Search

The combined use of the Analytical Hierarchy Process (AHP) and Fuzzy Inference Systems (FISs) can significantly enhance the effectiveness of transformative projects in organizations by better managing their complexities and uncertainties. This work develops a novel multicriteria model that integrates both methodologies to assist organizations in these projects. To demonstrate the value of the proposed approach, we present an illustrative example focused on the implementation of Industry 4.0 in SMEs. First, through a review of relevant literature, we identify the key barriers to improving SMEs' capability to implement Industry 4.0 effectively. Subsequently, the AHP, enhanced through Dong and Saaty's methodology, establishes a consensus-based assessment of the importance of these barriers, using the judgments of five experts. Next, a FIS is utilized, with rule bases automatically derived from the preceding weights, eliminating the need for another round of expert input. This paper shows and discusses how SMEs can use this model to self-assess their adaptability to the Industry 4.0 landscape and formulate improvement strategies to achieve deeper alignment with this transformative paradigm. [ABSTRACT FROM AUTHOR]

In this study, we have managed to model the energy surface of the aminophenol derivative azomethine molecule mathematically depending on two torsion angles SC1(C9C10C12N14) and SC2(C2C1C6C11). For this purpose, firstly, discrete data obtained from Density Functional Theory calculations have been converted into continuous data with the help of the Fuzzy Inference System. Thus, it is possible to calculate energy values for untested data, which are very costly in terms of time to obtain with other methods/experiments. Then, the continuous and non-smooth surface obtained from the fuzzy inference system and representing the energy values of the molecule has been transformed into a differentiable surface with the help of Bezier surfaces. Thus, an objective function has been obtained in which global optimization methods based on the derivative (or gradient) operator could be used. [ABSTRACT FROM AUTHOR]

The implementation of a circular supply chain (CSC) has potential to help the Irish dairy industry mitigate their negative environmental impacts. However, the industry does not have a clear understanding on their level of readiness to implement CSC in factors that ensure success. While there have been few studies that have identified barriers and critical success factors of CSC implementation, limited attention has been given to developing a comprehensive framework capable of measuring an industry's readiness for CSC implementation, especially in the dairy industry. This study provides novelty in the development and application of a novel hybrid approach based on best–worst method and fuzzy inference system (BWM–FIS) to evaluate readiness for CSC implementation in the Irish dairy industry. By identifying a comprehensive set of readiness measures and sub-measures and integrating them into the assessment framework, we provide a valuable tool for industry stakeholders to gauge their readiness level and make informed decisions regarding CSC implementation. The applicability of the proposed approach is then demonstrated with an empirical study of the Irish dairy industry. The data was collected from 34 supply chain and senior professionals from all 13 main processing and manufacturing companies in the Irish dairy industry. The empirical results for the Irish dairy industry suggests it has a moderate level of readiness on the CSC readiness scale. This indicates that dairy manufacturers in Ireland are not yet in an ideal state of readiness for CSC implementation. [ABSTRACT FROM AUTHOR]

The Type-2 fuzzy set is a fuzzy set with fuzzy membership degrees. This set is used when accurately determining the membership degree of a fuzzy set is challenging. It has been observed that higher-type fuzzy sets improve accuracy. However, to use fuzzy sets of higher types in deterministic space, the type needs to be reduced. Another critical challenge is ensuring hardware implementation capability and optimal performance in real-time applications while using fuzzy techniques. Memristor structures are emerging hardware platforms with biological similarities to the human nervous system, and its nanoscale implementation and low power consumption, making them suitable for hardware implementation. This paper introduces various approaches to implementing a fuzzy system with type-2 membership fuzzy sets, and for the first time, demonstrates the utilization of memristor structures to reduce the type. The suggested circuits allow the membership functions to have any shape and resolution, and the implementation results demonstrate the efficiency of the proposed hardware. The main goal of this paper was to showcase a hardware implementation that incorporates on-chip training, allowing adaptability to the environment without dependence on the host system (In-Situ Training). The ArC One hardware platform is used to demonstrate the results experimentally. In modelling and classification, the simulation and experimental results show an increase in accuracy more than 2% has been achieved, compared to previous works. [ABSTRACT FROM AUTHOR]

Patients with Parkinson's disease (PD) can present several biomechanical alterations, such as tremors, rigidity, bradykinesia, postural instability, and gait alterations. The Movement Disorder Society–Unified Parkinson's Disease Rating Scale (MDS-UPDRS) has a good reputation for uniformly evaluating motor and non-motor aspects of PD. However, motor clinical assessment depends on visual observations, which are mostly qualitative, with subtle differences not recognized. Many works have examined evaluations and analyses of these biomechanical alterations. However, there are no reviews on this topic. This paper presents a scoping review of computer models based on expert knowledge and machine learning (ML). The eligibility criteria and sources of evidence are represented by papers in journals indexed in the Journal Citation Report (JCR), and this paper analyzes the data, methods, results, and application opportunities in clinical environments or as support for new research. Finally, we analyze the results' explainability and the acceptance of such systems as tools to help physicians, both now and in future contributions. Many researchers have addressed PD biomechanics by using explainable artificial intelligence or combining several analysis models to provide explainable and transparent results, considering possible biases and precision and creating trust and security when using the models. [ABSTRACT FROM AUTHOR]

