Research Articles

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Comparing Dynamic Capabilities in Digital Banking: Economic Integration and Women in the East African Community
(Open Access, 2026) Bhoke Rotente
This study sought to identify the “comparing dynamic capabilities in digital banking institutions in developed and developing countries” through a Quantitative research approach. The population of the study comprised 101 participants who were the IT managers, innovation officers, compliance staff, and executives. They were selected through purposive sampling and Stratified Random Sampling techniques. Questionnaires and interviews were used to collect data. Data were analysed with the aid of the Statistical Package for Social Sciences (SPSS) version 25. The findings of the current study indicate a significant positive correlation with sustainable competitive advantage. Ambidexterity had the strongest correlation (r = .49), followed by UTAUT2 (r = .44), government regulation (r = .41), and digital transformation (r = .38). The strong inter correlations among UTAUT2, ambidexterity, and digital transformation highlight potential overlap in their influence. This means that digital banking in developed countries is more advanced than in developing countries. It is concluded that digital banking in developing countries, Tanzania, particularly, is not satisfied related to developed countries. It is recommended that there is a need to invest in digital technology, particularly for developing countries, to bridge the gap that exists between developed and developing countries.
Late Fusion-Based Multimodal Machine Learning for Driver Fatigue Detection in Emergency Response Driving Scenarios
(Open Access, 2026) Zulfa Sammadile, Deogratias Shidende , Bonny Mgawe , Sabine Moebs
Globally, road accidents are among the leading causes of death and injury, with driver fatigue being one of the main contributors. Driver fatigue is especially critical in emergency response driving, where factors such as long and overnight shifts are common. This study presents a multimodal driver fatigue detection Machine Learning (ML) model that utilizes a late fusion approach. A range of behavioral and physiological data was used to train base learners with various ML models, and their prediction probabilities were used to train four decision tree-based metaclassifiers (RF, XGB, LGB, and HGB) within a stacking late fusion framework. For a broader exploration, ensemble models of these metaclassifiers were trained using simple average, weighted average, majority voting, dynamic weighted average, and best confidence selection methods. Among the metaclassifiers, XGB and dynamic weighted average achieved the highest accuracies of 86.83% and 87.17% respectively, with XGB also yielding the lowest FNR, highlighting its inherent capability in handling heterogeneous and diverse features. To simulate real-world emergency driving conditions, where constant sensor inputs may be intermittent, missing modalities were simulated in Google Colab during training of the metaclassifiers. Later, the robustness of the metaclassifiers was analyzed across various missing modality scenarios, where up to three sensor inputs could be unavailable. In this analysis, XGB achieved the highest stability with the lowest SD of 0.039 across all missing modality scenarios, demonstrating its ability to maintain reliable driver fatigue detection performance even when some sensorinputs are missing.
Gated recurrent unit model for forecasting greenhouse gas concentrations with uncertainty quantification
(Frontiers in Artificial Intelligence, 2026) Erica Hargety Kimei, Devotha Godfrey Nyambo, Neema Mduma, Shubi Felix Kaijage
Reliable and accurate farm-level forecasting of greenhouse gas concentrations from dairy cattle is important to the formulation of climate change miti-gation strategies and policy planning in livestock systems. This study proposed an uncertainty-aware deep learning model that integrated data from multiple sources, including remote sensing and ground-based sensors, to forecast hourly concentrations of nitrous oxide, methane, and carbon dioxide in a controlled zero-grazing dairy system. The forecasting was implemented by formulating a causal multivariate time series using a 24-h lookback window with direct one-step-ahead prediction. A two-stage model evaluation protocol, model hyper-parameter tuning via rolling cross-validation, and holdout Test Evaluation were implemented during model development. The model was evaluated using the coefficient of determination, root-mean-square error, and mean squared error.To evaluate model reliability, the study employed a dual-output gated recurrent unit to estimate the conditional mean and heteroscedastic variance, and Monte Carlo dropout and Gaussian Negative Log-Likelihood to quantify epistemic and aleatoric uncertainty. Results indicate stable generalisation across temporal folds and strong probabilistic calibration, with empirical 95% coverage ranging from 93.6 to 94.8% on the holdout test set. Feature selection indicates that rainfall, normalised difference vegetation index, humidity, temperature, trend, and season influence the prediction of concentration. The study shows that incorporating exogenous variables improves model performance. The proposed framework demonstrates proof of concept for controlled zero-grazing systems, with poten-tial for broader application following multi-site validation.
