Kudakwashe Maguraushe

The Mpox outbreak has sparked extensive public discourse on social media platforms like Twitter (X), where users express emotions ranging from worry and suspicion to support. Understanding these emotions can be of great help to public health authorities attempting to communicate and respond while preparing for outbreaks like this one. However, conventional sentiment analysis approaches often fall short, as prior algorithms have failed to recognise syntactic categories in informal, almost carefree social media text. The study was carried out to evaluate the efficacy of transferrable transformer-based technologies in sentiment analysis of Twitter texts concerning the Mpox crisis. The researchers gathered 6,377 tweets using Apify scraping software from the Web and further filtered them to 5,142 English-language tweets after removing duplicates and non-English tweets. Sentences were classified using the VADER tool, then used to train a transformer-based classification model with AutoGluon's MultiModalPredictor, employing the pre-trained ELECTRA architecture. The model was then trained and tested with an 80:20 training-testing split and achieved an overall accuracy of 83.92%, with macro-averages of 79.78%, 79.67%, and 79.57% for precision, recall, and F1-Score, respectively. The comparison between the transformer and the VADER baseline was based on the transformer's significant leap in sentiment classification; it exhibited clear improvements. To demonstrate the practical applicability of the models, the trained model was deployed via an integrable Streamlit dashboard, where users could upload tweet datasets for the classifier to generate sentiment predictions. Some evaluations of sentiment trends were also conducted. The study also sheds light on how businesses based on transformer models have been capitalised to investigate large-scale social discourse and, in turn, to support digital epidemiology and public health monitoring during infectious disease outbreaks.

Purpose: Achieving global food security while maintaining environmentally sustainable agricultural systems remains a critical challenge amid population growth, climate variability, and resource constraints. Artificial Intelligence (AI) and the Internet of Things (IoT) have emerged as transformative technologies that support data-driven agricultural practices. This study systematically examines the applications, opportunities, challenges, and adoption factors of AI-IoT integration in smart agriculture, with particular emphasis on its potential contributions to food security and sustainable farming. Methods: A systematic literature review (SLR) was conducted following the PRISMA 2020 guidelines. Publications were retrieved from Scopus, IEEE Xplore, and ScienceDirect covering the period 2020-2024. From an initial 431 records, 17 empirical studies met the inclusion criteria and were analysed using narrative and thematic synthesis. Result: The review shows that AI-IoT technologies are primarily applied in crop disease detection, precision agriculture, environmental monitoring, yield prediction, and livestock health monitoring. These technologies enable real-time decision support, early disease detection, productivity improvement, and resource optimisation. However, challenges remain, including data limitations, infrastructure constraints, integration complexity, and high deployment costs. Novelty: The study proposes a layered smart agriculture framework linking technological infrastructure, application domains, adoption conditions, operational outcomes, and sustainability impacts. The findings highlight key factors necessary for successful implementation, including infrastructure readiness, affordability, technological reliability, and capacity development for farmers. Crucially, the review demonstrates that the field of AI-IoT smart agriculture is technically advanced but socio-technically incomplete: the evidence base is dominated by proof-of-concept studies from Asia, with no empirical representation from Africa or the Americas, creating what this study terms an AI-IoT agricultural equity gap that fundamentally limits the technology’s contribution to global food security. The five-layered framework introduced here provides the first inductively derived organising structure that explicitly connects AI-IoT infrastructure to SDG-aligned food security outcomes, offering a replicable analytical scaffold for future empirical and policy research in this domain.

The protection of personal information has become a defining challenge for higher education institutions, particularly in developing contexts where regulatory frameworks are often strong on paper but weak in practice. This study investigates student perceptions of privacy within Zimbabwe’s higher education system, focusing on three constructs: awareness, expectations, and confidence across nine core privacy components derived from international principles (FIPPs, OECD, GDPR) and the Zimbabwe Data Protection Act (ZDPA). Using survey data from 287 students across diverse programmes and modes of study, descriptive and comparative analyses reveal a striking pattern: students demonstrate high awareness and very strong expectations, yet their confidence in institutional compliance remains significantly lower. The largest deficits were found in privacy education, consent, and notice/openness, suggesting that institutions are perceived as technically competent in data handling but weak in transparency, accountability, and student engagement. The research extends privacy perception models by considering the discrepancy between the students’ expectations and the institutional trust. It also encourages universities to go beyond mere compliance by implementing concrete measures such as privacy training, clear consent, and frequent data audits. The findings contribute to global debates on privacy by offering evidence from the Global South, showing that the key challenge is not student ignorance but institutional trustworthiness. Bridging this awareness-confidence gap is essential for building a privacy-conscious academic environment.

This study critically examines the ethical dimensions of artificial intelligence (AI) and smart technologies in the financial sector, focusing on South Africa. Using bibliometric analysis of publications spanning 2006 to 2025, it maps research productivity, thematic orientations, and collaboration networks. The results reveal a significant growth in scholarly activity after 2018, coinciding with global regulatory debates and the rapid expansion of FinTech. South Africa emerges as a central actor in international bibliographic networks, underscoring its role as a regional leader. However, global authors and institutions continue to dominate, raising questions about representation and the contextual relevance of imported ethical frameworks. Critical issues such as trust, financial literacy, and financial performance appear marginal, despite their centrality to consumer confidence and inclusion. These findings indicate an urgent need for more context-sensitive scholarship reflecting South Africa's socio-economic realities, historical inequalities, and digital divides.