Proceedings of International Conference for ICT (ICICT) - Zambia

Adoption of Artificial Intelligence in Land Administration in Zambia

2025-01-14T10:44:12+00:00

The integration of Artificial Intelligence (AI) in land administration presents a transformative opportunity for improving efficiency, accuracy, and transparency in the management of land resources. Land administration in Zambia faces numerous challenges, including outdated records, manual processes, and limited accessibility to land information. These issues often result in land disputes, corruption, and inefficiencies in land transactions. The adoption of AI can address these challenges by automating routine tasks, enhancing data accuracy, and providing advanced analytics for decision making. This paper explores the potential and challenges of adopting AI technologies in the context of Zambia’s land administration system. This paper looks at how several AI technologies, like computer vision, natural language processing, and machine learning, can be used to important land administration tasks. By predicting changes in land value and identifying irregularities in land transactions, machine learning algorithms can lower the possibility of fraud and guarantee fair market practices. It is possible to manage land records and settle disputes more easily by using natural language processing to aid in the digitization and analysis of legal documents. With the provision of accurate and current spatial data, computer vision can support land surveying and mapping. Furthermore, the paper discusses the implementation framework required for the successful adoption of AI in Zambia's land administration. This includes the development of a robust digital infrastructure, capacity building among land administration professionals, and the establishment of clear legal and regulatory frameworks. Public-private partnerships and international collaborations are also highlighted as critical components for leveraging expertise and resources. The paper also addresses the potential challenges and risks associated with AI adoption, such as data privacy concerns, resistance to change, and the digital divide. Recommendations for mitigating these risks include establishing stringent data protection policies, conducting awareness, and training programs, and ensuring inclusive access to technology. In conclusion, the adoption of AI in land administration holds significant promise for enhancing the efficiency, transparency, and equity of land management in Zambia. By leveraging AI technologies, Zambia can modernize its land administration system, thereby fostering sustainable development and economic growth. This paper provides a comprehensive analysis of the benefits, implementation strategies, and potential obstacles, offering a roadmap for policymakers and stakeholders to navigate the transition towards AI-enabled land administration.

Advancing Cybersecurity Measures to Safeguard Critical Infrastructure and Data: Is Zambia Ready?

2025-01-14T12:14:56+00:00

As digital technologies become increasingly integral to critical infrastructure, emerging economies like Zambia are facing heightened cybersecurity threats. Current security measures are often inadequate, leaving essential services vulnerable to potential cyber-attacks. This research aims to develop a comprehensive cybersecurity framework tailored to safeguarding Zambia’s critical infrastructure and data. By identifying existing vulnerabilities and proposing innovative solutions, the study seeks to enhance the resilience of Zambia's critical infrastructure against cyber threats. The focus will be on practical tools and software to strengthen national security, ensure economic stability, and protect sensitive data. The findings will contribute to a stronger cybersecurity posture for Zambia, addressing the unique challenges faced by emerging economies in the digital era.

Cloud Computing Attack Resilience and its Implication for Public Institutions in Zambia: A Review and Adaptive Security Framework Proposal

2025-01-14T12:21:30+00:00

This research systematically examines the cybersecurity challenges facing public institutions in Zambia within the context of heterogeneous cloud networks. By reviewing recent literature (2016–2024) and conducting expert interviews, the study highlights the complexities of cloud adoption in resource-constrained environments. Key findings reveal vulnerabilities stemming from inconsistent cloud service policies, inadequate infrastructure, and limited technical expertise. To address these issues, the research proposes an Adaptive Security Framework that integrates continuous monitoring, real-time threat detection, dynamic policy adjustments, and Distributed Ledger Technologies (DLTs) to enhance data integrity and confidentiality. This framework aims to improve Zambia's cloud security resilience while providing practical policy recommendations to strengthen cloud security in public institutions. The study is built on over 50 scholarly references and offers a roadmap for enhancing cloud security management in Zambia’s public sector.

