Services
Various services provided by the University Library are listed below:
Research
The university advances knowledge through dedicated research teams in various departments, institutes, and centers, spanning arts, science, technology, and other key areas. We maintain a centralized database system to manage research information and publications efficiently.
Additionally, our university promotes interdisciplinary collaboration among faculty and students to drive innovative research across diverse academic fields, fostering an environment where knowledge and expertise from various disciplines converge to address complex global challenges.
News & Events
Stay Engaged: Dive into Our Library News & Events.
KNUST Library Hosts State University of Zanzibar Delegation for Benchmarking Visit.
The Kwame Nkrumah University of Science and Technology (KNUST) Library System recently welcomed a six-member delegation from the State University of Zanzibar (SUZA), Tanzania, as part of a benchmarking visit under the DANIDA-funded Building Stronger Universities (BSU4) Project. The visit sought to strengthen institutional collaboration through the exchange of best practices in grant management, research support, digital library services, and knowledge management. During the engagement, the KNUST Library presented an overview of its operations, emphasizing its strategic role in advancing teaching, learning, research, and innovation across the University. The presentation highlighted the Library's network, comprising the Prempeh II Library and six affiliated college libraries, serving a community of over 86,000 users with the support of 112 staff members. The delegation also gained insights into the Library's expanding digital infrastructure and research support services. These included access to more than 35,000 electronic journals through over 50 subscribed databases, the KNUSTSpace Institutional Repository, which hosts more than 16,821 scholarly records, the ongoing migration to the KOHA Library Management System, and the Research Commons, which provides dedicated support for faculty and postgraduate research. The presentation further underscored the Library's commitment to information literacy, scholarly communication, and research excellence. A major highlight of the visit was the delegation's engagement with the KNUST Library leadership. The University Librarian, Dr. Richard Bruce Lamptey; Deputy University Librarian, Dr. Edward Mensah Borteye; Library Registrar, Mrs. Felicia Amankwah, and the, Head of Collection Development and Management, Rev. Dr. Kwabena Asiamah; led discussions on the Library's evolving role in supporting research, innovation, teaching, and learning. During the session, Dr. Richard Bruce Lamptey demonstrated how the KNUST Library System has transformed beyond its traditional role into a dynamic research support partner. He highlighted the Library's comprehensive services that support faculty, researchers, and postgraduate students throughout the research lifecycle from information discovery and literature searches to research data management, scholarly publishing, research visibility, and open access initiatives. Dr. Richard Bruce Lamptey Speaking on behalf of the delegation, Deputy Vice-Chancellor for Academic, Research and Consultancy at SUZA, Professor Abdi Talib Abdalla, expressed his appreciation for the visit, describing it as an invaluable learning experience that exceeded the delegation's expectations. He commended KNUST Library Sysyem for its integrated approach to research support, digital transformation, and institutional collaboration. The delegation, comprising senior academic and research administrators from SUZA, also visited several strategic offices across the University to learn about KNUST's best practices in research governance, grant administration, innovation, and academic support services. The benchmarking visit concluded with a renewed commitment to collaboration between KNUST and SUZA. It reaffirmed the KNUST Library System's position as a leading partner in advancing research, innovation, digital transformation, and knowledge sharing, while strengthening partnerships that contribute to excellence in higher education across Africa.
