This research explains the perceptions of university students regarding the use of ChatGPT in their learning and research. The research is based on evidence provided in articles that were considered in a systematic literature review using the PRISMA model. The motivation for this study was informed by a realization that there are limited studies that provide a comprehensive understanding of students’ perceptions regarding the use of ChatGPT in learning and research. Many existing studies either focus on educators’ perceptions or isolated cases of empirical studies. It is therefore not clear what factors influence students’ adoption of ChatGPT. This gap was addressed by this study by conducting a systematic literature review to identify factors influencing the adoption of ChatGPT, the positive impact of ChatGPT on students’ education and the concerns raised by students regarding the integration of ChatGPT into academic practice. The research focus was on articles that discuss the students’ perceptions of ChatGPT, all of which were drawn from Google Scholar. In total 32 articles were considered for analysis for this study and in this paper, we present five constructs that influence the adoption of ChatGPT in academic activities. Even though the students’ perceptions of ChatGPT are widely varied, the findings of this research show that students concur that ChatGPT has a positive influence on their learning. The students further highlighted concerns relating to their use of ChatGPT in learning, which could be improved if the tool’s educational effectiveness is to be realized. The contribution of this study is three-fold. First, the findings of this research explain the perceptions of students about using ChatGPT in academic practice. Second, the results of this study inform the university management and policymakers about how to effectively integrate ChatGPT into the education system. Third, the study suggests, recommends, and guides educators who wish to incorporate this new, powerful tool (ChatGPT) into their teaching.

The re-surfacing of Monkeypox (Mpox) a zoonotic viral disease that causes painful blisters on the skin of the infected, as a global health concern has triggered the rapid and important development of useful and effective diagnostic tools to bolster the existing traditional methods such as contact tracing and timely intervention. Machine learning (ML) algorithms have shown promising results in the early detection and diagnosis of infectious diseases, including Mpox, by leveraging diverse data sources such as electronic health records, clinical images, and laboratory results. This Systematic Literature Review aims to assess the effectiveness of machine learning algorithms in detecting Monkeypox (Mpox). The main question is: ‘How well do machine learning algorithms perform in detecting Monkey Pox?”The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were employed for this review. An in-depth search was done spanning several databases, namely ScienceDirect, IEEE Xplore, ACM Digital Library, and Springer Link. Studies published between 2022 and 2024 were considered. After removing duplicates and screening for relevance and quality, a total of 30 papers were included in the final analysis. The outcomes indicate that ML algorithms, particularly convolutional neural networks (CNNs) and support vector machines (SVMs), have proven to have high accuracy and sensitivity in detecting Mpox. These algorithms perform strongly compared to traditional diagnostic methods like contact tracing, resulting in faster and more reliable results. The integration of ML with imaging techniques, such as dermoscopy and radiography, has further enhanced diagnostic precision. (ML)algorithms present a notable advancement in the detection of Monkeypox. They are very robust in the face of large datasets and give a very high level of accuracy and precision in interpreting the datasets, to this effect however further research is needed to address the challenges of data variability and the need for large, high-quality datasets. The review's findings offer practitioners and policymakers in healthcare important new information. The creation of novel diagnostic procedures and guidelines can be influenced by the proven efficacy of machine learning algorithms in Mpox detection. The results also emphasize the necessity of funding machine learning research and infrastructure to enhance disease surveillance and response tactics.

This paper investigates the factors influencing the adoption of AI-chatbots by non-governmental organizations (NGOs) in developing countries, a context faced with escalating social and environmental challenges. As poverty deepens, climate-induced droughts persist, and regional conflicts escalate, the demand for effective NGO intervention increases. NGOs are critical in addressing social, environmental, and economic issues, often filling gaps where governmental support is lacking. Despite a global increase in innovative technologies, the uptake of AI-chatbots by NGOs is still in its infancy. Furthermore, there is limited literature providing a comprehensive factors for NGOs to adopt AI-chatbots. This qualitative study interviews 30 managers for in-depth insights. The study examines technological, organisational, and environmental aspects affecting AI-chatbot adoption using the TOE paradigm. Thematic analysis of the qualitative data identified key factors such as a lack of technical expertise, financial constraints, resistance to change, data privacy and security concerns and infrastructure limitations as the most dominant factors in adopting chatbots. The factors address a gap in the literature by providing insights into the adoption of technology in resource-constrained settings, offering operational, technical, and strategic guidance to NGO managers and policymakers.

Diabetes is recognised as one of the world's most prevalent health problems. As diabetic patients grew, so did the percentage of diabetic hospital readmissions. Early readmissions can impact patient well-being, operational efficiency, and financial burden. This study uses machine learning approaches to predict hospital readmissions among diabetes patients. Data was collected from 130 US hospitals. CRISP-DM is used for analysis. Logistic regression (LR) and random forest (RF) classifiers were implemented. The classifier performance was compared. Random Forest outperformed the other model, with an accuracy of 0.89. The model was chosen to enable practical deployments. Researchers used a web-based interface to get data and receive real-time predictions. The results showed that the predictive model used alongside an interface creates a clear and understandable prediction platform. However, the research might involve various datasets and Deep Learning to improve models and findings, in future studies. Furthermore, the model could explore the integration of machine learning interpretability approaches to increase transparency and promote better comprehension of the model's predictions by healthcare practitioners.

Mental health is an important aspect of well-being as it encompasses emotional, psychological and social well-being. The use of patient portals in mental health care has gained attention as a potential tool to improve access to care for individuals with mental illness. Patient portals may be vulnerable to unauthorized access if appropriate security measures are not put in place. This study leverages blockchain technology to create tamper-proof patient records. The proposed solution uses an on-chain database that stores hashes and the actual medical record of a patient as well as an off-chain solution that handles encryption of each user’s medical record using their respective keys in a trustless manner before they are uploaded on-chain. A secure smart contract hosted on the Ethereum and the Byzantine Fault Tolerance consensus algorithm was used to ensure patient privacy. The research employed the Comparative Analysis Research Methodology as the research methodology and the Kanban methodology as the software development methodology. The research project concludes that the proposed solution addresses the current security issues and data privacy concerns in patient data. The decentralized nature of blockchain ensures security, transparency and tamper-proof storage of information. Further research is needed for future advancements, like integrating blockchain-based patient portals with wearable devices and IoT.