Mekelle Institute of Technology

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    DEVELOPING PREDICTIVE DATA MINING MODEL FOR GRADE 11TH STUDENT PERFORMANCE: IN-CASE OF ELU WOREDA SECONDARY SCHOOL
    (Mekelle University, 2025-08-24) BEDADA DEMTEW
    Education is a key factor for achieving continuing economic progress. Many students are getting less in their result for many reasons. More academic institutes now store massive student educational and related data. A large number of students will enter every year. So, the demanding growth of data in education sectors continues. Handling and analyzing such a high amount of raw data manually for performance evaluation creates dissatisfaction, boring and unsuccessful. Manually handling and evaluating such a big amount of data for performance evaluation leads to discontent, boredom, and failure. To put it another way, traditional methodologies are overly complex and difficult to analyze and evaluate. To solve such kind’s issues, we use a data mining technique, which is a set of machine learning algorithms, to examine the data .In other words automated discovery of previously unknown, valid, novel, useful, and understandable patterns in school databases. The study's major goal is to use data mining to create a predictive model for student academic performance in Oromia National Region State's South West Shoa zone Elu woreda Teji Senior Secondary School and Asgori secondary schools. This can greatly was support policymakers, planners, and education providers working on the control of student performance. The methodology used for this research will a hybrid six-step CRISP Knowledge Discovery Process. The essential data would be gathered from a school data ware house created specifically for student result purposes, would be collected from 2011 to 2016 E.C. The researcher used three popular data mining algorithms (J48 Decision Trees, Random forest and Naive Bayes Classifier) to develop the predictive model using a larger dataset (4000 cases). The researcher used 10-fold cross-validation and percentage split test mode for data mining algorithms of the three predictive models for performance comparison purposes. The results indicated that the J48 Decision tree with10-foldcross-validation mode and Random Forest with 85% split testis the better predictor due to the nature of data which is categorical for which this two algorithm is better. Both algorithm (the J48 Decision tree with10-foldcross-validation mode and Random Forest with 85% split test) has 100% accuracy on the given school dataset and Naïve Bayes came out to be the second with an accuracy of 97.5%
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    Design and Implementation of Big Data Analytics Framework at Abyssinia Bank: A Case Study in the Oromia Special Zone, Amhara Region
    (Mekelle University, 2025-08-24) Bayew Girma
    The Ethiopian banking industry is one of the fastest growing in the economy, Abyssinia Bank is one of the first banks in Ethiopia and it is among the top list of banks in the country. Big data analytics (BDA) has become an increasingly popular topic over the years amongst academics and practitioners alike. Big data, which is an important part of BDA, was originally defined with three Vs, being volume, velocity and variety. The rapid emergence of big data presents significant opportunities for organizations to enhance decision-making processes through data driven insights. This thesis explores the design and implementation of a big data analytics framework at Abyssinia Bank, focusing on its application in the Oromia Special Zone of the Amhara Region. This study aims to identify specific use cases where big data analytics can improve operational efficiency, customer service, and strategic decision-making. Through a mixed-methods approach that incorporates quantitative analysis of existing data and qualitative interviews with key stakeholders, this research will contribute to a deeper understanding of how big data can transform banking practices in emerging markets.
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    NEURAL NETWORK APPROACHES FOR ACCURATE AFAN OROMO SPELL CHECKING AND CORRECTION
    (Mekelle University, 2025-09-24) Ayenalem Dejene
    Afan Oromo, a widely spoken Cushitic language, lacks advanced natural language processing (NLP) tools like spell checkers due to limited resources and linguistic expertise. Both native and non-native speakers face challenges in writing Afan Oromo correctly, partly because its Latin-based Qubee script was adopted in 1991. Traditional spell-checking methods, such as dictionary lookup and rule-based approaches, are inadequate for Afan Oromo’s highly inflectional morphology. This thesis proposes a neural network-based spell checker using a sequence-to-sequence (Seq2Seq) model with Long Short-Term Memory (LSTM) layers. A corpus of 596,948 words was collected from BBC Afan Oromoo using Sketch Engine, ensuring compliance with BBC’s terms of service. The model was trained to detect and correct spelling errors, achieving 100% error recall and 52.47% precision. This work is the first to apply neural networks to Afan Oromo spell checking, offering a scalable solution for under-resourced languages.
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    Practices, Challenges and Implementations of ICT in Selected Secondary Schools of Zone Two Afar.
