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Item Shedding light on loadshedding with natural language processing: A social media case study on public perspectives of the South African electricity crisis in 2022(2025-11) Moodley, Avashlin; Naidoo, PrivolinIn times of collective discomfort and dissat isfaction, people often find solace in shared adversity on social media platforms like X (for merly known as Twitter). These platforms offer a unique window into the public’s emotions and viewpoints concerning common challenges. In 2022, South Africa experienced an electricity crisis, during which the country was subjected to rolling blackouts, commonly known as load shedding, by Eskom, the country’s primary electricity provider, to prevent a national elec tricity grid shutdown. This study conducted a data-driven exploration of the public discourse surrounding Eskom and loadshedding on X us ing natural language processing and data sci ence techniques. The dataset utilised for this study comprised tweets containing keywords related to Eskom and loadshedding. The study delved into the topics of discussion by apply ing topic modelling techniques to uncover la tent themes within the discourse. The topics were analysed through a multifaceted lens to unpack and highlight patterns within the sen timents, emotions and biases that underpin conversations related to loadshedding and Es kom. A notable inclusion in the analysis was the incorporation of sarcasm classifications, which enhanced the interpretation of the emo tion and sentiment within the topics discussed. The findings uncovered from the analysis were contrasted with loadshedding-related events in 2022 to understand the public discourse as the electricity crisis escalated. The methodology of this study provides a framework for utilis ing natural language processing techniques to uncover and examine the perspectives of a col lective within discourse related to events of shared interest.Item Peer-to-Peer based indoor localization using smartphones: A wi-fi RSSI and fingerprinting algorithm approach(2025-12) Sediela, MS; Gadebe, Moses L; Kogeda, OPThe evolution of smartphones with advanced wire less communication network capabilities has accelerated the adoption of Indoor Positioning Systems (IPS). These IPS desire to predict the position or location of various wireless devices. The (GPS) remains the widely adopted Location-Based Services (LB Global Positioning System) application for positioning and navigation in an outdoor setting. However, GPS is inefficient indoors due to the line-of-sight requirements to the satellites. The indoor environment is harsh with multipath effects that cause occlusion between the GPS receiver and transmitter. Short range technologies such as Wi-Fi are gaining popularity indoors to alleviate GPS as an alternative technology. However, Wi-Fi infrastructure can be costly. This paper presents a cost-effective localization solution that utilizes Android smartphones as the sole requirement, eliminating the need for additional hardware. The proposed IPS solution uses a fingerprinting algorithm and employs a Peer-to-Peer (P2P) localization approach to reduce the cost implications of Wi-Fi. Only the received signal strength indicator (RSSI) measurements from Wi-Fi Direct and allied devices are used as input during both the offline and online stages of the fingerprinting process. The proposed IPS developed an Android mobile application in Java programming using Android Studio, with SQLite and Firebase real-time Database for storage. We have tested the system in real-time and evaluated its performance; the system produced a high accuracy of 93.33% for monitoring.Item Dynamic deformation behavior of the TM380 mild steel subjected to blast(2025-06) Shoke, Lerato S; Sono, Tleyane J; Mutombo, Kalenda; Snyman, IMThe dynamic deformation behavior of the mild-steel TM380 subjected to explosive loading has been investigated. An imparted impulse and high pressure, from a PE4 explosive charge, interacted with the plate which is attached to a deflection gauge designed to measure the mid-point deflection time history and the imparted impulse. The shape of the bulge at the midsection of the plate was that of a paraboloid. The deflection-time curve is characterized by an escalation, followed by a very short plateau of a few microseconds at mid-point deflection, and finally a drop in deflection timespan. The dynamic strain, strain rate and impulse changes are revealed by deflection-time, velocity-time and hydrostatic pressure curves. Although no significant change in grain size and morphology occurs after shock wave loading, the pearlite lamellar structure transformed into spheroidized cementite as a result of shock induced phase transformation.Item Development of an architecture to detect DDoS attacks in end-to-end network slicing on 5G networks(2025-12) Joyi, P; Gurajena, C; Masonta, Moshe TNetwork slicing is a fundamental enabler of 5G networks, allowing the creation of multiple virtual networks on a shared