Multiple Criteria Decision Making Approach Research Articles

A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis

BackgroundDNA microarrays provide informative data for transcriptional profiling and identifying gene expression signatures to help prevent progression of latent tuberculosis infection (LTBI) to active disease. However, constructing a prognostic model for distinguishing LTBI from active tuberculosis (ATB) is very challenging due to the noisy nature of data and lack of a generally stable analysis approach.MethodsIn the present study, we proposed an accurate predictive model with the help of data fusion at the decision level. In this regard, results of filter feature selection and wrapper feature selection techniques were combined with multiple-criteria decision-making (MCDM) methods to select 10 genes from six microarray datasets that can be the most discriminative genes for diagnosing tuberculosis cases. As the main contribution of this study, the final ranking function was constructed by combining protein-protein interaction (PPI) network with an MCDM method (called Decision-making Trial and Evaluation Laboratory or DEMATEL) to improve the feature ranking approach.ResultsBy applying data fusion at the decision level on the 10 introduced genes in terms of fusion of classifiers of random forests (RF) and k-nearest neighbors (KNN) regarding Yager’s theory, the proposed algorithm reached a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. Finally, with the help of cumulative clustering, the genes involved in the diagnosis of latent and activated tuberculosis have been introduced.ConclusionsThe combination of MCDM methods and PPI networks can significantly improve the diagnosis different states of tuberculosis.Clinical trial numberNot applicable.

Multi-objective optimization of a non-uniform sinusoidal mini-channel heat sink by coupling genetic algorithm and CFD model

Wavy mini-channel heat sinks (MCHS) can disrupt the development of the thermal boundary layer, resulting in superior performance compared to straight MCHS. For complete utilization of the coolant, this study proposed and optimized a non-uniform sinusoidal MCHS with varying wavelength or varying amplitude along the flow direction. The optimization for five design variables was accomplished through a multi-objective genetic algorithm, where the thermal resistance θ or the pressure drop Δp were defined as two objectives. The computational fluid dynamics (CFD) software was employed to solve all direct problems encountered during the evolutionary process. Compared to the straight MCHS with different channel widths, the optimal designs achieved reductions of 27.74 % in θ or 59.51 % in Δp. Simultaneously, all the optimal values of the wavelength ratio and amplitude ratio indicate that enhancing the heat transfer performance downstream is more efficient. It was observed that among the five design variables, the number of wave cycles is the most relevant parameter with a Spearman’s correlation up to 0.983. Subsequently, a multiple-criteria decision-making approach was employed to ascertain the best compromise solution to balance the two objectives. Although the θ of the best compromise solution could be higher by 38.57 % than that of the lowest θ solution, the Δp presents a more substantial reduction of 92.45 % after the trade-off. Compared to the straight MCHS with the same Δp, the best compromise solution with varying wavelength reduces the θ and the standard temperature deviation by 26.20 % and 76.98 % respectively, while lowering the average base temperature by 3.07 K. Therefore, the optimal non-uniform wavy MCHS, along with the optimization approach presented, is meaningful for practical applications.

The Future of Energy Storage in Vietnam: A Fuzzy Multi-Criteria Decision-Making Approach to Metal-Ion Battery Assessments

