Book Chapters
Permanent URI for this collection${dspace.ui.url}/handle/20.500.12479/2944
Browse
Recent Submissions
Item Chapter 7 - Microbial threats in agricultural crops and their management through artificial intelligence approaches(Elsevier Inc., 2026-02-27) Neema, Mduma; Aloo, Becky ; Rajendrakumar, S.Agriculture is continuously threatened globally by various microbial pathogens that compromise yield, quality, and economic value. The conventional methods of managing these pathogens often rely on agrochemicals with ecological impacts and the development of pathogen resistance. Although artificial intelligence (AI) practices have been used to solve several global challenges, their applications in managing microbial pests in agriculture are still in their infancy. This chapter reviews the applications of AI in enhancing pathogen detection, disease prediction, and targeted interventions for microbial threats in agriculture. The chapter additionally evaluates the current application of AI tools in addressing microbial threats, the benefits of managing microbial threats in agriculture using AI, and the application challenges therein. The chapter also reviews various agricultural applications of AI tools, like machine learning algorithms for pathogen identification and predictive analytics for disease outbreak prevention. Using these AI-driven models and tools, farmers and agronomists can identify early signs of microbial infections for targeted interventions and management. Incorporating AI technology offers insights into a future where crop protection is more sustainable and precise without harming the ecology of the agricultural system. These technologies can be adopted for widespread use and managing microbial threats in agriculture for food security.Item AI-Powered Learning in the Global South: Lessons From ChatGPT Use in Tanzanian Universities(IGI Global Scientific Publishing, 2026) Mduma, NeemaThis chapter explores how ChatGPT, a popular Artificial Intelligence (AI) tool, is being used in Tanzanian universities to support teaching and learning. It presents findings from a study conducted at the University of Dar es Salaam, University of Dodoma and Nelson Mandela African Institution of Science and Technology. The chapter discusses how students and lecturers are using ChatGPT, the benefits they experience and the challenges they face such as poor internet access, lack of training and missing university policies. While the study focuses on higher education, the findings also inform K–12 education by showing how AI can serve as virtual tutors, support personalized learning and expose policy or infrastructure gaps in schools. The chapter offers recommendations, policy suggestions and areas for future research. It fills an important gap, since few studies examine AI use in Tanzanian higher education. The insights shared can help universities and policymakers make better decisions about using AI tools in a responsible way.Item Operationalizing the Triple Helix for Resource-Constrained Universities: A Nested Model of Micro-Credentials, Research Groups, and Centres of Excellence(IGI Global Scientific Publishing, 2026) Dida, MussaModern universities have shifted from basic teaching and research to becoming drivers of economic development and AI innovation. However, resource-constrained universities (RCUs) face a “vicious cycle” where limited funding, aging infrastructure, and a lack of specialized expertise prevent them from keeping pace with rapid technological shifts. Traditional academic structures are often too slow and rigid for the AI era, leaving many institutions marginalized in global innovation networks. To address this, the text proposes a “university-nested” ecosystem to operationalize the Triple Helix (university-industry-government) model through three mechanisms (1) Industry-co-designed micro-credentials for agile skills training, (2) Interdisciplinary Research Groups for collaborative problem-solving, and (3) Centres of Excellence to scale these partnerships sustainably. This roadmap enables RCUs to transform from passive degree-providers into proactive co-architects of technological innovation.Item AI-Driven Imaging and Diagnostics in Smart Healthcare Systems(Springer Nature, 2025-10-31) Ogundokun, Roseline; Owolawi, Pius; Mkoba, Elizabeth; Olugbemi, Adesanjo; Ogbuju, EmekaArtificial intelligence (AI) has embedded itself into the core of smart healthcare systems, transforming the face of medical diagnosis, especially AI-driven imaging and diagnostic tools. These developments have met the exponentially increasing demand for accuracy, efficiency, and interpretability in healthcare solutions. However, challenges remain regarding optimal performance across diverse datasets and the transparency of AI-driven decision-making. Conventional classification models do not handle the challenge of an imbalanced dataset and generally fail to maintain a balance between precision and recall. Moreover, non-interpretable AI models result in complex deployments in critical applications where decision-making should be transparent. This research article investigates the performance of the SHAP-AI-XNet model, a novel AI-driven imaging and diagnosis tool for smart healthcare systems. This also discussed the evaluation of this model using accuracy, precision, and recall parameters to find their interpretability and suitability to high-stake medical contexts. Overall, 13,244 samples are in the dataset. These are either positive or negative. Below is the description of the performance of a few models that used a confusion matrix and ROC curve; the PR curve also describes it. The results were interpreted using various visual tools, calculating accuracy, precision, recall, and F1-score. The SHAP-AI-XNet model yielded 100% accuracy, precision, recall, and an F1 score of 100%. The ROC curve analysis yielded a perfect