Fuzzy Cognitive Maps Research Articles

The role of network structure in integrated water management: a case study of collaboration and influence for adopting nature-based solutions

IntroductionIntegrated water management (IWM) involves a range of policies, actions, and organizational processes that go beyond traditional hydrology to consider multifaceted aspects of complex water resource systems. Due to its transdisciplinary nature, IWM comprises input from diverse stakeholders, each with unique perceptions, values, and experiences. However, stakeholders from differing backgrounds may disagree on best practices and collective paths forward. As such, successful IWM must address key governance principles (e.g., information flow, collective decision-making, and power relations) across social and institutional scales. Here, we sought to demonstrate how network structure impacts shared decision-making within IWM.MethodsWe explored a case study in Houston, Texas, USA, where decision-making stakeholders from various sectors and levels of governance engaged in a participatory modeling workshop to improve adoption of nature-based solutions (NBS) through IWM. The stakeholders used fuzzy cognitive mapping (FCM) to define an IWM model comprising multifaceted elements and their interrelationships, which influenced the adoption of NBS in Houston. We applied grounded theory and inductive reasoning to categorize tacit belief schemas regarding how stakeholders viewed themselves within the management system. We then used FCM-based modeling to explore how unique NBS policies would translate into more (or less) NBS adoption. Finally, we calculated specific network metrics (e.g., density, hierarchy, and centrality indices) to better understand the structure of human-water relations embedded within the IWM model. We compared the tacit assumptions about stakeholder roles in IWM against the quantitative degrees of influence and collectivism embedded within the stakeholder-defined model.Results and discussionOur findings revealed a mismatch between stakeholders' external belief statements about IWM and their internal assumptions through cognitive mapping and participatory modeling. The case study network was characterized by a limited degree of internal coordination (low density index), high democratic potential (low hierarchy index), and high-efficiency management opportunities (high centrality index), which transcended across socio-institutional scales. These findings contrasted with several of the belief schemas described by stakeholders during the group workshop. We describe how ongoing partnership with the stakeholders resulted in an opportunity for adaptive learning, where the NBS planning paradigm began to shift toward trans-scale collaboration aimed at high-leverage management opportunities. We emphasize how network analytics allowed us to better understand the extent to which key governance principles drove the behavior of the IWM model, which we leveraged to form deeper stakeholder partnerships by identifying hidden opportunities for governance transformation.

Modeling of Russian–Ukrainian war based on fuzzy cognitive map with genetic tuning

The Russian–Ukrainian conflict is considered as a dynamic system, whose variables are factors affecting the losses of the Russian army and the threat of the use of nuclear weapons. A fuzzy cognitive map (FCM) is used for modeling, that is, a directed graph whose vertices are model variables, and the weights of arcs are the degrees of positive and negative influences of variables on each other. The following factors influencing the losses of the Russian army and the threat of a nuclear strike were selected: resistance of the Ukrainian army, support of Ukraine with weapons, economic sanctions against Russia, opposition to the Russian government and its self-preservation instinct. The degrees of the influence of factors on each other and on the possibility of using nuclear weapons are evaluated by experts using fuzzy terms, which correspond to numeric values. To adjust the FCM, a genetic algorithm is used to select the degrees of influence of factors that minimize the discrepancy between the simulation results and expert estimations. The obtained FCM is used for scenario modeling of the conflict according to the “what if” scheme and ranking of factors according to their degree of influence on the level of nuclear threat.

Multi-scenario analysis on hydrogen production development using PESTEL and FCM models

Governments efforts to reduce future emissions and change the trajectory of fossil fuel consumption must include widespread adoption of the Hydrogen (H2) promise. However, from a long-term viewpoint, inherent uncertainties drive the strategic vision of H2 development, usage and establishment. The future trends created by a variety of exogenous and endogenous elements, including Political, Economic, Social, Technological, Environmental, and Legal factors (PESTEL) are significantly influenced by these unpredictable volatilities as they come into play. The objective of this study is to recognize, define, and analyze significant PESTEL aspects influencing the dynamics of H2 energy in an unpredictably changing environment. Fuzzy Inference systems (FIS) and Fuzzy Cognitive Maps (FCM) methodologies were used for the case scenario of Greece to analyze how interrelated criteria from the previous categories interact dynamically and behave in a holistic approach. With the help of diverse quadruple helix stakeholders, a survey and three workshops were conducted to establish a participatory modeling framework with multi-level scenario analysis phases for the Greek use case. Initially, an FCM model was determined as an amalgamation of qualitative and quantitative valued concepts. This model was augmented by four more concepts related to Greek H2 production plants projecting future trends of this industry. We run several scenarios (including best and worst case) to analyze the influence of politics and regional economy to the growth of H2 production industry. The scenario of energy crisis depicts a limited positive trend of the hydrogen production. While the economy grows, and the crisis diminishes results show a stronger turn to the hydrogen production. Societal factors affect the process in all phases and scenarios. Results span from deterioration trends to slight improvements depending on the starting values of key criteria.