physical infrastructure to meet diverse service requirements. However, this flexibility introduces critical security and privacy challenges, as shared control-plane components and inter-slice communication can be exploited by attackers to launch Distributed Denial-of-Service (DDoS) attacks, compromise data confidentiality, and disrupt service availability. To address these challenges, this study proposes a secure end-to-end 5G network slicing architecture integrating real-time traffic monitoring, anomaly detection, and slice-aware security policies to protect against DDoS attacks. The architecture was implemented using Open5GS as the core network and UERANSIM as the radio access network emulator, enabling the creation of multiple slices (eMBB, URLLC, and mMTC) with isolated SMF–UPF pairs and a shared AMF. Experimental evaluation involved generating legitimate and malicious traffic to analyze control-plane behavior at the AMF, slice resource utilization, and attack impact on packet flow. The proposed system achieved a DDoS detection accuracy of 98%, with a false positive rate of 2.3%, and demonstrated up to 40% faster response to signaling floods compared to baseline threshold-based detection approaches. These results confirm that the architecture can effectively detect and mitigate DDoS attacks while maintaining stable performance across multiple slices. This work contributes a practical and extensible security framework for 5G network slicing, offering improved resilience and reliability compared to existing solutions.Item Post-quantum cryptography standards for future proof IoT security: A literature review(2025-08) Nelufule, NthatheniThe advent of quantum computing technology has transformed computing technologies into a machine with higher computing power. However, this has also introduced vulnerability challenges, as traditional cryptographic systems are prone to vulnerability to cyber threats, particularly in the context of the IoT. This paper presents a systematic literature survey of the landscape of postquantum cryptographic systems and their impact on the security of connected systems. The paper has explored various key encapsulation mechanisms that are resistant to quantum attacks, existing post-quantum standards, and assessed their applicability in the IoT environments and implementation challenges. The paper highlighted some of the limitations for adopting post-quantum cryptography due to limited existing standards which can be used to enforce the confidentiality, integrity, security, and privacy of data transmitted particularly by IoT connected devices.Item Optimising South Africa’s power grid: An analysis of demand response and BESS integration(2025-09) Madhoo, H; Mdhluli, Sipho D; Makopo, Raisibe S; Chapman, DSouth Africa's energy landscape is currently strained due to an aging power generation fleet and financial constraints, leading to frequent load shedding. This study explores how integrating Demand Response (DR) and Battery Energy Storage Systems (BESS) can enhance grid resilience and economic efficiency. Using the PLEXOS techno-economic tool, the research models South Africa's 2027 power system to determine the optimal use of BESS and assess the impact of varying levels of DR and BESS capacity. The findings reveal that BESS revenue is maximized when 50% of its capacity is allocated to ancillary services and 50% to energy arbitrage, indicating a need for a balanced operational strategy. A flexible revenue-generating range exists between 30% and 65% allocation to ancillary services. Three scenarios were modelled: Low, Moderate, and High penetration of DR and BESS. The results show that increasing DR and BESS integration significantly improves energy reliability. Specifically, it leads to an 80% reduction in unserved energy and its associated costs and a 42% decrease in the use of costly Open-Cycle Gas Turbines (OCGT). These findings underscore the tangible economic benefits and operational flexibility gained from strategic investments in DR and BESS, positioning them as key enablers for a resilient and cost-effective energy future for South Africa.Item Impact of fluorescent yellow/green signs on the perception of road signage: South African eye-tracking study(2025-07) Cheure, Namatirai; Marole, Busisiwe C; Bosilong, Keolebogile KJ; Mongae, Tshegofatso; Venter, Karien; Matsaung, Ntsetsadi J; Mashaba, Hasane P; Mathonsi, Mbhoni R; Malima, Tembo; Kgoa, Lerato; Botha, RikaRoad signs play a vital role in ensuring the safety of both motorists and pedestrians on roadways. They support the overall goal of creating a safer, more forgiving and self explaining road environment. Among the various colours used for road signage, fluorescent yellow/green has emerged as an effective and attention-grabbing colour to highlight hazardous locations. Internationally, fluorescent yellow/green signage has become an indispensable tool in modern road safety efforts. However, it is currently not part of the South African Road Traffic Signs Manual (SARTSM), which is