The community is made up of conference proceedings, papers and presentations from conferences, symposiums, workshops and events held at or sponsored by Botswana International University of Science and Technology( BIUST). https://repository.biust.ac.bw/handle/123456789/65 2026-07-07T12:07:33Z 2026-07-07T12:07:33Z Parvathareddy, Sravani Yahya, Abid Amuhaya, Lilian Livutse Ravi, Samikannu https://repository.biust.ac.bw/handle/123456789/748 2026-07-07T10:13:17Z 2024-07-22T00:00:00Z

Hybrid machine learning and genetic algorithms for environmental impact and energy management to enhance mining sustainability Parvathareddy, Sravani; Yahya, Abid; Amuhaya, Lilian Livutse; Ravi, Samikannu The mining industry confronts the challenge of balancing economic prosperity and environmental sustainability. This research presents a comprehensive approach that leverages big data analytics and machine learning techniques to address this challenge. Unlike traditional approaches that focus on mitigating impacts post-occurrence, our method advocates for proactive measures throughout operational phases. We introduce a cohesive system integrating advanced technologies to analyze vast datasets, including real-time environmental sensor data, satellite imagery, company reports, and government records. The framework encompasses data pre-processing, model building, analysis, and recommendations. To predict environmental outcomes and assess sustainability, we employ a genetic algorithm (GA) and machine learning tools such as XGBoost, support vector regressor (SVR), and K-nearest neighbors (KNN) regressor algorithms. Data pre-processing ensures data accuracy and consistency. We use clustering and recommendation algorithms for analysis and suggestions, identify improvement areas, and propose environmental management solutions. This methodology underscores the importance of empowering stakeholders to anticipate environmental consequences, mitigate potential hazards, and continuously improve sustainability initiatives through real-time insights. By integrating big data analytics and machine learning, we enhance the environmental sustainability of mining operations, fostering a harmonious balance between environmental stewardship and economic returns. The benefits of our system are manifold, including improved environmental management, reduced environmental risks, and enhanced sustainability practices in the mining industry, thereby highlighting the crucial role of stakeholders in this process.

2024-07-22T00:00:00Z Moreri, Kealeboga K Segobye, Mooketsi Maphale, Lopang Peter, P https://repository.biust.ac.bw/handle/123456789/722 2025-11-27T08:41:08Z 2024-09-01T00:00:00Z

Issues to consider in incorporating artificial intelligence learning for geomatics education in Botswana Moreri, Kealeboga K; Segobye, Mooketsi; Maphale, Lopang; Peter, P In the last couple of years, Artificial intelligence (AI) applications in education have received a lot of attention from both the research and the general community at large. Several international reports describe AI in education as one of the emerging fields of interest in educational technology. Despite AI technology being around for almost 30 years, it is still unclear for geomatics educators how they can take pedagogical advantage of it on a broader scale and how it can impact meaningful teaching and learning in higher education. As a result, learners miss out on opportunities to study AI-propelled smart technologies and big data-driven approaches to solve societal problems. Moreover, they are deprived of developing skills to think in terms of AI use and AI-inspired innovations to improve current processes. It is necessary to investigate meaningful ways and approaches to incorporate AI literacy and AI thinking in the school curriculum to increase the knowledge gap among learners and encourage critical thinking. Therefore, this study investigates major issues to be considered in incorporating artificial intelligence concepts in geomatics education in Botswana particularly the opportunities, challenges, ethical considerations, and overall curriculum requirements for a successful integration. Furthermore, it has designed a conceptual framework that demonstrates how artificial intelligence and data science technologies can be incorporated into geomatics education in Botswana to produce well-rounded graduates. Proceedings of the International Conference on Engineering Education and Management (IC2EM’24), 23–25 September 2024, Palapye, International University of Science and Technology

2024-09-01T00:00:00Z Mpoeleng, Dimane https://repository.biust.ac.bw/handle/123456789/721 2025-11-26T08:22:28Z 2024-09-01T00:00:00Z

Spacex starlink: transforming Botswana's internet landscape Mpoeleng, Dimane The advent of SpaceX's Starlink technology is poised to revolutionize the internet landscape in Botswana, offering unprecedented opportunities for education, particularly in engineering. Starlink's advanced engineering, characterized by its innovative phased-array antennas, facilitates high-speed, low-latency internet access that surpasses traditional ISP offerings. This paper explores the transformative potential of Starlink in Botswana, a country where current internet services are limited and often unreliable. With Starlink, Botswana can experience the same rapid digital advancement observed in other countries, fostering an environment conducive to educational growth and technological innovation. The disruption of the current ISP market by Starlink's superior speed and reliability can lead to enhanced educational outcomes, empowering students and educators with the tools necessary for modern learning. Starlink's coverage will span 100% inch by inch of the entire country, ensuring that every corner of Botswana has access to reliable internet. Furthermore, Starlink will begin operations in Botswana through retail partnerships within the next two months, accelerating the country's digital transformation. This paper also reviews the engineering marvel of Starlink's antenna design, which plays a crucial role in delivering high-speed internet, and analyses the current state of Botswana's ISP market, highlighting the potential impacts and benefits of this revolutionary technology.

2024-09-01T00:00:00Z Mashaba, Kobamelo Monageng, Robert Ogolotse Kgwadi, Monageng Chikati, Ronald Majoo, P A https://repository.biust.ac.bw/handle/123456789/720 2025-11-26T08:21:13Z 2024-09-01T00:00:00Z

Challenges and opportunities in integrating artificial intelligence education at the university of Botswana: addressing infrastructure and educational priorities for a knowledge-based society. Mashaba, Kobamelo; Monageng, Robert Ogolotse; Kgwadi, Monageng; Chikati, Ronald; Majoo, P A Botswana, ranked 64th in economic performance and productivity according to the IMD World Competitiveness Yearbook 2024, sincince joing it has has made significant improvement in enhancing its skilled workforce and educational systems .Despite increasing awareness, the integration of artificial intelligence (AI) in education remains limited. AI, which automates tasks, holds significant potential for preparing engineers in a knowledge-based society. This study examines why the University of Botswana has not established robust AI programs and explores factors contributing to the absence of a dedicated AI degree. Key challenges investigated include inadequate infrastructure for fast connectivity, though efforts are underway with a fiber optic project from Namibia aimed at improving internet speeds. Significant investment is needed to modernize network capabilities and enhance economic competitiveness. While the Faculty of Computer Science offers related courses such as data structures, algorithms, and data mining, they focus more on theoretical and practical computing applications than standalone AI disciplines. Similarly, AI concepts in Electrical and Mechanical Engineering are integrated within broader engineering disciplines. Addressing these challenges requires understanding educational priorities and resource allocations necessary to develop comprehensive AI programs aligned with industry demands. The study employed a mixed-methods approach, and found that , the current AI-related courses are misaligned with industry demands and technological advancements, largely due to outdated content, insufficient faculty expertise, and inadequate infrastructure with Stakeholders expressing a strong need for the curriculum to evolve in order to better prepare students for the dynamic field of artificial intelligence and to keep up with technogical changes.

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