Distributed Genetic Algorithm for Community Detection in Large Graphs with a Parallel Fuzzy Cognitive Map for Focal Node Identification

This study addresses the importance of focal nodes in understanding the structural composition of networks. To identify these crucial nodes, a novel technique based on parallel Fuzzy Cognitive Maps (FCMs) is proposed. By utilising the focal nodes produced by the parallel FCMs, the algorithm efficiently creates initial clusters within the population. The community discovery process is accelerated through a distributed genetic algorithm that leverages the focal nodes obtained from the parallel FCM. This approach mitigates the randomness of the algorithm, addressing the limitations of the random population selection commonly found in genetic algorithms. The proposed algorithm improves the performance of the genetic algorithm by enabling informed decision making and forming a better initial population. This enhancement leads to improved convergence and overall algorithm performance. Furthermore, as graph sizes grow, traditional algorithms struggle to handle the increased complexity. To address this challenge, distributed algorithms are necessary for effectively managing larger data sizes and complexity. The proposed method is evaluated on diverse benchmark networks, encompassing both weighted and unweighted networks. The results demonstrate the superior scalability and performance of the proposed approach compared to the existing state-of-the-art methods.

Time-series forecasting based on fuzzy cognitive maps and GRU-autoencoder

Time-series forecasting based on fuzzy cognitive maps and GRU-autoencoder

Growth of digital brand name through customer satisfaction with big data analytics in the hospitality sector after the COVID-19 crisis

This study examines the best practices for the optimization of the corporate digital brand name by taking into consideration customers’ behavioral big data and web analytics. In the first stage of the study, customers’ satisfaction big data have been extracted with the assistance of a web scrapping tool from TripAdvisor for 189 hotels in Hubei province, and behavioral data from Hubei province hospitality websites have been gathered with the assistance of web analytics platforms for 5.7 million website visitors for the last 18 months. In the second stage of the research, those data have been statistically analyzed including descriptive, correlation, and regression analysis. Then, a fuzzy cognitive map has been created to present the intercorrelation between the parameters and two optimization scenarios have been developed for digital brand name and customer satisfaction. Finally, an Agent-based model has been created in order to simulate the customers’ behavior in the corporate website and TripAdvisor. The results indicated that hotels in Hubei province need to invest less in social media advertisements than search engine advertisements in order to achieve a competitive advantage and improve their digital brand name. Additionally, hotels need to develop their websites with more engaging content to maintain the customer on the corporate website for more time in order to optimize customer satisfaction in contrast to healthcare and libraries websites.

Fuzzy clustering algorithm for university students' psychological fitness and performance detection

Students' psychological fitness is unavoidable, hindering personal development, social interactions, peer influence, and adolescence. Academic stress may be the most dominant factor affecting college students' mental well-being. Therefore, improving the monitoring of mental health issues among college students is a vital topic for study. However, identifying the student's stress level is challenging, leading to uncertainty. Hence, this paper suggests Heuristic Fuzzy C-means Clustering Algorithm (HFCA) for analyzing college students' stress levels, psychological well-being and academic performance detection. The data are collected from the Kaggle stress dataset for predicting student mental health. This study investigates the psychological factors affecting students' academic performance using the suggested HFCA. Students' performance may be predicted using the Fuzzy Cognitive Map (FCM) in this study. This study used fuzzy clustering algorithms to discover the most crucial aspects of student success, such as student involvement and satisfaction. A better understanding of the risk factors for and protective factors against poor mental health can serve as the basis for developing policies and targeted interventions to prevent mental health problems and guarantee that at-risk students can access the help they need. The experimental analysis shows the proposed method HFCA to achieve a high student performance ratio of 96.7%, cognitive development ratio of 97.2%, student engagement ratio of 97.5% and prediction ratio of 95.1% compared to other methods.