Leveraging Cloud Computing and Artificial Intelligence to Enhance E-Government Services in Developing Countries: Insights from Zambia

2025-01-14T12:26:37+00:00

the rapid advancement of cloud computing offers a pivotal opportunity to transform e-government services, particularly in developing nations where conventional IT infrastructure often falls short. This study focuses on Zambia, aiming to enhance cloud-based e-government services by addressing key issues such as accessibility, security, and cost efficiency. The research thoroughly examines the primary barriers to effective implementation, including inadequate internet connectivity, data security concerns, and low user trust. Notably, integration challenges are significant, as evidenced by difficulties faced by Zambia’s Ministry of Health with the Smart Care application and the lack of cohesive systems across various departments under the Ministry of Home Affairs—such as the Zambia Police Service, Zambia Correctional Service, Drug Enforcement Commission, Immigration Department, Department of National Registration, and Passport and Citizenship. In response, the study proposes targeted cloud based strategies to improve service delivery, focusing on scalability, robust data protection, and cost efficiency. Cloud computing can address the unique challenges of e-government in resource-constrained settings. Despite existing integration challenges, the proposed solutions hold promise for significantly enhancing public service efficiency, accessibility, and security, thereby strengthening governance and public trust in the digital age. The findings provide actionable recommendations for policymakers in Zambia and similar developing countries, aiming to create a conducive environment for effective cloud based e-government implementation. Despite existing integration challenges, the proposed solutions hold promise for significantly enhancing public service efficiency, accessibility, and security, thereby strengthening governance and public trust in the digital age.

Mitigating Malware in Zambian Healthcare System: an AI Driven Approach

2025-01-14T12:34:53+00:00

Cyberattacks pose a severe threat to organizations worldwide, including Zambia. The Global Risks Report 2023 [1] notes that malware is the primary attack vector for cybercrime, which is becoming more common and dynamic. Malware, including worms, trojans, and ransomware, is typically disseminated through various methods, including social engineering and phishing, and can cause significant financial losses and data breaches. According to Siampondo [2], cybercrime is becoming increasingly prevalent in Zambia, highlighting the urgent need to address this issue. Conventional security solutions, such as signature-based detection, struggle to keep pace with malware's constant evolution. As a result, artificial intelligence (AI) has emerged as a powerful tool for detecting, predicting, and preventing threats in real-time. Deep learning and machine learning approaches enable effective threat pattern detection within large datasets. However, much remains to be learned about the adoption and efficacy of AI powered malware detection and protection systems in Zambia's cyberthreat reduction efforts, particularly within the healthcare sector. The Zambian healthcare industry has rapidly digitized with the expanded rollout of Electronic Health Records (EMRs) and networked systems. The healthcare sector is becoming increasingly vulnerable to cyberattacks due to various factors, including insufficient cybersecurity awareness, siloed systems, and outdated infrastructure. These issues, coupled with the sensitive nature of patient data, highlight the critical need for robust cybersecurity solutions. By investigating the feasibility, challenges, and benefits of AI-driven malware prevention and detection, we aim to contribute to enhancing the cybersecurity posture of public, private, and faith-based healthcare organizations. Our study focuses on how artificial intelligence can be utilized to detect and prevent malware in healthcare organizations, secure patient data, ensure service continuity, and develop resilience against cyberattacks, with a particular emphasis on minimizing malware incidents. Statistical analysis will be employed to test assumptions regarding the relative benefits of various AI technologies (e.g., machine learning vs. deep learning) and the impact of advanced security design. The experiences and perspectives of security professionals will be captured through thematic analysis of qualitative data. This research aims to contribute to the ongoing discussion on the adoption and integration of AI-powered security technologies in critical sectors, with a focus on healthcare in developing countries. The findings will be valuable to organizations considering deploying AI, stakeholders (Ministry of Health and cybersecurity vendors), and researchers developing measures to enhance cybersecurity in the field.

Applying Artificial Intelligence to Optimize Sustainable Energy Consumption and Management

2025-01-14T12:41:43+00:00

To solve the issues raised by climate change and guarantee long-term environmental and economic stability, a shift to sustainable energy systems is essential. To accelerate the transition to green economies, this article investigates the role of artificial intelligence (AI) in improving energy usage and management. AI technology, such predictive analytics and machine learning algorithms, may be used by energy systems to increase the integration of renewable energy sources, decrease waste, and improve efficiency. This paper examines the ways artificial intelligence is currently being used in energy management, such as demand response systems, smart grids, and predictive maintenance, and shows how these applications have the potential to change how energy is consumed. Additionally, the paper looks at the obstacles to the general adoption of AI, such worries about data privacy and technical constraints, and suggests solutions. The results highlight how AI can revolutionize sustainable energy practices, highlighting the necessity of ongoing innovation and well-thought-out legislative frameworks to facilitate its application. Considering climate change, this article concludes that artificial intelligence (AI) is an essential instrument for encouraging sustainable energy consumption and stimulating economic growth.

