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Background: In previous decades, large-scale research has been carried out on bacterial meningitis. In every field, citation analysis is the most significant contribution. The study's objective was to identify and analyze the 100 articles on bacterial meningitis that received the most citations between 2000 and 2023, highlighting the most significant developments in the field. Objective: The objective of this study was to find out what makes a highly influential article by identifying and analyzing the characteristics of the 100 articles in the field of bacterial meningitis that receive the most citations. The goal of this study was to find and examine the 100 articles on bacterial meningitis that received the most citations. Methodology: We identified the top 100 most-cited papers in the field of bacterial meningitis from 55 journals using the Dimensions AI database. The results of each author's analysis of 100 articles were then compared. We gathered fundamental data such as the journal's title, country of publication, and study type. Descriptive counts or percentages were used to compare the various categories. Results: Between the year 2000 and the year 2023, articles were published. The total number of citations ranged from 115 to 1176, with 42 papers receiving more than 200 citations. In 2008, 14 articles were published, followed by 10 in 2000 and 2007. One thousand one hundred and seventy-six times were given to the most-cited paper, whereas 115 times were given to the least-cited article. "Clinical Features and Prognostic Factors in Adults with Bacterial Meningitis," by Diederik van de Beek, et al. (2004) was the article that received the most citations. 1176 people have cited this article. van de Beek Diederik of the Academic Medical Center in The Netherlands is the author who has written the most articles, was mentioned in 14 of the top 100 articles. Papers were primarily published in Pediatrics (n = 9) publication with 1861 citations. The Netherlands came in second with 18 publications, followed by the United States (n = 46). Conclusion: Our study uses bibliometrics and visualization analysis of the most important articles in this field to show the current state of research in the area of bacterial meningitis, provide a history of research trends, and offer a perspective for future bacterial predicts the growth of meningitis. [ABSTRACT FROM AUTHOR]

Digital cytology and artificial intelligence (AI) are gaining greater adoption in the cytology laboratory. However, peer-reviewed real-world data and literature are lacking in regard to the current clinical landscape. The American Society of Cytopathology in conjunction with the International Academy of Cytology and the Digital Pathology Association established a special task force comprising 20 members with expertise and/or interest in digital cytology. The aim of the group was to investigate the feasibility of incorporating digital cytology, specifically cytology whole slide scanning and AI applications, into the workflow of the laboratory. In turn, the impact on cytopathologists, cytologists (cytotechnologists), and cytology departments were also assessed. The task force reviewed existing literature on digital cytology, conducted a worldwide survey, and held a virtual roundtable discussion on digital cytology and AI with multiple industry corporate representatives. This white paper, presented in 2 parts, summarizes the current state of digital cytology and AI practice in global cytology practice. Part 1 of the white paper is presented as a separate paper which details a review and best practice recommendations for incorporating digital cytology into practice. Part 2 of the white paper presented here provides a comprehensive review of AI in cytology practice along with best practice recommendations and legal considerations. Additionally, the cytology global survey results highlighting current AI practices by various laboratories, as well as current attitudes, are reported. • The American Society of Cytopathology in conjunction with the International Academy of Cytology and the Digital Pathology Association established a special task force comprising 20 members with expertise and/or interest in digital cytology to investigate the feasibility of incorporating digital cytology and artificial intelligence applications into the workflow of the laboratory. • The task force reviewed existing literature on digital cytology, conducted a worldwide survey, and held a virtual roundtable discussion on digital cytology and artificial intelligence (AI) with multiple industry corporate representatives. • Part 2 of the white paper presented here provides a comprehensive review of artificial intelligence in cytology practice along with best practice recommendations and legal considerations. [ABSTRACT FROM AUTHOR]

Extended reality (XR) enables both new opportunities but also introduces new barriers for inclusion in society. Furthermore, XR is less researched than web, desktop and mobile applications. This position paper presents the concept of an XR platform for inclusion, with the purpose to make people on the autism spectrum and with other disabilities, more independent of help from others in everyday life situations. Based on previous research, our position is that, through current and future XR technologies combined with civic and artificial intelligence, it is possible to create individually personalised support for this purpose, grounded in practice to ensure validation. [ABSTRACT FROM AUTHOR]

