https://ir.unisa.ac.za/handle/10500/460 Tue, 05 May 2026 11:04:04 GMT 2026-05-05T11:04:04Z https://ir.unisa.ac.za/handle/10500/32331 Investigating how Artificial Intelligence (AI) can be leveraged to optimise the distribution and allocation of social grants in South Africa Hlatshwayo, Mthokozisi Alfred This study investigates the potential of Artificial Intelligence (AI) technologies to enhance the distribution and allocation of social grants in South Africa, addressing inefficiencies, high costs, and administrative complexities. Social grants are vital for reducing poverty and improving welfare, but the current system faces challenges such as lengthy processing times, inconsistent data, and limited accessibility. A qualitative research design was employed, utilising semi-structured interviews with 20 stakeholders, including policymakers, officials from the South African Social Security Agency (SASSA), and beneficiaries. Data analysis used thematic analysis to assess the feasibility of AI integration. Pilot studies of Electronic Know Your Client (EKYC) and biometric verification across select provinces provided empirical insights. In order to enhance decision-making, lower fraud, expedite beneficiary verification, and optimise resource allocation within South Africa's social grant ecosystem, this study explores the potential applications of artificial intelligence (AI) technologies, including machine learning, predictive analytics, natural language processing, automated document verification, and anomaly detection. The study looks at international best practices, assesses the possible advantages and disadvantages of integrating AI in South Africa, and reviews policy preparedness in light of the nation's larger Fourth Industrial Revolution (4IR) goal. Findings reveal that AI technologies could mitigate inefficiencies by automating data verification, enhancing fraud detection, and streamlining decision-making processes. Pilot implementations of Electronic Know Your Client (EKYC) and biometric systems across Gauteng, Eastern Cape, and North West provinces demonstrated varied outcomes. Gauteng saw a 5% improvement in fraud detection but faced challenges with system integration. The Eastern Cape struggled with downtimes, while the North West achieved a 10% reduction in identity theft but encountered transaction inaccuracies. This research contributes a comprehensive AI integration model tailored to South Africa’s socio-economic context, providing actionable recommendations for policymakers. Key considerations include robust infrastructure, workforce training, regulatory updates, and ethical safeguards to ensure fair and inclusive AI deployment. The study advances the discourse on AI in public administration, offering practical insights for achieving greater efficiency, transparency, and equity in social grant distribution.; Lolu cwaningo luhlola ukuhlanganiswa kobuchwepheshe be-Artificial Intelligence (AI) ohlelweni lokusabalalisa izibonelelo zenhlalakahle eNingizimu Afrika ukuze kuthuthukiswe ukusebenza kahle, kuncishiswe ukukopela, futhi kwandiswe ukufinyeleleka. Uhlelo lwezibonelelo zenhlalakahle lwaseNingizimu Afrika, olulawulwa yi-South African Social Security Agency (SASSA), lubalulekile ekunciphiseni ububha nasekuthuthukiseni inhlalakahle yezenhlalo. Nokho, izinselelo ezinjengokungasebenzi kahle kokuphatha, ukukopela, nokulinganiselwa kwezinsiza kuthinta ukusebenza kahle kwalo hlelo. Lolu cwaningo lusebenzisa indlela yokucwaninga yekhwalithethivu, lusebenzisa izingxoxo ezihlelekile neziphathimandla ezibalulekile, okuhlanganisa izikhulu ze-SASSA, abenzi benqubomgomo, nabathola izibonelelo. Ukuhlaziywa kwezindikimba zedatha, okuhlangene nocwaningo lwamapayona lwe-Electronic Know Your Client (EKYC) kanye nebiometric verification ezifundazweni ezikhethiwe, kuhlaziywe imiphumela yocwaningo. Ucwaningo lwapayona lubonise amandla e-AI ekutholeni ukukopela, kwathi eGauteng kwaba nokwehla ngo-5% kokukopela, kwathi eNyakatho Ntshonalanga kwaba nehlazo lokuncipha kuka-10% ekuntshontshweni