KNUST Library System Spearhead Digital Transformation with Staff Training on AI Cataloguing Tools
The KNUST Library System has organised a capacity-building workshop on AI-Powered Cataloguing, equipping library professionals with practical knowledge on how artificial intelligence is transforming cataloguing, metadata creation, and information management in modern libraries. The workshop formed part of the Library's continuous professional development initiatives aimed at enhancing staff competencies and preparing them for emerging trends in librarianship. The training was led by Rev. Dr. Kwabena Ofori Asiamah, Head of the Collection Development and Management (CDM) Department, in collaboration with the Technical Support Department of the KNUST Library System. He was assisted by Mr. Setsoafia Afetsi Yao Humphrey-Ackumey, Assistant Librarian and Head of Library Systems and Support at the Prempeh II Library. During the workshop, participants were introduced to the concept of AI-powered cataloguing, exploring how technologies such as machine learning, natural language processing (NLP), optical character recognition (OCR), computer vision, and generative AI can automate and enhance the creation of bibliographic records, metadata extraction, subject classification, authority control, and information discovery. The session highlighted AI's ability to generate MARC21 records, recommend classification numbers, improve metadata consistency, and support semantic search within library catalogues. Rev. Dr. Asiamah also demonstrated a range of emerging AI cataloguing tools, including MarcAI, Cat & Class, Archival AI, Elicit, and evolving Koha AI integrations, illustrating how these technologies can streamline workflows, improve the management of digital collections, and strengthen institutional repositories. Participants learned that while AI significantly reduces repetitive cataloguing tasks and increases efficiency, human expertise remains indispensable in ensuring quality control, authority verification, ethical oversight, and adherence to international cataloguing standards. A key focus of the workshop was the potential for developing custom AI cataloguing solutions tailored to the unique needs of academic libraries. Discussions centred on AI-assisted metadata extraction, automated classification, intelligent subject heading recommendations, authority control, and integration with library management systems such as Koha. The facilitators emphasised that customised AI tools can enhance the discoverability, accessibility, and preservation of scholarly resources while supporting more efficient library operations. Speaking during the session, the facilitators encouraged participants to embrace AI as a collaborative tool that complements not replaces the expertise of professional librarians. They noted that the future of cataloguing lies in the strategic integration of artificial intelligence with librarians' professional judgement to improve research support, digital scholarship, and knowledge organisation. The workshop reflects the KNUST Library System's commitment to innovation, continuous staff development, and the adoption of emerging technologies that enhance library services. By investing in capacity building and digital transformation, KNUST Libraries continue to position themselves at the forefront of academic librarianship, supporting excellence in teaching, learning, research, and knowledge management.
Science Friday, Episode 7 : Listening to Mosquitoes Translating Flight Sounds into Data for Disease Monitoring
Our speaker for the 7th edition of ScienceFriday organized by KNUST Libraries is Prof. Kingsley Badu, an Associate Professor in Theoretical and Applied Biology at KNUST. His research focuses on Bioacoustics, Public Health, Entomology. He took us into the world of mosquito flight sounds and how they could become data for disease monitoring. Prof. Kingsley Badu According to him, the mosquito, responsible for over 700,000 deaths annually from diseases like malaria, dengue, and Zika, is the world’s deadliest animal. Traditional mosquito surveillance methods—such as human landing catches, CDC light traps, and microscopic identification—are labor-intensive, slow, and inadequate for real-time public health response. To address this, Professor Kinsley Bedu and his team at AI4PEP Ghana have developed an innovative, AI-driven mosquito surveillance system that leverages bioacoustics, edge computing, IoT, and machine learning to transform mosquito flight sounds and images into actionable data for disease monitoring. The system captures the unique wingbeat frequencies of mosquitoes, which vary by species, sex, age, and physiological state, enabling real-time classification with 92–93% accuracy. Using high-quality microphones in soundproof environments, the team records flight tones and trains convolutional neural networks (CNNs) to analyze spectrograms of these sounds. The AI model is compressed using TinyML to run directly on low-power edge devices deployed in mosquito traps, allowing on-device classification without cloud dependency. These devices also collect environmental data—temperature, humidity, GPS—and transmit summarized results wirelessly to a centralized dashboard. Complementing the acoustic system is Mosque Mesa Net, a mobile app developed by computer scientist Alice that uses computer vision to identify mosquito species from photos with up to 99.37% accuracy. Notably, it can identify not only adult mosquitoes but also aquatic stages—eggs, larvae, and pupae—enabling early