Over the past few years, explainable artificial intelligence (XAI) has become increasingly popular as a result of the demand for AI systems that are simpler to comprehend and with greater interpretability. This study provides a conceptual framework and a quick assessment of the work done in explainable artificial intelligence. Using the Vosviewer application, the researchers analyzed 4781 research publications from the Scopus database, spanning 2004 to 2023. Observations indicate a rapid and exponential growth in the quantity of publications, commencing in 2018. The importance of the study is shown by the analysis of publishing activities according to the year of publication and the geographical area, together with citation analysis, research methodologies, and data analysis techniques. The researchers have highlighted ten interesting areas that require further study from future researchers. Moreover, the work emphasizes the legal, ethical, and social consequences for the researchers.

This study aims to create a system for recognizing handwritten Devanagari letters using deep neural networks (DNNs) for accurate character identification. The research utilizes explicitly transfer learning techniques in combination with the VGG16 convolutional neural network (CNN) model. The authors have produced a new dataset for this research, which includes around 92,000 images. These images represent 46 characters from the Devanagari script, including consonants and digits. The VGG16 model, previously trained on extensive picture datasets, underwent fine-tuning using our Devanagari character dataset to enhance its performance for this particular application. The model attained an impressive recognition accuracy of 96.58%, a substantial improvement compared to previously reported techniques in the literature. The significant improvement in accuracy demonstrates the effectiveness of the transfer learning method and the resilience of the VGG16 architecture in dealing with the intricacies of handwritten Devanagari letters. The research showcases the promise of sophisticated deep-learning approaches in improving HCR systems. It also thoroughly compares with the previous methodology, highlighting the progress achieved. For future research, the authors intend to investigate deeper learning structures further and integrate a broader and more varied dataset to enhance the model’s accuracy and guarantee its suitability for different real-life situations.

Organizational behavior examines the interactions of individuals and groups within businesses, while human resource management (HRM) focuses on enhancing workforce efficiency through recruitment, training, and employee relations. The success of an organization depends on the relationship between employee engagement and performance, as engaged individuals enhance productivity and innovation. This study aims to conduct a comprehensive bibliometric analysis of the academic research on the relationship between artificial intelligence (AI), employee engagement, and performance. This study highlights trends, countries, sources, and keywords in this field. The authors analyzed 11,291 articles in the first phase, 42,358 articles were analyzed in the second phase, and 606 articles were analyzed in the third phase. This study highlights the growth of the research in this area and identifies the most productive years and regional contributions. The citation analysis is used to identify the relevant research and renowned authors. This study also addresses ethical concerns related to the implementation of artificial intelligence (AI) in the workplace. This study indicates theme variations among national contributions, highlighting differing socio-cultural and theoretical perspectives on AI adoption in HRM, from behavioral leadership models to efficiency-oriented frameworks. In summary, this bibliometric study provides valuable insights into the evolution of the research topics related to AI’s impact on employee engagement and productivity, spanning multiple disciplines, such as psychology, organizational behavior, and computer science. It is relevant for the researchers, practitioners, and businesses interested in understanding and utilizing AI in the workplace.

Digital marketing has become a game-changer by combining cutting-edge technologies, insights into how customers behave, and applicability across industries to change how businesses plan and how they interact with customers. Digital marketing is a key part of being competitive, sustainable, and innovative in a world where more and more people are using the internet and social media. Even though this subject is important, the study of it is still scattered, which shows that there is a need to systematically map out its intellectual structure. This research utilizes a bibliometric and topic modeling methodology, analyzing 4722 publications sourced from the Scopus database, including the string “Digital Marketing”. The authors employed Latent Dirichlet Allocation (LDA), a method from Natural Language Processing, to discern latent study themes and Vosviewer 1.6.20 for bibliometric analysis. The results explore ten main thematic clusters, such as digital marketing and blockchain, applications in the health and food industries, higher education and skill enhancement, machine learning and analytics, small and medium-sized enterprises (SMEs) and sustainability, emerging trends and ethics, sales transformation, tourism and hospitality, digital media and audience perception, and consumer satisfaction through service quality. These clusters show that digital marketing is becoming more interdisciplinary and is becoming more connected to ethical and technological issues. The report finds that digital marketing research is changing quickly because of artificial intelligence (AI), blockchain, immersive technology, and reflect it with a digital business environment. Future directions encompass the expansion of analyses to new economies, the implementation of advanced semantic models, and the navigation of ethical difficulties, thereby guaranteeing that digital marketing fosters both business progress and public welfare.

Frequent pattern mining is a fundamental method for Data Mining, applicable in market basket analysis, recommendation systems, and academic analytics. Widely adopted and foundational algorithms such as Apriori and FP-Growth, which represent the standard approaches in frequent pattern mining, face limitations related to candidate set generation and memory usage, especially when applied to extensive relational datasets. This work presents the Recursive Queried Frequent Patterns (RQFP) algorithm, an SQL-based approach that utilizes recursive queries on relational Mining Tables to detect frequent itemsets without the need for explicit candidate development. The algorithm was implemented using a Microsoft SQL Server and demonstrated through a custom-developed C# web application interface. RQFP facilitates easy integration with database systems and enhances result interpretability. Comparative analyses of Apriori and FP-Growth on an academic dataset reveal competitive efficacy, accompanied with diminished memory requirements and enhanced clarity in pattern extraction. The paper further contextualizes RQFP using benchmark datasets from the previous literature and delineates a roadmap for future evaluations in healthcare and retail data. The existing implementation is educational, although the technique demonstrates the potential for scalable, database-native pattern mining.

The need for easier-to-understand and more interpretable AI systems has led to the rise of explainable artificial intelligence (XAI) in recent years. In the realm of explainable artificial intelligence, this paper briefly reviews the work carried out, along with a conceptual framework. The researchers conducted a systematic review of 4781 research publications obtained from the Scopus database spanning the years 2004 to 2023 using the VOSViewer tool. The research shows that there has been exponential growth in terms of publications from the year 2018. The study establishes its prominence by studying the publication activities based on the year of publication and region, citation analysis, research designs, data analysis techniques, and findings from the selected articles.