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This paper aims to propose a new genetically evolved fuzzy cognitive mapping approach as a decision-making framework for analyzing the relationships between the drivers and strategies for green computing adoption.

A focus group discussion among stakeholders in the Philippines is used to establish the relationships between the drivers and strategies of green computing adoption.

The proposed approach significantly reduces the time complexity for developing the fuzzy cognitive maps and provides a basis for comprehensively clustering drivers and strategies that share similar characteristics.

This paper’s results provide insights into how the drivers and strategies of green computing adoption facilitate the intention of adopting stakeholders. Moreover, it provides a framework for analyzing structural relationships that exist between factors in a compliant manner.

To the best of the author’s knowledge, the paper is the first to analyze the drivers and strategies of green computing under a complex systems’ perspective. Moreover, this is the first study to offer lenses in a Philippine scenario.

This article proposes a novel hybrid simulation model for understanding the complex tobacco use behavior.

The model is developed by embedding the concept of the multistage learning-based fuzzy cognitive map (FCM) into the agent-based model (ABM) in order to benefit from advantageous of each methodology. The ABM is used to represent individual level behaviors while the FCM is used as a decision support mechanism for individuals. In this study, socio-demographic characteristics of individuals, tobacco control policies, and social network effect are taken into account to reflect the current tobacco use system of Turkey. The effects of plain package and COVID-19 on tobacco use behaviors of individuals are also searched under different scenarios.

The findings indicate that the proposed model provides promising results for representing the mental models of agents. Besides, the scenario analyses help to observe the possible reactions of people to new conditions according to characteristics.

The proposed method combined ABM and FCM with a multi-stage learning phases for modeling a complex and dynamic social problem as close as real life. It is expected to contribute for both ABM and tobacco use literature.

The purpose of this paper is to show that research on policy diffusion mechanism of Government Procurement of Public Services (GPPS) is beneficial to improve the efficiency of policy formulation and implementation.

In view of the four dimensions which are internal demand, external pressure, policy innovation environment and service characteristic, a system of factors affecting policy diffusion is established. On this basis, a Multilayer Fuzzy Cognitive Map (MFCM) model for policy diffusion of GPPS is constructed. Nonlinear Hebbian Learning algorithm and genetic algorithm are applied to optimize the two components of the MFCM model, which are relationship between nodes at the same layer and influence weights between nodes at different layers, respectively. Taking Nanjing municipal government purchasing elderly-care services in China as the empirical object, simulation of policy diffusion based on the MFCM model is carried out, aiming to obtain the key factors influencing policy diffusion and the dynamic diffusion mechanism of GPPS policy.

Research results show that, compared with monolayer Fuzzy Cognitive Map, the MFCM model converges faster. In addition, simulation results of policy diffusion indicate that economic development level of jurisdiction, superior pressure, administrative level and operability of services are key influencing factors which are under four dimensions correspondingly. And the dynamic influencing mechanism of key factors has also been learned.

This paper constructs the MFCM model, which is a new approach based on several monolayer FCMs, to study the policy diffusion mechanism.

The COVID-19 pandemic has proven that how supply chain management (SCM) can become a crucial process for sustainability of the world's production/service. The global supply chain crisis during pandemic has affected most of the sectors. Home and personal care products manufacturers are among them. In this study (1) the problems at SCM of personal and home care products manufacturers during pandemic are discussed with the help of medium-size manufacturer and (2) the factors affecting suppliers' performance for the relevant sector during COVID-19 are analyzed comprehensively.

The importance of the factors is evaluated using fuzzy cognitive maps that can help to reveal hidden casual relationships with the help of expert knowledge. In order to eliminate subjectivity due to usage of expert knowledge, the maps are trained with a hybrid learning approach that consists of Non-linear Learning and Extended Great Deluge Algorithms to increase robustness of the analysis.

The findings of the study indicate that the factors such as general quality level of products/services, compliance to delivery time, communication skills and total production capacity of suppliers have been crucial factors during pandemic.

While the implementation of the hybrid learning approach on supply chain can fill the gap in the relevant literature, the promising results of the study can prove the convenience of the methodology to model the of complex systems like supply chain processes.

The study aims to show how several factors interact to promote mobile game download: the number of games released by a publisher, the quality of the games released, the popularity of a game's genre, the quality of borrowed intellectual property, the frequency of recommendations, intragenre ranking, consumer rating and review quantity.

Signaling theory was used to classify the mobile game information displayed on the Apple App Store into four groups. A conceptual model was proposed to illustrate the complex relationship between the information and download. Based on information on 203 mobile games in the seven days following their release, the model was empirically tested to identify the influence of information configuration on game download by combining fuzzy qualitative comparative analysis (fsQCA) and a fuzzy cognitive map (FCM).

Three solutions were identified for high game download and two for low/medium. The number of previous games released by a publisher, intragenre ranking, consumer rating and review quantity are core conditions that reinforce high game download. The effects of one information type on another and on downloads change as coexisting information types change.

This study enriches existing knowledge about how combinations of multiple types of game information lead to game download and extends previous variance-based research. Combining an FCM with fsQCA can facilitate one’s understanding of the complex causal relationships between game information and download.

New approaches for non‐classical neural‐based computing are introduced. The developed approaches utilize new concepts in three‐dimensionality, invertibility and reversibility to perform the required neural computing. The various implementations of the new neural circuits using the introduced paradigms and architectures are presented, several applications are shown, and the extension for the utilization in neural‐systolic computing is also introduced.

