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Ranking relevant journals is very critical for researchers to choose their publication outlets, which can affect their research performance. In the management information systems (MIS) subject, many related studies conducted surveys as the subjective method for identifying MIS journal rankings. However, very few consider other objective methods, such as journals’ impact factors and h-indexes. The paper aims to discuss these issues.

In this paper, top 50 ranked journals identified by researchers’ perceptions are examined in terms of the correlation to the rankings by their impact factors and h-indexes. Moreover, a hybrid method to combine these different rankings based on Borda count is used to produce new MIS journal rankings.

The results show that there are low correlations between the subjective and objective based MIS journal rankings. In addition, the new MIS journal rankings by the Borda count approach can also be considered for future researches.

The contribution of this paper is to apply the Borda count approach to combine different MIS journal rankings produced by subjective and objective methods. The new MIS journal rankings and previous studies can be complementary to allow researchers to determine the top-ranked journals for their publication outlets.

About 10 years ago the Singapore Government realized that entrepreneurial spirit was lacking in its general population. These conclusions were confirmed by an empirical survey, the Global Entrepreneurship Monitor (GEM), an annual assessment of the national level of entrepreneurial activity. The paternalistic and authoritative approach of the government contributed to the general population’s averseness to participating in riskoriented ventures.

Removing impediments to entrepreneurship is a key challenge for the government and the business sector if the island republic is to maintain its national competitiveness. This article explores the various initiatives taken by the government to stimulate risk-taking and attempts to ascertain if the various measures can be used as key factors to strengthen the inherent cultural values that stimulate the entrepreneurial spirit.The observations can serve as a useful tool for academics and managers in recognizing the cultural traits that influence and help foster entrepreneurial tendencies.

Hundreds of thousands of deaths each year in the world are caused by breast cancer (BC). An early-stage diagnosis of this disease can positively reduce the morbidity and mortality rate by helping to select the most appropriate treatment options, especially by using histological BC images for the diagnosis.

The present study proposes and evaluates a novel approach which consists of 24 deep hybrid heterogenous ensembles that combine the strength of seven deep learning techniques (DenseNet 201, Inception V3, VGG16, VGG19, Inception-ResNet-V3, MobileNet V2 and ResNet 50) for feature extraction and four well-known classifiers (multi-layer perceptron, support vector machines, K-nearest neighbors and decision tree) by means of hard and weighted voting combination methods for histological classification of BC medical image. Furthermore, the best deep hybrid heterogenous ensembles were compared to the deep stacked ensembles to determine the best strategy to design the deep ensemble methods. The empirical evaluations used four classification performance criteria (accuracy, sensitivity, precision and F1-score), fivefold cross-validation, Scott–Knott (SK) statistical test and Borda count voting method. All empirical evaluations were assessed using four performance measures, including accuracy, precision, recall and F1-score, and were over the histological BreakHis public dataset with four magnification factors (40×, 100×, 200× and 400×). SK statistical test and Borda count were also used to cluster the designed techniques and rank the techniques belonging to the best SK cluster, respectively.

Results showed that the deep hybrid heterogenous ensembles outperformed both their singles and the deep stacked ensembles and reached the accuracy values of 96.3, 95.6, 96.3 and 94 per cent across the four magnification factors 40×, 100×, 200× and 400×, respectively.

The proposed deep hybrid heterogenous ensembles can be applied for the BC diagnosis to assist pathologists in reducing the missed diagnoses and proposing adequate treatments for the patients.

In today’s digital era, data pertaining to scientific research have attracted considerable attention of researchers. Data of scientific publications can be modeled in the form of networks such as citation networks, co-citation networks, collaboration networks, and others. Identification and ranking of important nodes in such networks is useful in many applications, such as finding most influential papers, most productive researchers, pattern of citation, and many more. The paper aims to discuss this issue.

A number of methods are available in literature for node ranking, and K-shell decomposition is one such method. This method categorizes nodes in different groups based on their topological position. The shell number of a node provides useful insights about the node’s importance in the network. It has been found that shells produced by the K-shell method need to be further refined to quantify the influence of the nodes aptly. In this work, a method has been developed, which ranks nodes by taking the core(s) as the origin and second-order neighborhood of a node as its immediate sphere of influence.

