Application of the full consistency method (FUCOM) - Cosine similarity framework in 5G infrastructure investment planning: An approach for telecommunication quality improvements.

In the rapidly evolving telecommunications landscape, the shift towards advanced communication technologies marks a critical milestone. This transition promises to revolutionize connectivity by enabling seamless data downloads, high-quality video streaming, and instant access to applications. However, adapting to these advanced technologies poses significant challenges for infrastructure expansion, requiring innovative investment and deployment strategies. These strategies aim not only to enhance service quality but also to ensure extensive network coverage. To address the need for systematic planning in infrastructure investment, this paper presents a novel methodology that combines the Full Consistency Method (FUCOM) with cosine similarity analysis. This integrated approach effectively prioritizes service areas for the deployment of 5G technology, emphasizing the importance of detailed planning in mobile strategy development. By leveraging FUCOM to determine the weights of various criteria and employing cosine similarity analysis to rank service areas, the methodology facilitates efficient resource allocation and service quality enhancements. Empirical validation using real data from a Turkish telecommunications company confirmed the effectiveness of the proposed algorithm. The results indicate that this integrated approach can significantly advance the telecommunications industry by providing essential insights for companies seeking to improve service quality amidst the transition to 5G and beyond. The successful implementation of the proposed algorithm demonstrates its effectiveness in addressing the challenges faced by telecommunications companies and underscores the importance of a data-driven approach in strategic decision-making and resource allocation. Furthermore, the findings suggest that the integrated FUCOM and cosine similarity analysis approach can offer a valuable tool for telecommunications companies worldwide, offering a systematic method for prioritizing infrastructure investments and enhancing network performance.

A complex fuzzy decision model for analysing the post-pandemic immuno-sustainability.

The post-effects of the COronaVIrus Disease (COVID-19) vary depending on socioeconomic and biological factors. Similarly, the effects of vaccination on people's immunity vary across several factors. After the pandemic, real-life post-vaccination anomalies significantly impact women's health, access to medical treatments and medications, mental well-being, and daily physical activities. However, there has been scant investigation into the physical, psychological, social, and economic ramifications of vaccine effects on women in the post-pandemic era. Therefore, conducting a comprehensive risk assessment is crucial to safeguard women from the post-vaccination effects.To address this issue, the research encompasses complex bipolar spherical fuzzy ℵ-soft set, which has two-sided periodic ambiguous data due to its parametric properties as an adaptable ℵ-soft set and distinguishing criteria as a complex bipolar spherical fuzzy set. In addition, some fundamental operations and properties are presented in a complex bipolar spherical fuzzy ℵ-soft environment. Furthermore, the robust assessment of a real-world application demonstrate the efficacy of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach to optimise the decision result. Finally, the provided decision-making approach is compared with existing techniques to illustrate their remarkable credibility and integrity.

Applying hesitant q-rung orthopair fuzzy sets to evaluate uncertainty in subsidence causes factors.

Land subsidence is a widespread problem impacting communities worldwide. Understanding the causes and factors of land subsidence is crucial for identifying and prioritizing effective mitigation measures. One of the main reasons for prioritizing land subsidence causes is the potential impact on infrastructure and the environment. The main objective of this paper is to emphasize the importance of prioritizing the causes of land subsidence. By understanding and prioritizing the factors contributing to land subsidence based on their impact and urgency, the aim is to develop targeted strategies for mitigation, inform policy decisions, and prevent further exacerbation of this problems. The study comprises three phases, where experts in the field provide their opinions and propose a robust hybrid framework. This framework integrates the Failure Mode and Effect Analysis (FMEA) and Step-wise Weight Assessment Ratio Analysis (SWARA) with Hesitant q-rung orthopair fuzzy set (Hq-ROFS). The performance of the proposed technique was then compared with two other decision-making techniques for evaluating and ranking land subsidence causes. According to the results, extraction of groundwater, excessive irrigation using groundwater, and oxidation and drainage of organic soils were identified as primary drivers of subsidence.

