Fuzzyfortify: a multi-attribute risk assessment for multi-factor authentication and cloud container orchestration

Nowadays, multi-fault diagnosis has become the most interesting topic for researchers, since it has lately attracted a substantial attention. The most published works recently have considered defects detection, identification, and classification as the toughest challenge for rotating machinery monitoring. As feature extraction requires robust techniques for online inspection with a high level of expertise to make automatic decisions on the running machine health status, a robust approach is required to adjust the misclassification of the extracted features, especially under various working conditions. In this paper, we propose the combination of two Time Domain Features (TDFs) in tandem with Singular Value Decomposition (SVD) and Fuzzy Logic System (FLS) to build an enhanced fault diagnosis technique for rolling bearing. The original vibration signal is divided first into several data samples. Thereafter, TDFs are applied on each sample to construct a feature matrix during the feature extraction step. Afterwards, SVD is performed on the obtained matrices in order to reduce their dimension and select the most stable vectors (singular values). Finally, FLS is employed as a powerful tool for automatic feature classification. Experimental results confirm that our suggested approach can enhance the ability to assess the degradation of bearing faults with a higher recognition sensitivity even under different working conditions.

This paper presents a fuzzy logic control strategy on four-wheel steering(4WS) vehicle based on a multi-body vehicle dynamic model. The multi-body vehicle dynamic model based on ADAMS can accurately predict the dynamics performance of the vehicle. Fuzzy logic is applied to track the yaw velocity of the two degrees of freedom ideal model through the co-simulation of ADAMS and Matlab Fuzzy control unit with the optimized membership function. The fuzzy control parameters are optimized and analyzed by a combined optimization algorithm (Genetic Algorithm (GA) and Nonlinear Programming Quadratic Line search (NLPQL) method) combined with response surface model (RSM). Single lane change test is chosen to validate the fuzzy control logic strategy. Simulation result shows that four-wheel steering vehicle with the fuzzy control logic strategy can improve vehicle handling stability greatly comparing with traditional front wheel steering.

Using Fuzzy AHP to manage Intellectual Capital assets: An application to the ICT service industry

Bearing diagnosis has attracted considerable research interest; thus, researchers have developed several signal processing techniques using vibration analysis to monitor the rotating machinery’s conditions. In practical engineering, features extraction with most relevant information from experimental vibration signals under variable operation conditions is still regarded as the most critical concern. Therefore, actual works focus on combining Time Domain Features (TDFs) with decomposition techniques to obtain accurate results for defect detection, identification, and classification. In this paper, a new hybrid method is proposed, which is based on Time Synchronous Averaging (TSA), TDFs, and Singular Value Decomposition (SVD) for the feature extraction, then the Adaptive Neuro-Fuzzy Inference System (ANFIS) which gathers the advantages of both neural networks and fuzzy logic is applied for the classification process. First, TSA is used to reduce noises in the vibration signal by extracting the periodic waveforms from the disturbed data; thereafter, TDFs are applied on each synchronous signal to construct a feature matrix; afterwards, SVD is performed on the obtained matrices to remove the instability of statistical values and select the most stable vectors. Finally, ANFIS is implemented to provide a powerful automatic tool for features classification.

This study presents a novel application of the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) to evaluate multi-dimensional risks in a motorcycle spare parts manufacturing setting, with a strong emphasis on environmental sustainability. Unlike conventional safety assessments that focus solely on worker injuries or equipment damage, this research integrates environmental impact, such as emissions and ecological harm as a core criterion within the multi-criteria decision-making process. By embedding environmental considerations alongside health, asset, and reputational risks, the proposed model offers a more comprehensive and sustainable risk analysis approach. Thirteen hazards across six production activities were assessed, resulting in classifications of extreme (7.7%), high (53.8%), and medium (38.5%) risk levels. Control strategies include elimination, substitution, engineering measures, administrative actions, and PPE. By addressing the complexity and uncertainty in risk prioritization through fuzzy logic and expert input, this study supports SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action): a rare intersection in risk analysis research that highlights the integration of safety and environmental stewardship in sustainable manufacturing.