Water scarcity is becoming a crisis and a significant threat to economic and agricultural development. In order to achieve long-term sustainability, it is suggested that local government should concentrate on evaluating the water resources' value with a combination of ecological, financial, and societal aspects. Hence, this paper's purpose is to devise an all-encompassing approach for evaluating the value of water resources, thereby providing a robust framework for their assessment. Firstly, a water quality, water quantity, and socioeconomic evaluation indicator system are established for Mashhad plain from 2016 to 2018. Subsequently, a fuzzy index was developed to measure the interrelationships and connections among natural, economic, and social systems, thereby enabling the determination of the value associated with water resources. This study's innovation is that each factor influencing this value was appraised utilizing the fuzzy approach. The water quality index shows poor to very poor fuzzy quality of water in all three years. Also, the water quantity index results show that variable's critical range. Also, based on the fuzzy water quantity index, the water quantity of Mashhad plain is in the critical range. The socioeconomic status assessment model results also showed that the Mashhad plain area has a good to moderate socioeconomic status. The statistics revealed that the valuation of water resources in the Mashhad plain exhibited values of 0.391, 0.399, and 0.416 during the years 2016, 2017, and 2018, respectively. [ABSTRACT FROM AUTHOR]

Segmentation of medical images is a critical step in assisting doctors in making accurate diagnoses and planning appropriate treatments. Deep learning architectures often serve as the basis for computer models used for this task. However, a common challenge faced by segmentation models is class imbalance, which leads to a bias towards classes with a larger number of pixels, resulting in reduced accuracy for the minority-class regions. To address this problem, the α -balanced variant of the focal loss function introduces a α modulation factor that reduces the weight assigned to majority classes and gives greater weight to minority classes. This study proposes the use of a fuzzy inference system to automatically adjust the α factor, rather than maintaining a fixed value as commonly implemented. The adaptive fuzzy focal loss (AFFL) achieves an appropriate adjustment in α by employing fifteen fuzzy rules. To evaluate the effectiveness of AFFL, we implement an encoder-decoder segmentation model based on the UNet and Transformer architectures (AFFL-TransUNet) using the CHAOS dataset. We compare the performance of seven segmentation models implemented using the same data partition and hardware equipment. A statistical analysis, considering the DICE coefficient metric, demonstrates that AFFL-TransUNet outperforms four baseline models and performs comparably to the remaining models. Remarkably, AFFL-TransUNet achieves this high performance while significantly reducing training processing time by 66.31–72.39%. This reduction is attributed to the fuzzy system that effectively adapts the α value of the loss function, stabilizing the model within just a few epochs. [ABSTRACT FROM AUTHOR]

Over the past few decades, the field of fuzzy logic has evolved significantly, leading to the development of diverse techniques and applications. Fuzzy logic has been successfully combined with other artificial intelligence techniques such as artificial neural networks, deep learning, robotics, and genetic algorithms, creating powerful tools for complex problem-solving applications. This article provides an informative description of some of the main concepts in the field of fuzzy logic. These include the types and roles of membership functions, fuzzy inference system (FIS), adaptive neuro-fuzzy inference system and fuzzy c-means clustering. The processes of fuzzification, defuzzification, implication, and determining fuzzy rules' firing strengths are described. The article outlines some recent developments in the field of fuzzy logic, including its applications for decision support, industrial processes and control, data and telecommunication, and image and signal processing. Approaches to implementing fuzzy logic models are explained and, as an illustration, Matlab (version R2024b) is used to demonstrate implementation of a FIS. The prospects for future fuzzy logic developments are explored and example applications of hybrid fuzzy logic systems are provided. There remain extensive opportunities in further developing fuzzy logic-based techniques, including their further integration with various machine learning algorithms, and their adaptation into consumer products and industrial processes. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive range of uncertainties is considered to assess the seismic abilities of a moment-resisting system. To incorporate the parameter of construction quality, which has a descriptive nature, a suitable fuzzy logic engine has been developed. This engine, for the first time, addresses the quantitative assessment of construction quality parameters based on linguistic variables, including map accuracy, worker skills, material quality, and site supervision conditions. Instead of using random selection, a self-organizing map (SOM) algorithm is employed to carefully select strong ground motion records, reducing time costs. By applying incremental dynamic analysis (IDA) results, analytical equations are derived for the response surface method. These equations determine the collapse fragility's mean and standard deviation. The material quality is modeled using the fuzzy inference engine, with the coefficient of logarithm response surface. Collapse fragility curves are created by taking into account many of their material quality values and utilizing the fuzzy model to estimate the modeling parameter based on the logarithm regression coefficients. These curves take into consideration various sources of uncertainty. In countries with inadequate material quality control, it is important to take cognitive uncertainty into account when developing fragility curves. This will help improve the overall risk management strategy. [ABSTRACT FROM AUTHOR]