Optimal selection of healthcare waste treatment devices using fuzzy-rough approach.

The escalating volume of healthcare waste (HCW) generated by healthcare facilities poses a pressing challenge for all nations. Adequate management and disposal of this waste are imperative to mitigate its adverse impact on human lives, wildlife, and the environment. Addressing this issue in Bosnia and Herzegovina involves the establishment of a regional center dedicated to HCW management. In practice, there are various treatments available for HCW management. Therefore, it is necessary to determine the priority for procuring different treatments during the formation of this center. To assess these treatment devices, expert decision-making employed the fuzzy-rough approach. By leveraging extended sustainability criteria, experts initially evaluated the significance of these criteria and subsequently assessed the devices for HCW treatment. Employing the fuzzy-rough LMAW (Logarithm Methodology of Additive Weights), the study determined the importance of criteria, highlighting "Air emissions" and "Annual usage costs" as the most critical factors. Utilizing the fuzzy-rough CoCoSo (the Combined Compromise Solution) method, six devices employing incineration or sterilization for HCW treatment were ranked. The findings indicated that the "Rotary kiln" and "Steam disinfection" emerged as the most favorable devices for HCW treatment based on this research. This conclusion was validated through comparative and sensitivity analyses. This research contributes by proposing a solution to address Bosnia and Herzegovina's HCW challenge through the establishment of a regional center dedicated to HCW management.

Multi-attribute group decision-making based on Pythagorean fuzzy rough Aczel-Alsina aggregation operators and its applications to Medical diagnosis.

The fusion of information is a very hectic process whenever we analyze the information. Several frameworks have been introduced to reduce the uncertainty while fusing the information. Among those techniques, the Pythagorean fuzzy rough set (PyFRS), which is based on approximations is a key idea for dealing with uncertainty when data is taken from real-world circumstances. Furthermore, the most adaptable and flexible operational laws based on the parameters for fuzzy frameworks are Aczel-Alsina t-norm (AATNM) and Aczel-Alsina t-conorm (AATCNM). The major goal of this work is to introduce some methods for the basic operations of the information in the shape of Pythagorean fuzzy rough (PyFR) values (PyFRVs). Consequently, the PyFR Aczel-Alsina weighted geometric (PyFRAAWG), PyFR Aczel-Alsina ordered weighted geometric (PyFRAAOWG), and PyFR Aczel-Alsina hybrid weighted geometric (PyFRAAHWG) operators are developed in this article based on AATNM and AATCNM. Further, some basic properties of the developed operators are observed and discussed. Further, the developed approaches are applied to the problem of multi-attribute group decision-making (MAGDM). The obtained results from the MAGDM problem are observed at various values of the parameters involved by AATNM and AATCNM. Moreover, the results are also compared with already existing techniques for the significance of the developed approach.

Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel-Alsina Function.

The COVID-19 pandemic led to an increase in healthcare waste (HCW). HCW management treatment needs to be re-taken into focus to deal with this challenge. In practice, there are several treatments of HCW with their advantages and disadvantages. This study is conducted to select the appropriate treatment for HCW in the Brcko District of Bosnia and Herzegovina. Six HCW management treatments are analyzed and observed through twelve criteria. Ten-level linguistic values were used to bring this evaluation closer to human thinking. A fuzzy rough approach is used to solve the problem of inaccuracy in determining these values. The OPA method from the Bonferroni operator is used to determine the weights of the criteria. The results of the application of this method showed that the criterion Environmental Impact ( C 4 ) received the highest weight, while the criterion Automation Level ( C 8 ) received the lowest value. The ranking of HCW management treatments was performed using MARCOS methods based on the Aczel-Alsina function. The results of this analysis showed that the best-ranked HCW management treatment is microwave (A6) while landfill treatment (A5) is ranked worst. This study has provided a new approach based on fuzzy rough numbers where the Bonferroni function is used to determine the lower and upper limits, while the application of the Aczel-Alsina function reduced the influence of decision-makers on the final decision because this function stabilizes the decision-making process.

