A bibliometric analysis study investigating chatbots' current state and developments in education research has not been adequately addressed in literature. Thus, this study highlights the current state, emerging research trends and directions of chatbots in education using bibliometric analysis. The significance of the study is to provide insights into the most recent developments of chatbots application in education and future research directions for academics and practitioners. A bibliometric analysis of publications on chatbots in education published between 2012 and 2022 was conducted. A total of 759 publications were collected from the Scopus database for the bibliometric analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was adopted to identify and screen the dataset. VOSviewer (version 1.6.18) was used to perform the following network analysis: co-authorship and co-occurrence. Also, Bibliometrix was used for the descriptive statistics of the bibliometric data and the publication trend. The study’s findings showed that fewer studies have been conducted from the African region on chatbot education research. Researchers from the United Kingdom, United States, Australia, China, India, Greece, Japan, Vietnam, Malaysia, and United Arab Emirates have collaborated significantly in chatbot education research. Also, the main keyword occurrences in research on chatbots in education are chatbots, students, artificial intelligence, natural language processing, education, and learning. The trends indicate a steady increase in research on chatbots over the past decade.

A thorough overview of desktop virtualization, a growingly popular technology that allows centralized and effective IT management, is provided in this review paper. Exploring desktop virtualization's benefits, drawbacks, various forms, impact on contemporary workplaces, and potential future trends are the main objectives. The advantages of desktop virtualization are emphasized in the report, including increased productivity, cost savings, and improved security. It explores the numerous forms of virtualization, such as client-based, server-based, and application virtualization, highlighting their special qualities and applicability for varied organizational purposes. The article also addresses how virtual desktop infrastructure (VDI) supports bring-your-own-device (BYOD) rules, allowing workers to access their work environments from any place and device. In today's dynamic workplace, this feature improves collaboration and overall efficiency. The study concludes by examining the potential of desktop virtualization and offering information on new trends and advancements that have the potential to influence the field. This review paper provides a thorough overview, making it a useful tool for businesses wishing to use desktop virtualization in their IT infrastructure.

The Decision Review System (DRS) in cricket has significantly improved decision-making accuracy, but there is immense potential for advancement through the integration of AI techniques. This paper explores the concept of advancing the DRS by harnessing AI capabilities to enhance decision-making in cricket matches. It presents an overview of the current state of the DRS, highlighting its components and limitations. The paper then delves into the possibilities offered by AI, including ball-tracking algorithms, predictive analytics, automated decision-making, and refining technology accuracy. Furthermore, it discusses the challenges associated with data availability, model transparency, and maintaining the integrity of the game. By harnessing AI techniques in the DRS, cricket can benefit from objective and data-driven decision-making, reducing human error and enhancing fairness in the game.

Modern technologies like 5G, the Internet of Things (IoT), and Artificial Intelligence (AI) have just come together, creating previously unheard-of chances for creative solutions. As a result, several IoT use cases have come to fruition, particularly in the healthcare industry, enabling the creation of eHealth and mHealth applications for ambient assisted living (AAL). However, there are practical issues with the current healthcare system, such as service delays and exorbitant expenses, which have had serious repercussions, such the untimely passing of famous people from heart attacks. Real-time patient monitoring and therapy with few delays are necessary to solve these pressing challenges. IoT has changed the game in this area by making it easier to establish Remote Patient Monitoring (RPM) systems. Vital indicators can be sent in real time to clinicians using IoT-enabled wearable devices (biosensors), enabling quick intervention and the start of treatment. This article gives an overview of the state-of-the-art in RPM using IoT, highlighting its potential to save time, lower healthcare expenses, and considerably raise patient quality of life and the caliber of healthcare services. It also identifies research holes and ways to use RPM systems, laying the groundwork for further development in this area.

This paper reviews recent advancements in machine learning (ML) driven automated trading systems (ATS). ATS has progressed from simple rule-based systems to sophisticated ML models like deep reinforcement learning, deep learning, and Q-learning that can adapt to evolving markets. These techniques have been successfully applied across various financial instruments to optimize trading strategies, forecast prices, and enhance profits. The literature indicates that ML improves ATS performance over conventional methods by identifying intricate patterns and relationships in data. However, risks like overfitting, instability, and low interpretability exist. Techniques to mitigate these limitations include cross-validation, careful model management, and utilizing more transparent algorithms. Although challenges remain, ML creates valuable opportunities for ATS via alternative data sources, advanced feature engineering, optimized adaptive strategies, and holistic market modelling. While research shows ML improves market quality through increased liquidity and efficiency, heightened volatility needs further analysis. Promising future research directions include leveraging innovations in deep learning, reinforcement learning, sentiment analysis, and hybrid systems. More work is also needed on evaluating different techniques systematically. Overall, the progress in ML-driven ATS contributes significantly to the field, but judicious application and balanced regulations are required to address risks. Further advancements in ML will enable more capable, nuanced, and profitable algorithmic trading.