Dynamics, stability, and bifurcation analysis of a fractional-order HIV–TB co-infection model with environmental transmission
(Elsevier, 2026) Dickson D. Luambano, Mussa A. Stephano, Maranya M. Mayengo
Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) co-infection remain a significant public health challenge, especially in high-burden regions where environmental contamination sustains Mycobacterium tuberculosis. To incorporate memory effects in disease progression and environmental transmission, we develop a fractional-order compartmental model using the Caputo derivative. The model includes an environmental reservoir for TB bacteria and differentiates between HIV, TB, and co-infected stages. We establish positivity and boundedness, derive the basic reproduction number , and conduct stability analysis. Global sensitivity analysis with Latin Hypercube Sampling and Partial Rank Correlation Coefficients highlights key parameters influencing disease spread. The model exhibits backward bifurcation, suggesting that reducing below unity does not necessarily eliminate the disease. Memory effects (lower fractional order) slow disease progression and decrease infection burdens, while environmental transmission considerably sustains disease persistence. HIV transmission rate (), TB transmission rate (), and environmental transmission rate () are the primary drivers of , whereas antiretroviral therapy (), TB treatment (), and environmental clearance rate () are the most effective suppressors. These findings emphasize the importance of integrated intervention strategies that combine direct transmission control, expanded treatment coverage, and environmental sanitation to effectively manage HIV–TB co-infection.
Fowlpox forecasting multimodal dataset from northern Tanzania
(Zenodo, 2026-04-27) Kabeya, Almasi
This dataset contains weather data, epidemiology data, vaccination data, and seasonality data for fowlpox incidence in northern Tanzania.
Optimizing LoRaWAN throughput in maritime environments through adaptive coding and modulation in Rayleigh fading channels
(Taylor & Fransic Online, 2026-06-01) Lyimo, Martine; Mgawe, Bonny; Leo, Judith; Dida, Mussa; Michael, Kisangiri
This article proposes an Adaptive Coding and Modulation (ACM) scheme to improve the throughput of Long-Range Wide Area Network (LoRaWAN) in a maritime environment where wireless communication faces severe challenges from multipath propagation, interference, and Rayleigh fading. Unlike conventional LoRaWAN systems that use fixed physical layer configurations, dynamic adjustment of spreading factor (SF), coding rate (CR), and bandwidth (BW), according to channel conditions based on real-time Signal-to-Noise Ratio (SNR), is another solution to respond to communication challenges in the maritime environment. Simulations using a Rayleigh fading model with six different configuration levels demonstrate that the proposed ACM can achieve a gain of up to 103 times the throughput improvement compared to fixed SF7 under low SNR conditions while maintaining link reliability. This work establishes the foundation for implementing efficient and reliable LoRaWAN networks in maritime Internet of Things (IoT) applications.
Can dual active ingredient Interceptor® G2 insecticide-treated net (ITN) replace indoor residual spraying (IRS) efficiently? A case study in Sakassou, Côte d’Ivoire
(Springer Nature, 2026-05-28) Chabi, Joseph; Shirima, Gloria; Masanja, Brian; Sylvester; N’Guessan; N’Guessan Gbalegba, Constant; Kouassi, Bernard; N’Guessan Broudje, Brice; Ako Edi, Constant; Yokoly, Firmain; Adimi, William; Kouame, Ruth-Marie; Anian, Valentin; Kyerematen, Rosina; Egyir-Yawson, Alexander; Kiware, Samson; Dadzie, Samuel
Background Following three successful rounds of indoor residual spraying (IRS) implementation in the district of Sakassou (Côte d’Ivoire), IRS was withdrawn and replaced by insecticide-treated nets (ITNs). This study evaluated the entomological and epidemiological impacts of Interceptor (IG2) ITNs distributed in Sakassou to determine whether the protection offered by IG2 ITNs was adequate to suppress malaria transmission post-IRS withdrawal. Methods This study is a quasi-experimental evaluation using historical data and additional data collections on entomological indicators and malaria incidence to assess malaria transmission trends in Sakassou. The vector control optimization model was adapted to evaluate the effectiveness of the IRS and IG2 ITN deployment on malaria transmission dynamics. Additionally, we used an interrupted time series model to analyze routinely reported malaria cases in the District Health Information