Solar PV power plant site selection using a GIS-based non-linear multi-criteria optimization technique.

The ongoing rise in energy consumption imposed serious environmental challenges by using fossil fuels. The use of renewable energy sources is being increasingly explored as a potential answer for achieving sustainable energy production and minimizing adverse environmental effects. In the modern day, photovoltaic (PV) systems are viewed as a possible replacement for fossil fuels as a clean energy source. The installation of solar PV power plants requires vast land and huge investment. Therefore, it is necessary to select a suitable site to achieve maximum efficiency and low cost. A feasible location of photovoltaic (PV) system must consider certain criteria including land restrictions, access to roads, and transmission lines. This study analyzed ten factors grouped into four categories: geographic, technical, economic, and flood susceptibility criterion. The data of each factor is extracted from various governments, United Nation (UN), and non-government organizational bodies. Weights were assigned to ten factors by using a non-linear multi-criteria optimization technique called full consistency method (FUCOM). A geographic information system (GIS) software, ESRI ArcGIS pro, performs the weighted overlay analysis of the ten factors with weighted importance calculated by the above technique. A suitability map is created showing that a total of 2.02% of the country's area is suitable for PV power plants, which are further divided into five suitability classes. The results highlight the distribution of suitable sites for the construction of solar PV power plant throughout the country. A sensitivity analysis is performed to highlight the impact of the factor on the final suitability map. These findings can promote the future widespread development and application of solar energy resources.

A modified EDAS model for comparison of mobile wallet service providers in India.

The present paper has two-fold purposes. First, the current work provides an integrated theoretical framework to compare popular mobile wallet service providers based on users' views in the Indian context. To this end, we propose a new grey correlation-based Picture Fuzzy-Evaluation based on Distance from Average Solution (GCPF-EDAS) framework for the comparative analysis. We integrate the fundamental framework of the Technology Acceptance Model and Unified theory of acceptance and use of technology vis-à-vis service quality dimensions for criteria selection. For comparative ranking, we conduct our analysis under uncertain environments using picture fuzzy numbers. We find that user-friendliness, a wide variety of use, and familiarity and awareness about the products help reduce the uncertainty factors and obtain positive impressions from the users. It is seen that PhonePe (A3), Google Pay (A2), Amazon Pay (A4) and PayTM (A1) hold top positions. For validation of the result, we first compare the ranking provided by our proposed model with that derived by using picture fuzzy score based extensions of EDAS and another widely used algorithm such as The Technique for Order of Preference by Similarity to Ideal Solution. We observe a significant consistency. We then carry out rank reversal test for GCPF-EDAS model. We notice that our proposed GCPF-EDAS model does not suffers from rank reversal phenomenon. To examine the stability in the result for further validation, we carry out the sensitivity analysis by varying the differentiating coefficient and exchanging the criteria weights. We find that our proposed method provides stable result for the present case study and performs better as ranking order does not get changed significantly with the changes in the given conditions.

Evaluation of the route selection in international freight transportation by using the CODAS technique based on interval-valued Atanassov intuitionistic sets.

