School-wide publications
Permanent URI for this collectionhttps://dspace.njala.edu.sl/handle/20.500.144402/182
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Item Review of Knowledge Management in Optical Networks, Lambda Architecture using Database Technologies in Cloud Settings(International Journal of Scientific and Research Publications, 2021-08-24) Fofanah, Abdul JosephIn this paper, we present concepts, theories, and overview of knowledge management in an autonomous optical networks and Lamba Architecture in cloud related environment.This study presents some illustrative cases that has been used to illustrate the potential application of KM architecture and to evaluate the various policies for the knowledge sharing and integration algorithm.Here the knowledge is used at the optical transponder system level, sharing, and integration implemented at the node level and supervising and data analytics (SDA) controller level.The KM process has been evaluated by integrating on a metro-network situation in terms of model error convergence time and the data shared among agents.Indeed, the propagation and reinforcement actions illustrated by similar convergence time than data-based policies at various phases of the network learning process without compromising the convergence accuracy of the model prediction.The Lambda Architecture is the new model for Big Data and database research focus, that helps in data processing with a balance on throughput, latency, and fault-tolerance.To provide a complete solution and better accuracy, low latency, and high throughput, there exists no single tool.This introduced the idea to use a set of tools and methods to build a comprehensive Big Data approach.Although this paper does not provide a developed and working tool, however, provides an outline and the methods used by researchers to overcome some of the shortcomings of Lambda Architecture.The Lambda Architecture defines a set of layers to fit in a set of tools and methods rightly for constructing a comprehensive Big Data scheme: Speed Layer, Serving Layer, Batch Layer.Each layer satisfies a set of features and builds upon the functionality delivered by the layers beneath it.The Batch Layer is the place where the master dataset is warehoused, which is an unchangeable and add-only set of raw data.Also, the batch layer computes before the results using a distributed processing system like Hadoop, Apache Spark that can manage large amounts of data.The Speed Layer encapsulates new data coming in real-time and processes it.The Serving Layer comprises a parallel processing query steam engine, that takes results from both Batch and Speed Layers and responds to questions and requests in real-time with low latency.Stack Overflow is a Question-and-Answer forum with an enormous user community, millions of posts with rapid growth over the years.Item Personalized learning through artificial intelligence: Revolutionizing education(GSC Online Press, 2024-12-20) Dumbuya, EmmanuelPersonalized learning, powered by Artificial Intelligence (AI), is reshaping education by addressing diverse student needs, bridging learning gaps, and enhancing academic outcomes. This paper explores how AI technologies, such as adaptive learning platforms, predictive analytics, and intelligent tutoring systems, contribute to more equitable and efficient education. Emphasizing the potential of inclusive access to AI tools, it highlights the role of technology in democratizing education globally. The research concludes with recommendations for integrating AI into educational practices to ensure broad, equitable impacts. This paper examines the applications, benefits, and challenges of AI-driven personalized learning systems and proposes strategies for their effective integration into educational frameworks.Item Developing a framework for revising the curriculum to incorporate future skills such as digital literacy, critical thinking and adaptability, while aligning with global standards(GSC Online Press, 2024-12-20) Dumbuya, EmmanuelIn response to the evolving demands of the 21st-century job market and society, this study proposes a comprehensive framework for curriculum revision that integrates essential future skills—digital literacy, critical thinking, and adaptability—while aligning with global standards. The framework draws upon established models, including the DQ Global Standards, which encompass 32 digital competencies across eight critical areas of digital life, such as identity, use, safety, and communication. Additionally, it considers the International Society for Technology in Education Standards which guide educators in leveraging technology to create impactful and equitable learning experiences. The study also references the Organization for Economic Co-operation and Development (OECD) Future of Education and Skills 2030 framework, which emphasizes the knowledge, skills, attitudes, and values students need in the 21st century. By synthesizing these frameworks, the proposed model aims to equip students with the competencies necessary to navigate and succeed in a rapidly changing global landscape. The implications of this research suggest that educational institutions adopting this framework can