Although nuclear energy has the capability of providing society with reliable low-carbon energy, its popularity and popularity among the population remains a significant challenge, particularly among the developing countries. Concerns about managing radioactive waste, high capital expenditure costs, and anxieties related to previous nuclear accidents like the ones at Three Mile Island (TMI), the Chernobyl Nuclear Power Plant, and the Fukushima Daiichi Nuclear Power Plant are frequently the main factors influencing stakeholder viewpoints. The fact that radiation is undetectable adds to these worries by making people feel more anxious and vulnerable. All newcomer countries should overcome these challenges by improving knowledge sharing regarding the advantages of nuclear power and, at the same time, overcome the myths and minimize the fear of the population of the effects of radiations and nuclear accidents. The negative perception of stakeholders in the past has proven that the perception may be a major impediment or even a barrier to nuclear power projects, which translates into the long-term economic and strategic loss to the country. For example, Austria completed the construction of its nuclear power plant but never brought it into operation for electricity generation following a public referendum (Kirchhof, 2020). And Poland faced great challenge in 1990 to construct their first nuclear power plant “Żarnowiec Nuclear Power Plant” due to its strong opposition from the environmental activists and other mix reasons like profitability, financing issues and government ended the project that time (Szulecka & Szulecki, 2017). It became extremely hard to restart the construction of nuclear power plants in Japan after the 2011 calamity that struck the Fukushima Daiichi Nuclear Power Plant due to the introduction of stringent precautionary steps post the economic disaster and the protracted of regulatory inspections. Several future nuclear developments were postponed as a result of both rising compliance costs and the heightened scrutiny of the Nuclear Regulation Authority and the need to restore confidence in the community (Japan Atomic Industrial Forum, 2023; Ebrahimifard et al., 2024). 

According to a study of Japanese public perception polls conducted over a 30-year period, opposition to nuclear power generation (NPG), which was formerly steady at 20–30%, increased to 70% in the four to six months following Fukushima, which coincided with increased popularity of renewable energy. Although 60 percent perceived NPG as essential and confirmed its usefulness in the case of power shortage anxieties, post-accident panic, mistrust, and risk aversion were increased, and little was known about NPG reduction trade-offs or willingness to accept renewable-induced electricity price increases (Kitada, 2016). The nuclear power initiative should be committed in ensuring the awareness and support of the technology and science to serve the interest of the population. The percep- tions of local communities and key stakeholders must be carefully addressed, regardless of whether those perceptions are technically justified or not. 

Numerous parties with various responsibilities and functions are involved in a nuclear power program. Broadly, the government initiates the program, which is implemented by the nuclear power operating organization and overseen by the nuclear regulatory body. In addition, numerous other entities-including environmental agencies, security forces and intel- ligence agencies, fire services, Universities, NGOs, local governments and municipalities, hospitals and medical sectors, and, most importantly, the local community - play key roles (IAEA, 2011). To achieve maximum support and commitment, all stakeholders must work and engaged simultaneously in their respective roles, coordinating effectively to ensure the safety and security of the plant. A nuclear power project's progress can be hampered by any change in enthusiasm, ignorance of technical or scientific concerns, or nervousness among stakeholders. These factors can also affect public opinion. To effectively engage all stakeholders, different organizations should take proactive initiatives to ensure the highest level of commitment, coordination, and motivation. This study uses fuzzy logic based intelligent system to design and improve the stakeholder participation in nuclear power initiatives. By using opinion-based findings gathered from organized surveys, interviews, and recorded documents, the suggested structure unifies various stakeholder groups. The statistical variables that are generated from these data and utilized as inputs to the fuzzy inference system (FIS) are stakeholder trust (ST), risk perception (RP), communication quality (CQ), and engagement effectiveness (EE). The model evaluates stakeholder satisfaction and strategic decision coherence by examining these inputs, pointing out areas that require consideration in the engagement procedure. 

The results of the model can be implemented through timely and targeted interventions targeted at specific stakeholder groups. To resolve the ambiguities in the perception and communication process of the stakeholders, the fuzzy inference system (FIS) systematically maps the input parameters to the helpful decision-support outputs. The study results for each stakeholder group may be combined to evaluate stakeholder satisfaction and the overall alignment of strategic choices. Targeted corrective action can then be implemented when the stakeholder groups that need improvements have been identified.

