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The study aims to analyze the barriers in the adoption of Industry 4.0 (I4.0) practices in terms of prioritization, cluster formation and clustering of empirical responses, and then narrowing them with identification of the most influential barriers for further managerial implications in the adoption of I4.0 practices by developing an enhanced understanding of I4.0.

For the survey-based empirical research, barriers to I.40 are synthesized from the review of relevant literature and further discussions with academician and industry persons. Three widely acclaimed statistical techniques, viz. principal component analysis (PCA), fuzzy analytical hierarchical process (fuzzy AHP) and K-means clustering are applied.

The novel integrated approach shows that lack of transparent cost-benefit analysis with clear comprehension about benefits is the major barrier for the adoption of I4.0, followed by “IT infrastructure,” “Missing standards,” “Lack of properly skilled manpower,” “Fitness of present machines/equipment in the new regime” and “Concern to data security” which are other prominent barriers in adoption of I4.0 practices. The availability of funds, transparent cost-benefit analysis and clear comprehension about benefits will motivate the business owners to adopt it, overcoming the other barriers.

The present study brings out the new fundamental insights from the barriers to I4.0. The new insights developed here will be helpful for managers and policymakers to understand the concept and barriers hindering its smooth implementation. The factors identified are the major thrust areas for a manager to focus on for the smooth implementation of I4.0 practices. The removal of these barriers will act as a booster in the way of implementing I4.0. Real-world testing of findings is not available yet, and this will be the new direction for further research.

The new production paradigm is highly complex and evolving. The study will act as a handy tool for the implementing manager for what to push first and what to push later while implementing the I4.0 practices. It will also empower a manager to assess the implementation capabilities of the industry in advance.

PCA, fuzzy AHP and K means are deployed for identifying the significant barriers to I4.0 first time. The paper is the result of the original conceptual work of integrating the three techniques in the domain of prioritizing and narrowing the barriers from 16 to 6.

The purpose of this paper is to develop a systematic approach for business customer segmentation.

This study proposes an approach for business customer segmentation that integrates clustering and multi-criteria decision making (MCDM). First, proper segmentation variables are identified and then customers are grouped by using hierarchical and partitional clustering algorithms. The approach extended the recency-frequency-monetary (RFM) model by proposing five novel segmentation variables for business markets. To confirm the viability of the proposed approach, a real-world application is presented. Three agglomerative hierarchical clustering algorithms namely “Ward’s method,” “single linkage” and “complete linkage,” and a partitional clustering algorithm, “k-means,” are used in segmentation. In the implementation, fuzzy analytic hierarchy process is employed to determine the importance of the segments.

Business customers of an international original equipment manufacturer (OEM) are segmented in the application. In this regard, 317 business customers of the OEM are segmented as “best,” “valuable,” “average,” “potential valuable” and “potential invaluable” according to the cluster ranks obtained in this study. The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation.

The success of the proposed approach relies on the availability and quality of customers’ data. Therefore, design of an extensive customer database management system is the foundation for any successful customer relationship management (CRM) solution offered by the proposed approach. Such a database management system may entail a noteworthy level of investment.

The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. By making customer segmentation decisions, the proposed approach can provides firms a basis for the development of effective loyalty programs and design of customized strategies for their customers.

The proposed segmentation approach may contribute firms to gaining sustainable competitive advantage in the market by increasing the effectiveness of CRM strategies.

This study proposes an integrated approach for business customer segmentation. The proposed approach differentiates itself from its counterparts by combining MCDM and clustering in business customer segmentation. In addition, it extends the traditional RFM model by including five novel segmentation variables for business markets.

The purpose of this paper is to present a modified failure mode and effects analysis (FMEA) in order to make the assignment of the scores for the occurrence factor more robust, and to link the FMEA chart directly to the maintenance activities.

A well-known clustering algorithm (i.e. K-means), along with a normalisation approach, are applied and compared for the assignment of the occurrence scores. Subsequently, the relationship between failures and maintenance operations is made explicit by a correlation matrix. Finally, the K-means algorithm is applied to the maintenance operations again in order to sort them into priority classes.