Lithium-ion (Li-ion) batteries, despite their prevalence, face issues of resource scarcity and environmental concerns, prompting the search for alternative technologies. This study addresses the need to assess and identify viable metal-ion battery alternatives to Li-ion batteries, focusing on the rapidly industrializing context of Vietnam. It acknowledges the criticality of developing a sustainable, cost-effective, and resource-efficient energy storage solution that aligns with the country’s growth trajectory. The primary objective is to evaluate the suitability of emerging metal-ion batteries—specifically sodium-ion (SIB), sodium-ion saltwater (SIB-S), magnesium-ion (MIB), and zinc-ion (ZIB)—for Vietnam’s energy storage needs, guiding future investment and policy decisions. A Fuzzy Multiple-Criteria Decision-Making (MCDM) approach is employed, incorporating both quantitative and qualitative criteria. This study utilizes the Fuzzy Best-Worst Method (BWM) to determine the relative importance of various performance indicators and then applies the Bonferroni Fuzzy Combined Compromise Solution (Bonferroni FCoCoSo) method to rank the battery alternatives. The SIBs emerged as the most promising alternative, scoring the highest in the overall evaluation. The MIBs and SIB-saltwater batteries displayed competitive potential, while the ZIBs ranked the lowest among the considered options. This research provides a strategic framework for energy policy formulation and investment prioritization. It contributes to the field by applying a fuzzy-based MCDM approach in a novel context and offers a structured comparative analysis of metal-ion batteries, enhancing the body of knowledge on sustainable energy storage technologies.

The performance analysis using Six Sigma DMAIC and integrated MCDM approach: A case study for microlens process in Vietnam

The define-measure-analyze-improve-control (DMAIC) cycle is used by the author in the context of this project to conduct research on optimizing production procedures at a business that manufactures optical components, mainly focusing on visual defects. The DMAIC cycle and techniques for quality control like Pareto charts, and experimental design and MCDM are used in this project's research to lower the error rate in the production process. Multiple criteria decision-making (MCDM) is utilized during the analysis phase of the DMAIC process to aggregate the insights of specialists and attain a more comprehensive understanding of the factors that substantially impact and contribute to microlens manufacturing defects. This enhances the Six Sigma process's agility and dependability. Microlens product is specifically the research object. After applying the DMAIC cycle, the two primary defects identified were contamination and not polishing S1. Implementing the suggested steam ratio and packaging modifications led to a noticeable enhancement. The reasonable daily product rate increased from 69 % to 79 %, saving $1932,367 annually.

A re-evaluation of how well each country is doing in terms of achieving the SDGs: An objective approach based on multi-criteria-decision-analysis

Adopting Agenda 2030, the UN issued a set of sustainable development report consisting of 17 goals. The indices created by these goals are beneficial in defining the position of a particular country across the years; however, it is relatively complex to compare its position to other countries. This is not just because it requires an extensive analysis but also because the weight of each goal must be different. It is hard to determine which goal is more important than the others, as it differs from one specialist to another. Then how should we determine the goal weights without falling into the trap of subjective suggestions? That is our question for this research paper. Therefore, we use the multiple-criteria decision-making approach (MCDM) in order to characterise each country's position more accurately. Doing so, we get diverse weights instead of just using the SDGs' arithmetical averages. Therefore, our findings show slightly different rankings than the UN’s SDG report 2022.

Comparison of Outlier Detection Methods in Linear Regression: A Multiple-Criteria Decision-Making Approach

Comparison of Outlier Detection Methods in Linear Regression: A Multiple-Criteria Decision-Making Approach

Retracted: A Novel Multiple-Criteria Decision-Making Approach Based on Picture Fuzzy Sets

Retracted: A Novel Multiple-Criteria Decision-Making Approach Based on Picture Fuzzy Sets

Mitigating uncertainty in the site selection process for a German deep geological repository through a risk-based and multi-criteria decision-making approach

