Mekelle University Institutional Repository
Discover scholarly works, research outputs, and institutional publications.
The Mekelle University Institutional Repository is a digital collection of scholarly and research outputs created by the university's faculty, students, and researchers. This repository provides open access to a wide range of materials, including articles, theses, dissertations, conference papers, books, and more."
By making our research available, we aim to
- Increase the visibility and impact of research conducted at Mekelle University.
- Promote knowledge sharing and collaboration within the academic community.
- Preserve and disseminate valuable scholarly works for future generations
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- A central archive for Mekelle University’s institutional abstract books from academic and research conferences.
Recent Submissions
Word Sequence Prediction Model for the Tigrigna Language Using a Deep Learning Approach
(Mekelle University, 2026-01-22) Yibralem Hagos Mekonnen
This research explores the development of a word sequence prediction model for the Tigrigna language using deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). Tigrigna, primarily spoken in Eritrea and Ethiopia, faces significant challenges in natural language processing (NLP) due to the scarcity of comprehensive computational resources and annotated corpora. This study addresses the urgent need for effective NLP tools tailored to Tigrigna, focusing on the fundamental task of word sequence prediction, which underpins various applications such as machine translation and text generation. Despite the limited dataset of 10,000 sentences compiled from diverse sources, the models were evaluated for their ability to predict and generate coherent word sequences. Results indicate that while LSTM and GRU models demonstrated potential in capturing Tigrigna’s unique linguistic characteristics, they faced issues with overfitting and underfitting, particularly influenced by the choice of embeddings Word2Vec and Keras Embedding. The findings highlight the necessity for improved regularization techniques and the importance of data augmentation to enhance model generalization. This research contributes to the nascent field of Tigrigna NLP by demonstrating the applicability of deep learning models in resource-scarce languages. The outcomes suggest pathways for future advancements in Tigrigna language technology, emphasizing the potential for enhanced predictive text applications and deeper insights into Tigrigna's grammatical structures. Ultimately, this work lays a foundation for further developments in Tigrigna NLP, advocating for increased investment in linguistic resources and innovative modeling techniques to support the digital representation of the Tigrigna language.
DEVELOPMENT OF A TEXT-BASED, AMHARIC-LANGUAGE CHATBOT FOR MATERNAL HEALTH CONSULTATION USING SUPERVISED MACHINE LEARNING
(Mekelle University, 2026-01-26) BIRTUKAN NGATU
Maternal health continues to be a critical concern in Ethiopia, where language barriers and limited access to healthcare information contribute to high rates of preventable pregnancy complications. Motivated by the need to improve maternal outcomes through accessible and culturally appropriate solutions, this study introduces an Amharic-based pregnancy chatbot. The chatbot is designed to provide expecting mothers with personalized, trustworthy, and timely maternal health guidance throughout their pregnancy journey. Using natural language processing (NLP), the chatbot interacts with users in Amharic, addressing common concerns and delivering information on prenatal care, nutrition, warning signs, emotional well-being, childbirth, and postpartum care. The methodology involves integrating the chatbot with local health resources and deploying it via mobile platforms to ensure 24/7 conversational support. The developed chatbot achieved approximately 100% training accuracy and 75% test accuracy in intent classification using an ensemble model averaging approach. Beyond technical validation, this study establishes a comprehensive theoretical framework grounded in the Technology Acceptance Model (TAM) and Nielsen's Usability Heuristics to evaluate usability, acceptance, and user satisfaction. This framework addresses the critical gap between technical functionality and real-world adoption, providing a methodological foundation for future empirical validation with target users in Ethiopia's maternal healthcare context.