Online shopping has experienced substantial growth over the past decade, and this trend is expected to persist. The convenience it offers consumers serves as a driving force behind this expansion. Online retailers stand to benefit from a comprehensive understanding of consumer behavior and online shopping habits, as it enables them to formulate more effective marketing strategies and tailor their communications to the preferences of online shoppers. This paper aimed to develop a bespoke questionnaire leveraging data from a EuroStat report in 2021. As novel methodology a Sugeno-type predictive fuzzy model was constructed using these data, empowering businesses to make more precise predictions regarding the requirements and behaviors of distinct consumer groups. The study examined the following areas of consumers: online shoppers belonging to the X, Y, and Z generations; living in small towns, towns, or in the capital; and studying, working, or both. In addition, the likelihood of spending money online was determined regarding the following product categories: Bills, utilities; (2) Food, shopping; (3) Entertainment; (4) Wellness, beauty; (5) Electronic items; (6) Fashion; (7) Home, decoration and (8) Other goods. The results of this survey, combined with the fuzzy model developed, serve as valuable resources for online retailers seeking to enhance their marketing strategies and gain a deeper understanding of customer preferences. The conclusions highlight patterns and preferences among different age groups and locations, providing valuable insights for online retailers to enhance their marketing strategies when identifying main target groups for specific products. Additionally, the research offers a more comprehensive understanding of demographic attributes associated with these age cohorts than EuroStat data. [ABSTRACT FROM AUTHOR]

Image tamper detection is a challenging area in multimedia research. The advances in photography technology have made it possible to capture real-time high-dynamic-range (HDR) images through an iPhone or an Android device, which highlights the need for rigorous research on HDR images for tamper detection, and localization. The algorithms built for standard images may affect the watermark visibility when it is applied to HDR images as this produces high perceptual variations in the image compared to the original. To tackle this problem, we presented a Proof of Work consensus blockchain watermarking scheme combined with a convolution attention model (PoWBWM) system for tamper detection and localization. The system utilizes a Convolution Attention model (CoAtNet) to generate robust watermarks. A quaternion graph-based transform (QGBT) for embedding, ensuring imperceptibility and robustness. A fuzzy inference system optimizes embedding regions and factors based on human visual system characteristics. The system's security is enhanced through blockchain's proof-of-work (consensus) mechanism, providing a semi-blind watermarking scheme that authenticates ownership and detects tampering efficiently. The security is ensured only when the embedded hash key is authentic with its previous block to proceed further extraction process. The proposed algorithm's performance is evaluated in terms of its visibility by Peak-Signal-to-Noise-Ratio (PSNR), Structural Similarity Index (SSIM), and the perceptual quality of an HDR image is additionally measured by the Visual Dynamic Predictor (VDP) metric. On the other hand, the robustness performance is measured by Normalized Correlation Coefficient (NCC) and Bit Error Rate (BER). The experimental results for CASIA images achieved the highest PSNR value of 63.84 dB, and the SSIM value of 1.000, whereas the maximum VDP value obtained for HDR images is 98.02. In comparison with the existing system, the experimental findings of the suggested model show an effective tamper detection watermarking system as well as a robust against both intentional and unintentional attacks with an average NCC value of 0.98.

Abstract To improve dynamic performance and steady-state accuracy of position leap control of the direct current (DC) servo motor, a fuzzy inference system (FIS) enabled artificial neural network (ANN) feedforward compensation control method is proposed in this study. In the method, a proportional-integral-derivative (PID) controller is used to generate the baseline control law. Then, an ANN identifier is constructed to online learn the reverse model of the DC servo motor system. Meanwhile, the learned parameters are passed in real-time to an ANN compensator to provide feedforward compensation control law accurately. Next, according to system tracking error and network modeling error, an FIS decider consisting of an FI basic module and an FI finetuning module is developed to adjust the compensation quantity and prevent uncertain disturbance from undertrained ANN adaptively. Finally, the feasibility and efficiency of the proposed method are verified by the tracking experiments of step and square signals on the DC servo motor testbed. Experimental results show that the proposed FIS-enabled ANN feedforward compensation control method achieves lower overshoot, faster adjustment, and higher precision than other comparative control methods.

