Scholarly interest in the emerging topic of multimodal learning and its ethical considerations, which integrates a variety of informational forms to enhance learning, has significantly increased during the past ten years. This paper aims to pinpoint the conceptual foundations and development trend of multimodal learning through thorough bibliometric research. By examining patterns, trends, and linkages within the published literature using a sizable collection of articles from the Scopus database, this research attempts to map the scientific landscape of multimodal learning. According to our study, multimodal learning is becoming more popular in a variety of academic disciplines. The major works and new trends in the subject were highlighted by emphasizing key publications and significant authors. While developing trends and recurrent study subjects were determined using keyword analysis, the major papers having the greatest influence on the field were found utilizing co-citation analysis. The collaborative network analysis also revealed a vibrant academic community with expanding global ties, fostering an atmosphere that is conducive to cutting-edge multimodal learning research. With its insights on the past, present, and likely future paths of multimodal learning, this study is an invaluable resource for academics, teachers, and decision-makers.

Medical image analysis has quickly advanced, with several advantages for research, diagnosis, and healthcare planning. We must have enough knowledge of current trends and developments in this field due to improvements in medical imaging technology and the accessibility of a wide range of medical image databases. This work covers the research environment for bibliometric analysis of academic articles as part of medical image analysis, utilizing the tools VOSviewer and Biblioshiny. The Scopus database contains 1,973 articles submitted between 1988 and 2023; all have been gathered and examined. The study has focused on the key bibliometric elements, such as authorship patterns, highly cited papers, prestigious journals, and collaborative networks. The outcomes of our inquiry demonstrate its interdisciplinary nature. This bibliometric analysis is a valuable resource for researchers, practitioners, and decision-makers, allowing them to identify significant trends, identify knowledge gaps, and explore prospects for further advancements in this critical field by providing a comprehensive overview of the literature on medical image analysis.

Recommender System (RS) is one of the most popular applications of Artificial Intelligence which attracted researchers all around the world. Many machine learning algorithms are used to develop RSs. Choosing the best machine learning algorithm to provide users with a product or service is the most challenging task in the area of RSs. Now we are witnessing a paradigm shift in the purchase habits of people from in-shop to online resulting in the availability of online information exponentially growing every day. The ever-increasing online information and the number of online users create new avenues in RS. In an online shopping scenario, these systems must be able to recommend relevant items to the users. The RSs have to deal with the huge amount of information by filtering the relevant information based on the analysis made on the inputs made by the users during their online sessions. These systems can recommend appropriate items to users based on their interest and previous preference which can lead to increased sales. The three major techniques used to build a RS are content-based, collaborative based and hybrid-based. This paper presents the various applications of RSs and makes a detailed comparative study of different machine learning approaches used. The methodologies used for identifying research articles for analysis, the merits and demerits of different techniques in RSs and domain-specific applications of these techniques are well explained here with scientific review analysis.

Background: One of the most critical contributors to air pollution is particulate matter (PM2.5) that its acute or chronic exposure causes serious health effects to human. Accurate forecasting of PM2.5 concentration is essential for air pollution control and prevention of health complications. A survey of the available scientific literature on random forest model for PM2.5 prediction is presented here. Methods: The scientific literature is extracted from Science Direct database based on a set of specified search criteria. The input features, data length, and evaluation parameters used in PM2.5 prediction were analyzed in this study. Results: The study shows that majority of the publications are aimed at the daily prediction of outdoor PM2.5. Most publications base their PM2.5 prediction on features aerosol optical depth (AOD) and boundary layer height (BLH). PM10 and NO2 are the main air pollutants employed in the PM2.5 estimation. Majority studies utilized input data lengths covering more than one year, and the effectiveness of prediction models are unaffected by the length of investigation. The coefficient of determination, R2 , is the primary evaluation parameter used in all publications. The majority of research study indicated R2 values greater than 0.85, demonstrating the reasonable dependability and efficiency of random forest regression-based PM2.5 prediction models. Conclusion: The study demonstrates that the publications use a variety of meteorological and geological features for PM2.5 estimation, depending on the context of the research as well as data accessibility. The findings demonstrate that it is hard to pinpoint the optimal model in any particular way.

Random forest regression (RFR) is a versatile, easy-to-use and efficient tree based machine-learning algorithm that utilizes the power of multiple decision trees for making decisions. So random forest is a subject of a great deal of research, in many of the machine intelligence applications. The objective of this research is to investigate the scientific output of research based on RFR and to explore its hotspots and frontiers through bibliometric analysis for the years 2007 to 2019. The data are collected from the Web of Science database. The total number of publications, the citations, and types of publications, publication countries, productive authors, prominent journals, and keyword co-occurrence of RFR research are examined, using VOSviewer software. There are 516 papers, published in 299 journals, of which researchers from the USA published 162 articles. The most prolific author, with 6 publications and 240 citations, is Martin H. Teicher. The most cited article is the research article entitled "Genomic selection in wheat breeding using genotyping by sequencing". Among the journals, the most articles (41 publications) are published by the Remote Sensing journal.