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Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis. While current research focuses mainly on measurement noise, measurement bias remains challenging. This study proposes a novel performance-based fault detection and identification (FDI) strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system. To handle ambient condition changes, we use parameter correction to preprocess the raw measurement data, which reduces the FDI's system complexity. Additionally, the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system. The data for designing, training, and testing the proposed FDI strategy are generated using a component-level turbofan engine model. The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression. A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases. The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies. Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection, isolation, and identification. The robust structure demonstrates a 2%–8% improvement in the success rate index under relatively large measurement bias conditions, thereby indicating excellent robustness. Accuracy against significant bias values and computation time are also evaluated, suggesting that the proposed robust structure has desirable online performance. This study proposes a novel FDI strategy that effectively addresses measurement uncertainties. [ABSTRACT FROM AUTHOR]

Earthquakes are one of the most destructive natural disasters, causing human casualties and economic losses. This paper presents a remote sensing method that produces earthquake damage map by fusing optical and radar post-event images, utilizing deep learning, fuzzy inference system, and segmentation. The paper’s first part involves the design of a convolutional neural network (CNN) and the classification of images at the pixel level using three different approaches, including 1-classification by a proposed CNN, 2-applying the proposed CNN as a feature extractor, and 3-classifiction via support vector machine and fuzzy inference system. The paper’s second part deals with implementing the superior approach in three modes: 1-using optical and radar data individually, 2-fusion of optical and radar images at the pixel level, and 3-exploiting just the optical image. Decision-level fusion is then performed over the damage maps generated in three different modes. At the end of these two parts, the segmented image is combined with the pixel-level damage map to generate the object-level damage map. Experiment results obtained for the city of Sarpol-e Zahab in western Iran, which had experienced an earthquake of magnitude 7.3 on November 12, 2017, showed that using the fuzzy decision maker on the features extracted by the CNN improves the results when compared to the CNN’s complete classification. It was also proved that using radar data in conjunction with optical data produces better results than using optical data alone, and decision-level fusion leads to accuracy improvements. Furthermore, in all investigations, object-based methods outperformed pixel-based methods. [ABSTRACT FROM AUTHOR]

The main goal of this study is to demonstrate how fusion methods can be used in mineral prospectivity mapping (MPM) through a comprehensive multi criteria decision-making (MCDM) analysis. MPM plays a crucial role in mineral exploration by aiming to lower exploration expenses through identifying potential ore-bearing targets and planning detailed exploration surveys, including drilling. This study utilises various methods including index overlay, Old TOPSIS, Modified TOPSIS, Adjusted TOPSIS, Old VIKOR, Modified VIKOR, fuzzy gamma operator, fuzzy ordered weighted averaging (FOWA) with different strategies, and fuzzy inference system (FIS). The study area chosen for comparison of various fusion techniques is the Sonajil Cu-Au porphyry deposit located in the east Azerbaijan province, which is the main focus of the investigation. Geological factors, including rock units and faults, remote sensing data for alteration, geochemical analysis using soil samples, and geophysical factors from reduced to the pole magnetic data, are employed in the indicator maps to assess the potential of the region. To delineate the prospective area in terms of ore-trapping favourability, a fractalbased analysis was applied to all fusion outputs, resulting in the identification of five distinct perspective areas. The efficacy of different MPMs was assessed and compared using subsurface data from 21 boreholes, showing consistent agreement rates among all methods. The FIS showed a 79% agreement, whereas the FOWA, with its optimal strategy, exhibited an 81% agreement, emphasising their effectiveness compared to alternative approaches. The predicted maps show a close connection with the geological features of the host rock, particularly granitoid and andesite porphyry from the Sonajil formation, along with the presence of potassic and phyllic alterations. The FIS and FOWA models have successfully pinpointed new drilling sites and assisted in evaluating mining potential for future exploration and development. By utilising this comparative model, which evaluates different algorithms to determine the most accurate prediction map, significant progress has been made in exploratory studies. [ABSTRACT FROM AUTHOR]

This article discusses the use of adaptive control in the sonar scanning sector within an obstacle detection system, to improve the effectiveness of collision avoidance for autonomous underwater vehicles (AUVs). An adaptive network-based fuzzy inference system (ANFIS) was used for dynamic calculations of the sonar scanning sector. Based on 100 simulation scenarios containing various trajectories created by the mission planner, with various shapes, dimensions and arrangements of static obstacles, and various arrangements and displacement vectors of dynamic obstacles, the effectiveness of the proposed system was tested in comparison with other classical approaches such as a single echosounder and sonar with a fixed scanning sector width. The above sensor configurations were evaluated in terms of the percentage of collision-free trials, the average percentage of trajectory completion, and the average number of activations of the collision avoidance system. Simulations conducted based on the mathematical model of the AUV confirmed that the proposed approach increased the effectiveness of collision avoidance systems for AUVs compared to classical echosounder and sonar-based systems. [ABSTRACT FROM AUTHOR]

This research addresses the challenge of optimizing rule creation for palm oil production at PT Tapiana Nadenggan. It deals with the complexity of diverse agricultural variables, environmental factors, and the dynamic nature of palm oil production. The existing problem lies in the limitations of conventional decision tree models--J48, reduced error pruning (REP), and Random--in capturing the nuanced relationships within the intricate palm oil production system. The study introduces hybrid decision tree models--specifically J48-REP, REP-Random, and Random-J48--to address this challenge via combination scenarios. This approach aims to refine and update the rule creation process, enabling the recognition of nuanced performance processes within the selected decision tree combinations. To comprehensively tackle this challenge and problem, the study employs Tsukamoto's fuzzy inference system (FIS) for a sophisticated performance comparison. Despite the complexity, intriguing results emerge after the forecasting process, with the standalone J48 decision tree achieving 85.70% accuracy and the combined J48-REP excelling at 93.87%. This highlights the potential of decision tree combinations in overcoming the complexities inherent in forecasting palm oil production, contributing valuable insights for informed decision-making in the industry. [ABSTRACT FROM AUTHOR]