Radiography education is pivotal in training skilled radiographers for diagnostic imaging and therapeutic applications. With technological advancements, interest in innovative educational tools to enhance traditional teaching methods is growing. This discussion paper explores the possibility of the integration of ChatGPT, a cutting-edge conversational AI language model, into radiography education. We report that ChatGPT offers interactive learning opportunities that can facilitate learning. It also provides self-paced learning, revision platforms, and supports educators in scenario creation, assessment development, group collaboration, and professional and research activities. Despite these benefits, it is important to carefully consider issues related to academic integrity and privacy, along with the opportunities and challenges presented by this new technology in radiography education. This paper highlights some of the prospects and limitations of the potential applications of ChatGPT in radiography education, underscoring the benefits for both students and educators. However, its implementation must be considered thoughtfully and ethically, taking into account its strengths and limitations. Integrating ChatGPT in radiography education has the potential to improve radiography education by improving digital literacy and graduate outcomes of students while streamlining the preparation process for educators. However, ethical implementation is vital for optimal outcomes. [ABSTRACT FROM AUTHOR]

This column will help the voice teacher better understand and evaluate voice research publications. Three main topics are discussed: 1) the basic structure of a scientific paper, 2) a method for quickly reading and understanding scientific papers, and 3) a method for evaluating voice science publications. A fictious ChatGPT-generated study is critiqued with the authors' method. [ABSTRACT FROM AUTHOR]

Artificial Intelligence in higher education (AIED) is becoming a more important research area with increasing developments and application of AI within the wider society. However, as yet AI based tools have not been widely adopted in higher education. As a result there is a lack of sound evidence available on the pedagogical impact of AI for learning and teaching. This conceptual paper thus seeks to bridge the gap and addresses the following question: is artificial intelligence really the new big thing that will revolutionise learning and teaching in higher education? Adopting the technological pedagogical content knowledge (TPACK) framework and the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical foundations, we argue that Artificial Intelligence (AI) technologies, at least in their current state of development, do not afford any real new advances for pedagogy in higher education. This is mainly because there does not seem to be valid evidence as to how the use of AI technologies and applications has helped students improve learning, and/or helped tutors make effective pedagogical changes. In addition, the pedagogical affordances of AI have not yet been clearly defined. The challenges that the higher education sector is currently experiencing relating to AI adoption are discussed at three hierarchical levels, namely national, institutional and personal levels. The paper ends with recommendations with regard to accelerating AI use in universities. This includes developing dedicated AI adoption strategies at the institutional level, updating the existing technology infrastructure and upskilling academic tutors for AI. [ABSTRACT FROM AUTHOR]

This paper presents the case of an adjunct university professor to illustrate the dilemma of using artificial intelligence (AI) technology to grade student papers. The hypothetical case discusses the benefits of using a commercial AI service to grade student papers—including discretion, convenience, pedagogical merits of consistent feedback for students, and advances made in the field that yield high-quality work—all of which are achieved quickly. Arguments against using AI to grade student papers involve cost, privacy, legality, and ethics. The paper discusses career implications for faculty members in both situations and concludes with implications for researchers within the discourse on academic integrity. [ABSTRACT FROM AUTHOR]

Change has become the norm and with the digital revolution we are seeing the demise of the printed word. Dentistry is a pictorial subject, but will pictures in a textbook or journal be surpassed by accessing digital ones on a screen? This article reviews the rapidly changing access and reading of dental publications. An argument is that the ease of access to electronic material, such as video and podcasts, may make traditional paper-based publications obsolete. Already dentistry is seeing such changes take place. With such revolutions come challenges. Misinformation is much easier to generate, and the evidence base may be diluted. Furthermore artificial intelligence is now able to produce text and papers that will make it more difficult to critically review the dental content on offer. The future will bring many challenges and this article provides an opinion on what dentistry might expect. CPD/Clinical Relevance: There are challenges posed by the increasing use of digital resources for information and how this might impact dental knowledge. [ABSTRACT FROM AUTHOR]