The nuclear sector presents a difficult situation in making decisions because of the presence of various stakeholders, high safety standards, long duration of nuclear projects, and high degree of uncertainty that comes with nuclear power development. Successful decision making thus goes beyond technical and financial aspects to cover the perception of the stake holders, risk communication, regulatory limitations, and wider socio-political elements. Nevertheless, the classical sharp decision-making models are not always able to reflect the ambiguity and subjectivity of such complex and multidimensional scenarios. 

In an attempt to overcome such limitations, fuzzy logic has proved to be an effective strategy modeling uncertainty and making informed choices concerning strategies. Fuzzy logic offers a mathematical frame- work to deal with linguistically expressed and imprecise data in that it can be a member of a set partially as opposed to a binary classification (Zadeh, 1965). This property renders it especially appropriate in decision settings that are typically characterized by human judgment, expert opinion and qualitative evaluations. In the nuclear sector, several investigations have used fuzzy-based decision-making models in the selection of sites, technology and also safety issues. As an example, fuzzy multi-criteria decision-making (MCDM) models have been employed to evaluate the appropriateness of nuclear power plant (NPP) locations by considering environmental effects, seismic risk, economic viability and social acceptance aspects (Abdullah et al., 2023). 

Fuzzy logic in such studies also helps decision- makers to overcome uncertainties in expert ratings and stakeholder interests than the deterministic methods. On the same note, hybrid fuzzy systems have been suggested that integrate approaches like Fuzzy VIKOR and Fuzzy Analytic Hierarchy Process (FAHP) to rank among the alternative nuclear sites and technologies thus enhancing the strength of the strategic planning process in face of uncertainty (Das, Kumar, & Dutta, 2024). In addition to technical planning, the stake holder engagement has also been incorporated into risk-informed decision-making frameworks. Branko et al. (Branko et al., 2022) illustrated the use of a structured framework where risk assessment is integrated with engaging stakeholders using case studies of naturally occurring radioactive material (NORM) and nuclear legacy sites. Their framework is effective in helping to have participative and transparent decision-making, but is mostly qualitative and lacks quantitative representations of stakeholder preferences and uncertainties. This shortcoming identifies a research gap on the development of advanced decision-support frame- works like fuzzy logic-based or multi-criteria frameworks that can be able to systematically capture stakeholder uncertainty and build robust risk-based decision-making.

The management of nuclear as well as radiological emergencies also requires transparency and an effective stakeholder engagement. Perko et al. (Perko, Martell, & Turcanu, 2020) also analyzed the concept of transparency, information sharing, and stakeholder participation in emergency management and revealed that all these factors greatly contribute to increased trust and quality of choices when faced with a nuclear or radiological emergency. Nevertheless, they are mainly interested in qualitative and governance approaches and do not include formal risk-informed or quantitative decision-support systems. These also provide support to the necessity of integrated frame-works that combine stakeholder engagement with powerful and quantitative decision-making methodologies in nuclear and radiological settings. The system based on fuzzy logic can also be applied in the evaluation of a chemical hazard assessment and avert the hazard in various industries sector. 

The Aziz, A et al. (Aziz et al., 2025) research suggested a fuzzy logic-based system to assess the risk and estimate the degree of precaution when handling explosive, flammable, and toxic chemicals The suggested system can properly address uncertainty in the severity of hazards, the nature of exposure, and human judgement, and it can be considered a strong way to use fuzzy logic in making decisions that are safety-related. Nonetheless, the framework is mainly concerned with the technical analysis of risks and the estimation of precautions and lacks the consideration of the involvement of stakeholders, regulatory views, and social-political aspects of the decision-making process.

The paper could not collect the real-world data from the chemical factories as there is lack of ability to measure the risk parameters in country like Bangladesh. There could be a research opportunity to develop a more robust model for measuring the intensities of the hazardous properties.  The integration of stakeholder judgment with fuzzy logic was examined by Kraidi et al. (Kraidi et al., 2020). Their research shows the fuzzy logic is a practical approach that can integrate expert and stakeholder views in determining the risk factor on complex and uncertain factor, which will improve the credibility of the infrastructure risk assessment. Relative to this work, the authors formulated a Computer-Based Risk Analysis Model (CBRAM) that is based on the theory of fuzzy logic in order to analyze the factors that affect risk systematically. The findings of the CBRAM have determined the most essential risk factors, especially those that were closely linked with the stakeholder judgment and the problems of data scarcity. Moon et al. (Moon et al., 2024) applies the fuzzy- Analytic Hierarchy Process (AHP) method to prioritize structural and human-related risks in dismantling radioactive concrete structures, identifying collision, jamming, and falling objects as critical hazards. Stakeholder engagement and more comprehensive risk-informed strategic decision- making are not included in the approach, which primarily concentrates on operational risk assessment even though it successfully supports task-level safety prioritization. This brings out the necessity of integrated fuzzy-based systems incorporating technical risk evaluation with stakeholder-oriented governance in nuclear programs.