intervention. Designed for citizen science, the app empowers communities, especially in remote areas, to contribute to surveillance using widely available smartphones. The real-time dashboard provides public health officials with interactive maps showing device locations, mosquito counts by species, sex, and age, environmental conditions, and device status. This enables targeted interventions such as spraying or bed net distribution and supports integration with national health systems. The team is working to synchronize the system with the Ghana Health Service’s database for seamless data sharing. Community engagement has been vital, revealing concerns about tiny, non-mosquito biting insects that were found to contain blood, prompting expanded research. The project also prepares for emerging threats like the invasive Anopheles stephensi, training AI models to recognize new species via image and sound data. Internationally recognized, the project has presented at global forums including the UNGA Science Summit and the 8th Global Symposium on Health Systems Research. Field testing is underway in the Philippines to enhance model generalizability across Africa and Asia. Funded by IDRC Canada, FCDO UK, NIH, and others with over $3 million, the initiative has received multiple awards, including KNUST’s best innovation and multidisciplinary awards. Future goals include detecting viral pathogens directly from sound or in-trap molecular probes, scaling device deployment, publishing research on transmission dynamics, and exploring AI-based detection of insecticide resistance through wingbeat analysis. With mobile penetration exceeding 100% in Ghana, the potential for widespread adoption and impact on global health is significant, aiming to shift from reactive to predictive disease control through intelligent, automated surveillance. The mosquito, responsible for over 700,000 deaths annually from diseases like malaria, dengue, and Zika, is the world’s deadliest animal. Traditional mosquito surveillance methods—such as human landing catches, CDC light traps, and microscopic identification—are labor-intensive, slow, and inadequate for real-time public health response. To address this, Professor Kinsley Bedu and his team at AI4PEP Ghana have developed an innovative, AI-driven mosquito surveillance system that leverages bioacoustics, edge computing, IoT, and machine learning to transform mosquito flight sounds and images into actionable data for disease monitoring. The system captures the unique wingbeat frequencies of mosquitoes, which vary by species, sex, age, and physiological state, enabling real-time classification with 92–93% accuracy. Using high-quality microphones in soundproof environments, the team records flight tones and trains convolutional neural networks (CNNs) to analyze spectrograms of these sounds. The AI model is compressed using TinyML to run directly on low-power edge devices deployed in mosquito traps, allowing on-device classification without cloud dependency. These devices also collect environmental data—temperature, humidity, GPS—and transmit summarized results wirelessly to a centralized dashboard. Complementing the acoustic system is Mosque Mesa Net, a mobile app developed by computer scientist Alice that uses computer vision to identify mosquito species from photos with up to 99.37% accuracy. Notably, it can identify not only adult mosquitoes but also aquatic stages—eggs, larvae, and pupae—enabling early intervention. Designed for citizen science, the app empowers communities, especially in remote areas, to contribute to surveillance using widely available smartphones. The real-time dashboard provides public health officials with interactive maps showing device locations, mosquito counts by species, sex, and age, environmental conditions, and device status. This enables targeted interventions such as spraying or bed net distribution and supports integration with national health systems. The team is working to synchronize the system with the Ghana Health Service’s database for seamless data sharing. Community engagement has been vital, revealing concerns about tiny, non-mosquito biting insects that were found to contain blood, prompting expanded research. The project also prepares for emerging threats like the invasive Anopheles stephensi, training AI models to recognize new species via image and sound data. Dr Richard Bruce Lamptey (University Librarian ) Internationally recognized, the project has presented at global forums including the UNGA Science Summit and the 8th Global Symposium on Health Systems Research. Field testing is underway in the Philippines to enhance model generalizability across Africa and Asia. Funded by IDRC Canada, FCDO UK, NIH, and others with over $3 million, the initiative has received multiple awards, including KNUST’s best innovation and multidisciplinary awards. Future goals include detecting viral pathogens directly from sound or in-trap molecular probes, scaling device deployment, publishing research on transmission dynamics, and exploring AI-based detection of insecticide resistance through wingbeat analysis. With mobile penetration exceeding 100% in Ghana, the potential for widespread adoption and impact on global health is significant, aiming to shift from reactive to predictive disease control through intelligent, automated surveillance.
14
August 2026Science Friday, Episode 8
2:00 PMPREMPEH II LIBRARY
8
May 2026Science Friday Episode 4
2:00 PMPREMPEH II LIBRARY
10
April 2026Science Friday Episode 2
2:00 PMPREMPEH II LIBRARY