With the increasing need for flexibility and adaptivity in computerized systems, the application of fuzzy expert systems is becoming increasingly commonplace in today's industry. Fuzzy logic expert systems often improve performance by allowing knowledge to generalize without requiring the knowledge engineer to anticipate all possible situations. Thus, for many types of applications, soft such as Fuzzy logic can incur lower overhead in terms of representing and engineering task knowledge. My project investigates the application of fuzzy expert systems to motion tracking. Previous research showed that Fuzzy Logic can be used to track the motion of a brightly colored object against a dark background, with relatively low development and run-time costs. The system I am developing identifies, tracks, and predicts the motion of multiple objects using unique identification patterns against a dark background. My poster will describe the fuzzy inference systems for tracking and motion prediction of such objects. An essential step to obtaining the fuzzy inputs for the motion tracking fuzzy inference system is to use convolution correlation data to obtain the centers of mass of the objects. Image processing information from the region around the center of each object provides good fuzzy inputs for recognizing object patterns and determining orientation. I am investigating two fuzzy inference systems for motion tracking and prediction in order to identify their strengths and weaknesses. In the long run, I hope to exploit the strengths of both by dividing the task between the two approaches. The Adaptive Neuro Fuzzy Inference System (ANFIS) is effective for pattern recognition. Fuzzy Proportional Derivative (FPD) is useful for computing errors and tuning inferences. Cascading the two fuzzy systems allows the system first to acquire generalized patterns and then fine-tune them for error tolerance. To perform this work, I am using the Matlab fuzzy logic toolkit, which is a simple and flexible tool for designing fuzzy inference systems.

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.

Introduction: With regard to increasing population of country, need to high agricultural production is essential. The most suitable method for this issue is high production per area unit. Preparation much food and other environmental resources with conservation of biotic resources for futures will be possible only with optimum exploitation of soil. Among effective factors for the most production balanced addition of fertilizers increases production of crops higher than the others. With attention to this topic, determination of soil fertility degree is essential tobetter use of fertilizers and right exploitation of soils. Using fuzzy logic and Analytic Hierarchy Process (AHP) could be useful in accurate determination of soil fertility degree. Materials and Methods: The study area (at the east of Rasht city) is located between 49° 31' to 49° 45' E longitude and 37° 7' to 37° 27' N latitude in north of Guilan Province, northern Iran, in the southern coast of the Caspian sea. 117 soil samples were derived from0-30 cm depth in the study area. Air-dried soil samples were crushed and passed through a 2mm sieve. Available phosphorus, potassium and organic carbon were determined by sodium bicarbonate, normal ammonium acetate and corrected walkly-black method, respectively. In the first stage, the interpolation of data was done by kriging method in GIS context. Then S-shape membership function was defined for each parameter and prepared fuzzy map. After determination of membership function weight parameters maps were determined using AHP technique and finally soil fertility map was prepared with overlaying of weighted fuzzy maps. Relative variance and correlation coefficient criteria used tocontrol groups separation accuracy in fuzzy fertility map. Results and Discussion: With regard to minimum amounts of parameters looks some lands of study area had fertility difficulty. Therefore, soil fertility map of study area distinct these lands and present soil fertility groups for better management of soil and plant nutrition. Weight of soil parameters was0.54, 0.29 and 0.17 for organic carbon, available phosphor and potassium, respectively. Fuzzy map of study area includes five soil fertility groups as: 22.9% very high fertility, 27.7% high fertility, 35.53% medium fertility, 10.48% low fertility and 3.39% very low fertility. Consequently, a separated map for soil fertility prepared to evaluate soil fertility of study area for rice cultivation. Toinvestigatethe efficiency of fuzzy model and AHP in increasing the accuracy of soil fertility map, soil fertility map with Boolean method prepared as well. Boolean map showed 58.88% fertile and 41.12% unfertile.15 soil samples from different soil fertility groups of study area were derived fromcontrol of maps accuracy. 13 renewed samples of 15 and 9 soil samples have matched with fuzzy and Boolean map, respectively. Comparison of parameters mean in fuzzy map fertility groups showed that parameters mean amounts of very high and high fertility groups are higher than optimum level except potassium that is a few lower than optimum level in high fertility group, therefore, addition of fertilizers in these groups could not be useful to increase rice crop production. Phosphorus parameter amount is lower than the critical level in very low, low and medium fertility groups, then in these groups phosphorus fertilizer should be added to the soil toincreaserice production. The amount of potassium parameter is higher than the critical level and lower than optimum limit in very low, low, medium and high fertility groups, then in these groups addition of potassium fertilizer will results in theincrease of production. Organic carbon amount is lower than optimum level in very low and low fertility groups. With regard to the relation between organic carbon andnitrogen and phosphorus, therefore, the addition of organic carbon fertilizer could compensate deficit of nitrogen and phosphorus in these groups as well. Attention to the presented explanations and comparison of fuzzy and Boolean maps using parameters amounts in renewed sampling points for control of maps accuracy, it is distinct that fuzzy logic could influencetheoptimum using of fertilizers with increasing map efficiency and accuracy. In addition, relative variance and correlation coefficient amounts showed that fuzzy map has separatedquite wellparameters changes. Conclusion: Effective parameters in soil fertility, includingorganic carbon, phosphorus and potassium were used topreparesoil fertility map for rice cultivation. With regard to the minimum amounts of parameters looks some lands of study area had fertility difficulty. Therefore, soil fertility map of study area distinct these lands and presents soil fertility groups tobetter management of soil and plant nutrition. Fuzzy and Boolean methods were used topreparesoil fertility map. Comparison of these two approaches showed that fuzzy method with AHP caused to increase theefficiency and accuracy of fertility map for rice. Separated and distinguish soil fertility groups in fuzzy map help suitable distribution and optimum use of fertilizers for rice production.