Management System (DHIS2) to determine the epidemiological impact of both interventions. Counterfactual trends were generated for the post-IRS withdrawal period during which IG2 ITNs were distributed. Results The results showed a 55.4% (95% CI 48.3–62.4%) reduction in the human biting rate (HBR) when IRS was deployed and 48.8% (95% CI 42.8–54.6%) when IG2 ITNs were distributed, compared with standard pyrethroid-only nets. No statistical difference was recorded between the HBR of IG2 ITNs and IRS (P = 0.164) during the implementation of IG2 ITNs and the counterfactual of IRS. Similarly, IRS resulted in a 64.7% (95% CI 56.6–72.8%) decline in EIR while IG2 ITNs resulted in a comparable reduction of 61.9% (95% CI 54.2–69.6% ;P = 0.616) over the same period. Furthermore, a 26% reduction in malaria cases was recorded immediately after spraying (IRR = 1.02; 95% CI 1.00–1.04; P = 0.005) with cumulative impact over time and spray rounds and was similar to IG2 performance (IRR = 1.03; CI 1.00–1.07; P = 0.040). Conclusions The study findings suggest that IG2 ITNs provided entomological efficacy comparable to clothianidin-based IRS but could not adequately suppress malaria cases after IRS withdrawal, possibly due to plastic-vector feeding behavior and late timing of IG2 deployment. Overall, the study shows how the dynamics of malaria transmission and operational decisions could impact the effectiveness of both IRS and ITNs as vector control tools. These findings provide key information for malaria programs and policymakers to consider when deploying vector control interventions as countries work toward malaria elimination.
Chance constrained nonlinear model predictive control for multiple systems with shared resources
(Elsevier B.V., 2026-06-04) Mugenga, Ineza; Geletu, Abebe; Mirau, Silas; Li, Pu
This paper proposes chance constrained nonlinear model predictive control (CCNMPC) for multiple interconnected systems sharing limited resources under uncertainty. Each subsystem with uncertainties has its local objectives and constraints and is coupled to the other subsystems through the shared resource. The shared resource limitation is satisfied by a global chance constraint which is decomposed into individual local chance constraints via the Bonferroni inequality, enabling a distributed solution approach. The chance constrained problem is then transformed into a sequence of smooth deterministic problems through the inner–outer approximation method, in which the smoothing parameter is shown to act as an implicit constraint-tightening mechanism that recovers the classical Gaussian tube-MPC back-off in the zero-smoothing limit. Within this framework, we prove probabilistic one-step recursive feasibility under unbounded disturbances. The resulting distributed nonconvex problem is solved using an adapted Augmented Lagrangian based Alternating Direction Inexact Newton (ALADIN) algorithm. The framework is validated on a shared battery energy storage system serving multiple microgrids with non-Gaussian demand forecast errors over a 24-h horizon: the empirical violation rates remain below the prescribed 5% threshold and the terminal invariance condition is satisfied with substantial margin throughout.
A labelled dataset of healthy and diseased common bean (Phaseolus vulgaris) from Tanzania
(Elsevier Inc., 2026-05-31) Mduma, Neema; Laizer, Hudson
Common bean (Phaseolus vulgaris) is an important food and cash crop in Tanzania, where it contributes to household nutrition and income among smallholder farmers. Its production is, however, constrained by diseases such as bean rust and bean anthracnose, which can cause substantial yield losses and reduce crop quality. This article presents a labelled image dataset of common bean leaves collected under field conditions in Northern Tanzania to support research and application development in computer vision, machine learning and digital crop health. The dataset comprises 155,842 labelled multi-season images belonging to three classes: healthy leaves, bean rust and bean anthracnose. Images were acquired from farms in Arusha, Kilimanjaro and Manyara regions using smartphone cameras and were subsequently reviewed and validated with support from agricultural extension officers and plant pathologists to improve annotation reliability. The dataset is organized into class-specific compressed files and is publicly available through the Zenodo repository. By providing a large field-based image resource captured under variable real-world conditions, this dataset can support the development, training and evaluation of image-based models for common bean disease identification.
