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The purpose of this paper is to propose hybrid revised weighted fuzzy c-means (RWFCM) clustering and Nelder–Mead (NM) simplex algorithm, called as RWFCM-NM, for generalized multisource Weber problem (MWP).

Although the RWFCM claims that there is no obligation to sequentially use different methods together, NM’s local search advantage is investigated and performance of the proposed hybrid algorithm for generalized MWP is tested on well-known research data sets.

Test results state the outstanding performance of new hybrid RWFCM and NM simplex algorithm in terms of cost minimization and CPU times.

Proposed approach achieves better results in continuous facility location problems.

The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

It is essential to validate whether a nation's economic strength always transpires into new business capacity. The present research strives to identify the key indicators to the proxy new business ecosystem of countries and critically evaluate the similarity through the lens of advanced Fuzzy Clustering Frameworks over the years.

The authors use Fuzzy C Means, Type 2 Fuzzy C Means, Fuzzy Possibilistic C Means and Fuzzy Possibilistic Product Partition C Means Clustering algorithm to discover the inherent groupings of the considered countries in terms of intricate patterns of geospatial new business capacity during 2015–2018. Additionally, the authors propose a Particle Swarm Optimization driven Gradient Boosting Regression methodology to measure the influence of the underlying indicators for the overall surge in new business.

The Fuzzy Clustering frameworks suggest the existence of two clusters of nations across the years. Several developing countries have emerged to cater praiseworthy state of the new business ecosystem. The ease of running a business has appeared to be the most influential feature that governs the overall New Business Density.

It is of paramount practical importance to conduct a periodic review of nations' overall new business ecosystem to draw action plans to emphasize and augment the key enablers linked to new business growth. Countries found to lack new business capacity despite enjoying adequate economic strength can focus effectively on weaker dimensions.

The research proposes a robust systematic framework for new business capacity across different economies, indicating that economic strength does not necessarily transpire to equivalent new business capacity.

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

This study proposes an objective assessment model to evaluate the performance of internal and external capabilities of firms. It facilitates firms to invest appropriate resources to cultivate the organizational capability necessary to meet the requirements of the performance indicators.

This study integrates the concepts of resource-based theory, the organizational capability concept, and conduct a performance analysis to the four perspectives of the BSC by implementing the fuzzy set theory and data employment analysis.

The findings show that the appropriate strategies help allocate available resources and capabilities during the different product life cycle, which provides practical guidelines for firms to achieve sustaining competitive advantage.

The selected factors were focused on four resources and capabilities rather than all possible factors.

An objective assessment model was created based on internal and external competitive performance efficiency in this research field. This model facilitates the ability of the top management to make decisions for resource allocation that will enhance firm's performance.

This study aims to evaluate a new fusion technique of visual and textual clusters of objects from a real multimedia data-driven collection to improve the performance of multimedia applications.

The authors focused on using multi-criteria for clustering texts and images. The algorithm consists of these steps: first is text representation using the statistical method of weighting, second is image representation using a bag of words feature descriptors methods and finally application of multi-criteria clustering.

As an application for event detection based on social multimedia data, in particular, Flickr platform. Several experiments were conducted to choose the appropriate parameters for a better scheme of clustering. The new approach achieves better performance when aggregate text clustering is done with image clustering for event detection.

Further researches would be investigated on other social media platforms such as Facebook and Twitter for a generalization of the technique.

This study contributes to multimedia data mining through the new fusion technique of clustering. The technique has its root in such strong field as the field of multi-criteria clustering and decision-making support.

Online customer relationship management (CRM) is an important issue for implementing digital marketing of electronic commerce or social commerce. The purpose of this study is to establish valuable markets for discovering customer knowledge from data-driven CRM systems for enhancing growth rates of businesses. Airline or travel agency industries are online businesses in the world. Therefore, the industries in Taiwan will be an empirical case for this study.

This research applied a procedure with an applied proposed model for establishing valuable markets from data-driven CRM systems. However, the study used a proposed customer value model (recency, frequency and monetary [RFM]; RFM model-based), the analytic hierarchy process (AHP) procedure and a proposed equation for estimating customer values.

For enhancing the data-driven CRM marketing of the industries, in this research, the market of air travelers can be partitioned into eight markets by the proposed model. As well, the markets can be ranked by the AHP procedure. Furthermore, the travelers’ customer values can be estimated by a proposed customer value equation.

Via the applied proposed procedure, online airlines, travel agencies or other online businesses can implement the research procedure as their data-driven marketing strategy on their online large-scale or Big Data customers’ databases for enhancing sales rates.

The purpose of this paper is introducing the image processing technology used for fabric analysis, which has the advantages of objective, digital and quick response.

This paper briefly describes the key process and module of some typical automatic recognition systems for fabric analysis presented by previous researchers; the related methods and algorithms used for the texture and pattern identification are also introduced.

Compared with the traditional subjective method, the image processing technology method has been proved to be rapid, accurate and reliable for quality control.

The future trends and limitations in the field of weave pattern recognition for woven fabrics have been summarized at the end of this paper.