Fuzzy-based Approach Research Articles

Performance Comparison of ANFIS, FOPID-PSO and FOPID-Fuzzy Tuning Methodology for Optimizing Response of High-Performance Drilling Machine

A fractional-order proportional integral derivative (FOPID)controller has replaced the classical PID controller used in industries for process control application. FOPID is less sensitive to the change of control parameters than PID Controller, due to which better results are obtained. The FOPID provides a robust and stable system for a higher-order system as of iso- damping property. This study aims to find a stable and controlled structure by tuning the FOPID controller with the Particle Swarm Optimization (PSO) algorithm, fuzzy-based logic approach, and Adaptive Neuro-fuzzy inference system (ANFIS). The tuning of FOPID is done to overcome deficits of PID controller using the different types of optimization method to overcome large overshoot and large settling time. This paper presented useful techniques based on PSO, fuzzy logic, and ANFIS to optimize FOPID controlled high-performance drilling machines. On the analysis and comparisons of simulation findings, it is observed that the FOPID-PSO approach provides better performance over the Ziegler-Nichols (ZN)-FOPID and the above-mentioned intelligent techniques in terms of less settling time (ts=0.823sec.) & optimized peak overshoot (Mp=2.44%) for the mentioned target.

Analysis on change detection techniques for remote sensing applications: A review

Satellite images taken on the earth's surface are analyzed to identify the spatial and temporal changes that have occurred naturally or manmade. Real-time prediction of change provides an understanding related to the land cover, environmental changes, habitat fragmentation, coastal alteration, urban sprawl, etc. In the current study, various digital change detection approaches and their constituent methods are presented. It was found that (i) change vector analysis method provides better accuracy among the algebra-based change detection approach, (ii) discrete wavelet transformation is better among transformation techniques, (iii) considering the artificial neural network and fuzzy-based approaches to analyze the prediction performance over the traditional state-of-the-art approaches, (iv) analyzing the promising outcomes generated by deep learning techniques for difference analysis related to the images captured at a different instance of time. The brief outlines of different change detection approaches are discussed in this study and addressed the need for improvement in the methods that are developed for the detection of a change in the remote sensing community.

A fuzzy-based approach for resource management in SDN-VANETs: Effect of trustworthiness on assessment of available edge computing resources

Vehicular Ad hoc Networks (VANETs) aim to improve the efficiency and safety of transportation systems by enabling communication between vehicles and roadside units, without relying on a central infrastructure. However, since there is a tremendous amount of data and significant number of resources to be dealt with, data and resource management become their major issues. Cloud, Fog and Edge computing, together with Software Defined Networking (SDN) are anticipated to provide flexibility, scalability and intelligence in VANETs while leveraging distributed processing environment. In this paper, we consider this architecture and implement and compare two Fuzzy-based Systems for Assessment of Neighboring Vehicles Processing Capability (FS-ANVPC1 and FS-ANVPC2) to determine the processing capability of neighboring vehicles in Software Defined Vehicular Ad hoc Networks (SDN-VANETs). The computational, networking and storage resources of vehicles comprise the Edge Computing resources in a layered Cloud-Fog-Edge architecture. A vehicle which needs additional resources to complete certain tasks and process various data can use the resources of the neighboring vehicles if the requirements to realize such operations are fulfilled. The proposed systems are used to assess the processing capability of each neighboring vehicle and based on the final value, it can be determined whether the edge layer can be used by the vehicles in need. FS-ANVPC1 takes into consideration the available resources of the neighboring vehicles and the predicted contact duration between them and the present vehicle, while FS-ANVPC2 includes in addition the vehicles trustworthiness value. Our systems take also into account the neighboring vehicles’ willingness to share their resources and determine the processing capability for each neighbor. We evaluate the proposed systems by computer simulations. The evaluation results show that FS-ANVPC1 decides that helpful neighboring vehicles are the ones that are predicted to be within the vehicle communication range for a while and have medium/large amount of available resources. FS-ANVPC2 considers the same neighboring vehicles as helpful neighbors only if they have at least a moderate trustworthiness value ( VT = 0.5). When VT is higher, FS-ANVPC2 takes into consideration also neighbors with less available resources.