The selection of a proper international freight transport route is one of the crucial tasks for decision-makers since it can affect costs, efficiency, and transportation performance. Besides, the selection of suitable and appropriate freight routes can also reduce external costs of transportation such as emissions, noise, traffic congestions, accidents, and so on. Route selection in international transportation is a complicated decision-making problem as many conflicting factors and criteria affect the assessment process. It has been observed that there is no mathematical model and methodological frame used for solving these selection problems, and decision-makers make decisions on this issue based on their own experiences and verbal judgments in the research process. Therefore, a methodological frame is required to make rational, realistic, and optimal decisions on route selection. From this perspective, the current paper proposes using the IVAIF CODAS, an extended version of the traditional CODAS techniques, and using the Atanassov interval-valued intuitionistic fuzzy sets (IVAIFS) for processing better the existing uncertainties. The proposed model is applied to solve the route selection, a real-life decision-making problem encountered in international transportation between EU countries and Turkey. According to the results of the analysis, option A6 (i.e., Route-6 (Bursa-Istanbul-Pendik-Trieste (Ro-Ro)-Austria-Frankfurt/Germany) has been determined as the best alternative. These obtained results have been approved by a comprehensive sensitivity analysis performed by using different MCDM techniques based on interval-valued intuitionistic fuzzy sets. Hence, it can be accepted that the proposed model is an applicable, robust, and powerful mathematical tool; also, it can provide very reliable, accurate, and reasonable results. As a result, the proposed model can provide a more flexible and effective decision-making environment as well as it can provide valuable advantages to the logistics and transport companies for carrying out practical, productive, and lower cost logistics operations.

Entropy and discrimination measures based q-rung orthopair fuzzy MULTIMOORA framework for selecting solid waste disposal method.

Fastest growing population, rapid urbanization, and growth in the disciplines of science and technology cause continually development in the amount and diversity of solid waste. In modern world, evaluation of an appropriate solid waste disposal method (SWDM) can be referred as multi-criteria decision-making (MCDM) problem due to involvement of several conflicting quantitative and qualitative sustainability indicators. The imprecision and ambiguity are usually arisen in SWDM assessment problem, and the q-rung orthopair fuzzy set (q-ROFS) has been recognized as one of the adaptable and valuable ways to tackle the complex uncertain information arisen in realistic problems. In the context of q-ROFSs, entropy is a significant measure for depicting fuzziness and uncertain information of q-ROFS and the discrimination measure is generally used to quantify the distance between two q-ROFSs by evaluating the amount of their discrimination. Thus, the aim of this study is to propose a novel integrated framework based on multi-attribute multi-objective optimization with the ratio analysis (MULTIMOORA) method with q-rung orthopair fuzzy information (q-ROFI). In this approach, an integrated weighting process is presented by combining objective and subjective weights of criteria with q-ROFI. Inspired by the q-rung orthopair fuzzy entropy and discrimination measure, objective weights of criteria are estimated by entropy and discrimination measure-based model. Whereas, the subjective weights are derived based on aggregation operator and the score function under q-ROFS environment. In this respect, novel entropy and discrimination measure are proposed for q-ROFSs. Furthermore, to display the feasibility and usefulness of the introduced approach, a case study related to SWD method selection is presented under q-ROFS perspective. Finally, comparison and sensitivity investigation are presented to confirm the robustness and solidity of the introduced approach.

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data.

This paper presents a new approach to solve multi-objective decision-making (DM) problems based on neural networks (NN). The utility evaluation function is estimated using the proposed group method of data handling (GMDH) NN. A series of training data is obtained based on a limited number of initial solutions to train the NN. The NN parameters are adjusted based on the error propagation training method and unscented Kalman filter (UKF). The designed DM is used in solving the practical problem, showing that the proposed method is very effective and gives favorable results, under limited fuzzy data. Also, the results of the proposed method are compared with some similar methods.

N-Cubic q-Rung Orthopair Fuzzy Sets: Analysis of the Use of Mobile App in the Education Sector.

This study analyzes the description to examine the results of a new study and create the technique and also demonstrate the effectiveness of this technique. In this ever-changing world, students are increasingly encouraged to use mobile phones primarily to learn for educational purposes. The learning process is continuous and the goal has now been achieved. It has been replaced by online learning. Due to mobile phones as well as the many feature-oriented applications, students can study at their own place and use the application to spend their time understanding, because everything is accessible with a single click. To carry on the study we applied mobile applications for online education system. Now, because the traditional method is taken into consideration, it is normal to carry a bag full of books and copies and immerse yourself in the tradition of learning to write. However, it has been found that not all students learn when he takes notes. Therefore, we must make sure that the student focuses only on one thing at a time. To continue the research, we apply the N-cubic structure to q-rung orthopair fuzzy sets in multi-attribute group decision-making problems. This structure solves the problems of multi-attribute group decision-making techniques more generally.