This paper proposed a Constructive Rapid Application Development Model based Software Cost Estimation Technique (CORADMO based SCET) for accurate software cost estimation. The data requirements, cost drivers, constraints and priorities are given as an input to the Fuzzy Inference System (FIS). The processed output such as effort, time and cost for nominal plan, shortest schedule plan, and least cost plan are computed in the Fuzzy Inference System (FIS). Further to reduce the effort time and cost means, the output optimized by Dragon fly Whale Optimization (DWO)which provides the best estimated effort, time and cost as an output for software development. The proposed CORADMO based SCET model is evaluated by NASA 93 dataset using MATLAB. The performance of the CORADMO based SCET approach is assessed in terms of Mean Magnitude of Relative Error, Pred (25%), and Magnitude of Relative Error attains the values of 98.77%, 92.55%, and 93.45% respectively. Finally, the CORADMO based SCET model justifies the suitability of Dragon fly Whale Optimization with the proposed fuzzy logic.

An artificial intelligence-based geostatistical optimization algorithm was developed to upgrade a test Iranian aquifer’s existing groundwater monitoring network. For that aquifer, a preliminary study revealed that a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) more accurately determined temporally average water table elevations than geostatistical kriging, spline, and inverse distance weighting. Because kriging is usually used in that area for water table estimation, the developed algorithm used MLP-ANN to guide kriging, and Genetic Algorithm (GA) to determine locations for new monitoring well location(s). For possible annual fiscal budgets allowing 1–12 new wells, 12 sets of optimal new well locations are reported. Each set has the locations of new wells that would minimize the squared difference between the time-averaged heads developed by kriging versus MLP-ANN. Also, to simultaneously consider local expertise, the algorithm used fuzzy inference to quantify an expert’s satisfaction with the number of new wells. Then, the algorithm used symmetric bargaining (Nash, Kalai–Smorodinsky, and area monotonic) to present an upgradation strategy that balanced professional judgment and heuristic optimization. In essence, the algorithm demonstrates the systematic application of relatively new computational practices to a common situation worldwide.

Abstract Cloud computing has become integral to modern computing infrastructure, offering scalability, flexibility, and cost-effectiveness. Trust is a critical aspect of cloud computing, influencing user decisions in selecting Cloud Service Providers (CSPs). This paper provides a comprehensive review of existing trust models in cloud computing, including agreement-based, SLA-based, certificate-based, feedback-based, domain-based, prediction-based, and reputation-based models. Building on this foundation, we propose a novel methodology for creating a trust model in cloud computing using digital twins for CSPs. The digital twin is augmented with a fuzzy inference system, which computes the trust score of a CSP based on trust-related parameters. The architecture of the digital twin with the fuzzy inference system is detailed, outlining how it processes security parameter values obtained through penetration testing mechanisms. These parameter values are transformed into crisp values using a linear ridge regression function and then fed into the fuzzy inference system to calculate a final trust score for the CSP. The paper also presents the outputs of the fuzzy inference system, demonstrating how different security parameter inputs yield various trust scores. This methodology provides a robust framework for assessing CSP trustworthiness and enhancing decision-making processes in cloud service selection.

Abstract College students face uncertainties during job searches due to a lack of career planning, unclear objectives, and ineffective search strategies, leading to poor employment outcomes. Fuzzy Control (FC) based Job Search Strategies (JS2) are proposed in this research as an optimized technique named FC-JS2-TSC. This technique combines Takagi-Sugeno (TS) fuzzy inference with Cuckoo (C) search optimization. The primary goals are improving individualized advice and creating an integrated system to deal with job search concerns. The FC uses fuzzy logic and sets to model uncertainties such as vague job desires and ever-changing market circumstances. Individual student profiles and preferences are used to fine-tune methods by cuckoo search. Through experimental validation, we can see that FC-JS2-TSC outperforms previous methods in terms of both job strategy selection and results. As a measure of system efficacy, the results demonstrate a high Cronbach's alpha reliability of 0.96, a low RMSEA of 0.04 and 96.6% regarding job offers. By adjusting tactics in response to uncertainty, the innovative FC-JS2-TSC algorithm facilitates data-driven, personalized decision-making, ultimately leading to more efficient job searches. It has an integrated design that combines optimization with fuzzy logic's uncertainty handling to ensure students have the best possible chance of success in their job searches.

Background Sustainable supply chains are more competitive than conventional supply chains. Supply chain sustainability performance needs to be carried out to determine sustainability under current conditions and to design appropriate strategies to increase sustainability. This study aims to design a sustainability performance assessment model for the sago agro-industry supply chain and identify critical indicators for sustainability improvement. Methods The Fuzzy Inference System (FIS) evaluates sustainability on three levels: economic, social, and environmental. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is then used to aggregate the overall sustainability performance. The cosine amplitude method (CAM) was used to analyze key indicators. This study assessed the sustainability performance on industrial- and small-medium-scale sago agro-industry. Results The results show that the supply chain sustainability performance on the industrial scale is 44.25, while it is 48.81 for the small-medium scale with the same status, almost sustainable. Key indicators for improving sago agro-industry supply chain sustainability performance include profit distribution among supply chain actors, institutional support for supply chains, waste utilization (reuse & recycle), and the availability of waste management facilities. The implication of this research for managers regards assessing the current status of sustainability performance and key indicators as a reference for formulating sustainability strategies and practices. Implication The sago agro-industry sustainability performance evaluation methodology uses industry-relevant metrics to assess supply chain sustainability, promoting collaboration among stakeholders and assisting in the creation of sustainable strategies. Conclusions The results of the study will enable supply chain actors to understand the key indicators for improving sustainability performance in the sago agro-industry supply chain, especially in Meranti Islands Regency, Riau Province. The proposed model can be applied to other agro-industries by adjusting the indicators used and assessing data availability and suitability for the research object.