In light of artificial intelligence and new technologies, organizations have to follow new ways of working with different skill sets to achieve strategic goals. Therefore, human resource analytics is the scientific solution that enables organizations to make important decisions related to human capital and strategic business and thus gain a competitive advantage. Through new technologies comes the role of big data, as it works to establish the reputation of human resources as a strategic business partner that makes decisions driven by analytics. Evidence-based decisions, therefore, all equal a significant competitive advantage. This conceptual paper aims to understand the relationship between human resource analytics and achieving a competitive advantage in the presence of big data. This study will use quantitative data through a survey list that will be distributed to middle managers and human resources employees of the telecommunications company sector in Egypt. [ABSTRACT FROM AUTHOR]

• A framework combining machine learning models and SHAP to interpret how peer review improves the research impact. • The importance of key linguistic, sentiment, and peer review features from peer review reports in determining the scientific significance of papers. • Valuable insights for authors to improve the quality of work and increase academic influence by paying closer attention to peer review characteristics. • Textual features of peer review reports play an important role in predicting post-publication scientific impact. Peer review is crucial in improving the quality and reliability of scientific research. However, the mechanisms through which peer review practices ensure papers become top-cited papers (TCPs) after publication are not well understood. In this study, by collecting a data set containing 13, 066 papers published between 2016 and 2020 from Nature communications with open peer review reports, we aim to examine how textual features embedded within the peer review reports of papers that reflect the reviewers' emotions may predict the papers to be TCPs. We compiled a list of 15 textual features and classified them into three categories: peer review features, linguistic features, and sentiment features. We then chose the XGBoost machine learning model with the best performance in predicting TCPs, and utilized the explainable artificial intelligence techniques SHAP to interpret the role of feature importance on the prediction results. The distribution of feature importance ranking results demonstrates that sentiment features play a crucial role in determining papers' potential to be highly cited. This conclusion still holds, even when the ranking of the feature importance changes in the subgroup analysis of dividing the samples into four disciplines (biological sciences, health sciences, physical sciences, and earth and environmental sciences), as well as two groups based on whether reviewers' identities were revealed. This research emphasizes the textual features retrieved from peer review reports that play role in improving manuscript quality can predict the post-publication research impact. [ABSTRACT FROM AUTHOR]

This study employs a bibliometric approach to analyze the landscape of artificial intelligence (AI) components used in performance measurement. As organizations increasingly leverage AI for optimizing processes and decision-making, understanding the trends in AI components becomes imperative. The identified AI components are classified based on their roles in enhancing performance measurement, offering insights into the prevalent methodologies and emerging technologies. The bibliometric analysis encompasses a comprehensive review of scholarly articles, conference papers, and patents, systematically exploring the evolving field. In this research, the methodology involves data extraction from reputable academic databases and patent repositories, followed by applying bibliometric techniques to quantify and visualize key aspects. The findings of this study contribute to the existing knowledge by mapping the intellectual structure of AI components for performance measurement. [ABSTRACT FROM AUTHOR]