Macroeconomic transmission effects on stock market performance in Tanzania: evidence from a structural VAR analysis
(Frontiers, 2026-05-20) Peter, Michael; Mirau, Silas; Sinkwembe, Emmanuel; Kasumo, Christian; Guambe, Calisto
This study examines the dynamic interactions between key macroeconomic indicators and stock market performance in Tanzania, a frontier market in sub-Saharan Africa. Using a Structural Vector Autoregression (SVAR) framework, we analyze monthly data for the Tanzania Share Index (TSI), gold returns, inflation rate, and electricity consumption employed as a high-frequency proxy for real economic activity to address the temporal limitations of quarterly GDP data common in developing economies. Johansen co-integration analysis identifies four stable long-run relationships, while a one-lag VAR specification suggests rapid adjustment dynamics. Impulse response functions reveal that shocks dissipate within three to five months. Notably, the TSI responds negatively to economic activity shocks, indicating a structural mismatch between stock market valuations and real-sector growth. Forecast error variance decomposition further underscores this decoupling: own shocks explain approximately 75%–80% of TSI fluctuations, suggesting that within this four-variable system, macroeconomic factors account for a modest share of equity variation. Gold returns, however, exhibit persistent positive responses to inflation shocks, confirming their role as an inflation hedge. Overall, the findings suggest Tanzania's financial market remains relatively disconnected from real-sector fundamentals, reflecting its early stage of financial deepening. Policy recommendations emphasize strengthening financial infrastructure, enhancing macro-financial linkages, and developing robust commodity-price monitoring systems to improve signal transmission to capital markets.
Integrating ML with Electronic Fiscal Devices for Real-Time Underpricing Detection in Tanzania
(Asosiasi Doktor Sistem Informasi Indonesia, 2026-05-26) Chengula, Benitho; Leo, Judith; Chimilila, Cyril
This study aims to develop a machine learning-based tool integrated into Electronic Fiscal Devices (EFDs) to detect underpricing fraud in real time in Tanzania. The motivation for this research arises from the limitations of existing EFD systems, which rely on manual and post-audit mechanisms that are ineffective in identifying fraudulent pricing during transactions. A mixed-methods approach was employed, combining qualitative insights from tax officers with quantitative data collected from traders and buyers. A dataset of 5,000 mobile phone sales transactions collected from Arusha, Dar es Salaam, and Iringa in Tanzania was pre-processed and used to train and evaluate multiple machine learning models, including Logistic Regression, Support Vector Machine, XGBoost, and Random Forest, using 5-fold cross-validation. The experimental results show that the Random Forest model outperformed other models, achieving an accuracy of 99.6% along with strong precision, recall, and F1-score values. To demonstrate practical applicability, the trained model was further integrated into a prototype EFD environment, where it enabled near real-time fraud detection and generated automated alerts for traders and tax authorities, with geolocation features supporting targeted enforcement. However, the dataset is limited to mobile phone transactions within selected regions of Tanzania, which may affect the generalisability of the findings. The novelty of this study lies in integrating machine learning–based price validation into EFD systems to support proactive detection of underpricing fraud at the point of transaction, thereby enhancing tax compliance and revenue protection.
Towards a Secure Central Bank Digital Currency System:A Case Study of Tanzania
(John Wiley and Sons Inc, 2026-04-05) Minja, Godbless; Sam, Anael; Nyambo, Devotha
This study contributes to the implementation of a secure Central Bank Digital Currency (CBDC) system in Tanzania. It identified the challenges faced by Tanzania's digital financial services (DFS), proposed relevant mitigations, and designed a secure CBDC system suitable for Tanzania. It also seeks to support ongoing CBDC research by the Bank of Tanzania (BoT) and provide insights to other stakeholders exploring CBDCs. It employed a mixed research approach to collect and analyse data from a representative Tanzanian population, revealing that security concerns (53.3%) and high transaction charges (21.9%) were the main challenges in adopting DFS. Additionally, unreliable electricity and internet connectivity and low digital literacy were reported. Despite showing potential to address the identified challenges, a CBDC must be carefully designed to attract users and integrate smoothly with existing DFS. Furthermore, efforts should be made to address infrastructural and digital literacy challenges to maximise the practical realisation of CBDC benefits in Tanzania. This study is the first of its kind in Tanzania to combine stakeholder-informed insights with a context-specific CBDC design focused on security. Furthermore, it contributes to BoT's ongoing CBDC research and to other countries’ CBDC initiatives, particularly in the developing world. Impact Statement: This study seeks to provide evidence-based insights to guide the design and implementation of a secure and context-aware CBDC for Tanzania. Its value lies in integrating stakeholder-informed analysis with a focus on the challenges within the DFS ecosystem of Tanzania. It supports the BoT's ongoing CBDC research efforts and contributes to the broader discourse on secure and context-aware DFS, particularly in the developing economies.