The new neural paradigms utilize new findings in computational intelligence and advanced logic synthesis to perform the functionality of the basic neural network (NN). This includes the techniques of three‐dimensionality, invertibility and reversibility. The extension of implementation to neural‐systolic computing using the introduced reversible neural‐systolic architecture is also presented.

Novel NN paradigms are introduced in this paper. New 3D paradigm of NL circuits called three‐dimensional inverted neural logic (3DINL) circuits is introduced. The new 3D architecture inverts the inputs and weights in the standard neural architecture: inputs become bases on internal interconnects, and weights become leaves of the network. New reversible neural network (RevNN) architecture is also introduced, and a RevNN paradigm using supervised learning is presented. The applications of RevNN to multiple‐output feedforward discrete plant control and to reversible neural‐systolic computing are also shown. Reversible neural paradigm that includes reversible neural architecture utilizing the extended mapping technique with an application to the reversible solution of the maze problem using the reversible counterpropagation NN is introduced, and new neural paradigm of reversibility in both architecture and training using reversibility in independent component analysis is also presented.

Since the new 3D NNs can be useful as a possible optimal design choice for compacting a learning (trainable) circuit in 3D space, and because reversibility is essential in the minimal‐power computing as the reduction of power consumption is a main requirement for the circuit synthesis of several emerging technologies, the introduced methods for non‐classical neural computation are new and interesting for the design of several future technologies that require optimal design specifications such as three‐dimensionality, regularity, super‐high speed, minimum power consumption and minimum size such as in low‐power control, adiabatic signal processing, quantum computing, and nanotechnology.

The purpose of this paper is to generate customer clusters using self-organizing map (SOM) approach, a machine learning technique with a big data set of credit card consumptions. The authors aim to use the consumption patterns of the customers in a period of three months deducted from the credit card transactions, specifically the consumption categories (e.g. food, entertainment, etc.).

The authors use a big data set of almost 40,000 credit card transactions to cluster customers. To deal with the size of the data set and the eliminated the required parametric assumptions the authors use a machine learning technique, SOMs. The variables used are grouped into three as demographical variables, categorical consumption variables and summary consumption variables. The variables are first converted to factors using principal component analysis. Then, the number of clusters is specified by k-means clustering trials. Then, clustering with SOM is conducted by only including the demographical variables and all variables. Then, a comparison is made and the significance of the variables is examined by analysis of variance.

The appropriate number of clusters is found to be 8 using k-means clusters. Then, the differences in categorical consumption levels are investigated between the clusters. However, they have been found to be insignificant, whereas the summary consumption variables are found to be significant between the clusters, as well as the demographical variables.

The originality of the study is to incorporate the credit card consumption variables of customers to cluster the bank customers. The authors use a big data set and dealt with it with a machine learning technique to deduct the consumption patterns to generate the clusters. Credit card transactions generate a vast amount of data to deduce valuable information. It is mainly used to detect fraud in the literature. To the best of the authors’ knowledge, consumption patterns obtained from credit card transaction are first used for clustering the customers in this study.

A recommendation system for an extended process of information retrieval in distributed information systems is proposed. This system is both a model of dynamic cognitive categorization processes and powerful real application useful for knowledge management. It utilizes an extension of fuzzy sets named evidence sets as the mathematical mechanisms to implement the categorization processes. It is a development of some aspects of Pask’s conversation theory. It is also an instance of the notion of linguistic‐based selected self‐organization here described, and as such it instantiates an open‐ended semiosis between distributed information systems and the communities of users they interact with. This means that the knowledge stored in distributed information resources adapts to the evolving semantic expectations of their users as these select the information they desire in conversation with the information resources. This way, this recommendation system establishes a mechanism for user‐driven knowledge self‐organization.

The aim of this study is to propose a deep neural network (DNN) method that uses side information to improve clustering results for big datasets; also, the authors show that applying this information improves the performance of clustering and also increase the speed of the network training convergence.

In data mining, semisupervised learning is an interesting approach because good performance can be achieved with a small subset of labeled data; one reason is that the data labeling is expensive, and semisupervised learning does not need all labels. One type of semisupervised learning is constrained clustering; this type of learning does not use class labels for clustering. Instead, it uses information of some pairs of instances (side information), and these instances maybe are in the same cluster (must-link [ML]) or in different clusters (cannot-link [CL]). Constrained clustering was studied extensively; however, little works have focused on constrained clustering for big datasets. In this paper, the authors have presented a constrained clustering for big datasets, and the method uses a DNN. The authors inject the constraints (ML and CL) to this DNN to promote the clustering performance and call it constrained deep embedded clustering (CDEC). In this manner, an autoencoder was implemented to elicit informative low dimensional features in the latent space and then retrain the encoder network using a proposed Kullback–Leibler divergence objective function, which captures the constraints in order to cluster the projected samples. The proposed CDEC has been compared with the adversarial autoencoder, constrained 1-spectral clustering and autoencoder + k-means was applied to the known MNIST, Reuters-10k and USPS datasets, and their performance were assessed in terms of clustering accuracy. Empirical results confirmed the statistical superiority of CDEC in terms of clustering accuracy to the counterparts.

First of all, this is the first DNN-constrained clustering that uses side information to improve the performance of clustering without using labels in big datasets with high dimension. Second, the author defined a formula to inject side information to the DNN. Third, the proposed method improves clustering performance and network convergence speed.

Little works have focused on constrained clustering for big datasets; also, the studies in DNNs for clustering, with specific loss function that simultaneously extract features and clustering the data, are rare. The method improves the performance of big data clustering without using labels, and it is important because the data labeling is expensive and time-consuming, especially for big datasets.