Plant diseases can have profound effects on the economy, impacting both local and global scales. These diseases can lead to substantial losses in agricultural productivity, affecting crop yields and quality. In this context, deep learning algorithms are widely acknowledged as effective solutions. However, the use of these black-box approaches raises concerns about trust in interpreting and validating the decisions generated by the models. This study proposes an explainable artificial intelligence (XAI) based plant disease classification system to classify and identify distinct ailments with improved accuracy. The system correctly identifies 38 different plant diseases with accuracy, precision, and recall as 99.69%, 98.27%, and 98.26%, respectively. These predictions are subjected to additional analysis employing the local interpretable model-agnostic explanations (LIME) framework to produce visual explanations aligning with prior beliefs and adhering to established best practices in explanations. This system will serve as a promising avenue for revolutionizing disease detection, fostering informed decision-making, and ultimately contributing to global food security.

Background/Objectives: Telemedicine has emerged as a transformative solution to healthcare access challenges in Sub-Saharan Africa, where many populations remain underserved. This systematic review focuses on the adoption, implementation, and technological prospects of telemedicine in South Africa, Kenya, and Nigeria, three countries leading the region in healthcare innovations. Methods: A systematic search of PubMed, Scopus, and Web of Science, guided by PRISMA protocols, identified 567 studies published between 2014 and 2024, of which 53 met the inclusion criteria with a focus on telemedicine adoption, implementation, and technological prospects in the selected countries. A structured critical appraisal was used to assess potential biases in the included studies’ design, selection criteria, and reporting, while findings were thematically analysed to provide actionable and comparative insights. Results: The findings reveal that South Africa has the highest adoption rate, focusing on specialist teleconsultations, chronic disease management, and mental health services. Kenya demonstrates strong mHealth integration and advanced mobile applications, particularly in maternal health, HIV care, and sexual and reproductive health. While facing infrastructural and regulatory constraints, Nigeria is advancing innovations for remote diagnosis and teleconsultation. Conclusions: By synthesising evidence from peer-reviewed literature, the review identifies adoption trends, enabling factors, and opportunities for scaling telemedicine in these contexts. Despite these advancements, challenges persist, including regulatory gaps, digital literacy limitations, and infrastructure constraints. Addressing these barriers requires targeted investments in broadband expansion, policy harmonisation, and healthcare workforce training to optimise telemedicine’s impact and ensure its sustainability as a healthcare delivery model in Sub-Saharan Africa.

The software project scheduling (SPS) problem deals with optimal allocation of employees to tasks such that the project is completed within budget and schedule. This is well addressed in the absence of dynamic events and has been demonstrated in SPS research with the static scenarios only. The software development methodologies have evolved where customer requirements are not initially locked but are flexible to take into account ongoing additional requirements or changes. In this regard, ‘new employee addition’ has emerged as one of the critical dynamic events that could significantly disrupt the project budget and schedule. In this paper, the SPS is modelled as multi-objective optimization problem and a novel approach is presented to deal with ‘new employee addition’ dynamic event; that uses domain knowledge to generate a robust schedule. The proposed heuristic is further evaluated on 18 dynamic benchmark scenarios and 3 real-world instances. The results show that the proposed heuristic besides its capability to handle dynamic events also demonstrates good convergence while maintaining good distribution of solutions in a dynamic environment. The principal target audience for this research are project managers in software development organizations who will get optimal project reschedule under ‘new employee addition’ dynamic event particularly in complex, large scale and multi-project settings.

In South Africa, social media usage has consistently increased over time, with young individuals being the most active users. However, there are concerns about mental illness, such as depression, as a result of the surge in social comparison. This study explored the relationship between social media usage and mental health among young adults in the Buffalo City Metropolitan Municipality. This study used a quantitative research approach and was based on the positivism paradigm. Data was collected from 127 young individuals aged 14 to 35 who live in the Buffalo City Metropolitan Municipality through the use of structured questionnaires. The data collected was analysed using a descriptive statistical package for the social sciences. The findings of the study amongst others showed that the young adults spend an average of 6 hours per day on social media platforms. Also, the fear of missing out (FoMO) was identified as another major reason why young adults stay on social media platforms for long hours every day. Based on the results presented in this study, one can convincingly state that the excessive use of social media is negatively contributing to the mental health status of young adults. Some of these detrimental impacts include depression, anxiety, low self-esteem, suicide and other mental health issues. The study recommends amongst others that an urgent and continuous well-structured awareness campaign be made to help sensitise the young adults on the danger of excessive use of social media platforms, both on their mental health status and life in general.

Social media platforms are popular among higher education students and have seen increased usage for academic purposes, especially during the COVID-19 pandemic. However, excessive use of social media can negatively impact students' academic performance. This preliminary study examines social media's impact on students' academic performance at Walter Sisulu University (WSU), Buffalo City campus. Using a positivist paradigm and a quantitative approach, randomly sampled data were collected from 71 students through a survey to identify trends and generate preliminary insights. Results indicate that while social media can facilitate academic work, it predominantly acts as a distraction, negatively affecting academic performance, particularly for first-year students. Notably, 84.5 per cent of the students spend more than four hours daily on social media, and 39.4 per cent agree that it negatively impacts their assignment completion. The study underscores the need for students to balance their social media use and academic responsibilities, highlighting the importance of this issue. Recommendations for achieving this balance, such as adopting time management strategies and integrating social media into teaching methodologies, are discussed.

As a typical scheduling problem, the software project scheduling (SPS) under uncertain and dynamic events has gathered attention of academic researchers and practitioners because it causes tangible and intangible loss and will also influence the project schedule and cost if not addressed in time. In this paper, a SPS model based on skills acquired by employees and required by tasks criterion is proposed to tackle the issue of ‘employee turnover’ along with novel multi-objective evolutionary optimization algorithm to handle such dynamic events by employing the domain knowledge for population initialization. This is critical for the success of a software project as when an employee leaves, the project is rescheduled with remaining resources to assess if the project can be completed on time and under budget; otherwise, an alternative resource is hired. The proposed model and algorithm are evaluated on 18 dynamic benchmark and 6 real-world problem instances. The experimental results indicate that if existing employees are reassigned to the tasks, the project cost and duration increases on average 22% and 4% respectively. However, hiring new employees with recommended skills and proficiency based on proposed approach provides 9% decrease in project cost with no increase in duration and 15% decrease in cost against 11% referenced increase in project duration, in comparison to classical rescheduling results. The results demonstrate that proposed model and algorithm handles dynamic event effectively and help in identifying the right set of skills for hiring of an alternate resource while optimizing project duration and cost.