Leveraging Machine Learning and Artificial Intelligence for Innovation and Sustainability in Small and Medium Sized Enterprises (SMEs): A Case Study of Kalumbila, Zambia

2025-01-14T12:46:58+00:00

Despite the rapid advancement and widespread adoption of Artificial Intelligence (AI) technologies across various sectors, the implementation of AI and Machine Learning (ML) in Small and Medium-sized Enterprises (SMEs) remains relatively underexplored. The incorporation of Artificial Intelligence and Machine Learning into Small and Medium sized Enterprises (SMEs) can transform conventional business procedures and provide substantial prospects for expansion and competitiveness. By utilizing AI and ML tools such as Predictive and Prescriptive Analytics, Deep Learning and Natural Language Processing, SMEs can streamline operations, increase productivity and make data-driven decisions. This study examines the various uses of AI and Machine Learning in SMEs, emphasizing how these technologies can enhance decision-making, optimize operational efficiency, and provide tailored consumer experience. The study focuses on SMEs within Kalumbila town, in North-Western Province of Zambia. Additionally, the paper highlights the challenges SMEs face in implementing these technologies, such as resource limitations, costs, and the requirement for specialized skills. Ultimately, the study shows that with right strategies and approaches, Machine Learning and Artificial Intelligence can be powerful enablers for innovation and sustainable growth for SMEs.

Optimizing Urban Traffic Management with Artificial Intelligence. A Case Study of Kitwe, Zambia for Enhanced Climate Resilience

2025-01-14T12:51:13+00:00

Zambia's Road Transport and Safety Agency (RTSA) reports a significant rise in active vehicles, reaching 695,740, which has exacerbated traffic control challenges as of July 2024. Traditional traffic management systems, reliant on fixed schedules, struggle to adapt to dynamic traffic conditions, leading to increased congestion, prolonged travel times, higher CO2, CO emissions, and inefficiencies in human-managed intersections. This study explores the application of Artificial Intelligence to optimize traffic light control in Kitwe, Copperbelt Province, aiming to mitigate congestion and reduce carbon emissions. By employing advanced Artificial Intelligence techniques such as machine learning and reinforcement learning, traffic light systems can dynamically adjust to real-time traffic patterns, thereby improving signal timings and overall traffic flow. Our research includes a comprehensive review of Artificial Intelligence driven traffic management systems globally, evaluating their benefits and challenges. Preliminary simulations and test scenarios in Kitwe suggest that Artificial Intelligence enhanced traffic control can significantly reduce wait times at intersections and lower vehicle emissions, thereby contributing to more efficient urban transportation. The findings highlight the potential for Artificial Intelligence to transform traffic management in Zambia, suggesting further research and pilot projects to address technical, infrastructural, and regulatory challenges and fully realize Artificial Intelligence benefits for sustainable urban mobility.

Development and Assessment of User Acceptability of a Clinical Nursing Mobile Application Tool for Patient Follow-Up in Lusaka, Zambia

2025-01-14T13:01:51+00:00

This study aimed to develop and assess user acceptability of a clinical nursing mobile application tool (CNMAT) to enhance follow-up care for patients in Lusaka, Zambia. A mixed-methods sequential exploratory design was employed, involving three phases: exploration of nursing procedures for home-based case, CNMAT design and prototyping, and user acceptance assessment. This write-up focuses on phase one which involves the exploring suitable clinical nursing care procedures for the CNMAT and assessing the intention to use the CNMAT through in-depth interviews with 13 clinical nurses. Findings suggest that a wide range of nursing procedures can be safely conducted at home, but successful implementation requires addressing challenges such as limited resources, patient factors, and the need for training and support. The CNMAT offers potential benefits in improving follow-up care, but the research is still ongoing for the subsequent stages.