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]

Upper-limb exoskeleton robots have a significant impact on rehabilitation, assistive technology, and human augmentation, as they can restore or enhance human physical abilities. This paper presents a novel control approach, called Adaptive Fractional Integral Terminal Sliding Mode (AFITSM), which combines an exponential reaching law with a unique interval type-2 Fuzzy Inference System (FIS). This controller is designed to achieve zero-force control of a 5- degree-of-freedom upper-limb exoskeleton robot, even in the presence of bounded uncertainties. The controller’s integral terminal sliding surface ensures that the system converges in a finite time, allowing the exoskeleton to reach its desired state quickly, which is critical in time-sensitive applications. The exponential switching control term reduces chattering and tracking errors, while the AFITSM controller’s adaptability, enabled by the interval type-2 FIS, allows it to adjust its parameters in real-time to handle uncertainties and external disturbances. Numerical simulations demonstrate the effectiveness and superiority of the proposed control method over traditional control approaches. [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]

The switch to sustainable, or green buildings (GBs) is likely to involve a range of governmental policies, with the mix of incentives dependent on the country. The question then becomes one of creating the mix such that it represents the minimum cost/effort whilst maximizing the delivery rate. This research aimed at quantifying the influence of differing GB incentives using a fuzzy inference system. Twenty-six incentives were identified through literature review and expert interviews (n = 36) using a 5-point pairwise questionnaire. These incentives can be naturally bundled into Technical, Financial and Legal groups. Ranking these suggests the Financial, Technical and Legal groups rank first to third, with weights of 0.42, 0.32 and 0.26, respectively. Then, to quantify the potential influence of individual incentives, a fuzzy inference model was created. The model was then employed to predict the influence of each incentive, individually and in combination, i.e. when part of a policy. Using heat maps and sensitivity analysis, it was shown how to increase the impact of a policy for encouraging GBs, by choosing an appropriate percentage contribution for each incentive. The model introduced here is a tool, showing how policy-makers can change/regulate their policies for GB adoption to receive the best results. [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]

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]

Abstract Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis. While current research focuses mainly on measurement noise, measurement bias remains challenging. This study proposes a novel performance-based fault detection and identification (FDI) strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system. To handle ambient condition changes, we use parameter correction to preprocess the raw measurement data, which reduces the FDI’s system complexity. Additionally, the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system. The data for designing, training, and testing the proposed FDI strategy are generated using a component-level turbofan engine model. The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression. A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases. The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies. Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection, isolation, and identification. The robust structure demonstrates a 2%–8% improvement in the success rate index under relatively large measurement bias conditions, thereby indicating excellent robustness. Accuracy against significant bias values and computation time are also evaluated, suggesting that the proposed robust structure has desirable online performance. This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.

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.

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 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 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.

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.

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.

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 engineering education utilizes the internet and information communication technologies as media of learning. Unfortunately, there is a dearth of frameworks developed that address the issues of online learning based on predictors of adaptability of engineering students and faculty members. This paper aims to identify the issues of online learning, establish the causal-effect relationship between socio-demographic factors and adaptability, and propose an Artificial Intelligence-driven framework using mixed methods. The author engaged 886 engineering students and 61 faculty members from the Technological University of the Philippines in Manila and Taguig Campuses in the Philippines. Based on test results, fewer disruptions, more ICT devices, lower monthly internet bills, faster internet speed, and age significantly predict the students' adaptability to online learning. Meanwhile, faculty members with faster internet speed and higher monthly income have significantly higher adaptability to online learning. The author proposed a framework using a Fuzzy Inference System, which can be used for an accurate and timely decision-making process. Further test results confirmed that the framework is consistently accurate and significantly faster than the conventional method Hence, the proposed framework is a viable decision-making tool for large datasets and complex use cases in online engineering education. [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]

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]

The construction industry has recognized the potential benefits of Lean methodology for improving project outcomes. However, the full utilization of Lean principles remains challenging in the industry. This study aims to address this issue by exploring integrating Building Information Modeling (BIM) into Lean Construction practices to enhance project success. The concept of success in construction projects encompasses various dimensions, including sustainability, time efficiency, costeffectiveness, and budget adherence. This study focuses on measuring success through the lens of sustainability, emphasizing the need to make construction projects more sustainable and effective. To achieve this, a survey was conducted to collect data on sustainable factors that influence project success. The research analysis focused on a selected company's capacity to utilize its existing BIM software to improve critical sustainable factors. The sustainable factors identified as crucial for project success were the relationship with subcontractors, site cleanliness and tidiness, safety, and solid waste production. Projects can enhance their overall success and sustainability by effectively managing these factors. To evaluate different BIM software options, a comprehensive review of available software was undertaken based on their ability to address the identified sustainable factors. The Fuzzy Weighted LC-BIM TOPSIS was proposed as the evaluation method to determine the best BIM software that could maximize project success. The findings of this study highlight the importance of considering sustainable factors in construction projects and leveraging BIM software to manage them effectively. Integrating BIM into Lean Construction practices can improve project success by enhancing collaboration, streamlining processes, and optimizing resource allocation. The implications of this study suggest that construction industry stakeholders should prioritize adopting lean construction principles and leveraging BIM as a valuable tool to enhance project success. Integrating BIM and Lean methodologies can drive sustainable practices, optimize project performance, and contribute to the long-term success of the construction industry. [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]

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]

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]

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]

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]