    (Mekelle University, 2025-09-24) Anwar Hussien
    Information and communication technologies (ICT) have become common place entity in all aspects of life. The purpose of this study is to assess the existing practices, challenges and prospects of ICT in in selected secondary schools Of Zone Two Afar. The main research questions raised is assessment of extent of ICT implementation in the schools, based on the perceptions of students’, teachers’ and principals. The research utilized a quantitative research method and a descriptive survey research design was used to analyze. From available sampling methods, Proportionate sampling method was employed to determine size of each subgroup from the selected sample population. The study used both primary and secondary data sources to gather input for the study. The researcher collected data from teachers, students, principals from the schools. The major data collection instruments include Questionnaires and document analysis. The major findings of the study are lack of infrastructure and teaching learning aid shortage in the school. This study showed additional ICT training has to be arranged to equip teachers and students with required level of operational skill. And on the other hand, there was good perception on use of ICT in educational process. Lastly, the study revealed that the implementation of ICT in Abala and Erebty secondary schools is being tested with various challenges. These challenges include absence of operational design or model as well as arrangement of ICT infrastructure, lack of training for teachers and limited ICT knowledge and skills with both students and teachers; limited technical support during teaching and learning process and lack of proper ICT policies the schools. There is a gap in teachers owning facilitation and improvement of continuous support, monitoring and evaluation of ICT Policies. Hence, the research concluded that the inadequate practice, poor perception and the above-mentioned challenges hindered a well-versed implementation of ICT in the schools.
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    Diagnosis of Diabetes Using Data Mining Techniques
    (Mekelle University, 2025-09-24) AMANA TESHI GEMMEDA
    Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels due to the body's inability to produce or effectively use insulin. It is a significant and growing health concern worldwide, affecting millions of individuals and posing a considerable burden on healthcare systems. Diabetes can lead to various complications, including cardiovascular disease, kidney damage, nerve damage, and vision impairment. Early and accurate diagnosis of diabetes is crucial for effective management and prevention of complications. Traditional diagnostic methods rely on clinical symptoms, medical history, and laboratory tests. However, with the advancements in technology and the availability of large healthcare datasets, data mining techniques have emerged as a promising approach for diabetes diagnosis. Diabetes diagnosis plays a crucial role in effective disease management and prevention of complications. This study explores the application of data mining techniques for diabetes diagnosis, aiming to improve diagnostic accuracy and support healthcare decision-making. Four classification models (Decision Tree, SVM, Naïve Bayes, and Neural Network) are constructed and evaluated using a comprehensive diabetes dataset. Performance evaluation metrics, including TP rate, FP rate, accuracy, precision, recall, and F1-score, are employed to assess the models. The results indicate that the Decision Tree model consistently outperformed the other models, achieving high accuracy and F1-scores in both evaluation scenarios. The Naïve Bayes, SVM, and Neural Network models also showed reasonable performance, although slightly lower than the Decision Tree model. Based on the findings, it is recommended to utilize the Decision Tree model for diabetes diagnosis due to its high accuracy and F1-scores. However, further research and validation on larger and more diverse datasets are needed to ensure the generalizability of these results. Additionally, exploring the combination of different data mining techniques or the incorporation of additional factors, such as genetic data or lifestyle factors, could enhance the predictive capabilities of the models.
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    ASSESSING THE IMPACT OF SOCIAL MEDIA MARKETING ON CONSUMERS' BRAND AWARENESS
    (Mekelle University, 2025-08-24) ADDISSIE MINUYELET
    Facebook, Twitter, LinkedIn, You Tube and Google+ are major Social media stages have changed the way companies advertise their items and administrations. The reason of this research has been to see at how social media can make brand mindfulness and it’s utilized in Ethiopia. This ponder is of an exploratory and graphic nature whose essential objective is to supply knowledge into a modern showcasing marvel. A combination of both quantitative and subjective strategy was utilized. It has been conceivable to pull back essential and auxiliary information by conducting an internet survey and expert interview conjointly by alluding related literary works separately. A combination of organized questioner study survey and organized meet is conducted to gather the desired data. Both essential and auxiliary strategies were utilized to assemble data to achieve best conceivable result of the research’s exertion. The finding of this research shows that most customers pay attention to notices suggested and shared by companions and contacts on social media systems instead of the coordinate data given or notice campaign made by companies. Suggestion of others which is known as electronic word of mouth is considered as the foremost reliable source of data to impact consumers’ discernment almost items and administrations and half of the shoppers purchase items based on the data they secure from social media systems. By the by, they are confronting a few challenges which prevent the smooth application of social media showcasing campaign. In any case, most neighborhood companies utilize other conventional shapes of limited time channels most such as TV and radio notices. The utilize of social media showcasing these days is getting to be exceptionally prevalent worldwide and it has changed the relationship between clients and commerce and this impact will continuously proceed to advance in Ethiopia as modern media implants the culture and society. Subsequently, the inquire about recommended that neighborhood companies ought to utilize social media arrange to present their brands with gigantic reach, perpetual communication conceivable outcomes and with an awfully restricted advancement taken a toll and get the conceivable openings of abusing the brand building potential through the wealthiest and quickest communication shape accessible. Be that as it may, Social Media as a frame of promoting will show an entirety unused stage challenges.