the guiding regulation for the application of road signs in South Africa. A study was conducted to measure the perception of fluorescent yellow/green (FY/G) signs using a drive lab and eye tracker to consider the measurement of the various perception variables for different genders and age groups. The results showed that the average fixation duration for the FY/G signs was higher compared to that of standard signs for all drivers, and females had a higher average fixation duration compared to males. The local and national governments can use these findings to make informed decisions regarding the implementation of standards that permit FY/G signs to be utilised.Item FAIR and Interoperable Coastal Ocean Forecasting: a South African Model for Resilient Digital Ocean Services(2026-02) Veitch, J; Chiloane, L; Fearon, G; Krug, M; Memela, N, P; Mvula; Smith, Marie E; Williams, LThe National Oceans and Coastal Information Management System (OCIMS) is a decision-support platform designed to advance South Africa’s Blue Economy while strengthening ocean governance and environmental protection. OCIMS facilitates the integration, management, and sharing of ocean and coastal data across public and private sectors to support informed, evidence-based decision-making. By promoting cross-government collaboration, communities of practice, and strategic partnerships, the system enhances coordinated marine and coastal management. Developed through ongoing consultation between system developers and stakeholders, OCIMS remains fit-for-purpose, locally relevant, and aligned with global best practices. The platform contributes to sustainable ocean development by balancing economic growth with environmental stewardship.Item Evaluating model compression techniques for efficient edge AI deployment(2025-11) Zungu, ML; Zwane, Skhumbuzo G; Adigun, MODeploying artificial intelligence models on edge devices presents challenges due to limited computational power, memory, and energy. Model compression is essential to reduce the size and computational requirements of AI models, enabling their deployment on smartphones, IoT sensors, and other edge devices. This paper evaluates three major compression techniques which are quantization, pruning, and tensor decomposition, applied to the YOLOv8n model for object detection. The study compares their impact on accuracy, model size, inference speed, and energy efficiency. Findings indicate that quantization achieves the best balance by reducing size and improving inference speed with minimal accuracy loss, pruning yields high energy savings but at the cost of accuracy, and tensor decomposition provides a balanced trade-off. The research underscores the importance of compression in enabling real-time Edge AI applications and highlights the quantization model compression technique as the most suitable technique for efficient edge deployment.Item Environmental impact analysis of traditional concrete road and ash-based concrete road construction(2025-10) Mashiyane, T; Mvelase, Gculisile MThis study compares the environmental performance of two pavement types: conventional concrete using cement, gravel, and crushed stone, and an ash-based mix incorporating legacy ash and ground granulated blast furnace slag (GGBFS). The ash-based option supports circular economy principles by repurposing waste and reducing dependence on virgin raw materials. Using life cycle assessment (LCA) in SimaPro, greenhouse gas emissions were evaluated across the pavements’ life cycles. Results show that conventional concrete generates about 66% more CO₂ emissions, mainly from cement production and aggregate processing. In contrast, the ash-based road achieved a 66% reduction in embodied carbon, highlighting its potential as a sustainable alternative. These findings demonstrate the value of circular economy strategies in road construction and confirm the role of secondary materials in lowering the sector’s carbon footprint.Item Benchmarking machine learning models for cloud resource forecasting: LSTM vs. SVR, Random Forest, XGBoost, and Prophet(2025-12) Mamushiane, Lusani; Hlophe, C; Kobo, Hlabishi I; Lysko, Albert A; Mwangama, JEffective cloud resource forecasting is essential for optimizing virtual machine (VM) provisioning in increasingly dynamic and resource-constrained environments, especially now as next-generation mobile systems (5G/6G) continue to emerge. This study examines a selection of learning-based forecasting approaches, including LSTM, SVM, Random Forest, XGBoost, and Prophet, for VM workload prediction using real-world data. The analysis emphasizes the balance that must be maintained between predictive performance and computational demand, two critical aspects for real-time and scalable deployment. Results show that while LSTM achieves superior accuracy, it incurs significant training overhead. Random Forest provides a more balanced trade-off, maintaining competitive accuracy with lower computational cost. These findings inform model selection in contexts like 5G/6G