Fuzzy Logic Decision Research Articles

Overcoming Real-World IoT Deployment Challenges with Enhanced Fuzzy Logic Decision Algorithms

Overcoming Real-World IoT Deployment Challenges with Enhanced Fuzzy Logic Decision Algorithms

Fuzzy Methods in Entrepreneurship Research

The use of fuzzy theory has grown across many academic disciplines, including business, management and economics. In entrepreneurship research, methods such as Fuzzy-set Qualitative Comparative Analysis (fsQCA), fuzzy analytic hierarchy process (fAHP), fuzzy Delphi, fuzzy logic and fuzzy Decision Making Trial and Evaluation Laboratory are increasingly used. The emerging nature of these methods necessitates a synthesis of extant research in this field. Therefore, this research study aims to provide a comprehensive review of the growth of fuzzy methods in entrepreneurship via a bibliometric analysis. Using Scopus as the main data source, a systematic approach for data retrieval, screening and analysis was used. The results include a temporal analysis of citations, and analyses of influence and productivity. Furthermore, using a scientific mapping approach, the main thematic clusters and emerging themes are unpacked. Dominant themes include fsQCA, entrepreneurial orientation, female entrepreneurship, sustainability, entrepreneurial intention and innovation. Emerging themes include COVID-19, sports, ecosystems and the configurational approach. This article concludes with suggestions for future research.

Application of Fuzzy Decision Support Systems in Risk Assessment of Southeast Asian Labor Market

The labor market in Southeast Asia has several complicated issues, including the quick changes in the economy, the various behaviors of employees, and the shifting regulatory settings. To foster sustainable regional development and make decisions based on accurate information, a comprehensive risk assessment is important. Conventional methodologies, on the other hand, often fail to reflect the multifaceted personality of issues about the labor market. In light of recent economic changes and the complexity of applicable policies, this paper recommends a fuzzy decision support system to enhance risk assessment (FDSS-ERA) that could enhance risk assessment in Southeast Asian labor markets. The model methodically assesses hazards, using a mixture of fuzzy logic sense and decision assistance, incorporating changes in employment trends and demographics. The results highlight fuzzy logic’s role in decision-making for effective risk management and policy interventions, showing improved risk comprehension. While educated policy choice making can lead to equitable development in Southeast Asia’s labor work marketplace, FDSS is a potential method. In conclusion, to overcome the many obstacles that stand in the way of chance assessment in the labor market in Southeast Asia, FDSS provides a powerful strategy. Because it offers more advanced analytical tools, this research assists decision-makers in the region in developing more effective guidelines, proactively reducing risks, and achieving projects that promote sustainable development.

EquiCity game: a mathematical serious game for participatory design of spatial configurations

We propose a mathematical framework for developing social-choice games that are designed to mediate decision-making processes for city planning, urban area redevelopment, and architectural configuration of urban housing complexes. The proposed framework features a digital serious gaming approach for participatory design to support transparency and inclusion in the process of decision-making and ensure an equitable balance of sustainable development goals in spatial design outcomes. The mathematical process consists of a Markovian design machine for balancing the design decisions of actors, a massing configurator equipped with fuzzy logic and multi-criteria decision analysis, algebraic graph-theoretical accessibility evaluators, and automated solar-climatic evaluators using geospatial computational geometry. We demonstrate the effectiveness of the framework by implementing a multi-player online game that facilitates a participatory decision-making workshop for forming multi-functional building complexes by providing a generative configurator equipped with automated appraisal/scoring mechanisms for revealing the aggregate impact of alternatives. The EquiCity game empowers a group of decision-makers to reach a fair consensual spatial design by mathematically simulating many rounds of reasonable trade-offs between their decisions, with different levels of interest or control over various types of investments. The novelty of the framework is in its capability to encompass decision-making about the most idiosyncratic aspects of a site related to its heritage status and cultural significance to the most generic aspects such as balancing access to sunlight for the site while respecting ‘the right to sunlight’ of the neighbours of the site, ensuring coherence of the entire configuration with regards to a network of desired closeness ratings, the satisfaction of a programme of requirements, and intricately balancing individual development goals in conjunction with communal goals and environmental design codes.

