Botho University Library

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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.

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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.
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.
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.
A Model to Provide a Reliable Infrastructure for Cloud Computing
(World Congress on Engineering, 2012-07-04) Srinivasan R, Srivaramangai.P
The cloud computing offers dynamically scalable resources provided as a service over the Internet. It promises the drop in capital expenditure. But practically speaking if this is to become reality there are still some challenges which is to be still addressed. Amongst, the main issues are related to security and trust, since the user's data has to be released to the Cloud and thus leaves the secured area of the data owner. The users must trust the providers. There must be a strong trust relationship exist between the service providers and the users. This paper provides a model based on reputation which allows only reliable providers to provide the computing power and the resources which in turn can provide a reliable infrastructure for cloud computing.
Memetic Optimization with Cryptographic Encryption for Secure Medical Data Transmission in IoT-Based Distributed Systems
(2021) Srinath, Doss; Paranthaman, Jothi; Gopalakrishnan, Suseendran; Duraisamy, Akila; Pal, Souvik
In the healthcare system, the Internet of Things (IoT) based distributed systems play a vital role in transferring the medical-related documents and infor mation among the organizations to reduce the replication in medical tests. This datum is sensitive, and hence security is a must in transforming the sensational contents. In this paper, an Evolutionary Algorithm, namely the Memetic Algo rithm is used for encrypting the text messages. The encrypted information is then inserted into the medical images using Discrete Wavelet Transform 1 level and 2 levels. The reverse method of the Memetic Algorithm is implemented when extracting a hidden message from the encoded letter. To show its precision, equiva lent to five RGB images and five Grayscale images are used to test the proposed algorithm. The results of the proposed algorithm were analyzed using statistical methods, and the proposed algorithm showed the importance of data transfer in healthcare systems in a stable environment. In the future, to embed the privacy-pre serving of medical data, it can be extended with blockchain technology