ETMS: Efficient Traffic Management System for Congestion Detection and Alert using HAAR Cascade

Rapid social development has resulted in the emergence of a new major societal issue: urban traffic congestion, which many cities must address. In addition to making it more difficult for people to get around town, traffic jams are a major source of the city's pollution crisis. In order to address the problems of automobile exhaust pollution and congestion, this paper uses the system dynamics approach to develop a model to study the urban traffic congestion system from the perspectives of trucks,private cars, bikes and public transportation. This project proposes a system for detecting vehicles and sending alerts when traffic levels rise to dangerous levels using Haar Cascade and Fuzzy Cognitive Maps (FCP). The proposed system uses Haar Cascade to detect moving vehicles, which are then classified using FCP. The system can make decisions based on partial or ambiguous information by utilising FCP, a soft computing technique, which allows it to learn from past actions. An algorithm for estimating traffic density is also used by the system to pinpoint active areas. In congested areas, the system will alert the driver if it anticipates a collision with another vehicle and also Experiments show that the proposed system is able to accurately detect vehicles and provide timely alerts to the driver, drastically lowering the probability of accidents occurring in heavily travelled areas.
 The importance of introducing such a system cannot be overstated in today's transportation system. It's a big deal for the future of intelligent urban planning and traffic control. Congestion relief, cleaner air, and increased security are just some of the long-term benefits that justify the high initial investment. To add, this system is adaptable to suburban and rural areas, which can also experience traffic congestion issues.

A hybrid model of implementing a smart production factory within the Industry 4.0 framework

PurposeThis study aims to introduce the model of implementation to run the smart production factories. The study also aims to investigate the Industry 4.0 technologies as enablers to deal with challenges in the way of implementation.Design/methodology/approachThis contribution benefits from two teams of experts to evaluate the challenges and technologies of Industry 4.0. The Hanlon method is applied to evaluate, rank and prioritise the challenges which are initially scored by experts’ Team 1. Then, the adjacency matrix among enablers and challenges is extracted through the opinions of experts’ Team 2. The study also uses fuzzy cognitive map (FCM) to evaluate the real weights of technologies and challenges, rank and prioritise subsequently.FindingsA total of 8 challenging obstacles and 24 key technologies have been evaluated. The findings reveals that recruit and retention of experienced managers, undefined return on investment and recruit and retention of multi-skilled workers are the most serious challenges in the way of implementing smart production factories. Furthermore, big data, IT-based management and Internet of Things are the top-ranked key enablers to face the challenges.Originality/valueTo the best of the authors’ knowledge, this study is one of the pioneering studies that uses Hanlon method to evaluate industrial challenges. Integrating Hanlon method and FCM leads to a comprehensive model of evaluation and ranking which is another novelty of this contribution. Although many research studies have been released to implement the smart factories, practical model of implementation for production factories is identified as a literature gap.

New Hybrid MCDM Approach for an Optimal Selection of Maintenance Strategies: Results of a Case Study

Summary Industrial systems are becoming more sophisticated, and their failure can result in significant losses for the company in terms of production loss, maintenance costs, fines, image loss, etc. Conventional approaches to modeling and evaluating the failure mechanisms of these systems do not consider certain important aspects, such as the interdependencies between failure modes (FMs) with information and data containing uncertainties as they are generally collected from experts’ judgments. These restrictions may lead to improper decision-making. The use of more advanced techniques to model and assess the interdependencies among components’ failures under uncertainties seems to be more than necessary to overcome these deficiencies. It is in this context that the proposed approach fits. It consists of proposing a hybrid multicriteria decision-aking (MCDM) approach that combines several techniques for a better selection of maintenance strategies. Using the failure mode and effects analysis (FMEA) technique, the potential FMs of components, along with their causes and effects, are identified. The relative importance (or weight) of these FMs is determined using the fuzzy simple additive weighing (FSAW) method based on how they affect the system’s goals. The causal relationships between FMs and their final weights are determined by the fuzzy cognitive maps (FCM) method and the nonlinear Hebbian learning and differential evolution (NHL-DE) algorithm. Finally, based on the final FM weights provided by the FCM, the simple additive weighing (SAW) method is used to select the optimal maintenance strategies. The results of applying the proposed approach to an operating compressor lubrication and sealing oil system demonstrate its importance and usefulness in assisting system operators to efficiently allocate the optimal maintenance strategies, considering the strong correlation between FMs and their effects on system performance while accounting for the uncertainties associated with experts’ judgments. These correlation effects have led to changes in the assigned weights of the selected FMs. Specifically, the FM related to the low output of the lube/seal oil pump, which was initially assigned a lower priority, and with the correlation effects has become the first critical FM. This shift in prioritization emphasizes the need to address this particular FM promptly. By focusing on addressing these high-priority FMs, maintenance efforts can be optimized to prevent or mitigate more severe consequences. Among the various maintenance strategies evaluated, it was determined that the combination of condition-based maintenance (CBM) and precision maintenance (PrM) yields the most favorable outcome in terms of mitigating the impact of accidental failures and undesired events on the selected system.

