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In this study, we developed a novel multi-criteria decision-making (MCDM) framework for evaluating and benchmarking hybrid multi-deep transfer learning models using radiography X-ray coronavirus disease (COVID-19) images. First, we collected and pre-processed eight public databases related to the targeted datasets. Second, convolutional neural network (CNN) models extracted features from 1,338 chest X-ray (CXR) frontal view image data using six pre-trained models: VGG16, VGG19, painters, SqueezeNet, DeepLoc, and Inception v3. Then, we used the intersection between the six CNN models and eight classical machine learning (ML) methods, including AdaBoost, Decision Tree, logistic regression, random forest, kNN, neural network, and Naive Bayes, to introduce 48 hybrid classification models. In this study, eight supervised ML methods were used to classify COVID-19 CXR images. The classifiers were implemented using the TensorFlow2 and Keras libraries in Python. A feature vector was extracted from each image, and a five-fold cross-validation technique was used to evaluate the performance. The cost parameter $c$ was set to 1 and the gamma parameter $\gamma$ was set to 0.1 for all classifiers. The experiments were run on a Windows-based computer with dual Intel I CoITM i7 processors at 2.50GHz, 8GB of RAM, and a graphical processing unit of 2GB. The performance metrics of the 48 hybrid models, including the classification accuracy (CA), specificity, area under the curve (AUC), F1 score, precision, recall, and log loss, were used as efficient evaluation criteria. Third, the MCDM approach was used, which included (i) developing a dynamic decision matrix based on seven evaluation metrics and the developed hybrid models, (ii) developing the fuzzy-weighted zero-inconsistency method for determining the weight coefficients for the seven-evaluation metrics with zero inconsistency, and (iii) developing the Višekriterijumsko Kompromisno Rangiranje method for benchmarking the 48 hybrid models. Our experimental results reveal that (i) CA and AUC obtained the highest importance weights of 0.164 and 0.147, respectively, whereas F1 and specificity obtained the lowest weights of 0.134 and 0.134, respectively, and (ii) the highest three hybrid model scores were painters neural network, painters logistic regression, and VGG16-logistic regression, making them the highest ranking scores. Finally, the developed framework was validated using sensitivity analysis and comparison analysis.

The proliferation of technology has facilitated data accessibility, leading to an expansion in the range of criteria employed in decision problem design. This situation offers an advantage for making precise and rational decisions, but when it comes to managing spending, it becomes a disadvantage. Specifically, the expense of acquiring expert views utilized in the computation of criteria weights by subjective approaches experiences a substantial rise. Hence, decision-makers may employ objective methodologies to determine criterion weights. Nevertheless, objective methods provide a more limited range of choices compared to subjective methods. The study aims to utilize two widely recognized fundamental statistical approaches in order to enhance the capabilities of objective methods. One of the suggested approaches is the dissimilarity-based weighting method, which calculates the differentiation of values within the criteria. Another approach is the weighting method, which relies on the interquartile range. The methods were adapted as means of weighting criteria. Explanatory examples were provided, simulation-based comparisons were conducted, and ultimately applied to an actual data set. The data from each scenario were compared using the factorial analysis of variance method. The findings produced demonstrate that the proposed methods align with other objective methodologies. Furthermore, the proposed approaches were observed to take more time to finish the procedure compared to the Entropy and Standard Deviation methods, but less time compared to the Critic and Merec methods. Consequently, the suggested techniques are introduced as alternative approaches derived from established fundamental statistical procedures, which are straightforward to comprehend and valuable for professionals.

Although landfills are among the most important structures for municipal waste disposal worldwide, they are sometimes less preferred due to their impact on people and the environment. This research examines the health, safety and environmental (HSE) factors affecting the municipal solid waste (MSW) landfilling process’s performance. Next, it prioritises these factors using the conventional AHP and fuzzy AHP methods. Based on the findings, three main sub-criteria were determined from the 26 sub-criteria, and the environmental, health and safety criteria ranked the first, second and third, respectively. Leachate permeation control, control of pollutant production and hazardous gas monitoring were ranked the highest in terms of relative weight. In the environmental criteria, leachate control, in the health criteria, pollution and in the safety-related criteria, hazardous gas control were ranked the first. In addition, leachate permeation control had the highest global weight. Overall, experts are recommended to pay more attention to environmental issues and prioritise controlling the environmental impact of landfills in their design, construction and operation.

