Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Nowadays, innovation is valued as being of utmost significance when evaluating measures of sustainable development and performance constructs. The current research develops investigations into the field of frugal innovation (FI) and how this process can contribute to business sustainability. This study performs a comprehensive evaluation of scientific production through the quantitative method of bibliometric analysis to facilitate our understanding of the current structure of studies and to highlight future research paths on FI and business sustainability. The paper maps the literature by narrowing its research topics and identifying certain patterns. Using the Web of Science database (WoS) on a sample of 2072 documents, descriptive and performance analyses were conducted. The VOSviewer software was used to perform the science mapping of the conceptual, intellectual, and social structure that provides scholars with a quantified and graphic representation of the FI and business sustainability field. The trend towards the analysis of business sustainability and consumer behaviour in the context of FI is highlighted. Annual scientific production and citation analysis reveal significant growth in international interest and scientific production, indicating an increasing line of inquiry for this topic at the start of the twenty-first century. The prominent scholars in the field are of European origin, while the highly active countries in the subject of FI and business sustainability are the USA, Germany, England, the Netherlands, and India, with major collaborations across the globe. This paper covers the limits identified by previous scholars by detailing the knowledge base and by using science mapping tools to document the structure of the literature; distinguish key journals, articles, and authors; and highlight new emerging topics of research.

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Exploring the Research Regarding Frugal Innovation and Business Sustainability through Bibliometric Analysis

The circular supply chain has recently received more attention as a relevant solution to effectively tackle environmental issues while simultaneously achieving resource recovery and circular business strategy benefits. This study builds a hierarchical circular supply chain structure from big data including qualitative and quantitative information. This study uses data-driven analysis to clarify circular supply chain trends and opportunities in practice. A valid hierarchical circular supply chain structure is composed of a big dataset. However, the attributes of the hierarchical circular supply chain structure must be explored to identify the opportunities and challenges of the circular supply chain. A combination of data-driven content and cluster analysis, including the fuzzy Delphi method, fuzzy decision-making trials, evaluation laboratories, and the entropy weight method, is utilized to address this gap. The study analyzes a set of five attributes from the literature, and 23 criteria are validated. The results show that resource recovery implementation, Industry 4.0 and digitalization, and reverse supply chain practice pertain to the causal group, while circular business strategy and life cycle sustainability assessment are included in the effect group. The conclusive criteria comprise material efficiency, waste-to-energy, machine learning, e-waste, plastic recycling, and artificial intelligence. 

Building a data‐driven circular supply chain hierarchical structure: Resource recovery implementation drives circular business strategy

Traditional educational systems and learning have been significantly impacted by the quick growth of information and communication technology. Moreover, the learning process is increasingly important for socioeconomic and business success in our modern society. Therefore, at present, cloud computing is crucially important in connection with e-learning systems. The goal of this research is to conduct a thorough assessment of scientific production in the field of e-learning and cloud technology fields using the bibliometric analysis method’s quantitative approach in order to comprehensively review and analyze the subject. The current study reviews the literature by focusing on specific research areas and revealing certain trends. The research examines 637 articles published between 2007 and 2022 in the Web of Science database (WoS) using the VOSviewer software version 1.6.18 (Leiden University, The Netherlands) and bibliometrix R-package. The goal of this research is to conduct a thorough assessment of scientific production in the field of e-learning and cloud technology fields using the bibliometric analysis method’s quantitative approach to comprehensively review and analyze the subject. Currently, there is no unified approach and extensive bibliometric review that tackle both of these topics cohesively; thus, this research aims to fill this gap. The results shed light on the structure, evolution, main trends, and effect of the research field of e-learning systems based on cloud computing by intensively evaluating and analyzing the scientific output, key contributions to the subject, and possible directions for future research. The most productive country in terms of scientific knowledge and number of citations is China. It is noteworthy that the interest of researchers comes from various regions of the world, while the most prolific authors come from Serbia, Japan, and Romania. The average citation number per document is 6.8, while the most citations were obtained by highly influential article about critical factors influencing learner satisfaction for successful e-learning. Regarding the conceptual structure that assists researchers to understand keyword evolution and trend, four clusters were identified, which reside around the topics “Technology”, “Education”, “Delivery Systems” and “Cloud services”. Given these points, the current study’s implications reveal the significance of e-learning technologies based on cloud computing, along with the direct correlation between these two elements.

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Mapping Knowledge Area Analysis in E-Learning Systems Based on Cloud Computing

The rapid development of the analytic hierarchy process (AHP) results numerous theoretical and empirical documents. However, as far as we know, there is no literature focus on the theme evolution and knowledge trajectory of the AHP field until now, which makes it difficult for scholars to grasp the research priorities in this domain. To fill this gap, this study conducts science mapping analysis and main path analysis (MPA) on 1326 documents retrieved from Web of Science (WoS) to explore knowledge development and the main research fronts in the AHP field. This study first reflects the evolution trend of the theme through science mapping analysis based on co-word analysis, and proposes six outstanding research areas in the AHP field. Then, through MPA based on citation relationship, this study finds that outsourcing is one of the future research trends. Overall, this paper not only provides researchers with a comprehensive overview of the AHP domain, but also helps practitioners find the cutting-edge application areas and methods. 

A theme evolution and knowledge trajectory study in AHP using science mapping and main path analysis

The research and practice of information systems have progressed by leaps and bounds due to the emergence of big data analytics and machine learning. The paper undertakes a bibliometric study to analyze the contributions of major authors, universities/organizations, and countries in terms of productivity, citations, and bibliographic coupling. A sample of 2160 articles from the Scopus for the period 2006–2020 is the basis of the study. The publications are grouped into five clusters, of which Cluster 1 is consistently dominant in the information systems publication landscape. Cluster 2 includes published studies on the Internet of Things, security, and cloud computing, which have also been widely researched. Cluster 3, the third-largest cluster, has attempted to investigate social media analytics. Cluster 4 aims to look into the impact of classification and predictive, which is found to have sustained research interest. Topics with scant coverage in terms of papers are primarily in Cluster 5, indicating saturation in the area and the need for conducting inter-disciplinary studies. The results of our study provide valuable insights for potential contributors and global audiences in terms of emerging topics for research.

Prajwal Eachempati

Papers

Big data analytics and machine learning: A retrospective overview and bibliometric analysis