Abstract. The site selection for final disposal of high-level radioactive waste is a complex process that requires careful considerations. In Germany, the site selection procedure for a deep geological repository occurs in multiple phases. Step one phase one involves the identification of 90 subareas of interest. In step two phase one, sub-regions are selected for surface exploration. Consequently, several dilemmas arise, such as the necessity to screen through a considerable number of subareas with large surfaces to select sub-regions that are most adequate for further investigation. The associated subsurface uncertainty and the high exploration and operational costs create an additional challenge when planning such a campaign. To address these challenges, we propose a method of sub-region prioritization that combines favourability indexes developed using host-rock and subarea-specific criteria with the probability of success and risk of failure estimates. This method considers not only geological factors but also the heterogeneous nature of subsurface data quality and quantity. This heterogeneity leads to uncertainties that may overshadow the advantages of specific sub-regions at the expense of others. The method focusses only on the geological barriers of the repository system: the containment providing the rock zone, the surrounding host rock, and the overburden. The geological barriers are dealt with as a multi-component, multi-physics system. For each component, a risk matrix is generated; consequently, a risk estimation and a classification are made. To estimate the risk of failure in selecting an adequate sub-region prior to exploration, we propose the following formula based on Aven and Krohn (2014), which is defined as follows: Risk=likelihood×consequence×knowledge. This formula evaluates risk based on three factors: the probability of a specific event occurring that may jeopardize the integrity of the geological barriers (likelihood), the potential impact of that event (consequence), and the level of understanding or uncertainty associated with that event (knowledge), which can influence the overall risk assessment. The method involves the identification and evaluation of subsurface risk factors using the multiple-criteria decision-making approach and the development of a risk matrix. The risk matrix is based on expert judgement and existing data to quantify the relative importance of each risk factor. The method is evaluated by applying it to one of the subareas designated by the German federal company for radioactive waste disposal for methodological developments. The developed approach provides a valuable tool for enabling stakeholders to consider a wide range of factors, including the level of uncertainty and lack of knowledge associated with subsurface risk factors, and make informed decisions during the site selection process.

Water Scarcity Zonation Mapping of Household in District Soreng West Sikkim by MCDM Method Using RS&GIS

Abstract: A major global problem, water scarcity has a negative impact on ecosystems, socioeconomic activity, and millions of people. Effective water resource management calls for well-informed decision-making processes that consider many factors and their respective importance. The goal of this case study is to use the Multiple Criteria Decision-Making (MCDM) method to address the problem of ranking water scarcity. The study starts out by developing a set of pertinent standards for evaluating water shortage, including water availability, population demand, storage capacity, total population of the household, accessibility of a nearby source, distance to a perennial source, use of rainwater harvesting, number of animals in the household, water obtained from a public supply, and manual water collection methods. Surveys are used to gather information on these criteria, as well as statistical reports, remote sensing, and expert opinions. To ensure comparability across many parameters, the collected data are subsequently analysed and standardized. The regions are then ranked according to the severity of their water scarcity using a suitable MCDM approach. Using the MCDM method, decision-makers can consider their preferences and opinions as well as give various criteria proportionate weights based on their importance. The case study demonstrates how MCDM was used in the study area's battle against water scarcity. Policymakers and stakeholders can utilize the data to identify families that are experiencing the greatest levels of water scarcity and then priorities solutions based on those findings. Furthermore, sensitivity analysis can be performed to assess how well the ranking results hold up under other weightings and criteria. The results of this case study provide a systematic and all-encompassing strategy, which advances water resources management tactics also the results can be further used to prepare a Water Scarcity Zonation Map in ArcGIS or QGIS using RS&GIS method

Impact of R&D and innovation in Chinese road transportation sustainability performance: A novel trigonometric envelopment analysis for ideal solutions (TEA-IS)

Previous road transportation sustainability studies not only neglected the epistemic uncertainty that surrounds the impact of innovation and Research and Development (R&D) expenditure on pollutant emissions performance but also failed in designing and simultaneously exploring the strengths of alternative MCDM (Multiple-Criteria Decision-Making) approaches to better discriminate performance scores. This paper focuses on these two gaps by presenting a road transportation sustainability performance assessment of 29 Chinese provinces for a 14-year period in light of relevant socioeconomic and demographic variables. First, a novel TEA-IS model is developed to assess road transportation sustainability performance. Besides possessing the beneficial features of each model, this hybrid DEA-TOPSIS can analyze the sustainability performance from the perspective of the synergistic effects among the criteria. From the socioeconomic and demographic perspectives, we use machine learning techniques for predicting high-low performance and synergistic Chinese provinces. Results suggest that the discriminatory power of TEA-IS is good and there is high synergy in Chinese provinces in terms of sustainable road transportation. We further find that there is a high level of heterogeneity in road transportation sustainability among different geographical locations in China. The results further suggest that higher levels of synergy are strongly associated with medium and high-performing provinces. Finally, we find that innovation index, foreign direct investment, and non-coastal cities have both higher performance and synergy levels. We recommend that Chinese government should further provide favorable policies to enhance innovation and efforts should also be made to provide better environment for attracting more foreign direct investment. Finally, instead of road transportation, other modes of transportation, such as air and sea, are recommended to be further developed and utilized.