Due to the substantial economic and environmental repercussions of improper E-waste management and treatment, the issue has gained indispensable attention. In recent years, the research has been done about the selection of proper E-waste management methods like site selection, disposal method selection, etc. with fuzzy environments. But in real situations, the major reasons for improper e-waste management are lack of awareness and improper disposal with sustainability. In finding a sustainable awareness-creating program and disposal method for E-waste, the uncertainty and its sustainability of the information make challenges to decision makers, so the problem becomes a two-dimensional problem with uncertainty. This paper uses complex fuzzy terms to illustrate this two-dimensional uncertain information, in which amplitude term represents the uncertainty about alternative and phase term represents the uncertainty about the sustainability of an alternative. First, we present a new framework MULTIMOORA under complex fuzzy structure. Then, in order to solve two-dimensional uncertain information about E-waste, the proposed complex fuzzy-MULTIMOORA method is applied, in which E-waste awareness-creating program and treatment technology selection are done. The sensitivity and comparative analysis are further delivered to validate the effectiveness of the proposed technique.

OWA (Ordered Weighted Averaging) aggregation operators have been extensively adopted to handle MCDM (multiple criteria decision making) problems. However, additive or multiplicative preferences should be aggregated with feasible operators. To resolve this problem, this study proposes a new MCDM aggregation model, capable of handling situational group MCDM problems based on the ME-OWA (maximal entropy ordered weighted averaging) and ME-OWGA (maximal entropy ordered weighted geometric averaging) operators. The proposed model is also applied not only to evaluate the service quality of airlines but also select the most appropriate desalination technology. The results of previous MCDM methods can be covered with proposed model.

This paper presents a framework of website quality evaluation for measuring the performance of government websites. Multiple criteria decision-making (MCDM) is a widely used tool for evaluating and ranking problems containing multiple, usually conflicting criteria. In line with the multi-dimensional characteristics of website quality, MCDM provides an effective framework for an inter-websites comparison involving the evaluation of multiple attributes. It thus ranks different websites compared in terms of their overall performance. This paper models the inter-website comparison problem as an MCDM problem, and presents a practical and selective approach to deal with it. In addition, fuzzy logic is applied to the subjectivity and vagueness in the assessment process. The proposed framework is effectively illustrated to rate Turkish government websites.

The present paper proposes an improved score function for solving multi-criteria decision-making (MCDM) problem with partially known weight information, In it, the preferences related to criteria are taken in the form of interval-valued Pythagorean fuzzy sets. Based on these preferences and an improved score function, a score matrix has been formulated and then a linear programming based method has been proposed to solve MCDM problems with unknown attribute weights. Some generalized properties have also been proven for justification. Illustrative examples have been given for showing the superiority of the approach with the other existing functions in the decision-making process.

This current research paper measures the performance of Indian private sector banks through various multi-criteria decision-making (MCDM) techniques. To measure the performance of the banks the data about various criteria such as profit after tax, borrowings, advances, adjusted EPS, enterprise value, and NPAs from the Annual reports of the banks were extracted. The MCDM techniques, SDV (standard deviation) CRITIC (CRiteria Importance Through Intercriteria Correlation), ARAS (Additive Ratio Assessment), MOORA (Multi-objective Optimization on the basis of Ratio Analysis) are applied to analyze the data and measure the performance of the banks. In MCDM techniques, different methods provide different weights of the criteria, and also different ranks are obtained by different methods. Sensitivity analysis was carried out by measuring the criteria weights by SDV and CRITIC and the alternatives are ranked using two MCDM techniques, ARAS and MOORA. The results of the study show that among the private banks, HDFC created a benchmark and leading while Yes bank has shown poor performance on the basis of annual reports of 2020.

Enterprise resource planning (ERP) projects are prone to risk from the strategic, operational, technical and organizational perspective. The assessment of project risks is a challenging task for project managers and is considered in a class of a multi-criteria decision making (MCDM) problem. In this work, an MCDM approach has been presented for risk assessment in ERP project by COPRAS under fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by triangular fuzzy number. Fuzzy COPRAS (COPRAS-F) has been used to determine the weight of risk criteria and then prioritize the risk factor based on the calculated weight of criteria. The prioritized risk factors have been classified in scale from almost certain to rare risks. The proposed approach is illustrated with a real case studying fertilizer plant and the associated results have been compared with fuzzy TOPSIS technique. The result of this study demonstrates that excessive customization, ineffective consulting services experiences, complex architecture and high number of modules, poor project team skills, inadequate change management, inadequate ERP selection, ineffective strategic thinking and planning, poor leadership, lack of business process re-engineering and low-key user involvement are top ten risks that need to be mitigated to avoid failure of ERP project.