A digital twin-based energy-efficient wireless multimedia sensor network for waterbirds monitoring

Wetlands play a critical role in maintaining the global climate, regulating the hydrological cycle, and protecting human health. However, they are rapidly disappearing due to human activities. Waterbirds are valuable bio-indicators of wetland health, but it is challenging to monitor them effectively. Wireless Multimedia Sensor Networks (WMSNs) offer a promising technology for monitoring wetlands. Nonetheless, these networks are constrained in terms of energy, and also encounter challenges associated with large-scale deployments under natural environmental conditions. These conditions introduce harsh circumstances that may not have been anticipated during the pre-deployment testing phase. This paper proposes a Digital Twin (DT) based energy-efficient WMSN monitoring system specifically tailored for waterbirds in wetlands. The system utilizes a unique approach that combines local audio identification and image compression with DT technology to optimize network performance and minimize energy consumption. To reduce unnecessary image transmissions, the system employs a real-time, low-complexity local audio identification phase before triggering image capture. A denoising step is employed to achieve highly accurate bird recognition despite surrounding noises. Each image undergoes a low-complexity compression scheme prior to transmission, further enhancing energy efficiency. To enhance the system’s overall efficiency and effectiveness, DT technology is integrated to create real-time replicas of the WMSN and the monitoring application. A synergistic interaction between the two DTs enables cooperative data-making decision that ensures both QoS (Quality of Service) and QoE (Quality of Experience) requirements are met. Transmission rate control is done using a fuzzy logic decision-making technique. Real-time feedback provides rapid and accurate analysis of the current state of the WMSN, allowing for dynamic adjustments. The ”what-if scenarios” feature of the implemented DTs has been effectively leveraged to find the most suitable settings for the controller. The effectiveness and performance enhancements achieved by integrating DT into our WMSN-based surveillance system are validated through comprehensive experiments in scenarios that correspond to a real-world wetland. Comparative analyses demonstrate the undeniable benefits of the DT-integrated system compared to a conventional WMSN-based surveillance setup. In particular, the results demonstrate the system’s superior performance in terms of energy efficiency, real-time monitoring capabilities, and ability to handle multiple video sources.

English Speaking Teaching Strategies Based on Natural Language Processing

The study of cognitive processes in English teaching methods from the perspective of HPM (History and Pedagogy of Mathematics) provides valuable insights into how students learn and understand the English language. Cognitive research in language learning focuses on the mental processes involved in acquiring, processing, and using language skills. By incorporating the principles of HPM, which examines the historical development and pedagogical approaches in mathematics education, researchers can draw parallels and apply similar methodologies to English language instruction. This paper constructed Bloom’s taxonomy Fuzzy Decision Support System (BTF-DSS). Bloom’s taxonomy provides information about the teacher’s understanding towards the teaching. Through BTF-DSS model offers teachers provides the knowledge and expertise for effective teaching with the fuzzy model. The BTF-DSS model uses the fuzzy logic decision support system for the appropriate significant teaching and learning experience. The simulation analysis of the BTF-DSS model expressed that the fuzzy model exhibits significant performance for the English teaching method in China through the HPM process.

Multi‐objective spectrum assignment in heterogeneous cognitive radio networks for internet of things

SummaryInternet of Things (IoT) applications have massively increased in the last few years. Future‐generation IoTs are going to be massive in number, heterogeneous in nature, and extremely demanding in terms of computation and communication requirements. Therefore, effective techniques are needed to optimize the use of scarce communication resources. This work proposes a novel multi‐objective framework to investigate the channel assignment problem in cognitive radio network (CRN)‐based IoT applications. The multi‐objective optimization framework comprises three objective functions. The first objective aims to maximize the overall system throughput fairness, the second objective aims to maximize the overall system residual energy, and the third objective aims to increase the user satisfaction level by maximizing the overall priority index. The problem of channel assignment is modeled and solved using Mixed Integer Linear Programming (MILP) and ‐constraint technique. This technique generates a set of Pareto efficient solutions, and an optimal solution among these solutions is selected using a fuzzy logic decision mechanism. Moreover, power level selection is incorporated by solving the problem multiple times for different power levels and obtaining a preferred solution for each power level. The simulations are conducted to unveil an interesting trade‐off between the conflicting system design objectives, that is, the overall throughput and the overall residual energy while incorporating the significance of primary user (PU) activity when preforming channel assignment in CRN.

