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This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

This paper aims to explore recent advances in drift compensation algorithms for Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline, online and neural network-based strategies. It offers a comprehensive review and covers causes of drift, compensation methods and future directions. This synthesis provides insights for enhancing the reliability and effectiveness of E-nose systems in drift issues.

The article adopts a comprehensive approach and systematically explores the causes of sensor drift in E-nose systems and proposes various compensation strategies. It covers both offline and online compensation methods, as well as neural network-based approaches, and provides a holistic view of the available techniques.

The article provides a comprehensive overview of drift compensation algorithms for E-nose technology and consolidates recent research insights. It addresses challenges like sensor calibration and algorithm complexity, while discussing future directions. Readers gain an understanding of the current state-of-the-art and emerging trends in electronic olfaction.

This article presents a comprehensive review of the latest advancements in drift compensation algorithms for electronic nose technology and covers the causes of drift, offline drift compensation algorithms, online drift compensation algorithms and neural network drift compensation algorithms. The article also summarizes and discusses the current challenges and future directions of drift compensation algorithms in electronic nose systems.

Experiential learning is crucial in education, as it offers hands-on, practical experiences that enable individuals to develop their skills and knowledge more engagingly and interactively. In recent years, experiential learning has become a significant aspect of education. To provide academic scholars with a thorough roadmap for further investigation, this study aims to provide useful insights into the bibliometric and content analysis of experiential learning, including keywords, well-known authors, publications, nations and topics.

This research does a rigorous bibliometric analysis to give a thorough and visually instructional assessment of the evolution and advancement of the literature on experiential learning. Its fast development between 1976 and 2022 is meticulously tracked in the research. By using VOSviewer and Biblioshiny tools, the present study presents a concise overview of 507 records retrieved from the Scopus database using the keyword “Experiential Learning”, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol. Deeper text mining was done using Python libraries “Pandas” and “Natural Language Toolkit” and regular expressions.

The findings reveal a surge in the number of publications on experiential learning and provide insights, particularly using the theory, context, characteristics, methodology analysis, supporting researchers and practitioners to understand learning better and provide perspectives for future research. Descriptive bibliometric analysis showed that most contributions are from the USA, the UK and Canada. In-depth content analysis revealed five clusters: developments in learning, management education, engineering curricula, organisational learning and knowledge management and entrepreneurship education. The keyword co-occurrence analysis enabled linkages between relevant fields of study and significant research domains. The most commonly used theories were: experiential learning theory, social learning theory, relational coordination theory, empowerment theory, feedback learning theory, effectuation theory and human capital theory.

This study uses information from the Scopus database to do a bibliometric analysis of experiential learning from 1976 to 2022. This study serves as a valuable resource for researchers in the field, helping them to position their work more explicitly within the existing literature and highlighting potential areas for future research. It does this by thoroughly analysing the literature on experiential learning using bibliometrics.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

The purpose of this paper is to explore future directions in E-learning research by analysing data from Scopus indexed publications in order to have a comprehensive overview of the trends and thematic focus post COVID-19 in Asian context.

The paper uses Vos viewer and Biblioshiny software packages to analyse the bibliometric data. This software helped in identifying the anatomy of E-learning and their themes which were instrumental in forecasting future trends.

The paper depicts the trends in post COVID-19 E-learning research in Asian context. It identifies key publications, authors and journals in the field, with a focus on numerous networks of collaboration between writers and nations, identifying keyword clusters and co-citation analysis clusters. This study also explored that China and the USA are having maximum number of collaborations, whereas, countries like India, the United Kingdom, Singapore and New Zealand have comparatively weaker collaboration networks. So there is lot of potential for these countries for such collaborations. India is the most cited country globally and China is having maximum number of scientific productions per year.

The paper has been written by exclusively referring to Scopus database papers. Collecting data from different databases would significantly improve the study. Future researchers can also focus on papers from psychology, computer science and engineering fields as current work is based on open access articles on social business, business and arts and humanities.

This research will be useful to educational institutions that use these platforms to offer E-learning content and match future trends. This study will help researchers in understanding the new dimensions in the field of E-learning.

