Using Artificial Intelligence to Mitigate Monkey-Human Conflicts in Hospitality Spaces in Zambia

Francis Chisenga, Brian Halubanza — Wed, 03 Dec 2025 00:00:00 +0000

This study aimed to assess the effectiveness of an Artificial Intelligence (AI)-based deterrent system in reducing monkey incursions at a hospitality establishment in Livingstone, Zambia. The research sought to understand not only the behavioural changes in the monkeys but also the perceptions of hospitality personnel regarding the system's impact on their work environment and guest experiences. The target population included free-ranging monkeys regularly intruding on the premises and 30 hospitality staff members employed at the establishment. The staff represented diverse roles, genders, and experience levels, and all had been employed for at least six months prior to the intervention. A mixed-methods approach was adopted. Quantitative data were gathered through systematic behavioural observations of monkey activity before and after system implementation. Qualitative data were collected through 30 in-depth semi-structured interviews with staff to explore their perceptions of the system’s effectiveness. Data were analysed using statistical techniques and thematic content analysis, respectively. Findings revealed a substantial decrease in both the frequency and severity of monkey incursions following the installation of the AI-based deterrent. Observable monkey behaviours shifted significantly from habituated and aggressive patterns to avoidance and flight responses. Interview data indicated improved staff morale, reduced workplace stress, enhanced guest satisfaction, and a more professional atmosphere. While some participants expressed concerns about potential long-term monkey adaptation, the overall sentiment remained strongly positive. The AI-based deterrent system proved effective in mitigating human-wildlife conflict within a hospitality setting. It created a safer, more controlled work environment and contributed positively to both operational efficiency and guest experiences. The study demonstrates that intelligent, context-sensitive technologies can yield meaningful behavioural changes in non-human species while supporting human-centred hospitality operations. This contribution extends prior Zambian AI/IoT field deployments for wildlife and pest monitoring by demonstrating effective, real-time deterrence in a hospitality context near Mosi-oa-Tunya.

Multimodal Deep Hashing Biometric Authentication Systems Based on Neural Networks Regional Applications in Digital IDs

Boyd Sinkala, Jackson Phiri — Wed, 03 Dec 2025 00:00:00 +0000

With a focus on applications related to digital identities, this paper provides an extensive overview of multimodal deep hashing biometric authentication systems. We lay out precise research goals and examine the most recent approaches, such as privacy-preserving strategies and deep neural architectures. Modern multimodal hashing frameworks are identified, template security and system interoperability issues are evaluated, and future research directions are recommended. We employ a systematic literature search with clear inclusion/exclusion criteria and categorize the works by technique (e.g., CNN, RNN, Transformer), application domain, and modality (e.g., face, fingerprint, iris). We discuss recent developments, including transformer-based biometric models [2][3] and privacy techniques (secure sketches, homomorphic encryption) [4][5]. Key studies are compiled in a standardized comparative table. With an emphasis on open-source platforms (like MOSIP [6][7]), privacy-by-design, and economic effects, we cover policy frameworks (GDPR, eIDAS, and African Union privacy charters) and provide helpful suggestions for implementing digital ID systems in Africa. Future studies and the implementation of safe, privacy-conscious biometrics for identity programs are intended to be guided by our findings.

Zambia ICT Journal

Using Artificial Intelligence to Mitigate Monkey-Human Conflicts in Hospitality Spaces in Zambia

Multimodal Deep Hashing Biometric Authentication Systems Based on Neural Networks Regional Applications in Digital IDs