Using Multicriteria Decision Making to Evaluate the Risk of Hydrogen Energy Storage and Transportation in Cities

Hydrogen is an environmentally friendly source of renewable energy. Energy generation from hydrogen has not yet been widely commercialized due to issues related to risk management in its storage and transportation. In this paper, the authors propose a hybrid multiple-criteria decision-making (MCDM)-based method to manage the risks involved in the storage and transportation of hydrogen (RSTH). First, we identified the key points of the RSTH by examining the relevant literature and soliciting the opinions of experts and used this to build a prototype of its decision structure. Second, we developed a hybrid MCDM approach, called the D-ANP, that combined the decision-making trial and evaluation laboratory (DEMENTEL) with the analytic network process (ANP) to obtain the weight of each point of risk. Third, we used fuzzy evaluation to assess the level of the RSTH for Beijing, China, where energy generation using hydrogen is rapidly advancing. The results showed that the skills of the personnel constituted the most important risk-related factor, and environmental volatility and the effectiveness of feedback were root factors. These three factors had an important impact on other factors influencing the risk of energy generation from hydrogen. Training and technical assistance can be used to mitigate the risks arising due to differences in the skills of personnel. An appropriate logistics network and segmented transportation for energy derived from hydrogen should be implemented to reduce environmental volatility, and integrated supply chain management can help make the relevant feedback more effective.

Evaluation of nanostructured electrode materials for high-performance supercapacitors using multiple-criteria decision-making approach

<abstract> <p>The enhancement of electrode materials' properties for improving mercantile supercapacitors' performances is a remarkable research area. Throughout recent years, a significant amount of research has been devoted to improving the electrochemical performance of supercapacitors via the improvement of novel electrode materials. The nanocomposite structure provides a greater specific surface area (SSA) and lower ion/electron diffusion tracks, consequently enhancing supercapacitors' energy density and specific capacitance. These significant properties offer a wide range of potential for the electrode materials to be applied in diverse applications. For instance, their applications are in portable electronic systems such as all-solid-state supercapacitors, flexible/transparent supercapacitors and hybrid supercapacitors. The authors of this paper introduced a multi-criteria model to assess the priority of nanostructured electrode materials (NEMs) for high-performance supercapacitors (HPSCs). This work combines Analytic Hierarchy Process (AHP) with the Evaluation Based on Distance from Average Solution (EDAS) and Grey Relational Analysis (GRA) methods. Herein, the rough concept addresses the uncertainties resulting from the group decision-making process and the vague values of the properties of the NEMs. The modified R-AHP method was employed to find the criteria weights based on the multi-experts' opinions. The results reveal that specific capacitance (SC) and energy density (ED) are the most important criteria. R-AHP was integrated with R-EDAS and R-GRA models to evaluate the fourteen NEMs. The results of the R-EDAS method were compared with those provided by the R-GRA method. The results of the proposed integrated approach confirmed that it results in reliable and reputable ranks that will provide a framework for further applications and help physicists find optimal materials by evaluating various alternatives.</p> </abstract>