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    ASSESSING CYBERSECURITY THREATS AND VULNERABILITIES IN ADDIS ABABA’S ZEMEN SPORT BETTING FINANCIAL SECTOR
    (Mekelle University, 2025-09-12) Abreham Sintayehu
    In recent years, the sport betting industry in Addis Ababa particularly the Zemen Sport Betting platform has witnessed significant expansion, driven by technological advancements, increased smartphone penetration, and widespread internet access. As a result, more individuals are engaging with online betting services, leading to a notable rise in digital financial transactions. While this trend contributes to economic growth and the digitalization of the local entertainment and financial sectors, it simultaneously exposes stakeholder’s operators, users, and financial intermediaries to a range of cybersecurity threats and vulnerabilities. This research seeks to critically assess the cybersecurity posture of Zemen Sport Betting’s financial systems, focusing on the detection, evaluation, and analysis of threats that could compromise data integrity, user privacy, and transaction security. The study aims to identify key vulnerabilities within their digital infrastructure, such as inadequate encryption mechanisms, poor user authentication protocols, and susceptibility to phishing attacks, data breaches, malware intrusions, and weak regulatory compliance. Particular attention will be paid to the financial transaction process, from user registration and digital wallets to payment gateways and backend data storage systems. Using a qualitative research methodology, this study will employ semi-structured interviews with cybersecurity experts, system administrators, and financial service providers; conduct surveys with end users to assess their awareness of online threats; and perform document analysis of policy frameworks, system architecture, and security audits (where accessible). This triangulated approach will ensure a comprehensive understanding of both technical and human-related vulnerabilities. The expected outcome of this research is to map the cybersecurity threat landscape facing the Zemen Sport Betting financial infrastructure and to develop actionable recommendations aimed at mitigating these risks. Recommendations will likely include the adoption of stronger access control measures, implementation of multi-factor authentication, improvement of network monitoring tools, regular penetration testing, staff cybersecurity training, and adherence to national and international data protection standards. Ultimately, the findings of this research are intended to contribute toward bolstering cybersecurity resilience within the Ethiopian betting industry and to support the development of a safer, more trustworthy digital financial ecosystem. As Ethiopia continues to digitize its economy, ensuring the protection of online financial transactions especially within rapidly growing sectors like sport betting will be critical in sustaining public trust, protecting consumer assets, and promoting responsible digital innovation
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    PREDICTING HIGHER EDUCATION STUDENTS’ PERFORMANCE USING DATA MINING TECHNIQUES: THE CASE OF ETHIOPIAN POLICE UNIVERSITY
    (Mekelle University, 2025-09-24) Abdureman Jula
    Predictive modelling for students’ performance is an innovative methodology that can be utilized by higher education institutions. Accurate and reliable student performance forecasts in higher education are crucial in order to minimize inefficient utilization of resources and funds at universities. The objective of this research is to develop a predictive model for higher education students’ performance using data mining techniques. The study followed the six-step hybrid methodology of the Knowledge Discovery Process model, such as understanding of the problem domain, understanding of the data, preparation of the data, data mining, and evaluation of the discovered knowledge and use of the discovered knowledge to achieve the goal. The study tries to understand factors affecting higher education student performance based on the data collected from Ethiopian Police University from the years 2008 up to 2012 E.C. After data preparation using data cleaning, classification algorithms such as J48 Decision Tree, PART Rule induction, Nave Bayes, Logistic regression, Support Vector Machines, and Multilayer Perception Neural Network were used for all experiments due to their popularity in recent related works. The study used a dataset containing 5254 instances, 18 attributes, and one outcome variable to run the experiments. The WEKA 3.9.5 open source software was used as a data mining tool to implement the experiments. The study also used a 10-fold cross validation and 66% split test modes for splitting the data into training and test datasets. The result of the study showed that the J48 decision tree algorithm has registered the best classification accuracy of 98.4%. The results obtained in this study are interesting and encourage the design of a model that predicts higher institution students' performance. The major factors affecting the students' performance were identified as previous study GPA, previous study field, program type, students’ performance, batch, financial source, and students’ status. In this study, an attempt was made to show the use of knowledge extracted by data mining. In the future, we recommend the automatic integration of data mining with a knowledge system so as to design an intelligent system.