kobunikazi. Nokho, izinselelo ezifana nokwehla kwezinhlelo zesistimu nokungahlangani kahle kwezinhlelo eMpumalanga Kapa ziveza isidingo sokuthuthukiswa kwezingqalasizinda. Imiphumela ikhombisa ukuthi i-AI ingathuthukisa kakhulu ukuphathwa kwezibonelelo zenhlalakahle ngokuzenzekelayo ekuqinisekisweni kwabamukeli, ekunciphiseni izikhathi zokucubungula, nasekukholeni ukusebenza kwamasistimu atholayo. Nokho, ukuthuthukiswa kwe-AI okuphumelelayo kudinga ingqalasizinda eqinile, ukuqeqeshwa kwabasebenzi, kanye nokuqapha kwezomthetho ukuze kuqinisekiswe ukusetshenziswa okuzuzisayo nokulungile. Lolu cwaningo lunikeza isibonelo sokuqaliswa kwe-AI esenzelwe isimo seNingizimu Afrika, lunikeza izincomo ezisebenzayo kubenzi benqubomgomo. Ucwaningo lufaka isandla engxoxweni enkulu mayelana nokwethulwa kwe-AI ezinsizeni zomphakathi, lugqamisa isidingo sendlela elinganiselayo eqhathanisa izinzuzo zokusebenza kahle kanye nemithelela yesimilo nezenhlalo. Ngokubhekana nezinselelo zokwethulwa kwe-AI, lolu cwaningo lunikeza isiqondiso sendlela yokusebenzisa i-AI ukwenza uhlelo lwezibonelelo zenhlalakahle eNingizimu Afrika lube olusebenzayo, olungcono, nolungagwegwesi.; Patlisiso ena e hlahloba ho kenyelletsoa ha mahlale a Artificial Intelligence (AI) tsamaisong ea kabo ea meputso ea sechaba Afrika Boroa ho ntlafatsa ts'ebetso, ho fokotsa bomenemene, le ho ntlafatsa phihlello. Tsamaiso ea meputso ea sechaba ea Afrika Boroa, e tsamaisoang ke South African Social Security Agency (SASSA), e bohlokoa ho fokotseng bofuma le ho ntlafatsa boiketlo ba sechaba. Leha ho le joalo, mathata a kang ho se sebetse hantle tsamaisong, bomenemene, le mefokolo ea lisebelisoa a sitisa katleho ea tsamaiso ena. Patlisiso ena e sebelisa mokhoa oa lipatlisiso tsa boleng bo phahameng, e sebelisa lipuisano tse hlophisitsoeng le ba boholong, ho kenyeletsoa liofisiri tsa South African Social Security Agency (SASSA), ba etsang melao, le ba amohelang meputso ea sechaba. Tlhatlhobo ea data e entsoeng ka ho sekaseka lihlooho tsa bohlokoa, hammoho le teko ea pele ea Electronic Know Your Client (EKYC) le biometric verification liprofinseng tse ling, e fana ka leseli ka bokhoni ba AI tsamaisong ena. Teko ena e bontšitse hore AI e ka fokotsa bomenemene, moo Gauteng ho bileng le phokotso ea 5% ea bomenemene, ha Profense ya North West e bone phokotso ea 10% bosholu ba boitsebiso. Leha ho le joalo, mathata a kang ho putlama ha tsamaiso le mathata a kopano ea sistimi Profinseng ea Eastern Cape a bontša tlhoko ea lintlafatso tsa motheo. Liphuputso li bontša hore AI e ka ntlafatsa tsamaiso ea meputso ea sechaba ka ho nolofatsa ts'ebetso ea netefatso ea ba amohelang meputso, ho fokotsa nako ea ts'ebetso, le ho ntlafatsa lits'ebetso tsa tlhahlobo ea bomenemene. Leha ho le joalo, katleho ea AI e hloka hore ho be le motheo o tiileng oa meaho, koetliso ea basebetsi, le taolo e matla ho netefatsa hore AI e sebelisoa ka toka le ka nepo. Patlisiso ena e fana ka mohlala oa ho kenya AI ts'ebetsong ka mokhoa o lumellanang le maemo a Afrika Boroa, e fana ka likhothaletso tse sebetsang ho ba etsang melao le ba ikarabellang ho tsamaiso ea meputso ea sechaba. Patlisiso ena e kenya letsoho lipuisanong tse pharalletseng mabapi le AI le lits'ebeletso tsa sechaba, e hatisa tlhokahalo ea mokhoa o leka-lekaneng o hlahlobang melemo ea ts'ebetso hammoho le litlamorao tsa boitšoaro le tsa sechaba. Ka ho sebetsana le mathata a kenyelletsong ea AI, patlisiso ena e fana ka tataiso ea ho sebelisa AI ka tsela e ntlafatsang le e lumellanang le litlhoko tsa sechaba tsa Afrika Boroa. Abstract in English, Isizulu and Sesotho Fri, 01 Aug 2025 00:00:00 GMT https://ir.unisa.ac.za/handle/10500/32331 2025-08-01T00:00:00Z https://ir.unisa.ac.za/handle/10500/32280 Mining Limpopo Education Management Information System (EMIS) to model intra-provincial learner migration/mobility patterns and their underlying causes Ramphele, Letsukulo Frans Spontaneous learner migration is an ongoing concern in South African schools, posing challenges to educational planning and resource allocation. This phenomenon refers to learners