It is found that this revised FMEA approach improves the standard one due to its more rigorous mathematical formulation and lean applicability in real operating environments.

The novel approach may be improved by a deeper statistical analysis and/or applying the fuzzy theory.

A real case study is introduced in order to show the applicability of this approach to the quality control of a blow moulding process. It is found that this approach reveals a high potentiality for dealing with real issues.

The paper provides a further step towards bridging the gap between theory and practical application of the FMEA approach.

Service-oriented architecture is an emerging software architecture, in which web service (WS) plays a crucial role. In this architecture, the task of WS composition and verification is required when handling complex requirement of services from users. When the number of WS becomes very huge in practice, the complexity of the composition and verification is also correspondingly high. In this paper, the authors aim to propose a logic-based clustering approach to solve this problem by separating the original repository of WS into clusters. Moreover, they also propose a so-called quality-controlled clustering approach to ensure the quality of generated clusters in a reasonable execution time.

The approach represents WSs as logical formulas on which the authors conduct the clustering task. They also combine two most popular clustering approaches of hierarchical agglomerative clustering (HAC) and k-means to ensure the quality of generated clusters.

This logic-based clustering approach really helps to increase the performance of the WS composition and verification significantly. Furthermore, the logic-based approach helps us to maintain the soundness and completeness of the composition solution. Eventually, the quality-controlled strategy can ensure the quality of generated clusters in low complexity time.

The work discussed in this paper is just implemented as a research tool known as WSCOVER. More work is needed to make it a practical and usable system for real life applications.

In this paper, the authors propose a logic-based paradigm to represent and cluster WSs. Moreover, they also propose an approach of quality-controlled clustering which combines and takes advantages of two most popular clustering approaches of HAC and k-means.

The purpose of this work is to implement an ambient intelligence (AmI) testbed to improve human sleeping conditions.

The implemented testbed is composed of the sensor node, sink node and actor node. As sensor node, the authors use a microwave sensor module (MSM) called DC6M4JN3000, which emits microwaves in the direction of a human or animal subject. These microwaves reflect back off the surface of the subject and change slightly in accordance with movements of the subject’s heart and lungs. As sink node, the authors use Raspberry Pi 3 Model B computers. In the sink node, the data are processed and then clustered by the k-means clustering algorithm. Then, the result is sent to the actor node (Reidan Shiki PAD module), which can be used for cooling and heating the bed.

The authors carried out simulations and experiments. Based on the simulation results, it was found that the room lighting, humidity and temperature have different effects on humans during sleeping. The best performance is shown when LIG parameter is 10 units, HUM parameter is 50 and TEM parameter is 25. Based on experimental results, it was found that the implemented AmI testbed has a good effect on humans during sleeping.

For simulations, three input parameters were considered. However, new parameters that affect human sleeping conditions also need to be investigated. Further, the experiments were carried out for one person. More extensive experiments with multiple people are needed to have a better evaluation.

In this research work, a new fuzzy-based system was implemented to improve human sleeping conditions. The authors presented three new input parameters to evaluate the output (sleeping condition). The authors implemented and evaluated a testbed and showed that the implemented AmI testbed has a good effect on humans during sleeping, thus improving their quality of life (QoL).

This paper aims to detect abnormal energy uses which relate to undetected consumption, thefts, measurement errors, etc. The detection of irregular power consumption, with variation in irregularities, helps the electric utilities in planning and making strategies to transfer reliable and efficient electricity from generators to the end-users. Abnormal peak load demand is a kind of aberration that needs to be detected.

This paper proposes a Density-Based Micro Spatial Clustering of Applications with Noise (DBMSCAN) clustering algorithm, which is implemented for identification of ranked irregular electricity consumption and occurrence of peak and valley loads. In the proposed algorithm, two parameters, a and ß, are introduced, and, on tuning of these parameters, after setting of global parameters, a varied number of micro-clusters and ranked irregular consumptions, respectively, are obtained. An approach is incorporated with the introduction of a new term Irregularity Variance in the suggested algorithm to find variation in the irregular consumptions according to anomalous behaviors.