AUTOMATED SELENIUM TESTING FOR THE QUALITY ASSURANCE OF MEKELLE UNIVERSITY WEB SITE
(Mekelle University, 2026-01-28) Berihu Gidey Gebremeskel
Websites serve as critical platforms for administrative, academic, and communication functions in higher education institutions. However, many institutional websites in Ethiopia, including that of Mekelle University, face persistent challenges such as outdated content, inconsistent navigation, broken links, and poor mobile responsiveness. Manual quality assurance is labor intensive, error prone, and insufficient to ensure consistent performance across diverse devices and user interactions. To address these limitations, this study develops an automated testing framework using Python and Selenium WebDriver to systematically evaluate the functionality, usability, responsiveness, and content accuracy of the Mekelle University website. The design and execution of nine test cases (TC001–TC009) addressed navigation, form validation, authentication, content verification, responsive design, homepage content verification, and link validation. The results show that the navigational components (TC001–TC003) are generally reliable and offer consistent access to the main sections of the website. However, the failure of form validation testing (TC004) revealed a significant flaw in data entry operations. This failure resulted from a combination of automation-related problems, such as uneven HTML structure and unstable element locators, and website-side issues, such as missing elements and unresponsive buttons. This mixed outcome demonstrates that while Selenium works effectively with well-structured underlying web components, interacting with poorly developed or dynamically loaded form elements reduces its ease of use. Content verification (TC006) exposed discrepancies in page titles and footer components, while authentication testing (TC005) confirmed that the website currently lacks a login feature for authenticated access. Homepage content verification (TC008) further identified accessibility issues, particularly the absence of a working mobile navigation menu. Responsive design testing (TC007) showed generally acceptable behavior across devices, and link validation testing (TC009) revealed that 5 out of 53 hyperlinks failed (9.4%), indicating broken links that undermine reliability and user trust. Overall, the study demonstrates that Selenium-based automated testing is effective in detecting usability issues, content inconsistencies, and functional flaws across large portions of the website. At the same time, the mixed results from TC004 highlight an important limitation: Selenium’s accuracy and ease of use depend heavily on the quality and consistency of a website’s underlying HTML structure. Thus, the findings emphasize both the value of automated testing and the need for improved web development standards and continuous quality assurance practices to enhance the reliability, accessibility, and overall user experience of Ethiopian higher education websites.
Integrating Ethereum Blockchain and GraphQL for a Secure Graduate Verification System at Mekelle University
(Mekelle University, 2026-01-28) Tilahun Mamuye Gidey
Ensuring the integrity and efficiency of academic record verification has become increasingly important for modern educational institutions. This study presents a blockchain-powered verification system specifically designed for confirming the credentials of graduated students from Mekelle University. By integrating Ethereum blockchain with GraphQL APIs, the system enhances transparency and reliability in the verification process. The university’s existing system, built with Ruby on Rails, lacked automated verification, relied heavily on centralized control, and was prone to delays and potential data tampering. To overcome these issues, a decentralized application (DApp) was developed using various tools, including Ethers.js, Node.js, Ganache, Apollo Server, GraphQL, and React. This application enables the secure submission and retrieval of student records through Ethereum smart contracts. Data can be uploaded via CSV files or manually entered through forms, and each record is retrievable using a unique student ID, ensuring data immutability and public verifiability.
Stakeholder feedback was gathered through interviews, and thematic analysis was used to assess the system’s usability, scalability, and trustworthiness. Findings showed strong support for the blockchain-based system, with over 90% of participants agreeing that it improves transparency and reduces the risk of credential fraud. This research demonstrates a feasible bridge between traditional university information systems and decentralized technologies, highlighting both the practicality and institutional readiness for adopting blockchain in higher education.
The Construction Management of High Voltage Transmission Lines
(Mekelle University, 2026-01-10) Wang Ning
This study examines the construction management performance of three high-voltage (HV) transmission line projects in Ethiopia—Azezo-Chilga, Fincha II-Shambu, and Metu-Masha 230 kV lines—which are vital for strengthening grid reliability and supporting national electrification goals. Managing these projects presents significant challenges due to complex technical requirements, difficult terrain, logistical constraints, and extensive coordination needs across civil, electrical, and administrative teams. The research aims to evaluate construction management practices, identify the most pressing challenges, and propose evidence-based strategies to improve project efficiency, quality, and safety. A descriptive and exploratory research design was used. Data were collected from 18 key informants (project managers, engineers, site supervisors) and 42 project documents, including contracts, design drawings, test reports, and progress records. The analysis focused on three core dimensions—planning and survey, material and quality management, and execution and commissioning. The findings reveal that planning and survey effectiveness averaged 82%, supported by accurate route selection and reduced rework. Material and quality management performance averaged 76%, though procurement delays and logistics constraints affected tower delivery and conductor stringing schedules. Execution and commissioning achieved 84% performance, driven by strong supervision, safety compliance, and coordinated team workflows. Key challenges included logistical delays (reported in 67% of sites), terrain-related access problems (52%), and coordination gaps among stakeholders (48%). These were mitigated through proactive planning, improved contractor–client communication, and adaptive risk management strategies. Overall, the study concludes that integrated and data-driven construction management significantly improves schedule adherence, cost efficiency, and technical quality in HV transmission line projects. The findings offer practical guidance for policymakers, engineers, and project managers seeking to enhance performance, reliability, and long-term sustainability in Ethiopia’s power transmission infrastructure