School-wide publications

Permanent URI for this collectionhttps://dspace.njala.edu.sl/handle/20.500.144402/182

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Start date: 2021Has files: No

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    Statistical Analysis on the Relationship Between Exports and Economic Growth in Sierra Leone (1990-2019)
    (Science Publishing Group, 2021) Turay, Matthew James
    In order to boost up the economic growth of any country, Exports should be considered as key factor as it contributes immensely in which Sierra Leone as a nation is no exception. Therefore, this research paper target to investigate the effect of export on the economic growth of Sierra Leone using time series data of exports and the gross domestic products ranging from 1990- 2019 which was source from the World Bank Indicators. In order to accomplish the aim of this paper, descriptive statistics, regression, correlation, Augmented Dickey- Fuller Unit root test, Johansen cointegration test, Vector Auto regression model, diagnostic test and the Pair- wise Granger causality test were the tools used to analyze the data. Based on the results from both regression analysis, it can be seen that export has a significant positive effect on the economic growth of Sierra Leone. Augmented Dickey-Fuller unit root test results shows existence of stationarity at first difference was detected, while the cointegration test results revealed that, there is no relationship between the exports and economic growth. However, the Vector Auto regressive model test revealed that, exports have a significant effect on the economic growth as well. Granger causality test, shows that both exports influence the Gross Domestic Product of Sierra leone.
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    Adoption of Technology-Driven Solutions Built on Strategic Decision Theories for Instructing Graduate Students in Applied Quantitative and Information Disciplines
    (2021-01-02) Sesay, Ibrahim
    This study investigates the adoption of technology-driven instructional solutions grounded in strategic decision theories for graduate education in applied quantitative and information disciplines. The increasing complexity of modern data-centric fields such as applied mathematics, data science, and information systems has necessitated the integration of advanced pedagogical technologies that are not only computationally sophisticated but also strategically aligned with institutional decision-making frameworks. This research proposes a conceptual and operational model that integrates strategic decision theories, including bounded rationality, game theory, and multi-criteria decision-making, with educational technologies such as adaptive learning platforms, intelligent tutoring systems, and analytics-driven instructional design. A structured mixed-methods approach is adopted, combining theoretical synthesis, system design modeling, and pedagogical analysis. The study explores how strategic decision frameworks influence the deployment, optimization, and effectiveness of educational technologies in graduate-level instruction. Findings suggest that aligning instructional technologies with structured decision models significantly improves learning efficiency, conceptual mastery, and student engagement in computationally intensive disciplines. Furthermore, the integration of decision-theoretic principles enhances institutional adaptability in curriculum design and resource allocation. The study also highlights the role of data-driven feedback loops in optimizing teaching strategies and improving learner outcomes. It demonstrates that strategic alignment between educational technologies and decision sciences creates a robust ecosystem for graduate education that is both adaptive and scalable. The research contributes to the growing body of literature on intelligent education systems by positioning strategic decision theory as a foundational framework for instructional innovation in higher education.
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    Adaptive Neuro-Fuzzy Inference System with Non-Linear Regression Model for Online Learning Framework
    (RELX Group (Netherlands), 2021-10-01) Fofanah, Abdul Joseph
    In this paper, we present a combined hybrid of an adaptive neuro-fuzzy inference system with an adaptive online learning model (ANFISAOLM) using a non-linear regression machine learning technique to construct the Neural Network. We proposed ANFIS-AOLM for learning by modeling and controlling imprecise defined, uncertainty system with a significant role of the neuro-fuzzy method. The various simulation exercise was conducted with define input and output values, define rules and the dataset was created and trained the Neural Network. Exploration of the dataset was tested for ANFIS-I using “Grid Partitioning”; ANFIS-II using “Subtractive Clustering”; and ANFIS-III using “Fuzzy Clustering Means”. The study was conducted in order to explore the effectiveness of the combined hybrid learning opportunities using ANFIS and Non-linear Regression models. The study was experimented using MATLAB programming language for the adaptation of the online learning framework with learner’s capability to learn from the existing information and adapt accordingly in the learning environment. The results obtained from the analysis shows that with a membership function of 20 and 25 an average of 97.7% accuracy was achieved. However, as the number of membership functions increases the better the performance of the ANFIS and the higher the computational response time. The non-linear regression model also indicated that the relationship of the ANFIS system and the proposed technique is adequate for learner’s adaptation and the decision made through learning mechanism.
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    Experimental Exploration of Evolutionary Algorithms and their Applications in Complex Problems: Genetic Algorithm and Particle Swarm Optimization Algorithm
    (2023-04-18) Fofanah, Abdul Joseph
    The primary aim of this study was to experiment and compare the empirical evidence of evolutionary algorithms and their applications using the Genetic Algorithm (GA) and Particle Swamp Optimization (PSO) algorithm. The objectives of the study included reviewing recent scholarly scientific research papers and practices, and algorithms with existing genetic algorithm techniques, identifying the need for lessons learned and leveraging best health practices garnered from the existing problems of optimization, reviewing and identifying the technological tools used in the healthcare domain, algorithms, and the effectiveness of GA and PSO techniques and to develop and analyse GA algorithms using datasets to predict and determine which of the two algorithms performs better. Evolutionary algorithms which are normally viewed as components of both evolutionary computing and bio-inspired computing use mechanisms inspired by nature and are indeed capable of solving problems through processes that emulate the behaviours of living organisms. Genetic algorithms (GAs), evolution strategies (ESs), differential evolution (DE), and particle swarm optimization (PSO) are typical examples. Therefore, evolutionary algorithms are typically used to provide good, estimated solutions to problems that cannot be solved easily using other techniques, and as such numerous optimization problems fall into this category. This work aims to provide a systematic evaluation of the literature on contemporary PSO methods for knowledge discovery in the area of illness detection. Although the major objective is to present recommendations for future enhancement and development in this field, the systematic analysis reveals the possible study topics of PSO techniques as well as the research gaps. The essential ideas, theoretical underpinnings, and traditional application domains are covered in this study. It is fervently anticipated that the findings will help researchers to thoroughly examine PSO algorithms for disease diagnosis. According to the current state of PSO strategies in healthcare, several difficulties that can be taken on to advance the sector are mentioned.
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    Exploring the Landscape of Open Access Fibre Optic Cable Network in Sierra Leone
    (RELX Group (Netherlands), 2024) Monson, Fred Konneh Songa
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    User-Centric Evaluation and Optimization of Resource Allocation in Edge Computing Environments
    (2025) Koroma, Mohamed
    The proliferation of mobile applications with intensive computational demands has necessitated the adoption of edge computing to reduce latency and energy consumption. However, edge servers face challenges such as limited resources, dynamic wireless conditions, and inefficient task offloading strategies, particularly in multiuser environments.
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    Turning Resources into Results: Accelerating Capacity Building in Digital Learning at Njala University
    (2025-02-18) Dumbuya, Emmanuel
    Njala University is actively integrating digital learning solutions to enhance educational outcomes. This paper examines strategies for transforming available resources into measurable results through capacity-building efforts. Utilizing a mixed-methods approach—including policy analysis, stakeholder interviews, and institutional data review—this paper identify key factors contributing to successful digital capacity building. The findings suggest that strategic resource allocation, collaborative partnerships, and targeted professional development initiatives are critical for sustainable digital transformation in higher education. It discusses implications for digital learning at Njala University and proposes a framework to guide similar institutions.
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