No set of global parameters in DBSCAN is found in clustering of load pattern data of a practical system as the data. The proposed DBMSCAN approach finds clustering results and ranked irregular consumption such as different types of abnormal peak demands, sudden change in the demand, nearly zero demand, etc. with computational ease without any iterative control method.

The DBMSCAN can be applied on any data set to find ranked outliers. It is an unsupervised approach of clustering technique to find the clustering results and ranked irregular consumptions while focusing on the analysis of and variations in anomalous behaviors in electricity consumption.

The immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. The purpose of this paper is to provide diversity among the classification algorithms of ensemble, which is a key issue in the ensemble design.

An ensemble scheme based on hybrid supervised clustering is presented for text classification. In the presented scheme, supervised hybrid clustering, which is based on cuckoo search algorithm and k-means, is introduced to partition the data samples of each class into clusters so that training subsets with higher diversities can be provided. Each classifier is trained on the diversified training subsets and the predictions of individual classifiers are combined by the majority voting rule. The predictive performance of the proposed classifier ensemble is compared to conventional classification algorithms (such as Naïve Bayes, logistic regression, support vector machines and C4.5 algorithm) and ensemble learning methods (such as AdaBoost, bagging and random subspace) using 11 text benchmarks.

The experimental results indicate that the presented classifier ensemble outperforms the conventional classification algorithms and ensemble learning methods for text classification.

The presented ensemble scheme is the first to use supervised clustering to obtain diverse ensemble for text classification

This paper aims to propose a novel hybrid-decision-making trial and evaluation laboratory-K means clustering algorithm as a decision-making framework for analyzing the barriers of green computing adoption.

A literature review is conducted to extract relevant green computing barriers. An expert elicitation process is performed to finalize the barriers and to establish their corresponding interrelationships.

The proposed approach offers a comprehensive framework for modeling the barriers of green computing adoption.

The results of this paper provide insights on how the barriers of green computing adoption facilitate the adoption of stakeholders. Moreover, the paper provides a framework for analyzing the structural relationships that exist between factors in a tractable manner.

The paper is one of the very first attempts to analyze the barriers of green computing adoption. Furthermore, it is the first to offer lenses in a Philippine perspective. The paper offers a novel algorithm that can be useful in modeling the barriers of innovation, particularly, in green computing adoption.

Vehicle fault diagnosis is a key factor in ensuring the safe and efficient operation of the railway system. Due to the numerous vehicle categories and different fault mechanisms, there is an unbalanced fault category problem. Most of the current methods to solve this problem have complex algorithm structures, low efficiency and require prior knowledge. This study aims to propose a new method which has a simple structure and does not require any prior knowledge to achieve a fast diagnosis of unbalanced vehicle faults.

This study proposes a novel K-means with feature learning based on the feature learning K-means-improved cluster-centers selection (FKM-ICS) method, which includes the ICS and the FKM. Specifically, this study defines cluster centers approximation to select the initialized cluster centers in the ICS. This study uses improved term frequency-inverse document frequency to measure and adjust the feature word weights in each cluster, retaining the top τ feature words with the highest weight in each cluster and perform the clustering process again in the FKM. With the FKM-ICS method, clustering performance for unbalanced vehicle fault diagnosis can be significantly enhanced.

This study finds that the FKM-ICS can achieve a fast diagnosis of vehicle faults on the vehicle fault text (VFT) data set from a railway station in the 2017 (VFT) data set. The experimental results on VFT indicate the proposed method in this paper, outperforms several state-of-the-art methods.

This is the first effort to address the vehicle fault diagnostic problem and the proposed method performs effectively and efficiently. The ICS enables the FKM-ICS method to exclude the effect of outliers, solves the disadvantages of the fault text data contained a certain amount of noisy data, which effectively enhanced the method stability. The FKM enhances the distribution of feature words that discriminate between different fault categories and reduces the number of feature words to make the FKM-ICS method faster and better cluster for unbalanced vehicle fault diagnostic.

Customer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.

The authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.

As a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.

The authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.