This paper proposed a Constructive Rapid Application Development Model based Software Cost Estimation Technique (CORADMO based SCET) for accurate software cost estimation. The data requirements, cost drivers, constraints and priorities are given as an input to the Fuzzy Inference System (FIS). The processed output such as effort, time and cost for nominal plan, shortest schedule plan, and least cost plan are computed in the Fuzzy Inference System (FIS). Further to reduce the effort time and cost means, the output optimized by Dragon fly Whale Optimization (DWO)which provides the best estimated effort, time and cost as an output for software development. The proposed CORADMO based SCET model is evaluated by NASA 93 dataset using MATLAB. The performance of the CORADMO based SCET approach is assessed in terms of Mean Magnitude of Relative Error, Pred (25%), and Magnitude of Relative Error attains the values of 98.77%, 92.55%, and 93.45% respectively. Finally, the CORADMO based SCET model justifies the suitability of Dragon fly Whale Optimization with the proposed fuzzy logic. [ABSTRACT FROM AUTHOR]

To improve dynamic performance and steady-state accuracy of position leap control of the direct current (DC) servo motor, a fuzzy inference system (FIS) enabled artificial neural network (ANN) feedforward compensation control method is proposed in this study. In the method, a proportional-integral-derivative (PID) controller is used to generate the baseline control law. Then, an ANN identifier is constructed to online learn the reverse model of the DC servo motor system. Meanwhile, the learned parameters are passed in real-time to an ANN compensator to provide feedforward compensation control law accurately. Next, according to system tracking error and network modeling error, an FIS decider consisting of an FI basic module and an FI finetuning module is developed to adjust the compensation quantity and prevent uncertain disturbance from undertrained ANN adaptively. Finally, the feasibility and efficiency of the proposed method are verified by the tracking experiments of step and square signals on the DC servo motor testbed. Experimental results show that the proposed FIS-enabled ANN feedforward compensation control method achieves lower overshoot, faster adjustment, and higher precision than other comparative control methods. [ABSTRACT FROM AUTHOR]

Contractors face most of the construction risks among stakeholders, and insurance is a common method to mitigate these risks. However, not all risks are insurable. While prior studies have typically assessed risk insurability through a binary approach (insurable versus non-insurable) and lacked clear criteria, this study offers a novel perspective by evaluating the insurability of construction risks based on four criteria: 'accidental events,' 'quantifiable,' 'numerous and homogenous,' and 'evaluable.' This study develops a fuzzy-based model to assess the degree of the construction risk insurability, accounting for the uncertainty, imprecision, and vagueness inherent in evaluating insurability against a specific criterion and criteria combinations. The model is applied to assess the insurability of several construction risks, illustrating its practical application. This paper concludes by discussing the model's limitations and suggesting directions for future research. [ABSTRACT FROM AUTHOR]

Code Smell is a term that indicates flaws in design and coding practice. God Class is a type of code smell that shows an irregular distribution of functionalities in large-sized classes. God Classes are less cohesive and more coupled in nature, thereby increasing software maintenance efforts and costs. Refactoring all such classes can disturb other related classes with code smell instances, puzzle the developers, and increase the refactoring budget. This paper proposes an automated method to prioritize God Class smell-associated classes with the fuzzy inference system. The fuzzy inference system is used to fuzzy the selected criteria—number of code smell instances, type of code smells, and changes in history. For effective refactoring, first, we moderate the dataset with the CodeMR tool and then highlight that the prioritization criteria are imperative after detecting code smells. Using five metric-based heuristics, a comparative result analysis is done to determine the fore reason for correlation (40–43%) with our results and the gravity of our prioritization criteria. Finally, we provide a severity index of classes with five type classifications and evaluate runtime performance (in seconds) to improve quality. [ABSTRACT FROM AUTHOR]

Aquatic ecosystems that are subject to urbanization and environmental changes, such as the Kapaleeswarar and Chitrakulam tanks, depend on evaluating water quality. Their complicated data present challenges for conventional approaches. The usefulness of the Mamdani fuzzy inference system in determining the water quality in these tanks is investigated in this work. It creates a comprehensive assessment based on subject-matter expertise by handling ambiguous descriptors with linguistic variables and fuzzy sets. The system's procedures for implementation are described in detail, with an emphasis on how well they can manage interrelated variables. The study shows how well the system measures the water quality in tanks and suggests ways to improve it. Tank evaluation that incorporates the Mamdani system encourages comprehensive resource management and cultural preservation. [ABSTRACT FROM AUTHOR]