The aim of this study is to present the possibilities of nurse education in the use of the Chat Generative Pre-training Transformer (ChatGPT) tool to support the documentation process. The success of the nursing process is based on the accuracy of nursing diagnoses, which also determine nursing interventions and nursing outcomes. Educating nurses in the use of artificial intelligence in the nursing process can significantly reduce the time nurses spend on documentation. Discussion paper. We used a case study from Train4Health in the field of preventive care to demonstrate the potential of using Generative Pre-training Transformer (ChatGPT) to educate nurses in documenting the nursing process using generative artificial intelligence. Based on the case study, we entered a description of the patient's condition into Generative Pre-training Transformer (ChatGPT) and asked questions about nursing diagnoses, nursing interventions and nursing outcomes. We further synthesized these results. In the process of educating nurses about the nursing process and nursing diagnosis, Generative Pre-training Transformer (ChatGPT) can present potential patient problems to nurses and guide them through the process from taking a medical history, setting nursing diagnoses and planning goals and interventions. Generative Pre-training Transformer (ChatGPT) returned appropriate nursing diagnoses, but these were not in line with the North American Nursing Diagnosis Association – International (NANDA-I) classification as requested. Of all the nursing diagnoses provided, only one was consistent with the most recent version of the North American Nursing Diagnosis Association – International (NANDA-I). Generative Pre-training Transformer (ChatGPT) is still not specific enough for nursing diagnoses, resulting in incorrect answers in several cases. Using Generative Pre-training Transformer (ChatGPT) to educate nurses and support the documentation process is time-efficient, but it still requires a certain level of human critical-thinking and fact-checking. [ABSTRACT FROM AUTHOR]

The existence of AI application examples is by itself an admirable achievement of engineers and professionals working on them, and certainly there is an interest to move forward to design AI algorithms to make it write proficiently with originality. Artificial Intelligence (AI) has become a trendy topic through advertisements, notes and even warnings about it, claiming that it can be used to answer almost any kind of question. Generating new content requires that AI conduct research work starting from, in principle, human proposed hypothesis, and then getting analyzable results to formulate conclusions. [Extracted from the article]

This article introduces a nonparametric methodology combining the strengths of binary regression and latent variable formulations, while overcoming their disadvantages. The mathematical results are implemented through a novel Bayesian Hierarchical estimation methodology called Latent Adaptive Hierarchical Expectation Maximization Like algorithm. Requiring minimal assumptions, it extends extant methodologies, and in simulation studies gives better prediction and inference performances for asymmetric data generating processes. A new classification statistic, called Adjusted Receiver Operating Curve Statistic is also introduced. Utilizing it we demonstrate better overall model fit, inference and prediction performance of the proposed methodology over widely used existing methods in the sciences. In addition, the methodology can be used to perform model diagnostics for any model specification. This is a highly useful result, and it extends existing work for categorical model diagnostics broadly across the sciences. Furthermore, the mathematical results also highlight important new findings regarding the interplay of statistical significance and scientific significance. Finally, the methodology is applied to identifying highly-cited papers in the social sciences in a joint estimation framework. The results indicate that the methodology outperforms widely used existing artificial intelligence and machine learning models with very few Monte Carlo iterations. In Scientometric application, it finds Journal Impact Factor to be more important than Keyword Popularity parameters for explaining citation outcomes in select social science fields. It further finds that the percentage change in Published Popularity may also help to explain citation outcomes in the field. The findings appear to be new to the Scientometric field. • Introduces new statistical method for regressions requiring few assumptions. • Outperforms existing artificial intelligence/machine learning algorithms. • Introduces ARS: A general statistic for predication/comparison of models. • Applies methodology to identify highly-cited papers in the social sciences. • JIF more important than keywords for getting cited in some social science fields. [ABSTRACT FROM AUTHOR]

[Display omitted] • Deep learning enabled predictive nucleic acid amplification tests analysis. • On-site fluorescence signal processing powered by cloud computing. • Accurate prediction can be obtained using the early 22.5% data. • Approach can be universally extended to other areas of biomedical research. Pandemics such as COVID-19 have exposed global inequalities in essential health care. Here, we proposed a novel analytics of nucleic acid amplification tests (NAATs) by combining paper microfluidics with deep learning and cloud computing. Real-time amplifications of synthesized SARS-CoV-2 RNA templates were performed in paper devices. Information pertained to on-chip reactions in time-series format were transmitted to cloud server on which deep learning (DL) models were preloaded for data analysis. DL models enable prediction of NAAT results using partly gathered real-time fluorescence data. Using information provided by the G-channel, accurate prediction can be made as early as 9 min, a 78% reduction from the conventional 40 min mark. Reaction dynamics hidden in amplification curves were effectively leveraged. Positive and negative samples can be unbiasedly and automatically distinguished. Practical utility of the approach was validated by cross-platform study using clinical datasets. Predicted clinical accuracy, sensitivity and specificity were 98.6%, 97.6% and 99.1%. Not only the approach reduced the need for the use of bulky apparatus, but also provided intelligent, distributable and robotic insights for NAAT analysis. It set a novel paradigm for analyzing NAATs, and can be combined with the most cutting-edge technologies in fields of biosensor, artificial intelligence and cloud computing to facilitate fundamental and clinical research. [ABSTRACT FROM AUTHOR]