The role of environmental contamination in the dynamics of HIV–TB co-infection with control strategies: Caputo fractional-order approach
(Elsevier B.V., 2026-03-06) Luambano, Dickson; Stephano, Mussa; Mayengo, Maranya
This study investigates the effectiveness of integrated environmental and medical interventions in controlling HIV–TB co-infection. A Caputo fractional-order modeling framework is developed to capture memory and nonlocal effects inherent in chronic infections, while explicitly incorporating environmental reservoirs of Mycobacterium tuberculosis as an additional transmission pathway. The mathematical well-posedness of the model is established through existence, uniqueness, positivity, boundedness, and Ulam–Hyers stability analysis. Numerical solutions are obtained using an Adams–Bashforth–Moulton predictor–corrector scheme, and unknown parameters are calibrated using World Health Organization HIV–TB co-infection data from Tanzania spanning 2000–2023. The estimated fractional order is , indicating strong memory effects that reflect the cumulative impact of past infection history, delayed immune responses, prolonged treatment effects, and persistent environmental contamination characteristic of HIV–TB co-infection dynamics. Model validation using an independent dataset from Kenya (2000–2023) demonstrates the robustness and geographic transferability of the proposed framework. Sensitivity analysis identifies key epidemiological parameters governing transmission and control, emphasizing the joint importance of clinical treatment and environmental sanitation. A comparative analysis between the fractional-order model ( ) and the classical integer-order model ( ) reveals that the fractional formulation provides an improved fit to observed data and captures long-term disease dynamics more accurately, particularly in reproducing persistent infection trends. Overall, the results show that coordinated medical interventions combined with environmental control substantially reduce HIV–TB co-infection prevalence, underscoring the necessity of integrated, multi sectoral strategies for sustainable disease management in high-burden settings.
Modelling the impact of adopting new-generation insecticide-treated nets on malaria transmission and insecticide resistance
(medRxiv, 2026-03-05) Gervas, Hamenyimana; Mayengo, Maranya; Chacky, Frank; Mlacha, Yeromin; Ngowo, Halfan; Okumu, Fredros; Selvaraj, Prashanth
Background The widespread insecticide resistance increasingly threatens malaria elimination, prompting a reassessment of vector control strategies. As Tanzania transitions from standard pyrethroid-only insecticide-treated nets (ITNs) to new-generation nets, evaluating the impact of this shift on malaria transmission and resistance is critical. Methods Using the agent-based malaria model, EMOD, we assessed the impact of three ITN types, standard pyrethroid-only nets, pyrethroid-PBO nets (Olyset® Plus/PermaNet® 3.0), and the dual active, Interceptor® G2 nets (IG2) on malaria transmission and the evolution of insecticide resistance. We also evaluated different sequences for introducing the new-generation nets, and the impact of combining ITNs with indoor residual spraying (IRS). The model was calibrated using incidence and prevalence data from two regions in northwestern Tanzania, incorporating seasonality, insecticide resistance, and behaviors of dominant vectors Anopheles funestus (highly anthropophilic, endophilic) and Anopheles arabiensis (more opportunistic readily biting non-human hosts outdoors). Results Changing from standard pyrethroid-only ITNs to pyrethroid-PBO and thereafter to IG2 ITNs reduced homozygous-resistant An. funestus and An. arabiensis by 62.2% and 92.8%, respectively, and reduced incidence and prevalence by 94% and 75.2% respectively, under conditions where the probability of mosquito pyrethroid resistance was 0.75. Deploying IRS before the peak malaria transmission season in mid-May, in the second year following pyrethroid-PBO ITNs distribution, and repeating this every three years, reduced malaria incidence and prevalence by 76.4% and 52%, respectively. Conclusion In contrast to continuous use of standard pyrethroid-only ITNs, which sustains resistance selection, transitioning to new-generation ITNs, with or without periodic IRS, can disrupt the evolutionary trajectory of pyrethroid resistance, reduce malaria burden, and strengthen progress toward elimination.