It is found that the performance of the proposed technique is either comparable or better than other methods in terms of correctness and accuracy. In case of assigning different ranks to nodes, the performance of the proposed technique is far more superior to existing methods. The proposed method can be used to rank authors, research articles, and fields of research.

The proposed method ranks nodes by their global position in a network as well as their local sphere of information. It leads to better quantification of a node’s impact. This method is found to be better in terms of accuracy and correctness. In case of assigning different ranks to nodes, the performance of the proposed technique is far more superior to existing methods.

Although journal rankings are important for authors, readers, publishers, promotion, and tenure committees, it has been argued that the use of different measures (e.g. the journal impact factor (JIF), and Hirsch’s h-index) often lead to different journal rankings, which render it difficult to make an appropriate decision. A hybrid ranking method based on the Borda count approach, the Standardized Average Index (SA index), was introduced to solve this problem. The paper aims to discuss these issues.

Citations received by the articles published in 85 Health Care Sciences and Services (HCSS) journals in the period of 2009-2013 were analyzed with the use of the JIF, the h-index, and the SA index.

The SA index exhibits a high correlation with the JIF and the h-index (γ > 0.9, p < 0.01) and yields results with higher accuracy than the h-index. The new, comprehensive citation impact analysis of the 85 HCSS journals shows that the SA index can help researchers to find journals with both high JIFs and high h-indices more easily, thereby harvesting references for paper submissions and research directions.

The contribution of this study is the application of the Borda count approach to combine the HCSS journal rankings produced by the two widely accepted indices of the JIF and the h-index. The new HCSS journal rankings can be used by publishers, journal editors, researchers, policymakers, librarians, and practitioners as a reference for journal selection and the establishment of decisions and professional judgment.

This paper aims to propose a hybrid multiple criteria decision-making (MCDM) technique for performance evaluation of banks in which the banks are assessed and ranked according to the criteria of the balanced scorecard (BSC) methodology and corporate social responsibility (CSR) views.

To clarify the performance of the proposed model, the MCDM technique was implemented in four banks in Iran as a pilot. First, proper criteria for banking industry are identified considering BSC and CSR. Consequently, analytic hierarchy process (AHP) and modified digital logic (MDL) techniques are used to determine the weights of criteria. The banks are ranked by fuzzy TOPSIS (FTOPSIS) and fuzzy VIKOR (FVIKOR). Using a combination of these techniques, four methods, namely, AHP-FTOPSIS, AHP-FVIKOR, MDL-FTOPSIS and MDL-FVIKOR, are obtained, each of which provides a different set of rankings for banks. Eventually, the obtained ranks are integrated using the Copeland method.

The results showed that the return on investment, debt ratio and lower energy consumption criteria are the most important, and enhancement of brand value, increasing customer loyalty and environmental care criteria have the lowest percentage of importance. Also, the final bank ranking is determined by the proposed method.

This paper identifies 6 criteria and 25 sub-criteria for evaluating the banks considering BSC and CSR viewpoints including some new sub-criteria that has not been considered before. Moreover, these hybrid approaches and especially MDL techniques have not been used by previous researchers.

Every day, the sun provides by far more energy than the amount necessary to meet the whole world’s energy demand. Solar energy, unlike fossil fuels, does not suffer from depleting resource and also releases no greenhouse gas emissions when being used. Hence, using solar irradiance to produce electricity via photovoltaic (PV) systems has significant benefits which can lead to a sustainable and clean future. In this regard, the purpose of this study is first to assess the technical and economic viability of solar power generation sites in the capitals of the states of Canada. Then, a novel integrated technique is developed to prioritize all the alternatives.

In this study, ten provinces in Canada are evaluated for the construction of solar power plants. The new hybrid approach composed of data envelopment analysis (DEA), balanced scorecard (BSC) and game theory (GT) is implemented to rank the nominated locations from techno-economic-environmental efficiency aspects. The input data are obtained using HOMER software.

Applying the proposed hybrid approach, the order of high to low efficiency locations was found as Winnipeg, Victoria, Edmonton, Quebec, Halifax, St John’s, Ottawa, Regina, Charlottetown and Toronto. Construction of ten solar plants in the ten studied locations was assessed and it was ascertained that usage of solar energy in Winnipeg, Victoria and Edmonton would be economically and environmentally justified.

As to novelty, it should be clarified that the authors propose an effective hybrid method combining DEA, BSC and GT for prioritizing all available scenarios concerned with the construction of a solar power plant.