The number of citations is often used to estimate the impact of a study. Previous studies have investigated what factors of publications affect citations and how they affect citations. However, the findings of the studies were unable to reach a consensus because of the limited sample size, domain, and measurement. This study reviewed previous studies that addressed factors influencing citations and then identified 14 measurable factors. Approximately 33 million publications from the Scopus database were used to train and validate a CatBoost model. A SHAP framework was used to interpret the trained model by focusing on how salient factors affect the number of citations. The results showed that the year is a significant factor affecting the citation but not the priority factor. A publication source was presented as the most important factor contributing to the citation. Several implications and strategic approaches to maximizing the impact of a study were discussed. • This study examines 14 factors that can influence the citation of papers. • CatBoost model and SCOPUS dataset are used to examine the influences. • SHAP interprets the model and suggests how the factors contribute to the citation. • The results show that selecting the right journal/conference is the most important. [ABSTRACT FROM AUTHOR]

We have assessed the chatbot Generative Pretrained Transformer, a type of artificial intelligence software designed to simulate conversations with human users, in an experiment designed to test its relevance to scientific writing. chatbot Generative Pretrained Transformer could become a promising and powerful tool for tasks such as automated draft generation, which may be useful in academic activities to make writing work faster and easier. However, the use of this tool in scientific writing raises some ethical concerns and therefore there have been calls for it to be regulated. It may be difficult to recognize whether an abstract or paper is written by a chatbot or a human being because chatbots use advanced techniques, such as natural language processing and machine learning, to generate text that is similar to human writing. To detect the author is a complex task and requires thorough critical reading to reach a conclusion. The aim of this paper is, therefore, to explore the pros and cons of the use of chatbots in scientific writing. [ABSTRACT FROM AUTHOR]

Society's interest in AI/Big Data: that governments, companies, and NGOs can invest in the development of the world and obtain a successful realization of the policy of CSR & SDG. discussed in this paper is to show how governments, businesses, and NGOs can prove that they are ready to make a difference and join the global commitment to building an integrated and sustainable community that will leverage this unique opportunity to promote the common social interest by utilizing such a promising and complex technology as artificial intelligence. Consequently, the current paper integrates a range of cases. It utilizes a mixed-methods design, establishing that restricted access to resources, primarily relating to education, health, and the economic status that can be traced to marginalization dimensions, determines increased suffering because of the pandemic. These recommendations amalgamate to perform the roles of the diverse sectors to assure that AI benefits all the stages of society by promoting the equity steps of welfare. The study applies a contingency framework, and from the framework, the study develops a plan for how best to approach inclusive innovation to mitigate inequalities, which in turn suggests that societal and environmental objectives should be integrated into the observed innovation to take sustainable development principles. The research contributes to how society can employ the prospects in AI to solve societal problems, notating that such employment of the advantage in AI must incite professionalism and incorporation of society in the design of the measures. On this basis, this study contributes to the theoretical development and the practical recommendations related to using potential AI capabilities to build long-term opportunities for firms and governments to support the sustainable development of the world economy based on achieving the goals set in the UN 2030 agenda. This piece seeks to analyze the events, achievements, and barriers that culminated in the formation of this new week and lays down directions on how the strategy that focuses on inclusive innovation can be implemented. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) holds the potential to revolutionize the way businesses operate. With the promise of generating creative solutions for complex problems, AI's transformative impact on the business landscape is undeniable. Against this backdrop, business schools become crucial in preparing future professionals with relevant tools and techniques associated with AI. This paper investigates the integration of GitHub Copilot in conjunction with Python libraries such as Pandas and Plotly, within two sections of an introductory information systems course. We examine how business students perceive GitHub Copilot's ease of use, utility, and relevance, particularly in tasks involving code generation for data analysis and visualization. Our findings indicate a generally positive perception of GitHub Copilot among business students in terms of its ease of use, utility, and relevance. However, notable gender differences emerged; male students reported finding GitHub Copilot easier to use and their interactions with the tool clearer and more understandable compared to their female counterparts. The paper also provides an overview of the course assignment and presents the visualizations produced, offering valuable insights into the practical application of GitHub Copilot in an information systems course setting. [ABSTRACT FROM AUTHOR]