The robust appraisal of exploration drilling concepts is essential for establishing the economic viability of a prospective recovery field. This study evaluates the different concept selection methods that were considered for drilling operations at the Trym field in Norway. The construction of drilling rigs is a capital-intensive process, and it involves high levels of economic risk. These risks can be broadly categorised as aleatoric (i.e. those related to chance) and epistemic (i.e. those related to knowledge). Evaluating risks in the investment appraisal process tends to be a complicated process. Project risks are evaluated using Monte Carlo simulation (MCS) and are based on the fuzzy analytic hierarchy process (AHP). MCS provides a useful means of evaluating variabilities (i.e. aleatoric risks) in oil drilling operations. However, many of the economic risks in oil drilling processes are unanticipated, and, in some cases, are not readily expressible in quantitative values. The fuzzy AHP is therefore used to appraise the qualitatively defined indirect revenues comprising risks that affect future flexibilities, schedule certainty and health and safety performance. Both the Monte Carlo technique and the fuzzy AHP technique found that a cumulative revenue variation of up to 30% is possible in any of the considered drilling options. The fuzzy AHP technique estimates that the chances of profitability being less than NOK 1 billion over a five-year period is 0.5%, while the Monte Carlo technique estimates suggest a more conservative proportion of 10%. Overall, the fuzzy AHP technique is easy to use and flexible, and it demonstrates increased robustness and improved predictability.

In order to select the best suppliers it is necessary to make a trade off between these tangible and intangible factors some of which may conflict. This article proposes a new approach based on the integration of the fuzzy logic with the classical multi-criteria methods on the one hand and taking into account the concept of supplier reliability for resolving a supplier selection and evaluation problem on the other hand. Indeed, we deal with the first approach called "Method with Constraints" (MC) which consists of combination of the "Fuzzy Analytical Hierarchy Process" (FAHP) with the "Goal Programming" (GP) methods. This method reflects the idea of supplier reliability and at the same time the quantitative and qualitative factors. Considering the fuzzy constraints, we propose the second approach called "Method with Fuzzy Constraint" (MCF) which consists of combination of the FAHP with the "Fuzzy Goal Programming" (FGP) methods. We used the gravity centre method to approach the best solution, the supplier reliability approach to identify the most reliable supplier and the fuzzy logic method to find solutions for the inaccuracy and uncertainty problems. A numerical example is presented to illustrate this new approach which includes comparing the advantages and disadvantages of the selection methods for resolving a supplier selection and evaluation problem.