Improving driving behaviour can be a fruitful intervention to improve traffic safety. This paper proposes a method to determine a driving behaviour score (DBS) for each driver based on their driving history. For this purpose, a fuzzy inference system (FIS) was used to calculate DBS for every driver between 0 and 1. The input variables for this model are the frequency and severity of previous crashes, near-crash situations, and aberrant driving behaviours. The calculated DBS can then be applied in implementing usage-based insurance schemes. The proposed method is used for a case study in Kerman (Iran). For this purpose, 40 young drivers were recruited in an experiment to record their previous history of crashes, aberrant driving behaviours, as well as surrogate safety measures while driving on a specific route. According to the results, DBQ is a useful indicator to measure a driver's level of safe driving style since it considers the history of crashes, near-crash incidents and dangerous driving behaviours. In this study, DBQ was primarily affected by the frequency of previous crashes. In Iran, drivers with dangerous driving behaviours pay the same insurance premium as those with relatively safer driving habits. Due to the disregard of a complete driving history, the insurance premiums determination process is not fair. According to this paper, usage-based insurance pricing can become fair and dependent upon a driver's behaviour by using DBQ. [ABSTRACT FROM AUTHOR]

An artificial intelligence-based geostatistical optimization algorithm was developed to upgrade a test Iranian aquifer's existing groundwater monitoring network. For that aquifer, a preliminary study revealed that a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) more accurately determined temporally average water table elevations than geostatistical kriging, spline, and inverse distance weighting. Because kriging is usually used in that area for water table estimation, the developed algorithm used MLP-ANN to guide kriging, and Genetic Algorithm (GA) to determine locations for new monitoring well location(s). For possible annual fiscal budgets allowing 1–12 new wells, 12 sets of optimal new well locations are reported. Each set has the locations of new wells that would minimize the squared difference between the time-averaged heads developed by kriging versus MLP-ANN. Also, to simultaneously consider local expertise, the algorithm used fuzzy inference to quantify an expert's satisfaction with the number of new wells. Then, the algorithm used symmetric bargaining (Nash, Kalai–Smorodinsky, and area monotonic) to present an upgradation strategy that balanced professional judgment and heuristic optimization. In essence, the algorithm demonstrates the systematic application of relatively new computational practices to a common situation worldwide. [ABSTRACT FROM AUTHOR]

This paper aims to compare the performance of four credit scoring models, namely logistic regression (LR), artificial neural network (ANN), fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) in predicting default probability. We use a sample of 1045 consumer credits (after oversampling the initial sample of 660 customers) granted by a Tunisian Islamic bank. The six explanatory variables retained to predict the probability of default are: residual wage, age, job tenure, profession, financing type and region of residence. Our findings reveal that ANFIS and LR have the highest discriminating power (AUC = 0.9). Regarding the type I error (false-positive) and the type II (false-negative) error, it has been proved that ANFIS has the lowest misclassification costs (MC = 0.15). The outperformance of the ANFIS comes from combining the advantages of neural networks with a fuzzy inference system. Thus, our results suggest that the ANFIS seems to be the most efficient and transparent technique for predicting credit risk in Islamic banks. Unlike ANN, the ANFIS allows bankers to justify the reasons behind the rejection of credit applications. [ABSTRACT FROM AUTHOR]

Cloud computing has become integral to modern computing infrastructure, offering scalability, flexibility, and cost-effectiveness. Trust is a critical aspect of cloud computing, influencing user decisions in selecting Cloud Service Providers (CSPs). This paper provides a comprehensive review of existing trust models in cloud computing, including agreement-based, SLA-based, certificate-based, feedback-based, domain-based, prediction-based, and reputation-based models. Building on this foundation, we propose a novel methodology for creating a trust model in cloud computing using digital twins for CSPs. The digital twin is augmented with a fuzzy inference system, which computes the trust score of a CSP based on trust-related parameters. The architecture of the digital twin with the fuzzy inference system is detailed, outlining how it processes security parameter values obtained through penetration testing mechanisms. These parameter values are transformed into crisp values using a linear ridge regression function and then fed into the fuzzy inference system to calculate a final trust score for the CSP. The paper also presents the outputs of the fuzzy inference system, demonstrating how different security parameter inputs yield various trust scores. This methodology provides a robust framework for assessing CSP trustworthiness and enhancing decision-making processes in cloud service selection. [ABSTRACT FROM AUTHOR]