The current study uses bibliometric analysis to examine the association between E-learning, higher education and COVID-19. It aids in the identification of new difficulties within the complex and expanding study fields in the world of E-learning. Newly published studies on E-learning trends can improve understanding and bridge the knowledge gap. As a result, recommendations can be made to improve and implement newer strategies in field of education.

Forecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.

This study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.

The model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R² values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.

This study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

India has been withstanding increasing pressure of enrolment in the higher education system, resulting in the creation of new universities in consonance with the recommendations of the Knowledge Commission (2007). Barring a few institutions of paramount excellence, the mushrooming universities fail to conform to equitability of quality and standards, that is teaching-learning-dissemination and research, except for accommodating higher gross enrolment ratio. It has resulted in an asymmetric and sporadic development of human resources, leaving a large basket of learners out of the pursuit for aspiring higher academic, research and professional enrichment. The country needs to develop an innovative common minimum curriculum and evaluation framework, keeping in view the trinity of diversity, equity and inclusion (DEI) across the Indian higher education system to deliver human resources with equitable knowledge, skill and intellectual acumen.

The paper has been developed using secondary information.

The manuscript has developed an innovative teaching-learning framework that would ensure every Indian HEI to follow a common minimum curriculum and partial common national evaluation system so that the learners across the country would enjoy the essence of equivalence.

This research has designed a comprehensive model to integrate the spirit of the “DEI” value proposition in developing curriculum and gearing common evaluation. This would enable the country to reinforce the spirit of social equity and the capacity to utilise resources with equitability and perpetuity.

This study aims to examine the impact of social media (SM) on the interactivity among teachers, among students and between students and teachers for sustainable academic performance and for achieving sustainable development (SD) in higher educational institutes. This study also investigates the moderating impact of knowledge creators (KNC) and knowledge seekers (KNS) on the collaborative learning environment using SM.

With the help of Vroom’s expectancy motivation theory (1964), collaborative learning theory and other theories, a theoretical model has been developed. This theoretical model has been tested using the structural equation modeling technique with 375 participants taken from different educational institutes. The respondent-–participants were both teachers and students.

The study found that SM plays a significant role in achieving SD al goals and enhances collaborative learning activities among teachers and students to improve academic performance to achieve SD in higher educational institutes. Also, the study highlighted that both “knowledge creators” and “knowledge seekers” have effective moderating impact on the linkage between “intention to use SM for knowledge sharing” and “collaborative learning using social media” to achieve SD al goals.

With the inputs from expectancy-instrumentality-valance theory and collaborative learning theory and existing literature, a theoretical model has been developed conceptually. Later, the model was successfully validated with an overall high explanatory power (72%) of this model. As the sample of the study do not represent a global representation of the population, thus the findings cannot be generalizable.

This study has provided valuable inputs to the SD practitioners and educational policymakers to formulate appropriate policies that enable SD al activities in higher educational institutes. This study also provides food for thought to the policymakers about the role of KNC and KNS toward the collaborative learning environment in achieving SD al goals in higher educational institutes.

The theoretical model developed in this study is unique. This study shows how both “knowledge creator” and “knowledge seeker” play a significant role toward collaborative learning and helps to achieve SD in higher learning institutes and improves their performance. The overall predictive power of the model is 72%, which also shows the effectiveness and uniqueness of the proposed model.

The purpose of this study is to review the application of social media for knowledge sharing (KS) in higher education. KS is the most crucial component in knowledge management. Higher education institutions (HEIs) are the epitome of knowledge creation and acquisition. With the advancement in technology and the embracement of social media, knowledge should be shared more freely and easily.

Using a bibliometric analysis, this study applies bibliographic coupling and co-word analysis to analyze the present and future trends on KS using social media in HEIs. 455 journal publications and 21,181 cited references were retrieved from Web of Science (WoS) database.

Findings show that most themes are categorized towards academics and students. Themes related to academics are the use of social media for expertise sharing and KS's impact on university-industry networking. In contrast, themes related to students revolved around the impacts of social media and academic performance.

Implications towards major social media practices on KS are discussed.

This study provides a novel, state-of-the-art bibliometric review of knowledge sharing via social media in the higher education context.