Security and privacy concerns in social networks mathematically modified metaheuristic-based approach

Potential risks and vulnerabilities in Social Networks (SN) that may threaten the confidentiality, accuracy, and accessibility of data provided by users have been identified as safety risks. Unauthorized usage of the system, breaches of data, privacy violations, cyber-attacks, and other malicious behaviors that negatively impact the SN’s reliability and security may be addressed within these. Innovative techniques that use scientifically enhanced metaheuristic algorithms are the subject of the study, which attempts to address evolving issues of security and privacy in SNs. In order to improve the security of online social networking data encryption, researchers recommend implementing the MMCSOA approach, which is the Mathematically Modified Cuckoo Search Optimization Algorithm. An enhanced tuning of elliptic curve parameters can be accomplished by adapting the Cuckoo Search method, which, in consequence, provides an encrypted environment for multi-party computing. Users’ preferences for confidentiality are considered while applying a fuzzy logic (FL) decision matrix to select individuals. Data security is improved, and users receive additional authority over data shared with MMCSOA. Research findings prove that MMCSOA is higher compared to other methods in securing users’ privacy.

Fuzzy Logic Decision Model for Robust Risk Management in ubiquitous environment- A Review

Soft computing models are invaluable resources for solving addressing real-world issues. Soft computing is the process of solving difficult computer problems with imprecise yet practical results through the application of approximate computations. The method enables the resolution of issues that might be too difficult or time-consuming to handle with the technology available today. The term ‘computational intelligence’ is often used interchangeably with soft computing. This article will discuss various soft computing strategies, with a particular focus on the fuzzy logic approach. The study delves into risk management in the real world using fuzzy logic techniques across multiple fields, providing a comprehensive review and comparing performance analyses.

Dynamic and real-time continuous look-ahead distance for autonomous vehicles: an explicit formulation

The advent of autonomous vehicles has brought about significant advancements in transportation technology, promising safer and more efficient means of travel. However, their full integration into society depends on the accuracy of path-tracking realised by a lateral controller. Lateral control is achieved by regulating the steering angle to minimise the lateral error between the vehicle and a target point at a look-ahead distance on the reference path. This paper investigates the look-ahead distance as it is considered a key parameter that impacts vehicle performance, stability, and energy consumption. A qualitative analysis is performed to deduct a set of rules to adapt the look-ahead distance to three parameters: vehicle velocity, road curvature, and road adherence. Then, an original explicit mathematical formulation is developed for the look-ahead distance as a function of the considered parameters. A fuzzy logic decision for the look-ahead distance is further established and compared with the formulated one. Both approaches are implemented on a look-ahead distance-based lateral controller based on the super-twisting sliding mode control. Simulation results carried out in a joint simulation between Simulink/MatLab and SCANeR TM Studio vehicle dynamics simulator demonstrate the effectiveness of the developed model on vehicle performance, stability, computational efficiency, and energy consumption.

A Fuzzy Multidimensional Risk Assessment Method for Sewer Asset Management

This paper presents a flexible framework that aims at estimating the risk of structural failure in sewer pipes by utilizing limited or imperfect data. To this end, classical risk analysis is enhanced by incorporating fuzzy logic and multi-criteria decision making. To account for the multi-dimensionality of collapse risk at the pipe level as a decision parameter, its distinct impacts on the environment, traffic and road condition, and quality of life are taken into account. The proposed method is applied to the sewer network of Tehran, the capital of Iran. Results show how the integration of different risk indexes can influence the criticality of pipelines for the selection of rehabilitation activities. While using the first individual risk index, only considering the risk posed to the natural environment by a collapsing pipe in terms of contamination, approximately half of the pipe lengths are classified as extremely critical by the clustering algorithm. However, when the integrated risk is calculated, this cluster encompasses only approximately 30% of the total pipe length. With a database that contains various levels of uncertainty (from 10 to 60%), the predictive reproducibility for the exact same risk cluster is above 20% and above 50% for the same or only marginally better or worse. Furthermore, pipelines that are predicted to have a better risk class than the situation without considering uncertainty, thereby underestimating the likelihood of failures or consequences, are below 15%, showing a measure of quite good robustness. Considering the budget constraints of utilities, the proposed method can be applied to any urban, aiding in the identification of high-risk sections. Nevertheless, incorporating physical validation might be beneficial for further improving the analysis.

Modelling the perception of visual design principles on façades through fuzzy sets: towards building an automated architectural data generation and labelling tool

Recent studies showed that deep learning techniques and image processing can identify the distinguishing design principles in architectural façades. However, predicting the strength of a principle is still a challenging task, as it requires a huge amount of annotated design variations. The difficulties in both searching such big numbers of data – and its labelling by experts – slow down the research. This paper proposes a computation approach for obtaining this type of data faster. With the help of parametric modelling and evolutionary algorithms, we could manipulate the design elements, and thereby generate different solutions. An integrated fuzzy logic decision mechanism could enable to carry human knowledge in the judging and labelling of alternatives automatically. The final synthetic data developed from real building images could be used for machine learning applications to enhance our understanding of artistic expression.

Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring.

The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed. Fog-Cloud architecture-driven for IoMT networks • An IoMT framework is designed with important components and functions such as fog and cloud node capabilities. •Real-time data has been obtained from WBANs in Riverbed Modeler for a more realistic performance analysis of IoMT. •Fuzzy logic and machine learning algorithms (RF, SVM, and ANN) are used for diabetes predictions. •Intra and Inter-WBAN communications (IEEE 802.15.6 standard) are modeled as essential components of the IoMT framework with all functions.

Empower saving energy into smart communities using social products with a gamification structure for tailored Human–Machine Interfaces within smart homes

Residential buildings can contribute to save energy and to decrement electricity consumption in the world. On the other hand, the Internet of Things has allowed the implementation of smart homes that can profile the users. Nevertheless, end-users are not accepting the smart homes due to behavioral problems and usability problems with the Human–Machine Interface (HMI) or with the household appliances. As a solution, social products promote the interaction between the smart home and the consumer by including gamification features in the interface. Thus, smart homes can interact and compete with other houses to reduce energy consumption. Therefore, this paper proposes a three-step framework that takes advantage of social products to promote interaction between smart homes within a smart community to reduce electrical energy consumption. The first step collects from the literature review, the characteristics of the end-users, the behavioral and usability problems, and the most common gamification elements that teach, engage and motivate the user to reduce energy consumption. The second step proposes the gamification elements required for a tailored HMI in each social product and smart home through a fuzzy logic decision. The third step evaluates the interaction between social products in smart homes and the users to test which smart home is reducing more energy consumption. Finally, a three-level tailored gamified mock-up is depicted: level 1 for a single social product, level 2 for the smart home, and level three for the smart community. This mock-up can be implemented in small communities as residential complexes.

Blank shape optimization in sheet metal processing using a hybrid fuzzy-sensitivity method

ABSTRACT In this paper, the blank shape is optimized by three approaches: a slow and accurate method, sensitivity analysis, a fast and approximate method, fuzzy logic decision-making, and finally, a novel quick and precise way was introduced by a combination of sensitivity analysis and fuzzy logic. It is shown that by using hybrid fuzzy logic-sensitivity, the number of iterations has been reduced down to 1 iteration in both cylindrical and square punch forming when the allowable geometrical error was considered to be less than the 1% of target size. The fuzzy logic code was written in Matlab software, and commercial finite element code Abaqus was used to simulate the forming process. Also, the results of the simulations obtained from finite element method analysis were verified experimentally on AL1050-O sheet for two different cylindrical and square die configurations. There was close agreement between the results of simulations and experiments. The difference was within the acceptable range of almost 3.5% in the square die and 1.8% in the cylindrical die.

Fuzzy logic is a powerful tool for the automation of milk classification

The demand for the consumption of milk and dairy products by the consumer market is very high. This makes it difficult to analyze the large number of milk samples for quality. In addition to the requirement to consider many quality attributes, there are usually large number of producers, who need daily milk evaluations. The aim of the study was to evaluate the efficiency of fuzzy logic in decision making for the classification of milk. In the fuzzification stage, physical and chemical characteristics of the milk were considered as input linguistic variables. For each linguistic variable, pertinence functions were created, and these were made considering the trapezoidal forms. In the inference stage, rules were established for the association of linguistic variables and output variables (adulterated, inadequate and adequate). To verify the efficiency of the modeled system, 1,000 adulterated, inadequate and adequate milk samples were computationally simulated. Precision was verified when automating decision making in the classification of milk by the fuzzy logic, totaling 100% of correctness. Therefore, the fuzzy system is an efficient tool for the classification of milk and can be used advantageously by professionals in the field in order to reduce human and financial resources.

