Botho University Library

Botho University Institutional Repository

Theses, dissertations, faculty research and archives from Botho University.

Quick Access

Everything you need, one tap away

Browse by Title Browse by Author Browse by Subject Submit an Item

Borrow · Botho University Library

The Library Catalogue

Books, journals and course reserves across all campuses. Check what is on the shelf, place a hold and manage your loans.

New Arrivals Most Borrowed Course Reserves My Account

Enter the catalogue

About the Botho University Library

The Botho University Library supports learning, teaching and research across the University’s campuses in Botswana, Lesotho, eSwatini and Ghana. Our collections span business, computing, engineering, health sciences, education and more, available to students, staff and researchers, on campus and online.

Explore the Collection University Website

Opening Hours

Mon – Fri8:00 – 20:00
Saturday9:00 – 13:00
SundayClosed

Ask a Librarian

Explore by Subject

Curated areas across Botho University

Now showing 1 - 5 of 9

Recent Submissions

Determinants of Dividends decisions cum dividend derivatives in Botswana and Zimbabwe
(International Journal of Advanced Business Studies, 2026-03-30) Wilbert Chidaushe; Tavonga Njaya; Precious Onneile Alade
The study examines the determinants of dividends decisions alongside dividends derivatives in Botswana and Zimbabwe. Further the study explored the importance of dividends derivatives for companies listed on both Stock Exchanges. The study was based on a mixed methods research approach. The research used simple random sampling of non-financial firms for the determination of dividend decisions. Documentary review research was also deployed in assessing the significance of dividends derivatives. The study was based on a sample of 27 companies listed on the Botswana and Zimbabwe Stock exchanges. Unit root analysis, test of normality, test of fixed effects and Granger causality tests were applied to evaluate the determinants of the of dividend decisions. Logistic regression analysis was applied to investigate the determinants of dividend decisions in Botswana and Zimbabwe. Granger causality was deployed to investigate the impact of short-term relationships influencing the propensity for dividend payout. The binary logit study revealed that the significant positive determinants of dividends payout were past dividends, investors preferences, derivative hedge and industry type. The fixed effect model revealed that a Chief Financial Officer possessing more than 15 years of experience, had a significant positive propensity for dividend payout in both countries. Firm size and free cash flows were viewed as having a significant and negative propensity for dividend payout from the fixed effect model. The determinants of dividend derivatives were identified as the rise of decrement indices, economic uncertainties and pandemic risks. The benefits of dividend derivatives were found as protection of investors against dividend risks during periods of existential threats, economic vulnerabilities and uncertainties.
Face Recognition Using Dual Tree Complex Wavelet Transform
(Society of Digital Information and Wireless Communications, 2012) Shanmugasundaram, Suresh; Chidambaram, Divyapreya
We propose a novel face recognition using Dual Tree Complex Wavelet Transform (DTCWT), which is used to extract features from face images. The Complex Wavelet Transform is a tool that uses a dual tree of wavelet filters to find the real and imaginary parts of complex wavelet coefficients. The DT-CWT is, however, less redundant and computationally efficient. CWT is a relatively recent enhancement to the discrete wavelet transform (DWT). We show that it is a well-suited basis for this problem as it is directionally selective, smoothly shift invariant, optimally decimated at coarse scales and invertible (no loss of information). Our face recognition scheme is fast because of the decimated nature of the DTCWT. Dual Tree methods are based on image at different resolution. Normalization is done to reduce dimensionality which will reduce memory problem and computation time. Here Principal Component Analysis which is a linear dimensionality reduction technique, that attempt to represent data in lower dimensions, is used to perform the face recognition. PCA is applied that deals with the decomposition of the training set into the Eigenvectors called Eigen faces. Various discrimination analyzes such as, Euclidean, L1, L2 and Cosine similarity are used for the recognition of face images.
The Emergent Requirements for Visual Teaching Tools for Supporting Introductory Programming: A Case Study of Botho University, Francistown
(2014-07-06) Samson-Zulu, Bonolo Elizabeth; Nkomo, William
Despite low levels of interest in programming by learners and perceptions that programming is difficult, it should be noted that programming’s importance has not diminished and should form core curricula of undergraduate computer science in education. A current challenge for the Computer Science department is to provide effective means of reducing attrition rates and enhance the potential of the department to recruit more students at Botho University. The purpose of the present study is to investigate how best students learning introductory programming can be supported and their interest cultivated by use of the ultimate visual support tools. This paper reports on the current problems faced by Botho University (Francistown). It first compares the attrition rates experienced by the Software Engineering and Network Engineering streams from January 2009 to August 2012. Secondly, the core requirements for ultimate visual tools for supporting novice programmers are presented together with a framework to standardize their design and development. Furthermore, the simple semiotic ladder model is proposed as a guide for restructuring the curricula for novice programmers. Finally, recommendations for further studies into the effects of the visual teaching aids for supporting introductory programming in object-oriented programming such as C sharp is recommended.
A User-Intelligent Adaptive Learning Model for Learning Management System Using Data Mining And Artificial Intelligence
(International Journal for Innovative Research in Science and Technology, 2015-03) Sivakumar, Subitha; Venkataraman, Sivakumar; Gombiro, Cross
Entire world is revolving towards digital space as a result of the Internet and other emerging web technologies are helping the society to reach the universe. ICT and e-learning are growing radically fast and have captured a major role in higher educational systems. To develop the coast-to-coast purposes, institutions are changing the teaching style from chalk and talk to Learning Management System (LMS), called e-learning systems. Impact of e-learning introduces various delivery methods for teachers and different platforms to learn for learners. Teaching through LMS is most challenging because the difference in the learning styles and the nature of the course. Materials used for delivering the course are mostly static for all types of learners. Students are learning from the LMS tools, but not most effectively. To overcome this issues, the author(s) are recommending a new user intelligent adaptive learning model to be used in LMS. This allows the model to identify the learners learning style and endorses the appropriate learning materials for the learners. This method of delivering will be effective and efficient for different LMS learners.
Efficient Active Learning Constrains for Improved Semi-Supervised Clustering Performance
(International Journal of Computer Science and Electronics Engineering, 2015) Eswaraprasad, Ramkumar; Vengidusamy, Shanmugam
This paper presents a semi supervised clustering technique with incremental and decremented affinity propagation (ID-AP) that structures labeled exemplars into the AP algorithm and a new method for actively selecting informative constraints to make available of improved clustering performance. The clustering and active learning methods are both scalable to large data sets, and can hold very high dimensional data. In this paper, the active learning challenges are examined to choose the must-link and cannot-link constraints for semi-supervised clustering. The proposed active learning approach increases the neighborhoods based on selecting the informative points and querying their relationship between the neighborhoods. At this time, the classic uncertainty-based principle is designed and novel approach is presented for calculating the uncertainty associated with each data point. Further, a selection criterion is introduced that trades off the amount of uncertainty of each data point with the probable number of queries (the cost) essential to determine this uncertainty. This permits us to select queries that have the maximum information rate. Experimental results demonstrate that the proposed ID-AP technique adequately captures and takes full advantage of the intrinsic relationship between the labeled samples and unlabeled data, and produces better performance than the other considered methods Empirically evaluate the proposed method on the eight benchmark data sets against a number of competing methods. The evaluation results indicate that our method achieves consistent and substantial improvements over its competitors.