Energy resources have gained importance due to the increasing need for energy every day. The lack of resources of developing countries, which use especially fossil fuels more, increases foreign dependency. Thus, it is important to use renewable resources, especially in the iron and steel industry, which is one of the sectors with intensive energy use. This study was designed to investigate the low use of renewable energy sources in the sector and implemented in Karabük, which is one of the pioneering cities in Turkey's iron and steel industry. In practice, AHP method, which is one of the Multiple Criteria Decision-Making methods, is combined with fuzzy logic and Fuzzy AHP (BAHP) method is applied. Four main criteria have been determined, namely "Technical", "Economical", "Social" and "Environmental" reasons that cause low use of renewable energy resources in the steel industry. With this study, which is a pioneer in revealing low use reasons of renewable energy sources in the iron and steel industry, effective factors were identified and the order of importance was found as economic, technical, environmental and social reasons.

To enhance profitability of production cycle, any manufacturer needs effective product design and evaluation procedures. This study proposed a novel approach combining fuzzy analytic hierarchy process (FAHP) and multi-layer fuzzy inference system (MFIS). It is based on consumer online comments to improve product design. This method possesses several advantages over traditional design evaluation methods. It can quickly acquire consumer preferences, effectively handle multi-criteria decision problems and integrate uncertain and fuzzy information. The Fuzzy Analytic Hierarchy Process–Multi-layer Fuzzy Inference System (FAHP-MFIS) involves the following steps: screening of factors, hierarchical modeling, quantification of qualitative factors, and conversion of these factors into quantitative values. It is a knowledge-based system that uses logical rules. The quantity and levels of input variables directly correlates with the quantity of logical rules. However, with multi-factor and multi-level inference, the establishment of a rule base becomes impractical due to the overwhelming number of rules. To address this issue, the Taguchi orthogonal table is applied to reduce the number of logical rules. Taking a household oxygen generator for medical devices as an example, the proposed model is applied in real-time. In the first stage, web crawlers are used to collect user reviews of the household oxygen generators on large e-commerce platforms. Latent Dirichlet Allocation (LDA) models are used to screen for principal and sub-factors in the second stage. Then, sub-factors of the FAHP screening are used as inputs, and the principal factors are used as outputs. In the third stage, priority indicators are established based on principal factors such as Appearance, Basic Function, and Advanced Function. Established evaluation models are then used to rank the selected designs. The results show that the higher the priority index value of the product design scheme, the better the scheme, and vice versa. This study holds significant reference value in aiding enterprises to enhance the efficiency of their manufacturing cycle and determining the direction of product design and innovation with improved pace and accuracy. Moreover, it can be applied to other fields such as supply chain management, risk assessment, and investment decisions.

Rapid population growth integrated with poor governance and urban planning is highly challenging resulting key for the selection of unsuitable landfill sites, particularly in developing counties. Therefore, the aim of this study is to investigate the suitable solid waste landfill sites in the capital of the country as a case study, by the integration of Geographical Information System (GIS) with fuzzy logic, analytical hierarchy process (AHP), and weighted linear combination (WLC) method based on multi-criteria decision-making (MCDM). We chose thirteen (13) criteria (9 factors and 4 constraints) and grouped them into two main categories (environmental and socioeconomic) to achieve the objectives. The AHP was employed to evaluate the relative importance of the factors followed by standardization of criteria factors based on fuzzy set theory. Subsequently, all criteria factors were combined based on AHP and fuzzy logic-WLC method in order to obtain land suitability map. Finally, the sites were identified by the intersection of two combined suitability index layers. The obtained results depicted that the integration of fuzzy logic, AHP, and WLC technique with GIS can produce satisfactory results for the suitable locations of solid waste landfill sites over complex topographic regions. Overall, the land suitability obtained based on fuzzy-WLC is more refined and smooth because of its better segregation and its potential to consider full tradeoff between factors and average risk. The AHP was identified (47km2) as high suitable while fuzzy-WLC generated 36km2 as suitable area. Finally, the intersection of both suitability index map shows numerous suitable landfill sites available in Islamabad city; however, the surface areas of the sites are small at individual level (less than 15ha).