Fuzzy-based approaches for agri-food supply chains: a mini-review

Fuzzy-based approaches for agri-food supply chains: a mini-review

Decentralized Quality of Service Based System for Energy Trading Among Electric Vehicles

This paper incorporates a new perspective into P2P energy trading coordination schemes for EVs by considering Quality of Service (QoS) management. QoS could be utilized as a control metric to facilitate resilient and reliable transactions according to user preferences. To that end, this paper proposes a novel decentralized QoS-based system for P2P energy trading among EV energy providers and consumers. The system utilizes smart contracts to carry out the matching between EVs and monitor the delivery of a QoS-based P2P contract without the presence of a third party. Two QoS-based mechanisms are proposed to match trading EVs in this system. The proposed mechanisms are designed to match single-consumer to multiple-providers and multiple-consumers to multiple-providers based on consumers’ and providers’ QoS requirements and offers, respectively. A fuzzy-based approach with minimum and intelligible input is introduced to determine the weight values of each QoS attribute. Further, a penalty mechanism is developed to discourage dishonest requests/offers and ensure that trading parties stick to their contractual obligations. Numerical simulations are conducted to validate the effectiveness of the proposed QoS-based mechanisms.

Inventory classification system in space mission component replenishment using multi-attribute fuzzy ABC classification

PurposeThis project attempts to present a space component inventory classification system for space inventory replenishment and management. The authors propose to adopt a classification system that can incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment.Design/methodology/approachA fuzzy-based approach with ABC classification is proposed to incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment of the soil preparation system (SOPSYS) which is used in grinding and sifting Phobos rocks to sub-millimeter size in the Phobos-Grunt space mission. An information system was developed using the existing platform and was used to support the key aspects in performing inventory classification and purchasing optimization.FindingsThe proposed classification system was found to be able to classify the inventory and optimize the purchasing decision efficiency. Based on the information provided from the system, implementation plans for the SOPSYS project and related space projects can be proposed.Research limitations/implicationsThe paper addresses one of the main difficulties in handling qualitative or quantitative classification criteria. The model can be implemented using mathematical calculation tools and integrated into the existing inventory management system. The proposed model has important implications in optimizing the purchasing decisions to shorten the research and development of other space instruments in space missions.Originality/valueInventory management in the manufacture of space instruments is one of the major problems due to the complexity of the manufacturing process and the large variety of items. The classification system can optimize purchasing decision-making in the inventory management process. It is also designed to be flexible and can be implemented for the manufacture of other space mission instruments.

Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches

Due to increase in the public and stakeholders’ awareness regarding economic, environmental, and social issues, the construction industry tends to follow the sustainability policies and practices in supply chain management. Hence, one of the most crucial aspects for a construction company in this regard is sustainable supplier selection, and, to this end, an accurate and reliable model is required. In this paper a hybrid fuzzy best-worst method and fuzzy inference system model is developed for sustainable supplier selection. In the first phase of this study, after determining 19 criteria in three main aspects, the final weight of each aspect and criterion is obtained using fuzzy best-worst method approach. In the second phase, the most sustainable supplier is selected by running the weighted fuzzy inference system both in aspect and criterion level, providing more accurate results compared to the use of other available models. Finally, two different tests are employed to validate the results and evaluate the robustness of the proposed model. The novel developed model enables the decision-maker to simulate the decision-making process, reduce the calculations loads, consider a large number of criteria in decision making, and resolve the inherited uncertainties in experts’ responses.

A Fuzzy-Based Approach to Enhance Cyber Defence Security for Next-Generation IoT

In the modern era, the Cognitive Internet of Things (CIoT) in conjunction with IoT evolves which provides the intelligence power of sensing and computation for next-generation IoT (Nx-IoT) networks. The data scientists have discovered a large amount of techniques for knowledge discovery from processed data in CIoT. This task is accomplished successfully and data proceeds for further processing. The major cause for the failure of IoT devices is due to the attacks, in which Web spam is more prominent. There seems a requirement of a technique which can detect the Web spam before it enters into a device. Motivated from these issues, in this article, a cognitive spammer framework (CSF) for Web spam detection is proposed. CSF detects the Web spam by fuzzy rule-based classifiers along with machine learning classifiers. Each classifier produces the quality score of the webpage. These quality scores are then ensembled to generate a single score, which predicts the spamicity of the webpage. For ensembling, the fuzzy voting approach is used in CSF. The experiments were performed using a standard data set WEBSPAM-UK 2007 with respect to accuracy and overhead generated. From the results obtained, it has been demonstrated that CSF improves the accuracy by 97.3%, which is comparatively high in comparison to the other existing approaches in the literature.