Development of a Model for Trust Management in the Social Internet of Things

The Internet of Things (IoT) has evolved at a revolutionary pace in the last two decades of computer science. It is becoming increasingly fashionable for the IoT to be rebranded as the “Social Internet of Things” (SIoT), and this is drawing the attention of the scientific community. Smart items in the Internet of Things (IoT) ecosystem can locate relevant services based on the social ties between neighbors. As a result, SIoT displays the interplay between various items as a problem in the context of the social IoT ecosystem. Navigating a network can be difficult because of the number of friends and the complexity of social ties. By identifying difficulties with standard SIoT devices’ interaction with social objects, truthful friend computing (TFC) is a new paradigm for tracing such difficulties by utilising a relationship management component to improve network navigability. The concept of trust management can be useful as a strategy during collaborations among social IoT nodes. As a result, the trustor can use a variety of measures to evaluate a smart object’s trustworthiness. Hence, this article demonstrates the need for the trustor to evaluate the extent to which a given metric has contributed to the overall trust score and illustrates profitability when engaging in a transaction with other nodes. With the help of the SIoT, this paper used a unified fuzzy-based computational technique and a multiple-criteria decision-making approach to evaluate the trust weights. The statistical findings show that the computing of “truthful friends” is the biggest challenge for successful SIoT implementation at the initial level.

Research on significant factors affecting adoption of blockchain technology for enterprise distributed applications based on integrated MCDM FCEM-MULTIMOORA-FG method

Blockchain technology-powered applications infuse trust in the system without intermediary entities. Researchers’ interest in blockchain technology has increased dramatically in recent years as a result of this distinguishing feature. However, the adoption of this novel paradigm in enterprise distributed applications is not very encouraging. To adopt a new technology in an industry, managers and technocrats have to do multiple-criteria decision-making (MCDM) in a fuzzy environment. The aim of this study is to propose a system model for the identification and monitoring of significant factors responsible for the slow adoption rate of blockchain technology. In this research, the MULTIMOORA method for group decision-making, a popular MCDM approach, has been further extended by integrating the information entropy weight–fuzzy comprehensive evaluation model (IEW-FCEM) for an uncertain environment. The proposed method of FCEM-MULTIMOORA-FG combines the benefits of four best-in-class ranking methods, namely, the fuzzy ratio system, the fuzzy reference point method, the fuzzy full multiplicative form, and the IEW-FCEM. The idea behind integrating the IEW-FCEM with MULTIMOORA-FG is that information entropy will derive the objective weights of the criteria, thus removing the dependency upon subject matter expert experience only. An empirical study demonstrates the efficacy and practicability of the FCEM-MULTIMOORA-FG method. The simulation results demonstrate that the proposed method is computationally straightforward with better position accuracy than previous algorithms and that its basic idea is logical and understandable, making it easier to apply in a computer system. Additionally, a comparative analysis with five more MCDM methods was executed to validate the results.

Prioritization of ecopreneurship barriers overcoming renewable energy technologies promotion: A comparative analysis of novel spherical fuzzy and Pythagorean fuzzy AHP approach

In a challenging environment, renewable energy (RE) entrepreneurs promote and encourage the enactment of new technologies and concepts, but they face several hurdles in promoting them. A basic research gap exists in investigating the ecopreneurship obstacles to RE development by using multiple-criteria decision-making (MCDM) approaches under uncertainty in a comprehensive study, this research thus intends to fill a research gap with the fuzzy MCDM approach to tackle the entrepreneurial obstacles to emerging economies' power generation growth, which can be divided into two parts. Firstly, a literature survey and discussion with experts were used to identify prospective obstacles. Secondly, a novel Spherical Fuzzy Analytical Hierarchical Process was used to finalize obstacle weights. The results showed that the ‘Inadequate government or policy support’ category, with a final weight of (0.2093), is the most essential among the major categories, and ‘inadequate access to institutional finance’ becomes the second rank. Compared with Pythagorean fuzzy AHP, our suggested technique incorporates more details considering the uneasiness of the decision-making atmosphere. In contrast, the overall global orders of obstacles revealed that ‘Accessing credit’, with a final weight of (0.0737), is more important than the other twenty-six obstacles.