Applications of the Multiattribute Decision-Making for the Development of the Tourism Industry Using Complex Intuitionistic Fuzzy Hamy Mean Operators.

In the aggregation of uncertain information, it is very important to consider the interrelationship of the input information. Hamy mean (HM) is one of the fine tools to deal with such scenarios. This paper aims to extend the idea of the HM operator and dual HM (DHM) operator in the framework of complex intuitionistic fuzzy sets (CIFSs). The main benefit of using the frame of complex intuitionistic fuzzy CIF information is that it handles two possibilities of the truth degree (TD) and falsity degree (FD) of the uncertain information. We proposed four types of HM operators: CIF Hamy mean (CIFHM), CIF weighted Hamy mean (CIFWHM), CIF dual Hamy mean (CIFDHM), and CIF weighted dual Hamy mean (CIFWDHM) operators. The validity of the proposed HM operators is numerically established. The proposed HM operators are utilized to assess a multiattribute decision-making (MADM) problem where the case study of tourism destination places is discussed. For this purpose, a MADM algorithm involving the proposed HM operators is proposed and applied to the numerical example. The effectiveness and flexibility of the proposed method are also discussed, and the sensitivity of the involved parameters is studied. The conclusive remarks, after a comparative study, show that the results obtained in the frame of CIFSs improve the accuracy of the results by using the proposed HM operators.

Sustainable Energy Consumption Model for Textile Industry Using Fully Intuitionistic Fuzzy Optimization Approach.

Consumption of renewable energy is on the rise because new technologies have made it cheaper and easier to meet the needs of a long-term energy source. In the present study, the idea of optimal usage of sustainable energy is discussed, taking into consideration the environmental and economic conditions that exist in Pakistan's textile manufacturing industry. By taking into account the regional potential for the application of renewable energy resources, solar energy generators are taken into consideration, and a fully intuitionistic fuzzy (FIF) textile energy model is constructed. Using the FIF model to determine the optimal distribution of solar energy units resulted in a tolerable number of unused energy units. These units may be returned to the central power supply station, which would save both money and energy.

Homomorphisms of Lattice-Valued Intuitionistic Fuzzy Subgroup Type-3.

The lattice-valued intuitionistic fuzzy set was introduced by Gerstenkorn and Tepavcevi as a generalization of both the fuzzy set and the L-fuzzy set by incorporating membership functions, nonmembership functions from a nonempty set X to any lattice L, and lattice homomorphism from L to the interval [0,1]. In this article, lattice-valued intuitionistic fuzzy subgroup type-3 (LIFSG-3) is introduced. Lattice-valued intuitionistic fuzzy type-3 normal subgroups, cosets, and quotient groups are defined, and their group theocratic properties are compared with the concepts in classical group theory. LIFSG-3 homomorphism is established and examined in relation to group homomorphism. The research findings are supported by provided examples in each section.

Evaluation of critical risk factors in the implementation of modular construction.

Modular construction is considered as a preferred construction method over conventional construction due to a number of benefits including reduction in project completion time, improved environmental performance, better quality, enhanced workers' safety and flexibility. However, successful implementation of modular construction is hindered by various risk factors and uncertainties. Therefore, it is imperative to perform a comprehensive risk assessment of critical risk factors that pose a negative impact on the implementation of modular construction. Moreover, there is also a relatively less rate of modular construction adoption in developing countries, highlighting the need to focus more on underdeveloped regions. This study aims to propose a risk assessment framework for identification, evaluation and prioritization of critical risk factors affecting the implementation of modular construction in Pakistan. 20 risk factors were identified from previous literature which were then evaluated to shortlist the most significant risks using Fuzzy Delphi. The most significant risk factors were then prioritized using a novel Full-Consistency Method (FUCOM). The results specified 'Inadequate skills and experience in modular construction', 'Inadequate capacity of modular manufacturers' and 'Inability to make changes in design during the construction stage' as top three critical risks in the implementation of modular construction. This is the first study to propose a risk assessment framework for modular construction in Pakistan. The results of the study are useful to provide insights to construction industry practitioners in highlighting and eliminating risks involved in modular construction planning and execution.

