safety optimization under probabilistic linguistic Sugeno-Weber aggregation information

Traffic accidents, a global concern, pose threats to lives and carry substantial economic and societal burdens. This paper explores the innovative integration of artificial intelligence (AI) into the ambiance of Multi Criteria Decision Making (MCDM) to address the challenge. We investigate AI's potential to enhance road safety globally, utilizing data analysis, predictive modeling, and intelligent traffic management systems. Our goal is to revolutionize accident prevention by optimizing decision-making processes and promoting safer and more efficient road environments. In the realm of information aggregation and fusion, growing interest among researchers has centered on probabilistic linguistic expression sets, which adeptly aggregate uncertain data. This article's primary aim is to explore methodologies for aggregating information within a probabilistic linguistic environment. To achieve this goal, we have introduced procedural laws based on the Sugeno-Weber (SW) framework for handling probabilistic linguistic term elements (PLTEs), rooted in both the product and sum of SW operations. Consequently, we have crafted a range of probabilistic linguistic aggregation techniques, encompassing the probabilistic linguistic SW Average (PLSWA) and Geometric (PLSWG), followed by weighted as well as ordered aggregation operators like PLSWWA and PLSWWG, PLSWOWA and PLSWOWG. By harnessing SW τ-norm and τ-conorm, we have developed versatile aggregation tools that facilitate information reinforcement. Through the utilization of proposed operators, we have presented strategies for effectively integrating probabilistic linguistic term sets (PLTs) in the context of MCDM. We have compared our suggested procedures with the TOPSIS approach and elucidated the diverse features inherent to these operators.

A hybrid BWM–CRITIC–VIKOR approach for assessing oil and gas risk scenarios in probabilistic linguistic term set

The oil and gas industry plays a crucial role in supporting energy supply and economic development. Mitigating risks in oil and gas projects is essential for reducing decision-making uncertainties, optimizing resource allocation, and fostering the competitiveness and sustainable growth of enterprises. Therefore, evaluating decision-making risks in the oil and gas sector is of significant practical importance. This paper proposes a comprehensive approach based on the BWM–CRITIC–VIKOR multi-criteria decision-making (MCDM) evaluation model. An evaluation index system encompassing six critical dimensions—technology, economics, environment, security, societal impact, and political factors—is established. To enhance assessment precision, the AHP interval estimation method is employed to identify and eliminate indicators with low contributions. Subsequently, the probabilistic linguistic term set (PLTS) is introduced into the best–worst method (BWM) and CRITIC methods to calculate the weights for each index. This approach helps to circumvent the subjectivity inherent in single subjective weighting methods. To evaluate and rank alternatives, the PLTS is used as an evaluation linguistic tool within the VIKOR framework. Finally, this methodology is applied to decision-making in the oil and gas industry, and its effectiveness and superiority are compared with the AHP–TOPSIS method and the BWM–CRITIC method.

An evaluation of the adaptive performance of architects in project teams in Ghana using fuzzy approach

PurposeThe adaptive performance of architects as a key professional in project delivery teams has become important for developing strategies, skills and cognitive behaviours for sustainability of working systems. However, the understanding and knowledge of adaptive performance of architects is lacking in the current literature. Thus, this study fills this gap by primarily assessing the adaptive performance of architects in project teams in project delivery.Design/methodology/approachBy adopting the widely used eight-dimension attributes of adaptive performance, a questionnaire survey was conducted among team participants and stakeholders who directly or indirectly work on projects with architects in the public and private sectors project delivery supply chain in Ghana. A total of 42 responses were subsequently used in a fuzzy set theory analysis being facilitated by a set of linguistic terms.FindingsFrom the assessment, the overall adaptive performance of architects from the eight-dimension attributes emerged to be fairly high. Additionally, the architects’ performance in the individual eight-dimensions showed varied results. High performance was registered in architects’ ability to handling work stress and cultural adaptability. Also, architects demonstrated a fairly high performance in dealing with uncertain or unpredictable work situations. However, in the cases of learning work tasks, technologies and procedures, interpersonal adaptability and handling crisis and emergency situations, architects were deemed to have low and fairly low adaptive performance among project teams.Originality/valueGiven the vagueness and complexities in understanding adaptability among teams and its assessment, through the use of fuzzy set theory based on a suitable set of linguistics terms, the study presents a novel understanding of the level of architects’ adaptive performance in project teams in project delivery. The findings are extremely useful in helping architects adapt and cope with changing competitive work environment by developing the right cognitive behaviours for task functions and organizational roles, disruptions and aiding their ability to self-regulate.