An integrated assessment of food waste model through intuitionistic fuzzy cognitive maps

In recent years, the waste management field has received substantial attention from policymakers, organizations, academics, and researchers due to increased focus on sustainability and carbon footprint reduction as well as concerns around rapid depletion of natural resources, public health, and environmental impact. Studies on food waste management have become especially important given the dramatic growth of the world's population and global hunger and malnutrition crisis. Considering the fact that one-third of the world's food supply is wasted or lost annually while hundreds of millions of people are living with food insecurity, it is easy to understand the importance of research studying appropriate food waste management actions for sustainability. Since the subject of food waste involves complicated linkages, the determination of a suitable model is pivotal. Integrated assessment models (IAMs) have been commonly used to uncover hidden patterns and present insights to policymakers. Furthermore, these models are well-designed to integrate data, information, and multidisciplinary knowledge into a single framework. This project presents a Fuzzy Cognitive Map (FCM) extended with intuitionistic fuzzy sets using the documentary coding method. This intuitionistic FCM (iFCM) is used to analyze the primary factors, explore the interactions between food waste factors, and prioritize some policies to reduce food waste by incorporating hesitancy weight factors representing the lack of information. Then several what-if scenario analyses are generated to review the interrelationships between factors in the developed model and facilitate a decision-making process for researchers. Eventually, it is concluded that food waste reduction is achievable with the implementation of the right policies, and this also improves the other concepts such as the intention not to food waste, shopping routines, and planning routines.

A hesitant approach to sustainable supply chain risk assessment

Supply chains (SCs) are increasingly vulnerable to disruptive events, posing risks to all involved parties. Managing and mitigating these risks is crucial for resilient and sustainable SC, aligned with global sustainability goals. This study proposes a new hesitant approach to classical fuzzy cognitive mapping (FCM) to examine the underlying structure of sustainable supply chain risks (SCRs) and their long-term impact on SC performance. By handling the uncertainty caused by human doubt, the proposed approach captures both interpersonal and intrapersonal hesitancy in complex SC systems with negative and positive causalities. The applicability of the proposed approach is demonstrated through an application for a generic SC. The findings reveal that risk events linked to the economic, social, and environmental dimensions of sustainability have a negative impact on SC performance. Supplier-related risks are identified as the most critical risks in sustainable SC systems, followed by operational risks and demand-side risks. The proposed approach enhances risk assessment accuracy and robustness, providing SC managers with reliable insights for effective risk mitigation strategies, and offering valuable guidance in managing SCRs in a sustainable manner. It highlights the significance of a holistic approach to supply chain management (SCM) by identifying critical concepts related to sustainable SCRs.

Research on the risk evaluation of urban wastewater treatment projects based on an improved fuzzy cognitive map

To address the issues of ambiguous uncertainty and conflicting evidence in risk evaluation of wastewater treatment public-private partnership (PPP) project, a risk evaluation approach based on an evidence-based reasoning (ER)-improved fuzzy cognitive map (FCM) and 15 secondary evaluation indexes system are proposed. Then, ER is used to fuse expert opinions to obtain a correlation matrix. Finally, the rank evaluation results are obtained by FCM inference. External environmental risk (S), investment and financing risk (F) are the risk factors that have the greatest impact and sensitiveness on PPP wastewater treatment projects. For predictive analysis, the maximum project risk value was 0.5523 when the S value was set to 1, followed by the project risk value when the F value was set to 1. For diagnostic analysis, the greatest change in S value converged to 0.4978 when all risk factors were set to a neutral value of 0.5, while the greatest change in project risk was observed when all risk factors were set to 1. In addition, policy and legal risk, economic risk and market risk are the most sensitive risk factors among all secondary risk factors. The results are able to provide reliable project risk assessment solutions for project decision makers.