An Analysis of Algebraic Codes over Lattice Valued Intuitionistic Fuzzy Type-3 R-Submodules.

In the last few decades, the algebraic coding theory found widespread applications in various disciplines due to its rich fascinating mathematical structure. Linear codes, the basic codes in coding theory, are significant in data transmission. In this article, the authors' aim is to enlighten the reader about the role of linear codes in a fuzzy environment. Thus, the reader will be aware of linear codes over lattice valued intuitionistic fuzzy type-3 (LIF-3) R-submodule and α-intuitionistic fuzzy (α-IF) submodule. The proof that the level set of LIF-3 is contained in the level set of α-IF is given, and it is exclusively employed to define linear codes over α-IF submodule. Further, α-IF cyclic codes are presented along with their fundamental properties. Finally, an application based on genetic code is presented, and it is found that the technique of defining codes over α-IF submodule is entirely applicable in this scenario. More specifically, a mapping from the ℤ 64 module to a lattice L (comprising 64 codons) is considered, and α-IF codes are defined along with the respective degrees.

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning.

The purpose of this study is to achieve a novel and efficient method for treating the interval coefficient linear programming (ICLP) problems. The problem is used for modeling an uncertain environment that represents most real-life problems. Moreover, the optimal solution of the model represents a decision under uncertainty that has a risk of selecting the correct optimal solution that satisfies the optimality and the feasibility conditions. Therefore, a proposed algorithm is suggested for treating the ICLP problems depending on novel measures such as the optimality ratio, feasibility ratio, and the normalized risk factor. Depending upon these measures and the concept of possible scenarios, a novel and effective analysis of the problem is done. Unlike other algorithms, the proposed algorithm involves an important role for the decision-maker (DM) in defining a satisfied optimal solution by using a utility function and other required parameters. Numerical examples are used for comparing and illustrating the robustness of the proposed algorithm. Finally, applying the algorithm to treat a Solid Waste Management Planning is introduced.

Sustainable supplier selection using HF-DEA-FOCUM-MABAC technique: a case study in the Auto-making industry.

The assessment of sustainable supplier is very significant for supply chain management (SCM). The procedure of sustainable supplier selection (SSS) is a complex process for decision experts (DEs) due to the association of diverse qualitative and quantitative attributes. As the uncertainty is usually ensued in the SSS and hesitant fuzzy set (HFS), an extension of fuzzy set (FS) has been demonstrated as one of the effective ways to treat the uncertain information in realistic problems. The objective of this paper is to propose an integrated hesitant fuzzy-data envelopment analysis (DEA)-full consistency method (FOCUM)-multi attribute border approximation area comparison (MABAC) method called HF-DEA-FUCOM-MABAC framework to assess the multi-attribute decision-making (MADM) problems on HFSs settings. In this line, first, the efficient alternatives are chosen using the DEA method. Second, The FUCOM is used to compute the subjective weight of attributes. Third, The HF-MABAC method is presented to prioritize the alternatives in an MADM problem. In the following, a case study of SSS problem for an Auto-making company is taken to show the practicality and utility of the presented approach. Next, we present a sensitivity investigation with different attribute weights set to observe the steadiness of the presented approach. Finally, we draw attention toward a comparison between presented approach with the extant HF-FOCUM-TOPSIS model to show its advantage and potency as well.