Automated Bottle-Drink Packaging Mechanism (ABd-PM): Using the Bidirectional Shift Register Mechanism was Envisaged

2025-01-14T13:08:02+00:00

The control bidirectional shift register bottle packaging mechanism system at the initial process is static, such that the operational signals remain ignited. In this way, the system fails to respond to the mechanism and the bottles to be filled-in with drink remain static. It is in view of this that the automated bottle and drink packaging mechanism, in this study abbreviated as Abd-PM was envisaged. The Abd-PM operational mechanism is composed of the four-tier combinational logic gates. The first layer of this mechanism has the four(4) set of D-Flip-Flops, second is a four(4) set of multiplexer (MUX), third a two(2) set of demultiplexer (DMX); and fourth is a partial register Decoders-basic binary function, which utilizes the two (2) AND gates and one (1) OR gate. The ABd-PM architecture has the following major components: conveyor part; swinging bottle lifter; try rotator; electrical motors and various specialized sensors. In addition, the ABd-PM has an automated mechanism which indicates the accomplishment of a task at particular stages such as: liquid filling-in; label fixing; lead-fixing and to further packaging processes.

Envisaging Ethical Artificial Intelligence Governance Frameworks for Public Sector Applications: Addressing Accountability, Transparency and Fairness

2025-01-14T13:15:11+00:00

The integration of Artificial Intelligence (AI) into the public sector presents significant ethical and governance challenges that necessitate a comprehensive framework to ensure responsible implementation. This paper aims to develop a robust ethical AI governance framework tailored for government and public sector institutions. The research addresses critical issues such as accountability, transparency, and fairness in AI systems, focusing on maintaining public trust and mitigating potential biases. We propose ethical principles for AI development, evaluate existing regulatory models, and offer recommendations for effective oversight and public engagement. By analysing current policy models and integrating ethical considerations into the AI lifecycle, this study seeks to balance innovation with ethical imperatives. The findings provide actionable insights for public sector leaders and policymakers to establish governance frameworks that promote ethical AI usage, manage associated risks, and enhance societal benefits, ensuring equitable outcomes in public sector applications.

Gesture Controlled Home Automation for People with Disabilities

2025-01-14T13:19:48+00:00

This study examined a Gesture-Controlled home Automation system integrated with Artificial Intelligence (AI) to address accessibility challenges faced by individuals with disabilities and older adults. These groups often encounter difficulties operating standard electrical appliances such as lights, fans, and door locks without assistance. By relying on natural, intuitive gestures such as hand movements, head tilts, or other routine body motions, the system enabled users to control household appliances remotely, removing the need for physical contact with switches or reliance on wearable devices. During the research, a camera and AI algorithms were used to detect and interpret distinct gestures, which were then mapped to various appliance controls. The results showed that this contactless approach improved user convenience and reduced the need for caregiving support, enhancing independence both at home and in office settings. User feedback indicated that the system’s straightforward, intuitive interface promoted confidence in everyday tasks, while also minimizing physical strain. Moreover, the findings suggested that such inclusive technologies could encourage broader social engagement by allowing people with mobility limitations to manage their environment more autonomously. Overall, the study demonstrated that adopting an AI-driven, gesture-based control system can significantly improve daily life for individuals with disabilities and older adults, further contributing to equitable and accessible living environments.

Multiple Crop Diseases Detection and Diagnosis Using AI

2025-01-14T13:25:05+00:00

This project presents a mobile application leveraging artificial intelligence (AI) for efficient crop disease detection and diagnosis. Traditional methods for identifying crop diseases are often slow, require specialized knowledge, and may not be accessible to all farmers. This application provides a fast, user-friendly solution, enabling farmers to diagnose diseases with high accuracy. By analyzing image data, the app employs advanced AI algorithms to compare results with a comprehensive disease database, identifying potential diseases and offering tailored management recommendations. This tool empowers farmers to make informed decisions, adopt sustainable practices, and optimize crop productivity. Collaboration with agricultural and AI experts is integral to refining the AI model and ensuring its accuracy across diverse farming environments. The mobile application aims to enhance food security and promote sustainable agricultural development, addressing critical challenges in modern agriculture