ANALISIS PENDEKATAN STATISTIK DAN FUZZY MAMDANI DALAM PREDIKSI PRODUKTIVITAS PADI

Indonesia has fertile soil, so it is suitable for agricultural land. However, Indonesia still needs to import rice from abroad because rice productivity in Indonesia is often inconsistent. Therefore, rice productivity in Indonesia needs to be estimated for the future. This study aimed to determine the amount of rice productivity from production factors and harvested area that occurred in Indonesia from September 2021 to June 2022. The data used were rice production and rice harvested from January 2019 to August 2021 sourced from the Central Statistics Agency, Indonesia. Data processing in this study uses the Polynomial Regression method to determine predictions of future rice production and harvest area and the Mamdani Fuzzy Logic method. Data processing in this study uses a prediction method, namely the Polynomial Regression method, to determine future production and harvested area predictions and the Mamdani method of Fuzzy Logic for decision making. The results obtained from the Mamdani polynomial and fuzzy regression methods, predictions of rice productivity in September 2021 to June 2022 have increased in the range of 23.33 to 27.91.Keywords: Fuzzy Mamdani, polynomial regression, prediction, rice productivityMSC2020: 62A86

Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature

ABSTRACT The machine learning method of Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed as a data-driven technique to model the dew point temperature (DPT). The input patterns, of T min, T max, and T mean, are utilized for the training. The results indicate thatANFIS method is capable of identifying data patterns with a high degree of accuracy. However, the approach demonstrates that processing time and computer resources may substantially increase by adding additional functions. Based on the results, the number of iterations and computing resources might change dramatically if new functionalities are included. As a result, tuning parameters have to be optimized inside the method framework. The findings demonstrate a high agreement between results by the proposed machine learning method and the observed data. Using this prediction toolkit, DPT can be adequately predicted based on the temperature distribution. The modeling approach has shown to be promising for predicting DPT at various sites. Besides, this study thoroughly compares the Bilayered Neural Network (BNN) and ANFIS models on various scales where the ANFIS model remains stable for almost all the numbers of the membership functions.

Fuzzy Logic Decision Support System to Predict Peaches Marketable Period at Highest Quality

Food waste occurs from harvesting to consumption. Applying procedures and technologies, changing attitudes, and promoting awareness have positive social, economic, and environmental impacts that can contribute to reducing food waste. The paper presents a decision support system (DSS) to predict the quality evolution of fruits and vegetables, particularly of peaches, and estimate its commercialization period at the highest overall perceived quality by consumers, thus contributing to reducing food waste. The Fuzzy Logic DSS predicts the evolution of the physical-chemical parameters of peaches (hardness, soluble solids content, and acidity) depending on the cultivar (Royal Summer and Royal Time), storage time, and temperature. As the range of the values of these physical-chemical parameters of peaches that consumers perceive to be at their highest quality are known, the DSS predicts the marketable period in days. Case studies were developed to analyze the influence of each physical-chemical parameter on the commercialization days (number and time to start). It is concluded that temperature is the most important parameter for fruit conservation. A low value of conservation temperature allows for the significant extension of the time that peaches can be sold at the highest quality. Hardness is used to determine the harvest date since it is an index of fruit ripeness. The same conclusion is obtained for the influence of the soluble solids content. The influence of acidity on marketable days is less than the other physical-chemical parameters. This DSS helps retailers to sell their peaches at the highest quality with benefits for all parties. It also helps in the decision-making concerning the actions to take when fruits are reaching the end of their highest quality by predicting the range of the commercialization days. This formulation can be extended to other fruits and vegetables and in the last instance contribute to the reduction of food loss and waste, consequently promoting social, economic, and environmental aspects of our daily life.

A GIS-based multi-criteria fuzzy approach to select a suitable location for a MSW-based power plant and landfill: a case study, Khuzestan province, Iran.

The aim of this study was to find the best location for municipal solid waste (MSW)-based power plant and landfill. Another goal was to investigate the energy recovery potential of MSW by anaerobic digestion and recycling process. A combination of multi-criteria fuzzy decision making system was applied in GIS environment to select the most suitable site for MSW-based power plant and landfill. The effective sub-criteria were classified into three main categories of environmental, social, and economic criteria, and fuzzy analytic hierarchy process was used to weight and integrate them. The method was used in a case study to find the best location of MSW-based plant and landfill in Khuzestan province, Iran. Based on the amount of MSW production in this province and as a result, the potential for energy savings by recycling the recyclable part and recovering energy from the digestible part, the amount of fossil fuel that can be saved and the emission of greenhouse gases that can be avoided were estimated. Based on the results, in total, recycling the recyclable portion (dry waste) and producing energy from the digestible portion (wet waste) of MSW in Khuzestan province, leads to avoid burning 7.4 billion MJ of fossil fuels and emitting 644,169 t of CO2eq GHG. The results also showed that the integration of fuzzy logic and multi-criteria fuzzy decision making system provides more flexibility in choosing the best location for the MSW-based plant. This method is flexible enough and in other situations can include other necessary criteria and can provide a comprehensive guideline for decision makers in choosing the optimal location for a power plant or a landfill.