base station association, network slicing orchestration, multi-access edge computing, intelligent handover decisions, and dynamic service function chaining, where both efficiency and precision are vital for maintaining service-level guarantees under constrained resources.Item A framework for resource overprovisioning with machine learning to maximise revenue from 5G Core network slices(2025-12) Mamushiane, L; Mwangama, J; Lysko, Albert A; Kobo, Hlabishi INetwork slicing has emerged as a key enabler for delivering diverse services in 5G and beyond networks, where each slice must be provisioned with sufficient resources while maintaining operator profitability. Traditional admission control strategies are often conservative, leading to underutilization, while aggressive allocation risks violating service-level agreements (SLAs). This paper proposes a machine learning-based forecasting and admission control framework that leverages overprovisioning to balance utilization, revenue, and reliability. The framework combines CNN-LSTM forecasting for predicting slice resource demand with a policy that admits slice requests based on forecasted aggregate utilization scaled by an overprovisioning factor. To evaluate the approach, we use over 500 VM workload traces from the Materna dataset, grouping VMs into slices of ten. Results demonstrate that CNN-LSTM forecasting achieves consistent accuracy across diverse workloads, and that overprovisioning-based admission control improves net profit while reducing SLA violations compared to conservative baselines. These findings validate the feasibility of ML-driven overprovisioning for efficient and reliable slice admission in 5G networks.Item Benchmarking machine learning models for cloud resource forecasting: LSTM vs. SVR, Random Forest, XGBoost, and Prophet(2025-12) Mamushiane, Lusani; Hlophe, C; Kobo, Hlabishi I; Lysko, Albert A; Mwangama, JEffective cloud resource forecasting is essential for optimizing virtual machine (VM) provisioning in increasingly dynamic and resource-constrained environments, especially now as next-generation mobile systems (5G/6G) continue to emerge. This study examines a selection of learning-based forecasting approaches, including LSTM, SVM, Random Forest, XGBoost, and Prophet, for VM workload prediction using real-world data. The analysis emphasizes the balance that must be maintained between predictive performance and computational demand, two critical aspects for real-time and scalable deployment. Results show that while LSTM achieves superior accuracy, it incurs significant training overhead. Random Forest provides a more balanced trade-off, maintaining competitive accuracy with lower computational cost. These findings inform model selection in contexts like 5G/6G base station association, network slicing orchestration, multi-access edge computing, intelligent handover decisions, and dynamic service function chaining, where both efficiency and precision are vital for maintaining service-level guarantees under constrained resources.Item Cybersecurity in smart grids using machine learning: A systematic literature review(2025-12) Nelufule, Nthatheni; Mudau, Tshimangadzo C; Nkwe, Boitumelo CA; Chishiri, Samson; Mncwango, Lungisani S; Mutenwa, MuyowaThe Smart Grids due to their reliance to the connectedness of Internet of Things technologies, advanced metering infrastructure, and real-time communication are faced with significant cybersecurity and privacy risks. In this work a synthesized insights from recent literatures are presented which critically evaluate Machine Learning, Deep Learning, and privacy-preserving techniques for securing smart grids against cyber-attacks. The critical analysis of this review spans across several themes and topics such as ensemble ML models, federated learning, graph neural networks and blockchain-based protocols, scalability, and privacy-aware communication frameworks. However, there are several limitations such as reliance on simulated datasets, computational complexity, adversarial vulnerabilities, scalability issues, and lack of real-time data validation which limits their practical deployment in a real-world environment. This survey has also proposed some research solutions such as standardized real-world datasets, lightweight algorithms, adversarial training, explainable AI, and interoperable blockchain frameworks to enhance robustness, scalability, and trust. This study addresses key research questions on the effectiveness of learning based techniques, and the role of emerging technologies such as 5G technologies and explainable AI in securing a smart grids environment.Item Building a low-cost cloud Native 5G network slicing experimental testbed: Open-source solutions, lessons learned and future directions(2025-11) Otieno, HO; Mamushiane, Lusani; Maurine, C; Mukute, T; Mwangama, J; Malila, B; Modroiu, ER; Magedanz, TResearch on network slicing has gained some ground since its introduction to 5G networking. Network slicing has evolved from ideas, concepts, and