Fuzzy Cognitive Networks in Diverse Applications Using Hybrid Representative Structures

Fuzzy cognitive networks (FCNs) arose from traditional fuzzy cognitive maps (FCMs) to have the advantage of guaranteed convergence to equilibrium points, thus being more suitable than conventional FCMs for a variety of pattern recognition and system identification tasks. Moreover, recent developments led to FCNs with functional weights (FCNs-FW), as a significant FCNs enhancement in terms of storage requirements, efficiency and less human intervention requirements. In this paper we proceed further by introducing hybrid deep learning structures, interweaving FCNs-FW with well established deep neural network (DNN) representative structures and apply the new schemes on a variety of pattern recognition and time series prediction tasks. More specifically, after discussing general issues related to the construction of deep learning structures using FCNs-FW we present three hybrid models, which combine the FCN-FW with convolutional neural networks (CNNs), echo state networks (ESNs) and AutoEncoder (AE) schemes, respectively. The hybrid schemes are tested on diverse benchmark data sets and prove that FCN-FW based hybrid schemes perform equally well or better than state-of-the-art DNN-based schemes, paving thus the way for using cognitive networks to deep learning representative structures.

Port resilience analysis based on the HHM-FCM approach under COVID-19

Ports are critical for global trade as they serve as essential gateways for the movement of cargo. To ensure uninterrupted transport services and minimize the impact of disruptive events, enhancing port resilience is of utmost importance. In this study, we developed a hybrid framework combining Hierarchical Holographic Modeling (HHM) with a fuzzy cognitive map (FCM) to analyze the interconnectedness of various factors that contribute to port resilience. Our significant contribution lies in adopting the “ship-port-cargo” perspective, which enabled us to effectively identify and filter relevant factors for enhancing port resilience. The results revealed that supply and demand balance, along with schedule reliability, are the most influential factors contributing to port resilience during the COVID-19 pandemic. The proposed method allows for the examination of both direct and indirect impacts, making network analysis and scenario simulations a valuable tool for studying the influence path. By leveraging this framework, port managers can make informed decisions to enhance port resilience and effectively mitigate the consequences of disruptive events.

Modeling Supply Chain Firms’ Stock Prices in the Fertilizer Industry through Innovative Cryptocurrency Market Big Data

Due to the volatility of the markets and the ongoing crises (COVID-19, the Ukrainian war, etc.), investors are keen to exploit any potential chances to make profits. For this reason, the idea of harvesting data from cryptocurrency market users takes an innovative step. Potential investors in supply chain firms in the fertilizer industry need to know whether the observation of data originating from the cryptocurrency market is capable of explaining their stock price variation. The authors identify the innovative utilization of cryptocurrency markets’ user analytical data to model and predict the stock price of supply chain firms in the fertilizer industry stock price. The main aim of this research is to evaluate the contribution of cryptocurrency market big data as a predicting factor for the stock price of fertilizer market firms. Such a finding improves the knowledge and decision-making of potential investors in the fertilizer market. Moreover, this study seeks to highlight the benefits of utilizing cryptocurrency market big data for other financial purposes, apart from stock price prediction. The analytical data was derived from cryptocurrency websites and applications and was then processed through statistical analysis (correlation and linear regressions), Fuzzy Cognitive Maps (FCM), and Hybrid Modeling (HM) modeling. The hybrid model’s simulation showed that analytical data from the cryptocurrency markets tend to explain and predict the stock price of supply chain firms in the fertilizer industry. Such data refer to Bitcoin’s website organic keywords and traffic costs, as well as paid traffic costs from cryptocurrency trade websites/apps. A rise in Bitcoin and cryptocurrency trade websites’ organic and paid traffic costs tend to increase supply chain firms in the fertilizer industry’s stock prices, while Bitcoin’s website organic keywords variation decreases accordingly.

Modelling Differential Diagnosis of Febrile Diseases with Fuzzy Cognitive Map.