Rule-based solutions for decision-making processes in complex and uncertain environments are beneficial because they are simple, transparent, and Effective. Considering that dependence on expert knowledge and human subjectivity of rule-based systems leads to inconsistencies or inaccuracies, approaches for the automatic rule selection process from training data are critical to minimize problems related to human interference. This study aims to apply genetic algorithms (GA) to automatically select IF-THEN rules in fuzzy inference systems to minimize the problems impacted by human involvement. To demonstrate the proposed approach's applicability, FIS with an automatized rules selection method based on GA has been applied for the classification of the Titanic disaster dataset. The executed experiments indicate improved classification performance by twice increasing the classification's F1-score from the initial generation to the last generation. Though the final metrics are less than the current state-of-the-art approach for the given dataset, the results approved the GA's eligibility for automatic rule selection. [ABSTRACT FROM AUTHOR]

The goal of this research is to create an intelligent system to assess the manufacturing system's level of risk for wheat four products. Five Fuzzy Inference Systems (FISs) are arranged in two layers of the model to assess the risk associated with a system that produces wheat four products. There are four FISs with three criteria (Occurrence, Severity, and Detectability) in the model's first layer. The final input for the manufacturing system will be determined from every physical, chemical, biological, and environmental failure. The suggested model, which is based on Mamdani FISs, ranks the manufacturing systems for wheat four products according to their performance. A four-step approach (i.e., eliciting hazard information for experts, fuzzification, inference, and defuzzification) brings to an evaluation of the final risk level in a real-world wheat four manufacturing system to 22.5%, which shows a fair situation, and it represents a manufacturing system with a high-risk level. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The equipment of airport security screening checkpoints undergoes frequent modifications due to technological or organizational changes. New solutions complement or replace existing ones to improve the effectiveness of the equipment or formal requirements. An example of this process is the replacement of the walk-through metal detection gate with a newer solution: the body scanner. The present study aimed to analyze the capacity of an airport security checkpoint under different operational conditions, depending on the equipment used. For this purpose, a previously created model (implemented as a colored, timed, stochastic Petri net) was used. Simulation studies were performed in four real-world operational scenarios, and their results were compared to those of a nominal scenario. The results show that, in terms of capacity, it may be advantageous to redirect a more significant stream of passengers to a station equipped with a specific device, depending on the specific operational situation. The results demonstrate the necessity of analyzing the applied strategy of operation with each change in the operational environment. In particular, for an airport with characteristics similar to Katowice Airport, using older technology is beneficial under nominal conditions and after increasing the staffing so that both genders work simultaneously. In other cases, the more intensive use of Body Scanner-equipped security screening checkpoints is advantageous. [ABSTRACT FROM AUTHOR]

A fuzzy rule generation method which applies Dempster–Shafer theory (DST) to fuzzy inference systems is proposed. The sample data located in the same fuzzy input subspace are regarded as evidences, and all candidate rule consequents constitute the frame of discernment. Dempster's combination rule is employed to fuse evidences to determine the rule consequent, which solves the problem of conflicting rules simultaneously. In addition, a BPA-based approach to optimize constant rule consequent is proposed. Experimental results illustrate that both the proposed rule extraction method and the optimization method for rule consequent have better performance. [ABSTRACT FROM AUTHOR]

Sustainable supply chain management (SSCM) is the integration of economic, social, and environmental concerns into the supply chain practices that plays an important role to minimize industrial pollution, global warming, and carbon emission and make a supply chain green. In the present article, a novel fuzzy analytical hierarchy process (AHP) as a fuzzy preference programming (FPP) approach has been combined with fuzzy inference system (FIS) for evaluating the performance of suppliers considering carbon management (CM) indicators. To do so, 12 indicators are classified in four basic perspectives applying the Delphi method. The importance weights of perspectives and their related indicators are calculated performing FPP. Accordingly, the weighted performance values that are inputs of FIS are calculated and finally, the weighted fuzzy system will be applied to obtain the total environmental performance of the suppliers. Moreover, we implemented our model within a home appliances manufacturing company in Iran to examine its efficiency. Results approve that the proposed approach can apply efficiently in real situations and are reliable-enough. Furthermore, findings provide more insight for the managers of the company toward the concept of the CM and make them able to find their weaknesses and work with their partners to fix them. [ABSTRACT FROM AUTHOR]