With the largest wireless market in the world, China's selection of the appropriate 3G licensing patterns will have some crucial impacts on the landscape of telecommunication industry in China and other parts of the world. Under its obligations to the World Trade Organization, the Chinese government wishes to demonstrate a relatively structured and transparent 3G licensing process. Therefore, evaluation criteria in designing licensing patterns for the home-grown 3G standard should satisfy multiple goals to meet the requirements of the different stakeholders such as government, consumers, the potential operators, and domestic manufacturers. The surveys in methodology of Multi-Criteria Decision Making (MCDM) are conducted and the results reveal that the research methodology is applicable to the evaluation and policy-making process under the transitional context.

The practice of solely relying on the human resources department in the selection process of external training providers has cast doubts and mistrust across other departments as to how trainers are sourced. There are no measurable criteria used by human resource personnel, since most decisions are based on intuitive experience and subjective market knowledge. The present problem focuses on outsourcing of private training programs that are partly government funded, which has been facing accountability challenges. Due to the unavailability of a scientific decision-making approach in this context, a 12-step algorithm is proposed and tested in a Japanese multinational company. The model allows the decision makers to revise their criteria expectations, in turn witnessing the change of the training providers' quota distribution. Finally, this multi-objective sensitivity analysis provides a forward-looking approach to training needs planning and aids decision makers in their sourcing strategy.

Challenging decision problems in changeable spaces are characterized by existence of complex decision parameters that are changing with time and situations, including criteria and alternatives. Some of these parameters may be critical for their effective solutions, but hidden in the depth of potential domains. In this rapidly changing world, including technology and attitude, without paying attention to the problems in changeable spaces, we could easily commit serious mistakes due to decision blinds, decision traps and/or decision shocks. The article starts with a brief description of the evolution of MCDM toward challenging problems in changeable spaces. Then it briefly sketches a dynamic human behavior mechanism and habitual domain theory which provide an effective list for us to search relevant decision parameters and pave the way for latter discussion. Competence set analysis, derived from habitual domain, is then introduced to exemplify decision blinds, decision traps and decision shocks in challenging decision problems. Checking lists and methods for discovering blinds and traps and for dealing with shocks are also provided. Innovation dynamics, a systematic network of thoughts, is introduced to further look out relevant key parameters in dynamic challenging problems. The related academic subjects in each link of the innovation dynamics are also explained, which allow us to see the complexity and interconnectivities among different challenging problems in changeable spaces. Finally we introduce three habitual domain tool boxes to empower ourselves to expand and enrich our thoughts into the depth of the potential domains of the challenging problems, which allows us to more effectively identify hidden parameters, problems and competence sets to reduce decision blinds, avoid decision traps and solve the problems, or dissolve the problems before they occur.

Construction processes planning and effective management are extremely important for success in construction business. Head of a design must be well experienced in initiating, planning, and executing of construction projects. Therefore, proper assessment of design projects' managers is a vital part of construction process. The paper deals with an effective methodology that might serve as a decision support aid in assessing project managers. Project managers' different characteristics are considered to be more or less important for the effective management of the project. Qualifying of managers is based on laws in force and sustainability of project management involving determination of attributes value and weights by applying analytic hierarchy process (AHP) and expert judgement methods. For managers' assessment and decision supporting is used additive ratio assessment method (ARAS). The model, presented in this study, shows that the three different methods combined (ARAS method aggregated together with the AHP method and the expert judgement method) is an effective tool for multiple criteria decision aiding. As a tool for the assessment of the developed model, was developed multiple criteria decision support system (MCDSS) weighting and assessment of ratios (WEAR) software. The solution results show that the created model, selected methods and MCDSS WEAR can be applied in practice as an effective decision aid.

The paper discusses the meaning and nature of urban cultural heritage, and the available methods for its valuation in the perspective of sustainable city development. From this perspective, decision-making problems of renovation often involve a complex decision-making process in which multiple requirements and conditions have to be taken into consideration simultaneously. In project development it is hardly possible to get exhaustive and accurate information. As a result, the situations occur, the consequences of which can be very damaging to the project. Sometimes the loss is related to symbolic values that the public perceive as disregarded by the project, despite the overall improved conditions. This paper presents the multiple criteria assessment of alternatives of the cultural heritage renovation projects in Vilnius city. The model consists of the following elements: determining attributes set affecting built and human environment renovation; information collection and analysis, decision modeling and solution selection. The main purpose of the model is to improve the condition of the built and human environment through efficient decision making in renovation supported by multiple attribute evaluation. Delphi, AHP and ARAS-G methods, considering different environment factors as well as stakeholders' needs, are applied to solve problem.