Perceived usability evaluation of learning management systems via axiomatic design with a real life application

PurposeUsage of learning management systems (LMSs) has become widespread with the disruption of face-to-face educations after the COVID-19 pandemic. There are several software products, usually named as LMS to enable and support distance education. However, selection of a suitable LMS is a complex multiple criteria decision making (MCDM) problem that requires consideration of many criteria and inputs from different parties like students, academicians, education managers, etc. Usability evaluation of LMS is one of the critical steps in deciding which LMS system to be adapted. There are several studies related to usability evaluation of LMS in the literature, but utilization of MCDM methods and real life case studies are very rare. Based on this motivation, perceived usability evaluation of SAKAI-LMS that is in use at an academic department is performed by employing axiomatic design procedure (ADP). This paper aims to discuss the aforementioned issues.Design/methodology/approachADP is considered as a suitable MCDM method for perceived usability evaluation as it allows an easy approach to data fusion and setting performance targets for decision makers. A questionnaire is developed to collect data from three types of system users about predetermined usability criteria and their importance. After detailed statistical analyses and weighting criteria via analytical hierarch process (AHP), ADP is carried out to evaluate usability of the LMS.FindingsIt is found that the proposed ADP based approach is easy to apply in practical circumstances and able to quantify perceived usability of the LMSs.Research limitations/implicationsThe proposed approach provides an easy and practical evaluation of perceived usability of the LMSs for decision makers who are responsible for the implementation of LMSs. The developed novel and practical MCDM-based perceived usability approach for LMS in this study has been verified through a real life case study at an academic department. Perceived usability results, therefore, reflects only the views of this focus group and are not generalizable.Originality/valueFirst time in the literature, a comprehensive ADP based MCDM approach is proposed based on the analyses of the related literature and information gathered from the system users.

Design of high-performance photovoltaic-thermoelectric hybrid systems using multi-objective genetic algorithm

Hybrid systems which integrate photovoltaic (PV) and thermoelectric generator (TEG) can improve the utilization of solar energy. In this study, a multi-objective genetic algorithm (MOGA) is adopted to maximize the output power and conversion efficiency for the PV-TEG system, where the CFD model is coupled in the optimization procedure and the penalty function is employed to ensure the appropriate operating temperature. Meanwhile, two geometric parameters along with two boundary conditions are selected as the design variables, while the effects of three cooling conditions are also discussed. Compared with the PV only system, the output power of the PV-TEG system is improved by up to 11.52% with the conversion efficiency also improved by 44.97%. At the same solar concentration condition, the optimal PV-TEG system could increase the output power by 36%. Based on the analysis of optimal solutions, it is demonstrated that the adopted TEG contributes significantly to the output power, while a better cooling condition also has a positive effect on the comprehensive performances. Furthermore, a multiple-criteria decision-making approach is applied to balance output power and conversion efficiency. Compared with the maximum efficiency solution, the best compromise solution increases output power by 120.02% and decreases the efficiency only by 14.83%.

Solar photovoltaics can help China fulfill a net-zero electricity system by 2050 even facing climate change risks

As China has pledged to become carbon neutral by 2060, electrifying its energy sector is no doubt one of the priority measures to support the transition towards a more sustainable and decarbonized energy system. Solar photovoltaics (PV) has been known as one of the most promising renewable technologies to facilitate the electrification of energy systems. The feasibility of utilizing PV to implement a nationwide decarbonized electricity system now becomes an urgent unanswered question, especially in the context of global climate change and rapid economic growth in China. Here, by using a GIS-based multiple-criteria decision-making approach we address this question by conducting a comprehensive feasibility analysis with consideration of various economic, technological, logistical, and climate change factors. We show that it is feasible for China to fulfill a net-zero electricity system by 2050, through the installation of 7.46 TW solar PV panels on about 1.8% of the national land area (mostly in western China) with a total capital investment of 4.55 trillion USD in the next 30 years. Besides, we show that future climate change may lead to a slight decrease (less than 5%) in solar energy potential, but this would not affect the capability of the nationwide PV system to meet the need for a fully-electrified energy system.