Copyright of Contaduría y Administración is the property of Facultad de Contaduria y Administracion-Universidad Nacional Autonoma de Mexico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Uncertainty is an inevitable characteristic in human life and systems, posing challenges in decision-making and data analysis. Fuzzy theory emerges to address this uncertainty by describing variables with vague or uncertain values, one of which is the Fuzzy Inference System (FIS). This research analyzes and compares the performance of FIS from previous studies as a solution to manage uncertainty. FIS allows for flexible and responsive representations of truth levels using human-like linguistic rules. Common FIS methods include FISM, FIS-T, and FIS-S, each with different inference and defuzzification approaches. The findings of this research review, referencing previous studies, indicate that the application of FIS in various contexts such as prediction, medical diagnosis, and financial decision-making, yields very high accuracy levels up to 99%. However, accuracy comparisons show variations, with FIS-M tending to achieve more stable accuracy based on the referenced studies. The accuracy difference among FIS-M studies is not significantly different, only around 7.55%. Meanwhile, FIS-S has a wider accuracy range, from 81.48% to 99% (17.52%). FIS-S performs best if it can determine influencing factors well, such as determining constant values in its fuzzy rules. Additionally, the performance comparison of FIS can also be influenced by other factors such as data complexity, variables, domain, membership functions (curves), fuzzy rules, and defuzzification methods used in the study. Therefore, it is important to consider these factors and select the most suitable FIS method to manage uncertainty in the given situation. [ABSTRACT FROM AUTHOR]

The review systematically explores the theoretical foundations of type-2 fuzzy sets and the associated mathematical frameworks of the advancements in type-2 fuzzy logic systems (T2-FLS), as well as a comprehensive overview of the state-of-the-art techniques and applications of type-2 fuzzy logic systems. The review's application-oriented section looks at a number of different areas where type-2 fuzzy logic has made a big difference. These areas include robotics, autonomous vehicle control, electric and electronic control, image processing, and railway control. This overview mainly discusses the past, present, and future trends in type-2 fuzzy logic applications. The primary contribution summarizes the most essential type-2 fuzzy logic research with theoretical and practical implications. This article will provide a critical path and a strong foundation for individuals interested in this field to pursue future research.

Air pollution from vehicle emissions, industrial activities, and medical facilities poses significant health risks in urban areas, underscoring the necessity for robust air quality index (AQI) monitoring. This paper presents a novel method for AQI prediction by integrating a fuzzy centre merge graph with an optimal value-based fuzzy inference system (FCMG-OP-FIS) and machine learning (ML). Traditional ML techniques encounter difficulties when converting regression datasets into classification formats, particularly when unable to label the dataset using the traditional method. The proposed FCMG-OP-FIS model efficiently converts regression data into a classification framework. Unlike traditional AQI prediction methods that rely solely on pollutant data, this approach incorporates both pollutant and meteorological data to improve prediction accuracy. The innovative fuzzy centre merge graph (FCMG) balances the dataset for optimal solutions and facilitates input grouping for Simulink, simplifying rule management. The FCMG-OP-FIS model generates a regression output for AQI, which is subsequently classified into levels (healthy, moderate, or unhealthy) using IF-THEN rules. To enhance accuracy further, a random forest classifier (RFC) is trained on the FCMG-OP-FIS classified output data. The regression output of the FCMG-OP-FIS model is validated using metrics such as RMSE (0.48), MSE (0.23), MAE (0.23), and MAPE (1.77%). Additionally, the classification output from the RFC model employs advanced validation techniques including stratified shuffle validation, grid search cross-validation, and confusion matrix analysis, achieving an accuracy rate of 99%, with the F1 score, precision, and recall over all at 99%. These results demonstrate the effectiveness of the proposed model in accurately labelling data for classification and predicting AQI through ML, highlighting its potential for practical application in environmental monitoring and management.

Sustainability in crop production is highly dependent on favorable indicators of germination and seedling establishment. In this research seed coating different treatments data prepared and membership function defined in fuzzy inference system then used rule editor tool for determining parameters weight for achieved the best coating treatment in salinity and drought stress conditions. Seed coating formulations were investigated in laboratory and greenhouse experiments for their potential to increase maximum germination, germination rate, germination uniformity, and seedling growth of sugar beet seeds. Sugar beet seeds were coated with different compounds and combinations, including micro- (Fe, Zn, Cu, Mn, Co, Mo) and macro- (N, P, K) nutrients, humic acid, gibberellic acid, kaolin, and chitosan. Coated and non-coated sugar beet seeds were evaluated for germination and seedling growth after 10 and 21 days, respectively. In total, 30 different treatments were used to assess the effects of seed coating treatments. In laboratory experiments, sugar beet seeds were placed on paper in Petri dishes and maintained in a germinator at 25 °C. Sodium chloride and polyethylene glycol 8000 were used to apply salinity and drought stresses at three levels each and achieve the results of seed coatings on reducing the effects of salinity and drought stresses. To determine the indices related to emergence and establishment, cultivation trays were utilized with four replications for each treatment. The trays were kept inside a greenhouse. Coating treatments significantly improved total germination percentage, germination rate, seedling growth, and uniformity compared with the non-treated controls. In all treatments, polyvinylpyrrolidone was utilized as a binder. The best treatment with respect to germination and seedling growth indices was number 21 (micro and macronutrients, humic acid, gibberellic acid) in salinity and drought stress conditions. The results by fuzzy inference system illustrated that micronutrients, humic acid, and gibberellic acid create the best seed coating for sugar beet seeds, especially when combined at the specified amounts.