The report of the World Health Organization (WHO) about the poor accessibility of people living in low-to-middle-income countries to medical facilities and personnel has been a concern to both professionals and nonprofessionals in healthcare. This poor accessibility has led to high morbidity and mortality rates in tropical regions, especially when such a disease presents itself with confusable symptoms that are not easily differentiable by inexperienced doctors, such as those found in febrile diseases. This prompted the development of the fuzzy cognitive map (FCM) model to serve as a decision-support tool for medical health workers in the diagnosis of febrile diseases. With 2465 datasets gathered from four states in the febrile diseases-prone regions in Nigeria with the aid of 60 medical doctors, 10 of those doctors helped in weighting and fuzzifying the symptoms, which were used to generate the FCM model. Results obtained from computations to predict diagnosis results for the 2465 patients, and those diagnosed by the physicians on the field, showed an average of 87% accuracy for the 11 febrile diseases used in the study. The number of comorbidities of diseases with varying degrees of severity for most patients in the study also covary strongly with those found by the physicians in the field.

Training Novel Adaptive Fuzzy Cognitive Map by Knowledge-Guidance Learning Mechanism for Large-Scale Time-Series Forecasting.

A fuzzy cognitive map (FCM) is a graph-based knowledge representation model wherein the connections of the nodes (edges) represent casual relationships between the knowledge items associated with the nodes. This model has been applied to solve various modeling tasks including forecasting time series. In the original FCM-based forecasting model, causal relationships among concepts of the FCM remain unchanged. However, causal relationships may change in time. Therefore, we propose a new learning method for training an FCM resulting in an adaptive FCM which consists of several sub-FCMs. It can select different sub-FCMs at different moments. In an active processing scenario, in which we deal with a large-scale time series with new data being continuously generated, a forecasting model built on the old data should be updated when the new data arrive. Furthermore, retraining an FCM from scratch entails increasing computing overhead that will become a serious obstacle in many practical scenarios. To overcome the above-mentioned shortcomings, this study offers an original design setting in which the FCM is updated by knowledge-guidance learning mechanism for the first time. Compared with the existing classical forecasting models, the proposed model shows higher accuracy and efficiency. Its increased performance is demonstrated through a series of reported experimental studies.

MODEL FCM JAKO METODA ZARZĄDZANIA PRZESTRZENIĄ PUBLICZNĄ

The work presents the results of research that focus on creating high-quality public space through the involvement of society and taking into account the opinions of various stakeholder groups. The main goal is to understand how the concepts of "man-space-management" can be integrated in the process of creating high-quality public places in accordance with the principles of UN-Habitat. An important research aspect is the development of FCM (Fuzzy Cognitive Maps) models and various scenarios for the development of public space, which will enable the effective use of financial resources by the government, non-governmental organizations and private investors. The research is based on the opinions of the Zielona Góra community, which were collected through "placegame" workshops and questionnaires.

Efficient strategies for finger movement classification using surface electromyogram signals.

One of the famous research areas in biomedical engineering and pattern recognition is finger movement classification. For hand and finger gesture recognition, the most widely used signals are the surface electromyogram (sEMG) signals. With the help of sEMG signals, four proposed techniques of finger movement classification are presented in this work. The first technique proposed is a dynamic graph construction and graph entropy-based classification of sEMG signals. The second technique proposed encompasses the ideas of dimensionality reduction utilizing local tangent space alignment (LTSA) and local linear co-ordination (LLC) with evolutionary algorithms (EA), Bayesian belief networks (BBN), extreme learning machines (ELM), and a hybrid model called EA-BBN-ELM was developed for the classification of sEMG signals. The third technique proposed utilizes the ideas of differential entropy (DE), higher-order fuzzy cognitive maps (HFCM), empirical wavelet transformation (EWT), and another hybrid model with DE-FCM-EWT and machine learning classifiers was developed for the classification of sEMG signals. The fourth technique proposed uses the ideas of local mean decomposition (LMD) and fuzzy C-means clustering along with a combined kernel least squares support vector machine (LS-SVM) classifier. The best classification accuracy results (of 98.5%) were obtained using the LMD-fuzzy C-means clustering technique classified with a combined kernel LS-SVM model. The second-best classification accuracy (of 98.21%) was obtained using the DE-FCM-EWT hybrid model with SVM classifier. The third best classification accuracy (of 97.57%) was obtained using the LTSA-based EA-BBN-ELM model.