This paper delves into the deployment of artificial intelligence (AI) tool as innovative methods for language learning. With the rapid advancement of AI technologies, there is a growing interest in leveraging these tools to enhance language acquisition processes. This paper explores various AI-driven approaches, including language learning apps, virtual tutors, chatbots, and translation services, and examines their effectiveness in facilitating language learning across different proficiency levels and learning styles. Furthermore, it is about benefits and challenges associated with the integration of AI Tools into language education, highlighting the potential for personalised learning experiences, real-time feedback, and enhanced engagement. Through a review of existing research and case studies, this paper provides insights into the practical implementation and implications of deploying AI tools as language learning methods. [ABSTRACT FROM AUTHOR]

This paper explores the effectiveness of AI-driven language learning tools such as Duolingo, Babbel, and ELSA Speak in teaching English as a second language. These platforms employ artificial intelligence to offer personalized learning experiences, catering to various aspects of language acquisition. Duolingo, with its gamified approach, focuses on building vocabulary and basic grammar, making it suitable for beginners. Babbel provides a more structured curriculum emphasizing practical conversations and real-world application of English, targeting learners at beginner and intermediate levels. ELSA Speak, meanwhile, specializes in pronunciation training, using AI to deliver real-time feedback on spoken English. While each tool demonstrates the potential of AI to enhance English language learning, they also present limitations, such as insufficient depth in conversational skills (Duolingo), limited advanced content (Babbel), and narrow focus on pronunciation (ELSA Speak). This paper concludes that a combined use of these AI tools can provide a more comprehensive learning experience, addressing diverse learner needs and supporting proficiency across multiple language skills. [ABSTRACT FROM AUTHOR]

With the rise of ChatGPT, the anthropomorphic dialogue form of chatbots is sought after and relied on by a large number of users. Compared with traditional social real objects, chatbots are more controllable in social relationships. At the same time, compared with traditional real social objects, chatbots may show better emotional identification with objects. The structural equation modeling is a path analysis method based on the factor analysis and linear regression, which can be used to analyze the path relationship between intricate variables. Based on the structural equation modeling, this paper analyzes the potential pathways for artificial intelligence chatbots to induce the user's emotional dependence. The results show that the "sense of identification" and "sense of control" will cause people to have emotional dependence on the artificial intelligence chatbots, with p < 0.05. However, the psychological distance plays a regulating role in the identification-emotional dependence and the sense of control-emotional dependence, respectively. The research results in this paper show that there is a certain theoretical basis for the chatting intelligent robots to transform from a tool role to a social and emotional role. [ABSTRACT FROM AUTHOR]

With the rapid development of science and technology, artificial intelligence technology benefits the field of marketing. Based on artificial intelligence technology, media communication marketing has enriched marketing means and adopted virtual endorsers for marketing publicity. From the perspective of virtual endorsers interaction, this paper studies the impact of virtual endorsers interaction on consumers' brand attitude, conducts empirical analysis through situational experiments and explores the internal mechanism, and finds that: (1) Product involvement moderates the impact of "whether virtual endorsers interact with people" on consumers' brand attitudes. For products with high involvement, virtual endorsers have a significant positive impact on consumer brand attitudes when interacting with people. For low-involvement products, "whether virtual endorsers interact with people" has no significant impact on consumers' brand attitudes; (2) Self-identity plays a mediating role in this regulation. This paper verifies the feasibility of the application of artificial intelligence in media communication marketing to a certain extent, which will help to enrich the related research of virtual spokesmen. At the same time, it provides suggestions and references for enterprise marketing practice. [ABSTRACT FROM AUTHOR]