Developing a novel stock index trend predictor model by integrating multiple criteria decision-making with an optimized online sequential extreme learning machine.

It has always been the goal of many researchers to gain a thorough understanding of the patterns in the stock market and forecast the trends it will follow. The use of an advanced forecasting model can assist with accurately forecasting the future price of stocks, their fluctuations in the markets, as well as make profits in trading. With this motivation, in this study, a novel stock index trend predictor model is designed by integrating Multiple Criteria Decision-Making (MCDM) with an optimized Online Sequential Extreme Learning Machine (OSELM). Forecasting the future stock index prices and analyzing the upward or downward trends of these price forecasts are the two objectives of the proposed model. As the performance of OSELM is heavily dependent on the activation functions used in it, suitable selection of the activation function for OSELM is addressed as a MCDM problem. According to this approach, the trend prediction performance of six popular activation functions is assessed based on five regression-based and five classification-based criteria. In this investigation, three MCDM approaches are used to assess the performance matrix and determine which activation function is the best for OSELM based on six alternative models and ten criteria. To further optimize OSELM's performance, a hybrid crow search algorithm (hCSA) is incorporated in its training phase. By introducing the chaotic map and mutation operator in position update scheme and catfish behavior in the search process of original CSA, the proposed hCSA is able to achieve the right balance between exploration and exploitation improving the convergence. The proposed trend predictor model is empirically evaluated over historical data of three stock indices such as BSE SENSEX, S&P 500 and DJIA collected during pre-COVID and COVID time frame. In most of the test cases, the hCSA-OSELM model outperforms the state-of-the-art baseline models in terms of all evaluation criteria. When compared to the second-best baseline model, the suggested model is able to achieve the MSE improvements of 4–6%, 25–31%, and accuracy improvements of 0.4–0.8%, 0.9–1.3% over the pre-COVID and COVID time-frames, respectively. The statistical test also reveals the better performance of the proposed model. The robust and reliable MCDM-based model selection, superior prediction and classification outcomes clearly reveal that the proposed model can be used for financial time-series forecasting amid daily volatility as well as highly volatile markets.

Analysis of barriers in e-waste management in developing economy: an integrated multiple-criteria decision-making approach.

An increasing amount of electronic waste (e-waste) is not a new concern. It has been causing trouble globally. This waste comprises valuable metals and harmful compounds that lead to detrimental environmental conditions. Managing this kind of waste in developing economies is difficult due to different barriers hindering the process. Therefore, the goal of this research work is to determine the barriers while taking expert opinions and through available literature, and subsequently prioritize them to address the challenges in e-waste management. Moreover, this study utilizes an integrated Fuzzy Decision-Making Trail and Evaluation Laboratory (F-DEMATEL) and Fuzzy Interpretive Structural Modeling (F-ISM) approaches to determine the interrelationship between these identified barriers. Performance data obtained from this combined approach is applied to determine an overall rank for 15 identified barriers. The F-DEMATEL technique facilitates in obtaining the influence of barriers on each other and categorizes them into causal or effect groups. In addition, a Fuzzy Matrice d'impacts Croisés Multiplication Appliquée an un Classeement (F-MICMAC) analysis is exercised to sort them into dependentor driving factor. The findings suggest that the underlying cause barriers include "lack of customer awareness about return," "less policies addressing e-waste problem," "lack of long-term planning," and "insensitiveness of public towards environmental issues." The methodology is integrated with fuzzy logic to take uncertainty in the data gathered into consideration. This approach aids policymakers and decision-makers in determining the barriers' mutual relationships and interconnections.