simulations to real-world implementations. With this advancement, the realization and experimentation of network slicing remain a challenge due to the complexity involved. Research and experimentation testbeds have been built to explore and advance network slicing. However, most are quite expensive to set up or complicated for the novice researcher exploring 5G network slicing. In this work, we present a comprehensive and repeatable methodology that can be used to realize network slicing. Our approach focuses on building a low-cost, experimental cloud-native testbed using open-source solutions. The testbed leverages containerization and orchestration platforms to deploy the core network. We demonstrate end-to-end network slicing by extending the Open Air Interface’s reference setup with custom Helm charts. We validate the process of the user equipment requesting a slice on the network, to admission and management of the slice using slice-specific identifiers like Slice Service Type and Slice Differentiator. We highlight the lessons learned when automating this testbed setup to ensure the testbed is repeatable, consistent and proper resource isolation is achieved per slice as envisioned in network slicing. This work serves as a practical guide for researchers to experiment with and explore network slicing using readily available off-the-shelf hardware.Item Radar backscatter extraction from high resolution C-band airborne SAR measurements(2025-10) Tema, Thabo H; De Witt, Josias J; Blaauw, Ciara; Mosito, Katlego E; Nel, Willem AJ; Gaffar, YAIn this paper backscatter extraction and analysis from high-resolution, C-band, airborne Synthetic Aperture Radar (SAR) data acquired by the DSI CSIR SAR system are presented. First key aspects related to the radiometric calibration of SAR systems are described and related to the strategy used to calibrate the DSI CSIR SAR system. Backscatter statistics measured at slant- and cross-range resolutions of 0.5 m and 1 m, respectively are then presented for farmland and informal urban clutter types. The latter represents an important clutter type for urban sprawl monitoring for which little high-resolution C-band clutter measurements have been presented in literature. The measured mean backscatter results are shown to align well with published data, verifying the system's radiometric calibration. Amplitude distribution model fitting indicates that Rayleigh and Log-Normal distributions show good fits for farmland and informal urban clutter, respectively. Extracted amplitude model parameters for both types of clutter are presented.Item Proceedings of the 2025 29th International Conference Information Visualisation(2025-08) Banissi, E; Datia, N; Moura Pires, J; Ursyn, A; Nazemi, K; Secco, CA; Kovalerchuk, B; Andonie, R; Gavrilova, M; Mabakane, Mabule SIn the current information era, all aspects of society—from scientific research and economic planning to cultural practices and daily decision-making—are increasingly driven by data, information, and knowledge systems. The quality of our information infrastructure hinges not only on access to data but on the integrity, structure, and interpretability of that data, including its origin, completeness, classification, and transformation into higher-level knowledge. As such, the fields of Information Visualization, Analytics, and Machine/Deep Learning—underpinned by Artificial Intelligence (AI) advances—have become indispensable to how we interrogate, represent, and utilize data at scale. AI-enabled visualization represents a critical convergence of computational intelligence and human cognitive insight. These systems can discover latent structures, reveal non-obvious correlations, and enable intuitive interaction with complex, high-dimensional data. They now form the cornerstone of automated and semi-automated knowledge discovery pipelines, with increasing influence on strategic decision-making in science, policy, and industry. This transformation is not merely technological but epistemological, reshaping how knowledge is created, validated, and communicated. As data becomes increasingly heterogeneous, dynamic, and uncertain, a key research challenge is modelling uncertainty and risk throughout the data-to-knowledge lifecycle. Visualization—especially with AI—is vital in rendering uncertainty intelligible, making patterns and anomalies more interpretable, and supporting real-time exploration in complex domains. The evolution of this field thus reflects a broader shift toward explainable, interactive, and human-centric AI systems. The 29th International Conference on Information Visualization (IV2025), coupled with the 6th International Conference on AI & Visualization, highlights these developments and presents a curated selection of peer-reviewed papers exploring the frontiers of AI-driven visual analytics, interactive systems, human-computer interaction, and domain-specific applications. This year’s contributions represent over 100 institutions from 20+ countries, collectively showcasing contemporary