transitioning prematurely to alternative learning spaces without prior planning. It is more prevalent in rural provinces of South Africa, including Limpopo, where urban schools tend to be more affluent and well-resourced than rural schools. The complex interplay of personal, environmental, and socio-economic factors driving learner migration decisions often complicates predictive efforts, necessitating robust computational models for improved understanding and decision-making. This study utilised a longitudinal dataset from the Limpopo Education Management Information System (EMIS) records, spanning ten years (2011–2020), and identified biographical and structural variables that influence learner migration. These variables were used to develop three theory-based learner-migration indices: likelihood of migration, reason for migration, and distance of migration. The study applies Cross-Industry Standard Process for Data Mining (CRISP-DM) to guide the technical data-mining process and Design Science Research (DSR) to provide a broader framework that positions learner-migration computational models as reusable artefacts for educational planners. This methodological framework was grounded in Ravenstein and Everett Lee’s theories of migration and Hein de Haas’s aspiration–capability framework. Building on this methodological foundation, Feature Selection (FS) was performed using four techniques – Boruta, RPART, AdaBoost.M1, and J48 – to determine salient input features for the predictive models. Boruta demonstrated the most consistent feature importance scores, with a variance of 19.85 compared to 21.39 (RPART), 24.60 (AdaBoost.M1), and 24.60 (J48). The learner migration indices were optimised using the Social Ski-Driver (SSD) and Culture Algorithm. Both optimisers achieved commendable and comparable results, with the average F1 score metric for the three indices consistently surpassing 0.8 on a time series learner migration dataset spanning ten years. The CA-derived hyperparameter set was selected for the final model due to its low variance in the F1 score weights of the three indices and strong alignment with the Berger-Tal multidisciplinary framework's convergence principles on the exploration-exploitation trade-off. While previous studies on learner migration have primarily emphasised external factors such as a school’s poverty ranking, curriculum performance, the language of instruction, and legislative frameworks as the sole drivers of migration, this study reveals that migration is also influenced by biographical factors such as learner age, gender, home language, and socio-economic status. These insights are not just academic findings but may have direct implications for educational policy development and resource allocation strategies, offering a balanced understanding of migration dynamics. The developed models and their indices and metrics may support education planners in responding proactively to learner migration challenges. Abstract and text in English Fri, 01 Aug 2025 00:00:00 GMT https://ir.unisa.ac.za/handle/10500/32280 2025-08-01T00:00:00Z https://ir.unisa.ac.za/handle/10500/32236 Proceedings of the 5th/Vth Southern African Computer Symposium, Holiday Inn, Sandton, 29 November - 1 December 1989 (Full book) Kritzinger, P. (Ed.) Conference proceedings Wed, 29 Nov 1989 00:00:00 GMT https://ir.unisa.ac.za/handle/10500/32236 1989-11-29T00:00:00Z https://ir.unisa.ac.za/handle/10500/32234 Integrated network management Roos, J.D.; Von Backström, L. Over the last decade, networks have evolved into sophisticated, multi-vendor, multi-protocol networks, which need to be properly managed and protected. The preferred way of accomplishing this, is through Integrated Network Management (INM). This paper deals with the concepts and advantages of INM, and addresses the trends in INM, particularly the role of Automated and Open Systems Interconnection (OSI) Network Management (NM). Performance evaluation Wed, 29 Nov 1989 00:00:00 GMT https://ir.unisa.ac.za/handle/10500/32234 1989-11-29T00:00:00Z