Reliability-driven large group consensus decision-making method with hesitant fuzzy linguistic information for the selection of hydrogen storage technology

The use of hydrogen storage technology (HST) as a bridge for producing and utilizing hydrogen energy in the hydrogen industry chain is significant, and its evaluation has attracted the interest of researchers. Since there are different types of HSTs, selecting the most appropriate one requires the participation of plenty of experts with different professional backgrounds, which makes this be modeled as a large group decision-making problem. This paper develops a reliability-driven large group consensus decision-making (LGCDM) method for HST selection using the hesitant fuzzy linguistic terms set (HFLTS) as the evaluation representation format. Specifically, the expertise level of individuals and the reliability of group opinions are measured based on the set variables, and then the dimensionality of large groups is reduced based on the reliability of subgroup opinions. Furthermore, an opinion reliability rating mechanism is designed and, when consensus is not satisfactory, a feedback recommendation mechanism and consensus optimization mechanism are developed for implementation. Finally, the proposed reliability-driven LGCDM approach is applied to the HST selection for THVOW Company, and the comparison with existent related approaches indicates that it not only is practical and reasonable, but also provides a technical path for relevant departments to make decisions on practical issues.

Sarcasm Detection on Social Media: Addressing the Issues of Short and Long Texts Using Machine Learning and Deep Learning Approaches

Sarcasm is a linguistic term that conveys a meaning that is different from what is meant to be said, frequently used to mock, taunt, or convey disdain. Sarcasm is a complicated social phenomenon that can be recognized by its tone of voice, exaggeration, or context. Because sarcastic discourse is nuanced, detecting sarcasm in Natural Language Processing (NLP) has become a major difficulty. This paper thoroughly examines the tradition machine learning approaches like SVM, Naive Bayes and Random Forest well as advanced deep learning methods such as RNN, LSTM, and transformer based models, like BERT, that has demonstrated superior performance used for sarcasm detection, and also the paper offers more sophisticated data preprocessing techniques that comprise several stages, each focusing on a different facet of the fragmented and informal character of the social media material. It focuses mainly on short and long texts that may be found in news headlines and social media sites like Facebook, Instagram, X (formerly known as Twitter), and others. The paper also explores various challenges like cultural and linguistic barriers, and increased use of audio and visual sarcastic content on social media. At last paper concludes with possible guidelines for future works including the development of real-time, multilingual systems to examining holistic strategies that can encompass the complexity of sarcasm in multimodal communications. Key Words: sarcasm, NLP, social media, machine learning, deep learning

A three-way decision method based on COPRAS in the weak probabilistic linguistic term set information systems

A three-way decision method based on COPRAS in the weak probabilistic linguistic term set information systems

Site Selection for Girls Hostel in a University Campus by MCDM based Strategy

The area of interest based on girls’ hostel site selection is an important topic nowadays. The research directly impacts the well-being, safety, and academic success of female students who are taking the facilities for accommodation while pursuing their academic courses. An efficient hostel location not only supports the physical safety of students but also takes care of their psychological and emotional well-being by providing a suitable and helpful living atmosphere. Different valuable criteria and sub-criteria are considered for evaluating the most suitable site for a girls' hostel. The security and protection, proximity to academic buildings, accessibility, health and safety, and environmental factors as criteria with their associated sub-criteria are considered for this study. Two decision makers are given data sets in linguistic terms and further converted into crisp numbers. The weights of the criteria and sub-criteria are determined by a well-known multi-criteria decision-making (MCDM) technique called the Entropy weighted method. Further, the most prioritized location for the girls' hostel on the university campus was evacuated using the weighted aggregated sum product assessment (WASPAS) based ranking MCDM method. Additionally, the sensitivity analysis was conducted to check the stability and flexibility of the results.

Z-number linguistic term set for multi-criteria group decision-making and its application in predicting the acceptance of academic papers

Z-number linguistic term set for multi-criteria group decision-making and its application in predicting the acceptance of academic papers

Reliability and criticality analysis of a large-scale solar photovoltaic system using fuzzy-fault tree analysis approach

Over time, solar Photovoltaic (PV) systems experience a decline in performance and reliability due to various environmental factors. Fault Tree Analysis (FTA) can be used to assess the reliability of these systems and identify faults and failure modes that can significantly impact the entire PV system’s performance. However, in practice, obtaining accurate failure probability values for the components of a solar PV system is challenging since systems operate in an ever-changing environment, resulting in a scarcity of data for statistical estimation. This paper proposes a fuzzy theory-based FTA approach to obtain the failure probabilities of the faults more accurately. The Fuzzy-FTA methodology converts experts’ subjective opinions expressed in linguistic terms into a Failure Possibility Score (FPS), which is then converted into a failure probability value. The results of the proposed approach are compared to those obtained through the conventional FTA to assess its effectiveness, applicability, feasibility, and efficiency. Soiling, dust accumulation, inadequate system maintenance, bad system configuration, bird dropping, delamination, improper installation, shading, grounding, and discoloration are the most critical faults and impact on the performance and reliability of solar PV systems.

Fuzzy Bayesian inference for under-five mortality data

Under-five mortality remains a significant global health challenge, with millions of children dying before their fifth birthday each year. This study explores the application of fuzzy Bayesian inference for under-five mortality data using Tanzania as a case study. Fuzzy Bayesian inference has emerged as a promising technique that combines the flexibility of fuzzy set theory with the probabilistic framework of Bayesian inference. The study employs fuzzy sets and membership functions to represent the linguistic terms and their degrees of membership, along with the Poisson distribution to model the mortality rate. The results demonstrate the potential of fuzzy Bayesian inference for analyzing under-five mortality rates. This approach provides a more nuanced understanding of the complex mortality patterns.