Blockchain technology has potential to strengthen electronic voting, yet evidence from developing nations remains limited, particularly under constrained connectivity and centralised electoral governance. This study combined a PRISMA 2020-guided systematic review with empirical evaluation of a permissioned blockchain prototype for the Tanzanian electoral context. The review synthesised 113 peer-reviewed studies published between 2018 and 2026 and identified four recurring vulnerability classes: human-factor threats at the edge, latency and partition risks, algorithmic opacity, and challenges of coercion resistance. A Hyperledger Fabric prototype was implemented with separate voter-registry and vote-casting chaincodes linked through a one-way cryptographic token. Evaluation used a synthetic dataset of 25,000 voters across 50 polling stations. All nine functional security tests passed, and Hyperledger Caliper completed 1,800 transactions without failure, achieving 99.8 vote-casting transactions per second at sub-second average latency in the tested laboratory configuration. Performance was also stable between the earlier 1,000-voter and 25,000-voter loaded-state runs. These findings support the technical feasibility of the implemented permissioned architecture under controlled conditions, but they do not establish national-scale performance or complete electoral security. Geographically distributed testing, production identity-system integration, coercion-resistant mechanisms, data-protection validation and usability evaluation remain necessary before operational deployment.
Emmanuel Elly Mushi, Gustaph Sanga, G. Tesha· East African Journal of Info...· 0 citations
Automated bullying-content detection has advanced rapidly for English and other high-resource languages, yet comparable evidence for Swahili remains limited, particularly for short message service (SMS) communication in Tanzania. This study developed and evaluated a context-aware deep learning model for binary classification of bullying and non-bullying Swahili SMS messages. A multi-source corpus of 7,228 messages was initially assembled from prior Swahili datasets, voluntary student contributions, and Google Forms; duplicate records were removed during data cleaning before the train-validation partition was created. Four Kiswahili graduates applied a common annotation framework that considered the target, communicative intent, and surrounding linguistic context rather than treating offensive vocabulary as a sufficient label criterion. The messages were normalised, tokenised with a 10,000-token vocabulary, padded to 100 tokens, and represented using trainable 300-dimensional FastText embeddings. A Bidirectional Long Short-Term Memory network used 160 units in each direction, followed by dropout, a 96-unit rectified linear dense layer with L2 regularisation, and a two-class Softmax output. Candidate configurations were assessed through Keras Tuner and validation-based model selection. On the 1,470-message internal validation set created after duplicate removal, the selected model achieved 90.07% accuracy, 90.09% macro precision, 90.05% macro recall, and 90.06% macro F1-score. The confusion matrix contained 641 true negatives, 683 true positives, 80 false positives, and 66 false negatives. Bullying recall reached 91.19%, indicating that the model identified most harmful messages, although the 8.01-percentage-point training-validation gap and divergent loss curves showed moderate overfitting. The study contributes a Tanzania-focused Swahili SMS resource, an empirically evaluated FastText-BiLSTM architecture, and deployment guidance that positions automated detection as a triage mechanism for human review rather than an autonomous enforcement tool.
Andrea Peter, Gustaph Sanga, G. Tesha· East African Journal of Info...· 0 citations
Mobile money is critical financial infrastructure in Tanzania, but impersonation-based fraud can bypass technical safeguards by inducing users to authorise payments themselves. This study examined the techniques, patterns, and vulnerabilities facilitate impersonation-based fraud and the factors predicting user compliance with fraudulent instructions in Tanzania. A mixed-methods design combined a survey of 231 active mobile money users in Dar es Salaam and Kibaha, eight key informant interviews and a review of regulatory and industry documents. Impersonation attempts were pervasive, with 72.3% of users targeted and 86.2% of those targeted were repeatedly. Attackers requested PIN in only 0.6% of cases but inducing 98.8% to send money themselves. Among targeted users, 74.3% complied, and 97.6% of compliers lost money. Logistic regression identified trust in providers-appearing communications as the only significant unique predictor of compliance (OR = 2.50). Users strongly preferred transaction-context safeguards, with 93.1% wanting cancellation capability and 87.4% wanting more verification time. Impersonation-based mobile money fraud is primarily an authorised-payment problem rooted in trust exploitation. Mitigation should therefore strengthen safeguards at the point of payment authorisation
Gregory Aloyce, C. Budoya, G. Tesha· East African Journal of Info...· 0 citations
An AI-driven risk-adaptive Zero-Trust framework that incorporates real-time patient deterioration into access control decisions and suggests that integrating clinical deterioration predictions into Zero-Trust access control can improve emergency responsiveness while preserving security and accountability.
Arthur Nashon Malingo, C. Budoya, G. Tesha· East African Journal of Info...· 0 citations
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