An objective decision support for designing virtual enterprises using a fuzzy-based approach

An objective decision support for designing virtual enterprises using a fuzzy-based approach

FQ‐MEC: Fuzzy‐Based Q‐Learning Approach for Mobility‐Aware Energy‐Efficient Clustering in MANET

Different schemes have been proposed for increasing network lifetime in mobile ad hoc networks (MANETs) where nodes move uncertainly in any direction. Mobility awareness and energy efficiency are two inescapable optimization problems in such networks. Clustering is an important technique to improve scalability and network lifetime, as it relies on grouping mobile nodes into logical subgroups, called clusters, to facilitate network management. One of the challenging issues in this domain is to design a real‐time routing protocol that efficiently prolongs the network lifetime in MANET. In this paper, a novel fuzzy‐based Q‐learning approach for mobility‐aware energy‐efficient clustering (FQMEC) is proposed that relies on deciding the behavioral pattern of the nodes based on their stability and residual energy. Also, Chebyshev’s inequality principle is applied to get node connectivity for load balancing by taking history from the monitoring phase to increase the learning accuracy. Extensive simulations are performed using the NS‐2 network simulator, and the proposed scheme is compared with reinforcement learning (RL). The obtained results show the effectiveness of the proposed protocol regarding network lifetime, packet delivery ratio, average end‐to‐end delay, and energy consumption.

An Efficient Threshold-Fuzzy-Based Algorithm for VM Consolidation in Cloud Datacenter

Cloud computing has grown exponentially in the recent years. Data growth is increasing day by day, which increases the demand for cloud storage, which leads to setting up cloud data centers. But they consume enormous amounts of power, use the resources inefficiently, and also violate service-level agreements. In this paper, an adaptive fuzzy-based VM selection algorithm (AFT_FS) is proposed to address these problems. The proposed algorithm uses four thresholds to detect overloaded host and fuzzy-based approach to select VM for migration. The algorithm is experimentally tested for real-world data, and the performance is compared with existing algorithms for various metrics. The simulation results testify to the proposed AFT_FS method is the utmost energy efficient and minimizes the SLA rate compared to other algorithms.

A psychologically-inspired fuzzy-based approach for user personality prediction in rumor propagation across social networks

Social networks have emerged as a fertile ground for the spread of rumors and misinformation in recent times. The increased rate of social networking owes to the popularity of social networks among the common people and user personality has been considered as a principal component in predicting individuals’ social media usage patterns. Several studies have been conducted to study the psychological factors influencing the social network usage of people but only a few works have explored the relationship between the user’s personality and their orientation to spread rumors. This research aims to investigate the effect of personality on rumor spread on social networks. In this work, we propose a psychologically-inspired fuzzy-based approach grounded on the Five-Factor Model of behavioral theory to analyze the behavior of people who are highly involved in rumor diffusion and categorize users into the susceptible and resistant group, based on their inclination towards rumor sharing. We conducted our experiments in almost 825 individuals who shared rumor tweets on Twitter related to five different events. Our study ratifies the truth that the personality traits of individuals play a significant role in rumor dissemination and the experimental results prove that users exhibiting a high degree of agreeableness trait are more engaged in rumor sharing activities and the users high in extraversion and openness trait restrain themselves from rumor propagation.

Implementation of Fuzzy-based Model for Prediction of Thalassemia Diseases

Thalassemia is known as one of the blood disorder diseases that is inherited by parents. There are several types of Thalassemia, namely as Thalassemia major, minor, and intermedia. Among them, Thalassemia major is the most dangerous and needs more attention. Generally, it can be detected since the child is one year old. Late detection of this disease can have adverse consequences and various complications. This study aims to develop a new model for the prediction of thalassemia for children. The model adopts a fuzzy-based rule. The novelty in this article is that our model has 4 outputs, namely thalassemia major, intermedia, minor and not thalassemia. In the previous article it only had 3 outputs. In this study, we intend to implement a model that we developed using a fuzzy-based approach to classify thalassemia diseases based on CBC data. This article describes how to build a model and implement it in software. We compare the test results with the opinion of pediatricians regarding thalassemia. The final results of testing 4 CBC data show that our proposed model has successfully identified the type of thalassemia.

A Fuzzy-Based Optimization Technique for the Energy and Spectrum Efficiencies Trade-Off in Cognitive Radio-Enabled 5G Network

In 5G systems, enhancing the energy efficiency (EE) and spectrum efficiency (SE) is a significant task that is capable of fulfilling the demand of future wireless networks and providing Quality of Service (QoS) provisioning, such as high throughput with low power consumption. In the present work, a look-up table enabled a fuzzy-based approach to investigate the SE and EE issues in 5G networks and obtained an efficient symmetrical trade-off between them to enhance the system’s overall performance. We completed simulations in NS-2.31 software version 2.31, and MATLAB is used to visualize the results. The proposed model achieved maximum values of EE and SE of 0.92 bit/J/Hz with a sensing time τs(LUT) = 20 ms. Furthermore, optimizing the sensing time and the secondary user (SU) transmission power, yielded a maximum EE and provided a QoS provisioned cognitive radio-enabled 5G network.