The above literature shows that fuzzy logic is efficient in providing the uncertainty modeling and technical risk assessment (Hossain et al., 2018) in the nuclear and other safety-related industries whereas independent research emphasizes the significance of stake-holder involvement and disclosure. Nevertheless, there are still isolated strategies, and few are using quantitative fuzzy models and formal involvement of stakeholders. Thus, there is a research gap in the development of a holistic fuzzy logic framework that will integrate the element of risk-informed analysis and stakeholder involvement in strategic decision-making in nuclear programs.

Overall System Architecture

This proposed model is designed to evaluate strategic decision alignment (SDA) and stakeholders' satisfaction (SS) by gathering the concerns from all stakeholder groups of a new build nuclear power plant, taking into consideration the input variables Stake-holder's Trust (ST), Risk Perception (RP), Communication Quality (CQ), and Engagement Efficiency (EE). These input variables are the information that may be collected from the stakeholders' part. Accumulated data can then be processed by different methods like distribution analysis, mean approach, or weighted center to identify the input values for fuzzification. These discrete values of input are then converted to linguistic variables via a fuzzification interface with predetermined membership functions. Fig. 1 represents the proposed model that was studied in the research.

SDA/SS = (ST, RP, CQ, EE)                  (1)

Fig. 1: Flow chart of the Fuzzy Logic-Based Framework for nuclear stakeholder engagement and decision-making approach.

A rule-based inference engine that is built on the principles of the expert knowledge evaluation and interaction with stakeholders is used to assess the interaction between the input variables. The results of the fuzzy inference are then pooled and transformed to crisp outputs to Strategic Decision Alignment (SDA) and Stakeholder Satisfaction (SS) through a defuzzification strategy. Equation (1) is applicable to evaluate SDA and SS. The last step in the process is the analysis of the outputs at the individual stakeholder-group and integrated system levels in order to detect misalignments and implement specific, evidence-based engagement strategies. 

Implementation of Fuzzy Inference System (FIS)   

This assessment model uses a Mamdani type fuzzy interface system. To implement this FIS model to establish stakeholder engagement enhancement, the dataset made from taking inputs from the stake- holders used for input of this model. These four input variables (ST, RP, CQ & EE) are taken as input for fuzzification process. By fuzzification process, all the non-fuzzy inputs (crisp values) convert into fuzzy inputs in the fuzzy inference system. It is a fundamen-tal component in the implementation of the fuzzy system, as it allows the necessary information to be provided that the interference engine needs in order to make a decision. In a typical fuzzy system based on fuzzy logic, crisp inputs are converted into a fuzzy set by the use of a membership function. In this research, the membership function maps the input into a range from 0 to 10, representing how well the input maps into a specific fuzzy category. This allows the fuzzy system to deal with the inherent uncertainty in the inputs in a much more effective way, leading to a smoother, more adaptive, and more human-like decision-making process. For input and output parameters, three possible linguistic variables “Low”, “Medium” and “High” were used. If more input variables have been used for interface rules, the system would become more complex and vice versa. To use fuzzification, several ways are there like triangular, trapezoidal, and Gaussian membership functions. The categories of language were modeled into trapezoidal membership functions to represent the type of “Low” and “High” and triangular membership functions were considered to represent the category of Medium to the input variables due to the simplicity of these functions, modeling efficiency and the appropriateness of the functions in getting the system representation. A similar way has been adopted for membership function selection of output variables. In this study, the Mam- dani method of max–min inference and the centroid method of center of gravity defuzzification were used, as they offer smooth and continuous interpolation between the output values of the two rules. The FIS rule editor was then used to optimize and modify the rule base, as necessary. The system is designed with four input variables (ST, RP, CQ & EE) and two outputs (CQ & SS) resulting from the ‘IF' and ‘THEN' rules which relate the input and outputs. A number of 81 fuzzy interface rules are applied to the system based on expert knowledge to gain the results. The rue editor used in the FIS model is shown in Fig. 3 a general fuzzy “IF–THEN” rule can be expressed as follows (Equation 2):