better prepare learners for future challenges, fostering a workforce that is proficient in digital technologies, capable of critical analysis, and adaptable to change.Item Technological Advancements of Energy Storage Systems Technologies in Africa: A Review(Scientific Research Publishing, 2025) Koroma, MohamedThe global energy landscape is transforming to tackle climate change, improve energy security, and meet growing energy demands. Energy storage technologies are vital for incorporating “renewable energy”, stabilizing electrical network, and advancing electrification. This review paper provides a comprehensive analysis of the technological advancements in energy storage systems (ESS) and their applicability in Africa. The study highlights the potential of ESS to address Africa’s energy challenges, including grid instability, rural electrification, and renewable energy integration. The paper critically evaluates various ESS technologies, such as lithium-ion batteries, pumped hydro storage, and flywheels, and assesses their economic, environmental, and technical feasibility in different African regions. The findings suggest that tailored ESS solutions, supported by innovative financing models and regional-specific policies, are essential for sustainable energy transitions in Africa. The paper concludes with recommendations for policymakers, researchers, and industry stakeholders to accelerate the adoption of ESS technologies across the continent.Item Quantum computing in financial security: A risk management framework for systemically important financial institutions(GSC Online Press, 2025-01-30) Jhessim, EricThis research explores the application of quantum computing in enhancing financial system security, focusing on developing a robust risk management framework for systemically important banks in Sierra Leone. By evaluating 2 leading banks in the country, this study identifies key vulnerabilities, assesses potential quantum computing solutions, and provides a blueprint for safeguarding financial systems in the quantum era. Through implementation of the proposed framework, participating institutions demonstrated significant improvements in threat detection, encryption strength, and risk mitigation, with quantum-enhanced systems showing 99.8% accuracy in detecting sophisticated financial fraud attempts while reducing false positives by 90% compared to traditional systems. The implementation resulted in a 60% reduction in security incident response time and a 75% improvement in encryption strength against potential quantum attacks. These advancements provide crucial protection for financial infrastructure while ensuring regulatory compliance and operational resilience.Item Insights into breast cancer: A simple machine learning method for early disease detection(GSC Online Press, 2025-01-22) Sowah, AnthonyBreast cancer remains a significant global health challenge, where accurate prediction plays a vital role in early diagnosis and effective treatment, ultimately saving lives. This study evaluates the performance of three machine learning models; Support Vector Machine (SVM), Random Forest Classifier, and XGBoost for breast cancer prediction. Using the Wisconsin Breast Cancer Dataset, the models were assessed based on their accuracy. The experimental results demonstrated that SVM outperformed the other models, while both XGBoost and the Random Forest Classifier achieved just slightly lower accuracies. This research underscores the potential of machine learning models in enhancing breast cancer prediction and highlights their importance in advancing early detection and treatment strategies.Item IMPACT OF CLIMATE SMART RESILIENT PRACTICES ON ENHANCING SUSTAINABLE AGRICULTURAL PRODUCTIVITY. CASE STUDY OF RICE, COCOA AND OIL PALM VALUE CHAINS DEVELOPMENT(2025-09-26) M and E/GIS Officer-Agricultural Value Chain Development Project(AVDP), Ministry of Agriculture, Forestry and Food Security (MAFFS) Sierra Leone .The goal of the study was to evaluate the impacts of agricultural productivity through climate smart agricultural practices for selected value chains (IVS-Rice, Cocoa and Oil Palm) in a bid to boost resilience and enhance livelihood opportunities for rural farmers in Bo District. The objectives were to determine the effectiveness of climate-smart agricultural practices in increasing crop yields and productivity, and identify barriers to the adoption of climate-smart practices among rural farmers. The study adopted a mixed method approach using both quantitative and qualitative research method approaches. The findings revealed that climate smart agricultural (CSA) practices led to improved yields across all value chains with cocoa and IVS rice farmers achieving higher yields (above 25%) compared to moderate gains (10-25%) among Oil Palm farmers.The study concludes that Climate Smart Agricultural (CSA) practices have significantly enhanced agricultural productivity of farmers dealing with the IVS rice, cocoa, and oil palm value chains in Bo District. Although farmers continue to face challenges such as financial constraints, limited training opportunities, uncertain weather conditions, and restricted access to inputs, the evidence demonstrates that CSA practices