Toward a Fuzzy-based Approach for Computational Load Offloading of IoT Devices

Technological development and market expansion offer an increased availability of resources and computing power on IoT nodes at affordable cost. The edge computing paradigm allows keeping locally on the edge of the network a part of computing, while keeping all advantages of the cloud and adding support for privacy, real-time and network resilience. This can be further improved in IoT applications by exibly harvesting resources on IoT nodes, by moving part of the computing tasks related to data from the edge server to the nodes, raising the abstraction level of the data aspects of the architecture and potentially enabling larger IoT networks to be efficiently deployed and managed, in a stand-alone logic or as a component of edge architecture. Anyway, an e_cient energy management mechanism is needed for battery powered IoT networks, the most exible implementations, that dynamically balances task allocation and execution in order to In this paper we present a fuzzy logic based power management strategy for IoT subsystem that aims at maximizing the duration of the network by locally migrating part of the computing tasks between nodes. As our goal is to enable the deployment of semi-autonomic large IoT networks, our proposal does not rely on external resources for migration control and operates on a local basis to ensure scalability: at the best of our knowledge, this differentiates our proposal with respect to similar solutions available in literature.

Automated assessment of architectural spatial layout configurations using fuzzy logic

PurposeConventional approaches of evaluating spatial layout configurations typically involved universal understandings of aspects like connectivity, proximity and visibility, while possibly discarding both partially true solutions and ranges of parameters affecting detailed spatial relations. With the growing need to address spatial uncertainty and ambiguity, the incorporation of methods that embrace soft qualities in design is becoming increasingly significant in spatial layout planning.Design/methodology/approachThe authors introduce a fuzzy-based approach for the automated assessment of architectural spatial layout configurations while addressing ambiguity in layout design. The authors evaluate soft interdependent design qualities like connectedness, enclosure and spaciousness to satisfy multiple mutually inclusive criteria and account for all logical solutions without discarding likely or less likely solutions. The authors analyze spatial entities, parameters and relations and identify rulesets for logical configurations using linguistic variables, fuzzy sets, membership functions and descriptive rule blocks. As a case study, the authors use grasshopper and fuzzyTECH to represent four pilot layout alternates with varying attributes and a case study focusing on one specific spatial criterion.FindingsMultiple complex and nuanced spatial relations were inferred by evaluating spatial outputs and their inherent discrepancies and correlations, thus confirming the assumption that fuzzy-based systems could potentially satisfy multiple mutually inclusive criteria and account for exhaustive logical solutions without discarding preferable, likely or less likely solutions.Originality/valueMost precedent approaches focus on spatial layout design from an occupancy-centered perspective, where occupancy patterns and possibilities are identified in loosely defined spaces or behavioral usage patterns. The added value in this paper involves including a wide array of spatial inputs to describe soft spatial qualities using nuanced rule-based descriptors.

Integration of adaptive neural fuzzy inference system and fuzzy rough set theory with support vector regression to urban growth modelling

Land change models are amongst the most widely developed tools for spatial decision support. Despite this progress, only a few models have been created thus far that simulate urban growth that incorporate two important aspects of uncertainty inherent to land use dynamics: fuzziness and roughness. Combining fuzziness and roughness into models will enhance the use of these tools for decision support. This study applied and evaluated a fuzzy-based approach to the feature selection effects on the accuracy of a land change model. Fuzzy rough set theory (FRST) was employed here as feature selection method and was integrated with a support vector regression (SVR) algorithm to simulate urban growth of Tabriz mega city in northwest Iran. In order to apply feature selection to a FRST algorithm, incoming data has been first fuzzified by an adaptive neural fuzzy inference system (ANFIS). To evaluate the application of FRST, SVR was used with and without FRST (SVR and SVR-FRST), while for performance evaluation logistic regression (LR) and kernelled LR (KLR) models were integrated with and without FRST (LR, LR-FRST, KLR, and KLR-FRST). The accuracy of the simulated maps of all models were evaluated by calculating the overall accuracy (OA), true positive rate (TPR), true negative rate (TNR), total operating characteristic (TOC) and their area under curve (AUC). The results showed that integrating FRST with the above-mentioned models enhanced the overall performances based on the above criteria. Among the above mentioned models, SVM-FRST and KLR-FRST yielded the best goodness of fit measures. Moreover, SVM-FRST with 83.6% OA, 41.6% TPR, and 90.4% TNR performs better than KLR-FRST with 82.4% OA, 37.4% TPR, and 89.8% TNR. However, KLR-FRST has more AUC, less green area destruction, more barren to urban areas conversion, and fast tuning process related to SVR-FRST. Finally, we suggest that KLR-FRST and SVR-FRST are, among those evaluated, the most appropriate models for urban growth modelling of the Tabriz mega city of Iran when considering uncertainty.

Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders.

The main aim of this paper is to optimize the output of diagnosis of Cardiovascular Disorders (CVD) in Photoplethysmography (PPG) signals by utilizing a fuzzy-based approach with classification. The extracted parameters such as Energy, Variance, Approximate Entropy (ApEn), Mean, Standard Deviation (STD), Skewness, Kurtosis, and Peak Maximum are obtained initially from the PPG signals, and based on these extracted parameters, the fuzzy techniques are incorporated to model the Cardiovascular Disorder(CVD) risk levels from PPG signals. Optimization algorithms such as Differential Search (DS), Shuffled Frog Leaping Algorithm (SFLA), Wolf Search (WS), and Animal Migration Optimization (AMO) are implemented to the fuzzy modeled levels to optimize them further so that the PPG cardiovascular classification can be characterized well. This kind of approach is totally new in PPG signal classification, and the results show that when fuzzy-inspired modeling is implemented with WS optimization and classified with the Radial Basis Function (RBF) classifier, a classification accuracy of 94.79% is obtained for normal cases. When fuzzy-inspired modeling is implemented with AMO and classified with the Support Vector Machine–Radial Basis Function (SVM–RBF) classifier, a classification accuracy of 95.05% is obtained for CVD cases.

Large-Scale Malicious Software Classification With Fuzzified Features and Boosted Fuzzy Random Forest

Classification of malicious software, especially in a very large dataset, is a challenging task for machine intelligence. Malware can have highly diversified features, each of which has highly heterogeneous distributions. These factors increase the difficulties for traditional data analytic approaches to deal with them. Although deep learning based methods have reported good classification performance, the deep models usually lack interpretability and are fragile under adversarial attacks. To solve these problems, fuzzy systems have become a competitive candidate in malware analysis. In this article, a new fuzzy-based approach is proposed for malware classification. We focused on portable executable files in the Windows platform and analyzed the distributions of static features and content-oriented features. Fuzzification was used to reduce the ubiquitous impact of noise and outliers in a very large dataset. Finally, a novel boosted classifier consisted of fuzzy decision trees and support vector machine is proposed to perform the malware classification. By using fuzzy decision trees, the inner structure of the classifier can be readily interpreted as discriminative rules, whereas the novel boosting strategy provides state-of-the-art classification performance. Extensive experimental results showed that our method significantly outperformed several state-of-the-art classifiers.

A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns

Relief distribution and victim evacuation are the crucial emergency relief operations after sudden-onset disasters to alleviate the repercussions of catastrophes in the concerned areas, although aftershocks and unfair distribution of relief items can affect these planning and beget plenty of undesirable reflections. In this paper, a new multi-objective reliable optimization model to organize a humanitarian relief chain is rendered to make a broad range of decisions, including reliable facility location-allocation, fair distribution of relief items, assignment of victims, and routing of trucks. For this purpose, the first objective function is to minimize the total logistics costs, the second one is to minimize the total time of relief operations, and the third one minimizes the variation between upper and lower bounds of transportation cost of distribution centers to regulate the workload of them. What is more, due to the fact that the uncertain essence of catastrophes, such as demand, the capacity of facilities, miscellaneous costs and transportation times, a novel uncertainty approach, including robust optimization and the neutrosophic set, is proposed to surmount these obstacles. Ultimately, a real case study is examined to illustrate the validity of the proposed model and solution method. The obtained results reveal that via increasing the capacity of the emergency centers by 30%, the total cost of the humanitarian relief network is reduced by 18%, and the operating time is reduced by 9%. What is more, if the probability of disruption in one of the distribution centers reaches zero, the logistics costs will be reduced to approximately 20%, and also the distance between the maximum and minimum distance traveled will be reduced by 30%, and the workload between distribution centers will be more balanced.