Under the background of digitalization and networking, the fashion industry is undergoing unprecedented changes. In such a big environment, online clothing design based on artificial intelligence technology is a brand-new innovation and development direction. In this paper, the online fashion design is taken as the research object, and the latest artificial intelligence algorithm is combined to discuss the fashion design automation and personalized design method based on AI(Artificial Intelligence). The online clothing design system based on artificial intelligence uses deep learning, image recognition and machine learning methods to analyze user preferences, historical data and fashion trends, and automatically generate personalized and innovative clothing design schemes. In addition, this study also compares the differences between artificial intelligence-driven clothing design and traditional clothing design in creativity, cost-effectiveness and user satisfaction. In the AI design method, the design cycle is generally shorter, in which the automatic primary, intermediate and advanced design takes 8 days, 6 days and 4 days respectively, while the expert-aided design and fully customized design cycle takes 5 days and 7 days respectively. The research results of this paper will promote the automation and personalization of fashion design, and open up a brand-new working mode for the development of fashion industry. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution is forthcoming, bringing with it revolutionary alterations in connectivity, work dynamics, and everyday operations. The fundamental foundation of this transformation, artificial intelligence (AI) is at its core. The purpose of this research is to identify new and distinctive features of how artificial intelligence may improve and enhance business knowledge management systems. For this purpose, systematic literature review (SLR) was used as the research method and preferred reporting items for systematic reviews and meta-analyses (PRISMA) as an approach. Articles based on specific search queries from 2013 to 2023 were selected from major scientific databases such as Scopus, Web of Science, and ScienceDirect, with a focus on AI, knowledge management systems, and corporate organizations for the PRISMA. It covers four main research questions by utilizing 14 selected papers. The main findings of this research paper call attention to AI’s vital role in shaping the future of knowledge management which helps organizations make informed decisions and remain competitive and relevant in this ever-changing world of commerce. This study also explores the importance of data security in AI systems which need ethical considerations and responsible practices to address the vulnerabilities. [ABSTRACT FROM AUTHOR]

Air is very beneficial and crucial for every living creature on earth, hence it is very important to protect the air quality in order to avoid diseases. However, due to the increase in population, some human activities have been causing detrimental damage to the air quality. Some people have realized the potential impact of this problem and have done studies on predicting future air pollution. Some implemented machine learning models such as random forest regression, support vector machine, etc, while others have utilized deep learning models in their research.. This study will implement five different models, specifically linear regression, ridge regression, random forest regression, and multilayer perceptron regression. There will be two datasets used in this paper which will be merged and processed. There will be three key evaluation metrics being used in this paper namely root mean squared error, mean absolute error, and r-squared. The results from all of the models have concluded that more optimization and factors are needed in order to boost the final result of this study. [ABSTRACT FROM AUTHOR]

In the contemporary society Cloud computing and artificial intelligence (AI) are extremely pertinent. These technologies are enhancing various growth possibilities for companies, not only the effectiveness of work but also new ideas are developed every day. Most enterprises are incorporating these technologies because these enhance computing capabilities and reduce expenses. AI means that we are able to comprehend and apply data effectively - it contributes to the acceleration of processes such as supply management or considering the events of sports matches. In this paper we feel the effects that are going to be caused by AI and Cloud computing on Industry and competition in our modern economy fueled by data. This paper presents the impact of cloud computing and AI on industry dynamics and competition. It also discusses the various challenges, considerations, case studies, industry-specific uses and trends, key developments and applications in this domain. [ABSTRACT FROM AUTHOR]