consistently improve yields, with rice and cocoa farmers recording the highest gains and adoption rates are encouraging.Moreover, stakeholders acknowledge that CSA awareness is steadily increasing, particularly among younger male farmers, and that practices like inter-cropping are becoming more common. Overall, the findings affirm that CSA is a viable pathway to strengthen resilience, boost rural livelihoods, and support sustainable agricultural development in the district, provided that financial, institutional, and policy support systems are strengthened to scale adoption.Item Evaluating the Efficiency and Performance of Minimalist Neural Network Architectures with Hybrid Activation Functions(2025-10-23) Makieu, PeterThis research introduces an Adaptive Hybrid Activation Function (AHAF) for minimalist neural networks, addressing efficiency and performance challenges in resource-constrained applications.Unlike existing hybrid activations (e.g., Swish) or minimalist approaches (e.g., Lottery Ticket Hypothesis (LTH)), AHAF incorporates dynamic α-adaptation (0.50-2.13 range) with theoretical guarantees, achieving O(1/√T) convergence (Theorem 1) and gradient saturation mitigation (Lemma 2).Evaluated on Iris and Modified National Institute of Standards and Technology (MNIST) datasets, 8-unit AHAF networks matched 128-unit Rectified Linear Unit (ReLU) models in accuracy (96.67%) while reducing parameters by 84% and improving convergence speed by 18-22%.AHAF demonstrated superior adversarial robustness under Projected Gradient Descent (PGD) attacks (ϵ=0.1),showing only 1.18% accuracy drop versus ReLU's 3.42% (p 1.0).These results challenge conventional assumptions about hybrid activation overhead, establishing AHAF as a framework for sustainable Artificial Intelligence (AI) deployment in edge computing and adversarial-prone environments.The study bridges two key paradigms -dynamic activation adaptation and minimalist architecture design -offering both theoretical advances (formalized convergence proofs) and empirical validation (energy efficiency metrics, adversarial testing).By demonstrating that small networks with meta-learned activations can outperform traditional architectures, this work provides actionable insights for developing efficient, robust AI systems without sacrificing accuracy.The replicable methodology and open benchmarks further support adoption in real-world applications where computational resources and model stability are critical constraints.Item Artificial Intelligence and Equity(2025-11-06) Dumbuya, EmmanuelThis paper examines the potential of Artificial Intelligence (AI) to address educational inequities and enhance the quality of education in Sierra Leone. It explores the current state of education in the country, its challenges, and how AI-driven solutions can be strategically implemented to bridge gaps in access, improve learning outcomes, and promote equitable educational opportunities for all students, regardless of their socioeconomic background or geographic location. The paper also addresses potential ethical considerations and challenges associated with AI adoption in education and offers recommendations for responsible and effective implementation.Item Impact of ICT Infrastructure on Academic Staff Productivity: Evidence from Eastern Technical University, Sierra Leone(IORO Publications, 2026-09-02) Joseph KortuThis study examined the impact of Information and Communication Technology (ICT) infrastructure on the productivity of academic staff at Eastern Technical University (ETU), Sierra Leone. Guided by three objectives: to examine the availability and types of ICT infrastructure, to assess the extent of its utilization, and to evaluate its impact on teaching, research, and related activities, the study adopted a descriptive survey design. Data were collected from forty-nine academic staff using a structured, five-point Likert-scale questionnaire and analyzed with descriptive statistics (frequencies and percentages) in SPSS and Microsoft Excel. The findings reveal a clear paradox. Availability of ICT infrastructure was limited and inconsistent, with unreliable electricity and the absence of digital learning platforms (both 44.9% disagreement) identified as the weakest areas, while utilization was moderate to low and fewer than half of respondents (44.9%) reported adequate ICT skills. In sharp contrast, an overwhelming majority affirmed that ICT strongly enhances productivity improved teaching effectiveness (91.9%), faster task completion (91.8%), and improved overall job performance (89.8%). Respondents also strongly affirmed persistent barriers, including unreliable internet connectivity (83.7%), inadequate training (85.7%), and lack of technical support (83.7%). The study concludes that although staff clearly recognize the value of ICT, its realization is constrained by infrastructural, skills-related, and support-related gaps. It recommends targeted investment in reliable electricity and internet, deployment of digital learning platforms, regular capacity-building, and a dedicated technical support unit. **Keywords:** higher education; ICT infrastructure; academic staff productivity; Technology Acceptance Model; Sierra Leone