Board gender diversity and sustainable growth rate: Chinese evidence

This study investigates the impact of women directors on a firm’s sustainable growth rate. Using data from 2003 to 2017 for Chinese listed firms, we found a positive relationship between women directors and a sustainable growth rate. Our study also contributes to institutional theory by providing evidence that this positive relationship is more effective in legal-person-controlled firms than state-controlled firms. In comparison, women independent directors have a stronger influence than women executive directors on sustainable growth. Similarly, board gender diversity with three or more female directors substantially affects firms’ sustainable growth, consistent with critical mass theory. Our study’s findings are robust in terms of alternative estimations techniques, variable specifications, and different identification strategies, such as two-stage least squares and propensity score matching. Our study provides novel evidence on women directors’ role in increasing firms’ sustainable growth rate by adding a new dimension to the ongoing debate in the gender diversity literature

Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

Pattern recognition is seen as a major challenge within the field of data mining and knowledge discovery. For the work in this paper, we have analyzed a range of widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. This study also focuses on each of the algorithm’s strengths and weaknesses for finding patterns among large item sets in database systems

Novel centroid selection approaches for KMeans-clustering based recommender systems

Recommender systems have the ability to filter unseen information for predicting whether a particular user would prefer a given item when making a choice. Over the years, this process has been dependent on robust applications of data mining and machine learning techniques, which are known to have scalability issues when being applied for recommender systems. In this paper, we propose a k-means clustering-based recommendation algorithm, which addresses the scalability issues associated with traditional recommender systems. An issue with traditional k-means clustering algorithms is that they choose the initial k centroid randomly, which leads to inaccurate recommendations and increased cost for offline training of clusters. The work in this paper highlights how centroid selection in k-means based recommender systems can improve performance as well as being cost saving. The proposed centroid selection method has the ability to exploit underlying data correlation structures, which has been proven to exhibit superior accuracy and performance in comparison to the traditional centroid selection strategies, which choose centroids randomly. The proposed approach has been validated with an extensive set of experiments based on five different datasets (from movies, books, and music domain). These experiments prove that the proposed approach provides a better quality cluster and converges quicker than existing approaches, which in turn improves accuracy of the recommendation provided

Inference of Activities with Unexpected Actions Using Pattern Mining

Recognition of activities in an unobtrusive manner has attracted the attention of context aware systems, which provide end users with services based on everyday activities that are recognised without infringing the privacy of the end user. Current work has generally focused on applying a range of traditional classification and semantic reasoning based techniques in order to recognise these activities. However, the ability to recognise unexpected actions while the activity is being conducted remains a challenge. In this paper, we present an approach that is able to recognise activities regardless of the order of tasks/actions used to perform the activity. The proposed recognition framework extends an existing activity recognition approach by deploying a frequent pattern mining technique to find patterns among different streams of captured sensor events in order to increase the adaptive learning of the proposed recognition approach

Recognition Framework for Inferring Activities of Daily Living Based on Pattern Mining

Ambient assisted living applications are very much dependent on robust activity recognition frameworks, which allow these applications to provide services based on the contextual information that has been discovered. Existing frameworks have generally focused on the application of traditional classifiers and semantics reasoning to recognize activities. Nevertheless, being able to recognize unexpected actions remains a challenge. The work in this paper presents an approach that is able to recognize activities that have been conducted in an unordered manner. The recognition framework extends an existing approach that recognizes activities by exploiting the different levels of abstraction within an activity. A frequent pattern mining algorithm has been applied to the recognition framework in order to find patterns within the stream of captured events, which in turn increases the adaptive learning ability of the proposed recognition framework. This paper also presents experimental results that validate the recognition ability of the recognition framework. The motivation of this work is to be able to detect the functional decline among elderly people suffering from Alzheimer’s disease by recognizing their daily activities

A Systematic Review of Supply Chain Management Using Bibliometric Analysis

Purpose: This study investigates the many variables that support the expanding SCM-based literature published in the Web of Sciences till July 2023. Design/Methodology/Approach: This study separated the research front of Supply chain management using bibliometric coupling and then examined the conceptual structure of each element. SCM literature is highly published in 2021, with publication of Cleaner Production being the top publication for publishing SCM studies. The United Kingdom (UK) was the most often cited country for SCM research. Findings: The current study's aims are fourfold. To begin, we intend to investigate existing practices in the literature of SCM in provisions of key authors, fields, key journals, major institutes, associated nations, research kind, and economy. Second, we plan to identify important research tendencies in the field of SCM. Third, to comprehend the academic framework related to the research of SCM and how it has evolved over time Implications/Originality/Value: This study recommends a future research agenda for determining the rational formation, significant elements, and theoretical framework of literature based on SCM, as shown by the questions of the research suggested in this study. SCM stakeholders and organizations developing SCM might acquire helpful insights into the repercussions of SCM.  Because of these unique traits, this work makes an important contribution to the burgeoning literature on SCM