A well known method to control dynamic systems is the dynamic model inversion technique. Compensators obtained by this method may be used as feedforward controllers in the open-loop or closed-loop schemes. This technique has been widely used in the control of robotics arms. The method is conceptually simple but often requires a large amount of computation. In order to overcome this problem, a new method to design a fuzzy logic feedforward controller is presented in this paper. When designing a fuzzy logic controller, most of the time is spent in developing the fuzzy rules base to describe how the fuzzy controller should respond to the various inputs. Obtaining the rule base is usually a trial and error process. A starting set of rules are obtained and tested. The results are studied and the rules are then adjusted. The process is repeated until the desired results are achieved. An automated process would greatly simplify the design process of a fuzzy logic controller. In this paper, a two-level learning algorithm is used to obtain the inverse dynamics of a system. Changes to the learning algorithm that improves the performance of the existing algorithm are also presented. The learning algorithm allows the rules to be obtained by processing data collected from the system. The inverse dynamics are then implemented as a feedforward controller. To demonstrate the effectiveness of the learning algorithm, simulation results are presented.

Introduction: This paper presents a novel approach for classifying different types of steel rods using a combination of pre-trained convolutional neural networks (CNNs) and fuzzy logic systems. We utilize VGG16, ResNet50, and InceptionV3 models to extract features from grayscale images of steel rods, which are then classified using Support Vector Machines (SVM). Furthermore, we incorporate a fuzzy logic system to improve the final classification accuracy by combining the individual accuracies of each CNN model. Experimental results demonstrate the effectiveness of our approach in achieving high classification accuracy compared to traditional methods. Objectives: This research presents a novel multi-model approach for steel rod classification utilizing three distinct fuzzy logic systems: Fuzzy Recurrent Neural Network (RNN), Fuzzy Convolutional Neural Network (CNN), and Fuzzy Neural Network. The proposed system addresses the critical challenge of accurate defect detection and classification in steel manufacturing processes. By combining these complementary approaches, we achieve enhanced feature extraction capabilities and improved classification accuracy. Experimental results demonstrate that our multi-model system achieves a classification accuracy of 97.8%, surpassing single-model approaches by an average of 8.2%. The system successfully identifies surface defects, structural anomalies, and dimensional variations with high precision, making it suitable for real-time industrial applications. Methods: This paper introduces a novel approach for the feature extraction and classification of steel rods by integrating multiple models and utilizing fuzzy logic systems. The methodology leverages pre-trained convolutional neural networks (CNNs) for extracting comprehensive feature representations from steel rod images. These extracted features are then combined and integrated using fuzzy logic to enhance classification accuracy and reliability. By addressing the limitations of traditional manual inspection methods and single-model approaches, this integrated multi-model system offers a robust solution for real-time industrial implementation. The proposed system aims to improve the efficiency and effectiveness of quality control processes in steel rod manufacturing, ensuring consistent and accurate classification even under varying conditions and complexities. This innovative approach paves the way for more advanced and adaptable automated inspection systems in the industry. Results: The paper aims to develop an integrated multi-model system that combines three distinct fuzzy logic approaches to enhance feature extraction capabilities through complementary methodologies. By improving the classification accuracy and reliability, the system addresses the limitations of existing methods. The goal is to create a robust and adaptable system that is suitable for real-time industrial implementation for steel rod analysis and classification. This comprehensive approach leverages the strengths of multiple models and fuzzy logic techniques, ensuring accurate and efficient classification processes, even under complex and varying conditions. The proposed multi model system is designed to meet the stringent quality control requirements of industrial applications, ultimately improving productivity and product quality through reliable automated inspection and classification for steel rod with accuracy of 97.80%, precision of 97.30% and recall 97.50%. Conclusions: This paper presents a novel approach for the classification of steel rods using a combination of pre-trained CNN models and fuzzy logic systems. The experimental results demonstrate the effectiveness of our method in achieving high classification accuracy. This research demonstrates the effectiveness of a multi-model fuzzy logic approach for steel rod classification. The system achieves significant improvements over single-model approaches while maintaining real-time processing capabilities. Our approach provides a foundation for further research and development in automated industrial image classification.