visualization research's vitality and interdisciplinary nature. A key feature of IV2025 is the introduction of the Researchers Link initiative—an interdisciplinary design workshop focused on aligning AI and visualization research with the United Nations 2030 Sustainable Development Goals (SDGs). Facilitated in collaboration with Darmstadt University of Applied Sciences and coordinated by an international cohort of senior researchers, this initiative invites early-career and established academics to co-develop fundable research proposals during the conference. This model of embedded research generation reflects a growing emphasis on translational impact and cross-sector collaboration. Collectively, the contributions to this volume illustrate the dynamic state of the field, where data visualization, augmented by AI, continues to redefine both technical and societal boundaries. As we advance into the era of intelligent systems, the challenge is not only to innovate but to ensure that these innovations remain interpretable, ethical, and inclusive.Item A comparison of various machine learning algorithms on ISAR image classification of complex targets with varying levels of Gaussian noise(2025-08) Stewart-Burger, CD; Ludick, DJ; Potgieter, MoniqueThis paper investigates the ability to classify complex targets using inverse synthetic aperture radar (ISAR) images with varying noise levels. ISAR is an imaging technique used to generate high resolution two-dimensional images of radar targets. ISAR images contain more information about targets than one-dimensional data, such as high- resolution range (HRR) profiles. ISAR images therefore have useful applications in target classification.Item Climate resilience in urban transport planning: A case study of Cosmo City’s wetland dilemma(2025-07) Kgoa, LeratoUrban transport planning in climate-sensitive regions requires balancing accessibility needs with environmental sustainability, particularly in informal settlements. This paper examines the dilemma surrounding an operational informal taxi rank located in an ecologically sensitive area near wetland in Cosmo City, Johannesburg. Ideally situated for community access, the rank serves as a vital transit hub for workers, scholars, and traders. However, its location raises concerns about climate resilience and ecological impact, as the area is prone to flooding and holds significant biodiversity value. While formalising the rank would enhance safety and infrastructure, relocation to a non-wetland area risks underutilisation, as seen with a nearby formal facility that remains unused due to its inconvenient location. Using available environmental, land use and transport data, this paper explores the trade offs between environmental risks and community benefits in maintaining the taxi rank’s current location. By weighing climate resilience considerations against transport accessibility needs, the study offers insights into adaptive strategies for transport infrastructure in vulnerable urban areas. This case presents a broader challenge for Southern African cities, emphasising the need for context-sensitive solutions that address both climate resilience and societal demands. The paper invites debate on sustainable urban transport planning in high-density, climate-sensitive regions.Item Operational readiness for e-governance in local government: A case study of the City of Mbombela with a focus on e-participation(2025-11) Thulare, Tumiso; Maremi, Keneilwe JThis paper assesses the operational readiness of the City of Mbombela for implementing e Governance, focusing specifically on e-Participation to improve service delivery. Using a qualitative case study design approach, the research involves document analysis, secondary data, and key informant interviews to evaluate four readiness areas: digital infrastructure, institutional capacity, legal frameworks, and stakeholder engagement. Findings reveal that while Mbombela demonstrates policy alignment and partial ICT adoption, challenges persist in bridging the digital divide, strengthening interdepartmental coordination, and promoting inclusive citizen participation. Comparative insights from global examples like Seoul, Nairobi, Reykjavik, Cape Town, and Estonia suggest adaptable strategies such as mobile-first platforms, community access points, participatory budgeting, and open data dashboards. Building on these lessons, the paper proposes a phased roadmap for Mbombela, including broadband expansion, pilot digital engagement programs, digital literacy initiatives, and multi-stakeholder partnerships. The study contributes to e-Governance scholarship by contextualising operational readiness within the realities of South African municipalities, offering both a conceptual framework and practical strategies for institutionalising inclusive e-Participation. The paper concludes that sustainable e-Governance in Mbombela requires bridging technical, social, and institutional gaps while embedding citizen engagement into governance structures.