AUC of 1.00, indicating a perfect separation between the classes. A similar shape of the PR curve also attested to a high precision value at all recall values, showcasing that the model was reliable and strong. The results stated that the SHAP-AI-XNet model performed very well with a limited number of misclassifications and was highly interpretable. It is an efficient AI-based tool for imaging and diagnostics in smart healthcare systems, ensuring accuracy and reliability for medical decisions. Future work should confirm the scalability and generalizability of the model with more and larger diverse datasets. Besides, enhancing multi-class classification and real-time deployment could strengthen its application in smart healthcare environments.Item Integration of AI-Based for Seamless Operations in Smart Healthcare Systems(Springer Nature, 2025-10-31) Falola, Peace; Awotunde, Joseph; Adeniyi, Abidemi; Mkoba, ElizabethBy improving diagnosis, treatment, and patient management, the fusion of artificial intelligence (AI) into intelligent healthcare systems has completely transformed the way healthcare is delivered. Natural language processing (NLP), deep learning (DL), and machine learning (ML) are examples of AI-driven solutions that improve healthcare decision-making, promote smooth interoperability, and allow for individualized treatment regimens. Applications of AI in electronic health records (EHRs), robotics-assisted surgery, and diagnostic imaging have greatly enhanced patient outcomes and healthcare efficiency. Notwithstanding these developments, issues including algorithmic bias, privacy concerns, and data security still exist. This chapter examines how AI is revolutionizing smart healthcare, highlighting significant technical developments, ongoing uses, and potential future developments.Item Evidence-Based Practices on Co-operative Societies Information Record Management(Springer Nature, 2024-06-30) Germinous, George; Dida, MussaCo-operatives have proved to be one of the driving forces in the socio-economic empowerment of its members. The Government of Tanzania has been implementing the Poverty Reduction Strategy by encouraging people to form co-operatives in order to improve their economic prospects. Establishing a primary co-operative society involves a process which passes through the district co-operative office, regional co-operative office, and the registrar of co-operatives’ office at the national level. These processes bring about the issue of documentation and record keeping. The processing of records is done manually by using pen and paper, or electronically using computers, smart phones, and cameras. The research work essentially used questionnaires, interviews, observations, and document reviews to gather data. The findings of this study show that 8 out of 13 respondents (61.5%) responded to physically visiting co-operative offices to acquire existing records about co-operative societies. This vividly explains the use of manual procedures for co-operative records management thus leading to unsolved challenges such as; data inaccuracy and inconsistency, bureaucracy during physical access, money and time consumption due to geographical challenges, lack of transparency, and improper presentation of co-operative information in request. This ongoing research work avails a web-mobile approach named Co-operative Records Management System (CRMS). CRMS offers a solution that will enable District Co-operative officers (DCOs) to record, process, and generate electronic reports on co-operative societies’ records thus mitigating challenges about, but not limited to; time wastage, inconsistency in recording financial records as well as reducing costs for data acquisition. Thus, this paper presents evidence-based practices on co-operative societies’ information record management, a case of the Kilimanjaro region with a designed proposed solution.Item A Loan Application Management System for Efficient Loan Processing: A Case of Muhimbili SACCOS LTD(Springer Nature, 2024-06-30) Murimi, Luciana; Siebert, Marius; Salira, Godwin; Mkoba, Elizabeth; Ally, MussaMajor parts of the population in emerging markets are still unbanked. The 2021 Global Findex Database shows that only 52% of Tanzanian adults own a formal financial account. Unbanked individuals cannot access capital to grow their businesses. In Tanzania, Savings and Credit Cooperative Organization Societies (SACCOS) have traditionally provided services and products such as loans and savings tailored to fit the needs of the financially excluded. By doing so, tremendous success has been achieved in attaining financial inclusion. However, inefficient manual business processes still pose a great challenge, hindering SACCOS performance and sustainability. Whereas digital solutions such as web and mobile applications have been widely adopted to improve business processes in various sectors, this adoption has been quite slow in the SACCOS sector. Lack of affordable entry-level solutions has resulted in most SACCOS relying on manual paper-based processes. There is therefore need, for the design and implementation of affordable, entry-level digital solutions. This study presents the implementation of a loan application management system: a case of Muhimbili SACCOS LTD. Qualitative methods of data collection were used in identifying system requirements. An Android tablet-based loan application management system was implemented, allowing loan officers to capture rich information required to determine members’ loan eligibility. Through the application, loan officers can retrieve stored loan applications and generate the required templates needed for further processing. For integration with the Core Banking System (CBS), a schema is generated that can be uploaded to the CBS for further loan processing. Thus, achieving an efficient loan application process.