Daystar University Repository

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Conference/ Workshop/ Seminar/ Proceedings/ Lecture Series
A collection of conference, workshop, seminar, proceedings, and lecture series showcasing diverse topics and cutting-edge research from faculty and staff of Daystar University.
Daystar Archives
An archival collection chronicling the institutional history, academic achievements, and diverse heritage of Daystar University.
Daystar University Publications
A collection of Publications by faculty and staff showcasing research, academic achievements, and institutional insights of Daystar University.
Lectures and Speeches
A collection of Lectures and Speeches from distinguished speakers across various disciplines of Daystar University.
Policies
A collection Policies and Operational Manuals from different departments of Daystar University.

Recent Submissions

MPlist: Context Aware Music Playlist
(IEEE, 2016) Maake, Benard; Ngwira, Seleman; Ojo, Sunday; Zuva, Tranos
The choice of music in everyday life is greatly influenced by contextual circumstances surrounding music listeners. Music lovers create music playlists for various contexts and activities they are engaged in, and this is done manually by updating and loading new playlist each time a user changes activity or context. This does not make music listening enjoyable as much time and effort is spent on searching for songs that befit the current context and activity. This paper proposes a personalized context-aware music recommendation system, called MPlist, that dynamically and automatically creates a music playlist for music lovers based on their context (i.e., current location and activity), listening preference, nearby users listening profiles, other users listening preferences and music from labels and tags mined from music experts and the web. MPlist collects data from multiple sensors in a user’s smart mobile device and uses them to infer the user’s context and activity, thereby generating a playlist based on contextual preference. This approach has the advantage of solving the well-known cold start problem yet giving music lovers a personalized anywhere anytime music listening experience. MPlist classifier is built using both kNN and rule-based learning algorithms using sensor datasets and context-aware listening profile dataset. The contextanalytic engine infers basic user activities and sends all the inferred content to the content provider so that the server can learn the music preferences given a particular context. The system exhibit performance as follows: accuracy is 0.944, Fmeasure is 0.945, and RMSE is 0.0978. This tends to suggest that context-aware music recommendation systems is probably what music lover expect from music stores.
A Serendipitous Research Paper Recommender System
(International Journal of Business and Management Studies, 2019) Maake, Benard; Ojo, Sunday O.; Zuva, Tranos
In recent times, the rate at which research papers are being processed and shared all over the internet has tremendously increased leading to information overload. Tools such as academic search engines and recommender systems have lately been adopted to help the overwhelmed researchers make right decisions regarding using, downloading and managing these millions of available research paper articles. The aim of this research is to model a spontaneous research paper recommender system that recommends serendipitous research papers from two large and normally mismatched information spaces using Bisociative Information Networks (BisoNets). Set and graph theory methods were employed to model the problem, whereas text mining methodologies were used to process textual data which was used in developing nodes and links of the BisoNets graph. Nodes were constructed from weighty keywords while links between these nodes were established through weightings determined from the co-occurrence of corresponding keywords originating from both domains. Final results from our experiments ascertain the presence of latent relationships between the two habitually incompatible domains of magnesium and migraine. Word clouds indicated that there was no obvious relationship between the two domains, but statistical significance investigations on the terms indicated the presence of very strong associations that formed information networks. The strongest links in the established information networks were further exploited to show bisociations between the two habitually incompatible matrices. BisoNets were consequently constructed, exposing terms and concepts from two discordant domains that were bisociated. These terms and concepts were utilised in querying the one domain for recommendations in another domain. Hence, serendipitous recommendations were made since our bisociative knowledge discovery methodologies revealed hidden relationships between research papers from diverse domains. Finally, it was postulated that latent relationships exist between two incompatible domains, and when well exploited, it leads to the discovery of new information and knowledge that is useful to researchers in various fields, especially those engaged in multi-disciplinary research. Further research is being conducted to identify outlier linkers and connectors between domains of diverse subjects
Hindrance of ICT Adoption to Library Services in Higher Institution of Learning in Developing Countries
(Computer Science and Information Technology, 2013) Awuor, Fredrick Mzee; Rabah, Kefah; Maake, Benard
The adoption of ICT has revolutionized service provision in libraries and their general information management systems. This has transformed most services to digital: e-database (e-resources), e-catalogs, e-library and use of archiving technology like DSpace. Today, within the developing world, most libraries are moving towards transforming their existing traditional library services to digital systems - allowing them to tap and benefit from the vast advantages of ICT, for example, operation costs reduction, increased efficiency, an on-the-fly availability of information. Even with such numerous benefits, most Higher Institutions of Learning (HILs) in developing countries still lag behind on adoption of ICT in their library services. This paper seeks to investigate the challenges that hinder the adoption of ICT in libraries with special attention to HILs in developing. Further, solutions and recommendations to address these challenges are presented with case study analysis.
A Survey on Data Mining Techniques in Research Paper Recommender Systems
(IGI Global, 2019) Maake, Benard
In this chapter, we give an overview of the main data mining techniques that are utilised in the context of research paper recommender systems. These techniques refer to mathematical models and tools that are utilised in discovering patterns in data. Data mining is a term used to describe a collection of techniques that infer recommendation rules and build models from research paper datasets. We briefly describe how research paper recommender systems’ data is processed, analysed and then finally interpreted using these techniques. We review different distance measures, sampling techniques and dimensionality reduction methods employed in computing research paper recommendations. We also review the various clustering, classification and association rule mining methods employed to mine for hidden information. Finally, we highlight the major data mining issues that are affecting research paper recommender systems.
Sociodemographic and Associated Risk Factors for PTSD and Depression among Select Urban Refugees in Nairobi
(International Journal of Humanities Social Sciences and Education, 2025-11) Murunga, Beatrice A.
This study investigated the sociodemographic and trauma-related risk factors associated with posttraumatic stress disorder (PTSD) and depression among urban refugees seeking services at an international organization in Nairobi. Using PCL-5 and BDI-II, and a sociodemograhic questionnaire, data were collected from refugee participants to assess the prevalence and predictors of mental health conditions. The findings revealed that gender and education level were significant predictors (gender: χ²(2) = 10.23, p = .006; education: χ²(4) = 9.90, p = .042), with female refugees and those with lower educational attainment showing higher rates of PTSD and depression. While variables such as country of origin, marital status, and age did not significantly predict mental health outcomes, having family in Kenya and a higher number of children were associated with elevated symptoms (Wilks’ Λ = .953, F = 4.666, p = .011, effect size was partial η² = .047). Specific traumatic experiences—particularly torture (torture: F(1, 264) = 7.189, p = .008, η² = .027), sexual assault (sexual assault: F(1, 264) = 10.368, p = .001, η² = .038), and abduction (abduction: F(1, 264) = 6.511, p = .011, η² = .024)—were strong predictors of PTSD and depression, with the nature of the perpetrator influencing symptom severity. A cumulative trauma effect was observed, although resilience appeared to increase among those with extensive trauma exposure. Post-migration stressors, including unemployment, housing insecurity, and lack of access to services, were positively correlated with poor mental health. The study recommends targeted psychosocial interventions, education and income-generating programs, and stronger enforcement of refugee rights to mitigate these risks and improve mental health outcomes among urban refugee populations.