Mathematical model for the control of HIV/TB coinfection incorporating dual stigma
(Bulletin of Biomathematics, 2026-04-29) Lengiteng'i, Lalashe; Mbalawata, Isambi; Luboobi, Livingstone; Mirau, Silas; Nyerere, Nkuba
The co-epidemics of HIV and Tuberculosis, intensified by dual stigma, pose major challenges to effective disease control. Understanding the interaction between coinfection dynamics and stigma is essential for designing targeted interventions. This study develops a deterministic mathematical model incorporating dual stigma and control strategies to analyze HIV/TB transmission dynamics. Separate analyses of HIV-only, TB-only, and coinfection sub-models are conducted. Numerical simulations assess the impact of treatment and stigma-reduction interventions, while sensitivity analysis and contour plots evaluate the influence of key parameters on the basic reproduction numbers. Results indicate that increased treatment rates and awareness campaigns significantly reduce infection prevalence and stigma, thereby lowering transmission. Stigma is shown to exacerbate disease spread by limiting treatment uptake and increasing susceptibility. Sensitivity analysis identifies treatment efficacy and awareness-related parameters as the most influential factors in reducing reproduction numbers. Overall, integrating medical treatment with stigma-reduction strategies is highly effective in controlling HIV/TB coinfection. These findings highlight the importance of combined biomedical and social interventions and provide practical insights for policymakers.
Development of information system for enhancing communication between members of parliament and citizens: A study of dodoma, Tanzania
(Elsevier Inc., 2026-04-12) Mtoi, Mary; Ally, Mussa; Sam, Anael; Mbelwa, Hope
In Tanzania, members of parliament rely on both traditional channels and social media to gather citizens’ concerns. However, limited access to internet-enabled devices, the unavailability of members of parliament, poor attendance at public meetings, and high internet costs create a communication gap in civic participation. Existing civic technology solutions often depend on internet-based platforms, excluding users who rely on feature phones and highlighting a technical gap in prior research and system design. This study develops a context-aware, inclusive digital communication system that bridges infrastructural and technological barriers to foster civic engagement in low-resource environments. Through a literature review, gaps in existing civic technology implementations were identified, and the system was developed using the Extreme Programming Agile Methodology to produce a hybrid platform combining a web application and a two-way messaging system. The resulting Public Participation Information System allows citizens to raise concerns offline via feature phones or online through a bilingual web application in Swahili and English. The system prioritizes accessibility for low-resource users and incorporates features such as availability scheduling, task delegation, and concern tracking. Feedback indicates that it effectively reaches under-connected populations and promotes more inclusive civic engagement. Overall, the system addresses the digital divide in civic communication and offers a scalable framework that integrates low-technology access with modern information systems to support democratic participation in resource-constrained settings.
Chance-Constrained Distributed Optimization with Shared States
(MDPI, 2026-04-23) Mugenga, Ineza Remy; Geletu, Abebe; Mirau, Silas; Li, Pu
Interconnected systems have attracted significant attention in numerous engineering applications such as energy, water, and oil, as well as gas distribution networks. However, due to their high complexity, it is enormously difficult to ensure a reliable and effective cooperation of such interconnected systems under limited communication and interaction capacities. In addition, uncertainty consideration poses further challenges in developing an efficient distributed optimization approach to such interconnected systems. Furthermore, satisfying the constraints of shared states between subsystems under uncertainty leads to conflict issues and has not been properly studied yet. This study proposes a chance-constrained distributed optimization approach to the optimal operation of interconnected systems by considering conflicting reliability levels of satisfying shared state constraints. A compromised reliability level for such constraints is determined by an averaged weighting. We establish that the optimal cost is Lipschitz-continuous with respect to the compromised reliability level, providing a theoretical basis for quantifying the marginal cost of reliability, and we show that the compromise is robust to perturbations in the subsystem weights. We develop a numerical solution framework based on the inner–outer approximation method. For the efficient computation of the high-dimensional integrals, we use Hoeffding’s inequality to determine a suitable sample size. The optimal operation of three interconnected energy distribution networks is used as a case study to demonstrate the proposed approach.