Product Development Process (PDP) has been recognized as a source of competitive profits, and thus, it has received increasing attention. The existing methods for monitoring PDP performance, however, are either cumbersome or fail to connect the competitive priorities to the performance management process. Accordingly, in this paper, a model to evaluate PDP performance is considered as a multi-criteria decision-making problem; and a solution method, based on DEMATEL and ANP, is proposed. The evaluation criteria set was developed using the concept of competitive priorities, and the overall model was applied to an actual data set from a group of machine manufacturers in Turkey. The use scenarios for the model are also discussed.

Real estate brokerage services have developed from individual stores into a chain-store system, and the location of those stores plays a key role in their operation. The purpose of this study is to define and quantify the factors that affect the selection of a site for real estate brokerage services. Mutual relationships between the factors and sub-factors for site selection and their relative weights are also discussed to provide a complete set of decision evaluation models, then how to reduce the gaps to achieve the aspiration level. This research uses a new hybrid Multiple Criteria Decision Making (MCDM) model, combining the Decision Making Trial and Evaluation Laboratory (DEMATEL), DEMATEL-based Analytic Network Process (DANP), and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) methods to solve these problems. The DEMATEL technique is used to build an influential network relations map, and DANP is expected to obtain the influential weights using the basic concept of Analytic Network Process (ANP), to solve the dependence and feedback problems in the real world. Then, the VIKOR method is used to integrate the performance gaps from criteria to dimensions and overall. As the result shows, there is an interactive and auto-feedback relationship among the four dimensions. Among the 11 evaluation criteria, the income and consumption level is the most important consideration for selection of the site. The number and density of population ranks second in this regard. This study uses VIKOR method for selection of the best site, among three potential sites. Site A is closest to the aspiration level. Site A is better in this regard than the other two sites. The study develops and provides a decision-making system for the site selection in the real estate brokerage services.

Multi-criteria decision-making (MCDM) is one of the most widely used decision methodologies. Because every kind of MCDM approach has unique strengths and weaknesses, it is difficult to determine which kind of MCDM approach is best suited to a specific problem. Therefore, a new decision-making method is proposed herein, based on linguistic information and intersection concepts; it is called the linguistic intersection method (LIM). Notably, the linguistic variables are more suited to expressing the opinion of each decision maker. There are four MCDM methods: TOPSIS, ELECTRE, PROMETHEE and VIKOR which are included in the LIM. First, each MCDM approach is used to determine the ranking order of all alternatives in accordance with the linguistic evaluations of decision makers. Then, the intersection set is determined with regard to the better alternatives of all methods. Third, the final ranking order of alternatives in the intersection set can be determined by the proposed method. Lastly, an example is given to describe the procedure of the proposed method. In order to verify the effectiveness of the proposed method, a simulation test is provided to compare the LIM with the linguistic MCDM method. According to the comparison results, the proposed method is more stable in determining the ranking order of all decision alternatives.

In recent years several previous scholars made attempts to develop, extend, propose and apply Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for solving problems in decision making issues. Indeed, there are questions, how TOPSIS can help for solving these problems? Or does TOPSIS solved decision making problems in the real world? Therefore, this study shows the recent developments of TOPSIS approach which are presented by previous scholars. To achieve this objective, there are 105 reviewed papers which developed, extended, proposed and presented TOPSIS approach for solving DM problems. The results of the study indicated that 49 scholars have extended or developed TOPSIS technique and 56 scholars have proposed or presented new modifications for problems solution related to TOPSIS technique from 2000 to 2015. In addition, results of this study indicated that, previous studies have modifications related to this technique in 2011 more than other years.

The environmental decision problems often are divisive, even in a technical realm, decision makers with strong personalities influence outcomes. The purpose of this study is to define and quantify the factors that affect the conservation objectives of a national natural park located in Colombia, South America adding the judgments of six decision makers with different knowledge (every decision maker is also a stakeholder representative). This paper uses a hybrid multiple criteria group decision-making model (MCDM), combining the social network analysis (SNA), analytic hierarchy process (AHP) and similarity measures to solve the consensus and anchoring problem among environmental decision makers. The SNA technique is used to build an influential network relation map among decision makers and to obtain their weights for applying a weighted AHP. Then, the final decision matrices for every decision maker are compared between them in order to identify the consensus level of the problem.

In multiple criteria evaluation, criteria weights are of great importance. In practice, subjective criteria weights determined by specialists/experts are commonly used. The types of elements of a decision matrix also play an important role in the evaluation of alternatives. The objective weights help to estimate the structure of data. The entropy method is widely used for determining the weights (significances) of criteria. A new method of the criterion impact loss, CILOS, is used for determining a relative impact loss experienced by the criterion of an alternative, when another criterion is chosen to be the best. The authors of the paper have combined the best features of the entropy method and the CILOS approach to obtain a new method – Integrated Determination of Objective CRIteria Weights, or (IDOCRIW).