Hospital evaluations create competition between healthcare providers. In this study, a multi criteria decision-making (MCDM) method is used to evaluate criteria that affect hospital service quality. The paper aims to discuss these issues.

Criteria affecting hospital service quality are identified. Four Iranian public hospitals are evaluated using these criteria. Four hybrid methods, including modified digital logic–technique for order of preference by similarity to an ideal solution, analytical hierarchy process–technique for order of preference by similarity to an ideal solution, analytical hierarchy process–elimination and choice expressing reality and modified digital logic–elimination and choice expressing reality are used to evaluate hospital service quality. Results are aggregated using the Copeland method and final ranks are determined.

The four main criteria for evaluating hospital service quality are: environment; responsiveness; equipment and facilities; and professional capability. Results suggest that professional capability is the most important criterion. The Copeland method, used to integrate four MCDM hybrid methods, provides the final hospital ranks.

The criteria the authors identified and their weight help hospital managers to achieve comprehensive organizational growth and more efficient resource usage. Moreover, the decision matrix helps managers to identify their strengths and weaknesses.

New and comprehensive criteria are proposed for hospital quality assessments. Moreover, a new hybrid MCDM approach is used to achieve final hospital rankings.

The purpose of this paper is to investigate a fuzzy hybrid approach for ranking the flare gas recovery methods and allocating to refineries.

The proposed approach is containing four stages: in the first stage, experts' assessment is applied to identify relevant criteria and sub-criteria in the evaluation of flare gas recovery methods. In the second stage, the corresponding weights of criteria and sub-criteria are determined via fuzzy decision-making trial and evaluation (DEMATEL)-analytical network process (ANP) (DANP) method. In the third stage, the flare gas recovery methods are ranked using fuzzy weighted aggregated sum product assessment method (WASPAS) multi-criteria decision-making (MADM) technique. In the fourth stage, an optimization model is developed to allocate gas recovery methods to refineries while maximizing the total utility of allocations based on model constraints.

According to the results of fuzzy DANP method, technical and operational criterion was the most important followed by economic, political, managerial and environmental criteria. With respect to sub-criteria, international sanctions and political stability were the most important. The results of fuzzy WASPAS method indicated that gas injection was the first ranked alternative. Finally, the mathematical modeling allocated the recovery methods to five refineries of South Pars gas field in Iran based on budget and time constraints.

The proposed approach provides a systematic tool in the selection of flare recovery methods and allocation to refineries. This approach uses a new combination of fuzzy DEMATEL-ANP (DANP) method, fuzzy WASPAS method and mathematical programming. The approach is effectively implemented in a case study for ranking the flare gas recovery methods and allocating to refineries of South Pars gas field in Iran.

Excessive tool wear is responsible for damage or breakage of the tool, workpiece, or machining center. Thus, it is crucial to examine tool conditions during the machining process to improve its useful functional life and the surface quality of the final product. AI-based tool wear prediction techniques have proven to be effective in estimating the Remaining Useful Life (RUL) of the cutting tool. However, the model prediction needs improvement in terms of accuracy.

This paper represents a methodology of fusing a feature selection technique along with state-of-the-art deep learning models. The authors have used NASA milling data sets along with vibration signals for tool wear prediction and performance analysis in 15 different fault scenarios. Multiple steps are used for the feature selection and ranking. Different Long Short-Term Memory (LSTM) approaches are used to improve the overall prediction accuracy of the model for tool wear prediction. LSTM models' performance is evaluated using R-square, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters.

The R-square accuracy of the hybrid model is consistently high and has low MAE, MAPE and RMSE values. The average R-square score values for LSTM, Bidirection, Encoder–Decoder and Hybrid LSTM are 80.43, 84.74, 94.20 and 97.85%, respectively, and corresponding average MAPE values are 23.46, 22.200, 9.5739 and 6.2124%. The hybrid model shows high accuracy as compared to the remaining LSTM models.

The low variance, Spearman Correlation Coefficient and Random Forest Regression methods are used to select the most significant feature vectors for training the miscellaneous LSTM model versions and highlight the best approach. The selected features pass to different LSTM models like Bidirectional, Encoder–Decoder and Hybrid LSTM for tool wear prediction. The Hybrid LSTM approach shows a significant improvement in tool wear prediction.