Adaptive Decision-Level Intrusion Detection for Known and Zero-Day Attacks
(MDPI, 2026-04-09) Mchina, Joseph; Mduma, Neema; Sinde, Ramadhani
Network Intrusion Detection Systems (NIDS) face increasing challenges from sophisticated cyber threats, particularly zero-day attacks that evade signature-based methods. While supervised learning is effective for known attack classification, it struggles with novel threats, whereas anomaly-based approaches suffer from high false positive rates and unstable thresholds. To address these limitations, this paper proposes a decision-level adaptive intrusion-detection framework combining hierarchical CNN-based closed-set classification with autoencoder-based zero-day detection in a cascade architecture. The framework enables deployment-time adaptation by dynamically adjusting class-specific confidence thresholds and fusion parameters without model retraining. Experiments on the CSE-CIC-IDS2018 dataset demonstrate strong closed-set performance, achieving 98.98% accuracy and a macro-F1-score of 0.9342, with improved recall for minority attack classes under adaptive thresholding. Under a zero-day evaluation protocol in which Web_Attacks and Infiltration are excluded from training and validation, the proposed approach achieves an F1-score of 0.9319 while maintaining a low false positive rate of 0.0019. The framework is further evaluated on the Simulated University Network Environment (SUNE) dataset representing campus network traffic, achieving 96.18% closed-set accuracy and 97.54% accuracy in the integrated cascade setting. These results demonstrate that the proposed framework effectively balances minority attack detection, zero-day identification, and false-alarm control in dynamic and resource-constrained network environments.
A Mathematical Model for Smooth Muscle Cell Phenotype Switching In Atherosclerotic Plaque
(Springer Nature, 2026) Ndenda, Joseph; Watson, Michael; Misra, Ashish; Myerscough, Mary
Smooth muscle cells (SMCs) play a fundamental role in the development of atherosclerotic plaques. They ingest lipids in a similar way to monocyte-derived macrophages (MDMs) in the plaque. This can stimulate SMCs to undergo a phenotypic switch to a macrophage-like phenotype. We formulate an ordinary differential equation (ODE) model for the populations of SMCs, MDMs and smooth muscle cell-derived macrophages (SDMs) and the internalised lipid load in each population. We use this model to explore the effect on plaque fate of SMC phenotype switching. We find that when SMCs switch to a macrophage-like phenotype, there is an increase in the lipid quantity in the model plaque that is internalised inside cells. Additionally, removal of SMCs from the model plaque via phenotype switching reduces the number of SMCs in the fibrous cap, increases the lipid in the necrotic core, and increases plaque inflammation. These features are hallmarks of vulnerable plaques, whose rupture can cause heart attacks or strokes. When SDMs are highly proliferative or resistant to cell death, the model plaque becomes increasingly pathological. The model suggests that the switch of SMCs to a macrophage-like phenotype may drive the development of unstable and pathological plaques.
A Hybrid Machine Learning and Signature-Based Approach for Detecting Network Pivoting in BYOD Environments
(Asosiasi Doktor Sistem Informasi Indonesia, 2026-02-25) Amour, Nassor; Leo, Judith; Dida, Mussa
This study addresses the challenge of detecting network pivoting, a lateral movement technique that is difficult to identify in insider and BYOD environments because malicious transitions can resemble normal internal activity. The objective was to improve detection of both known and unknown pivoting behaviours while supporting practical triage in resource-constrained institutions. A hybrid detection framework was developed that fuses Snort signature alerts with machine learning classification and unsupervised anomaly detection using behavioural features derived from BYOD-like network traffic. The approach was evaluated in a controlled testbed and supported by organisational survey findings on awareness and monitoring practice. Results show the hybrid system achieved 96.2% classification accuracy with a 4.5% false positive rate when distinguishing normal traffic, suspicious activity, and pivoting attacks. Compared with signature-only and machine-learning-only baselines, the hybrid design detected simulated pivoting attempts earlier and more consistently. User acceptance testing also reported strong satisfaction with the integrated dashboard for monitoring, filtering, and reporting. The key contribution is a unified, dashboard-oriented fusion of signature and behavioural evidence that strengthens early lateral movement detection and reduces manual correlation effort.

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