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    ASSESSING CYBERSECURITY THREATS AND VULNERABILITIES IN ADDIS ABABA’S ZEMEN SPORT BETTING FINANCIAL SECTOR
    (Mekelle University, 2025-09-12) Abreham Sintayehu
    In recent years, the sport betting industry in Addis Ababa particularly the Zemen Sport Betting platform has witnessed significant expansion, driven by technological advancements, increased smartphone penetration, and widespread internet access. As a result, more individuals are engaging with online betting services, leading to a notable rise in digital financial transactions. While this trend contributes to economic growth and the digitalization of the local entertainment and financial sectors, it simultaneously exposes stakeholder’s operators, users, and financial intermediaries to a range of cybersecurity threats and vulnerabilities. This research seeks to critically assess the cybersecurity posture of Zemen Sport Betting’s financial systems, focusing on the detection, evaluation, and analysis of threats that could compromise data integrity, user privacy, and transaction security. The study aims to identify key vulnerabilities within their digital infrastructure, such as inadequate encryption mechanisms, poor user authentication protocols, and susceptibility to phishing attacks, data breaches, malware intrusions, and weak regulatory compliance. Particular attention will be paid to the financial transaction process, from user registration and digital wallets to payment gateways and backend data storage systems. Using a qualitative research methodology, this study will employ semi-structured interviews with cybersecurity experts, system administrators, and financial service providers; conduct surveys with end users to assess their awareness of online threats; and perform document analysis of policy frameworks, system architecture, and security audits (where accessible). This triangulated approach will ensure a comprehensive understanding of both technical and human-related vulnerabilities. The expected outcome of this research is to map the cybersecurity threat landscape facing the Zemen Sport Betting financial infrastructure and to develop actionable recommendations aimed at mitigating these risks. Recommendations will likely include the adoption of stronger access control measures, implementation of multi-factor authentication, improvement of network monitoring tools, regular penetration testing, staff cybersecurity training, and adherence to national and international data protection standards. Ultimately, the findings of this research are intended to contribute toward bolstering cybersecurity resilience within the Ethiopian betting industry and to support the development of a safer, more trustworthy digital financial ecosystem. As Ethiopia continues to digitize its economy, ensuring the protection of online financial transactions especially within rapidly growing sectors like sport betting will be critical in sustaining public trust, protecting consumer assets, and promoting responsible digital innovation.
  • Item
    PREDICTING HIGHER EDUCATION STUDENTS’ PERFORMANCE USING DATA MINING TECHNIQUES: THE CASE OF ETHIOPIAN POLICE UNIVERSITY
    (Mekelle University, 2025-09-24) Abdureman Jula
    Predictive modelling for students’ performance is an innovative methodology that can be utilized by higher education institutions. Accurate and reliable student performance forecasts in higher education are crucial in order to minimize inefficient utilization of resources and funds at universities. The objective of this research is to develop a predictive model for higher education students’ performance using data mining techniques. The study followed the six-step hybrid methodology of the Knowledge Discovery Process model, such as understanding of the problem domain, understanding of the data, preparation of the data, data mining, and evaluation of the discovered knowledge and use of the discovered knowledge to achieve the goal. The study tries to understand factors affecting higher education student performance based on the data collected from Ethiopian Police University from the years 2008 up to 2012 E.C. After data preparation using data cleaning, classification algorithms such as J48 Decision Tree, PART Rule induction, Nave Bayes, Logistic regression, Support Vector Machines, and Multilayer Perception Neural Network were used for all experiments due to their popularity in recent related works. The study used a dataset containing 5254 instances, 18 attributes, and one outcome variable to run the experiments. The WEKA 3.9.5 open source software was used as a data mining tool to implement the experiments. The study also used a 10-fold cross validation and 66% split test modes for splitting the data into training and test datasets. The result of the study showed that the J48 decision tree algorithm has registered the best classification accuracy of 98.4%. The results obtained in this study are interesting and encourage the design of a model that predicts higher institution students' performance. The major factors affecting the students' performance were identified as previous study GPA, previous study field, program type, students’ performance, batch, financial source, and students’ status. In this study, an attempt was made to show the use of knowledge extracted by data mining. In the future, we recommend the automatic integration of data mining with a knowledge system so as to design an intelligent system.