Computer Science

Permanent URI for this collectionhttps://etd.hu.edu.et/handle/123456789/76

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A MODEL TOWARDS PRICE PREDICTION FOR COMMODITIES USING DEEP LEARNING: CASE OF ETHIOPIAN COMMODITY EXCHANGE
(Hawassa University, 2022-10-03) SOLEN GOBENA
The development of information technology makes it possible to collect and store large amounts of data every second. Market Enterprises are generating large amounts of data, and it is difficult to use traditional data analysis methods to analyze and predict their future market price. Price predictions are an integral component of trade and policy analysis. The prices of agricultural commodities directly influence the real income of farmers and it also affects the national foreign currency. Haricot bean is produced in many areas of Ethiopia and it is rich in starch, protein, and dietary fiber, and is an excellent source of minerals and vitamins. Haricot bean is also the main agricultural commodity traded on the Ethiopian commodity exchange (ECX) market for the past 10 years. Though there are price prediction works for various crops in Ethiopia and abroad using machine learning and deep learning approaches, price prediction for Haricot bean has not been studied using machine learning as to the best of our knowledge,. The main objective of this study is to develop a price prediction model that can predict future prices of Haricot Bean traded at the ECX market based on time series data. Past 10 years, data has been obtained from the Ethiopian commodity exchange (ECX) with sample dataset size of 12272. Simple linear regression (SLR), multiple linear regression (MLR), and long short term memory (LSTM) were evaluated as predictive models. The results showed that LSTM outperformed other predictive models in all measures of model performance for predicting the Haricot Bean prices by achieving a coefficient of determination (R2 ) of 0.97, mean absolute percentage error (MAPE) of 0.015, and mean absolute error (MAE) of 0.032.
A URL-based Phishing Attack Detection and Data Protection Model
(Hawassa University, 2021-09-10) Yonathan Bukure Racho
Internet users are increasing rapidly in an uninterrupted way that is influencing the way of living. Every day billions of websites are accessed over the globe to facilitate different usage to people. This positive reinforcement is also resulting in internet abusing by hackers for their benefits. Most of the time internet abusing has experimented with over mobile phones or emails. The users are victimized by those abuse even without knowing that they are misused by hackers. Social engineering has become the tool for the hacker to manipulate users psychologically to reveal secret information. Phishing is a kind of social engineering attack with the potential to do harm to the individual or overall organization. Cybercriminal called Phisher comes up constantly in contact with individuals with creative ways to compromise the secret assets. Phishers uses the malicious URLs that are embedded over the webpage with severe threat and appears legitimate. When user clicks these links, redirects to malicious webpage where attackers ask some secrete information by misguiding user. Such kinds of attacks must be properly addressed. This thesis is focused on URL based phishing detection and data protection against such kind of attacks. Thus, the contribution of this thesis is divided into two phases that are: (1) URL based phishing attack detection, and (2) Protection of individual/organization assets. For the first phase, this thesis explored and implemented four machine learning algorithms like Decision tree, Random Forest, Naive Bayes, and Logistic Regression. Further performances of these algorithms are evaluated and compared against training and testing dataset. Based on performance result obtained, the best algorithm is recommended. For the second phase, thesis proposed a data protection model using a hybrid encryption method that combined AES and RSA algorithms. This model ensures the confidentiality of information assets as well as protect them against various kind of attacks. Overall proposed work is implemented in the Python programming language. The phishing detection phase concluded that Random forest outperforms and gave the highest accuracy of detection after important feature selection as compared to other algorithms. Results analysis conclude 96.89% and 99.06% detection accuracy over testing and training dataset respectively in Random forest. Similarly, the data protection phase encrypted and decrypted the data files very fast i.e., within few milliseconds and ensured the confidentiality of data in transit
AMHARIC EXTRACTIVE TEXT SUMMARIZATION USING AmRoBERTa –BiLSTM MODEL
(Hawassa University, 2024-05) EDEN AHMED
Extractive text summarization is a crucial task in natural language processing, allowing users to quickly grasp the main ideas of lengthy documents. The manual summarization process is often labor-intensive and time-consuming. As the volume of information in the Amharic language continues to grow, the need for effective summarization systems has become essential. While various summarization techniques have been developed for multiple languages, research specifically focused on Amharic remains limited. Most existing studies rely on traditional methods that often lack of contextual embeddings, which are crucial for understanding the meaning within the text. Additionally, current approaches often struggle to capture long-range dependencies among sentences and none of the existing studies have utilized hybrid deep models, which have demonstrated state of-the-art performance in summarization tasks for other languages. This study addresses the challenge of extractive text summarization for Amharic news articles by proposing a hybrid deep learning model that combines the contextual understanding of AmRoBERTa with the sequential processing capabilities of Bidirectional Long Short-Term Memory. A dataset of 1,200 Amharic news articles, covering a variety of topics, was collected. Each article was segmented into sentences and labeled by experts to indicate their relevance for summarization. Preprocessing was conducted, including normalization and tokenization using AmRoBERTa, to prepare the data for modeling. The proposed model was trained using various hyperparameter configurations and optimization techniques. Its effectiveness was evaluated using ROUGE metrics. The results demonstrate that our model achieved significant performance, with a ROUGE-1 score of 44.48, a ROUGE-2 score of 34.73, and a ROUGE-L score of 44.47.
AMHARIC MULTI-HOP QUESTION ANSWERING IN HISTORICAL TEXTS: A DEEP LEARNING APPROACH
(Hawassa University, 2024-11) BEREKET ENDALE
In our daily lives, questioning is the most effective way to gain knowledge. However, manual extraction of answers is time-consuming and requires expertise in the field. As a result, implementing fully question answering could accelerate extraction times and reduce the requirement for human labour. Numerous studies have been done on question answering in full resource languages like English, and others using various recent techniques. However, unlike previous research, which concentrated exclusively on single hop question answering, this thesis proposes the concept of multi-hop question answering in Amharic. Until yet, no studies have investigated multi-hop question answering in the context of the Amharic language, which includes reasoning over numerous pieces of evidence or documents to generate an answer. Furthermore, there is no existing question answering data set to address these issues; therefore, this study used deep learning for the Amharic multi-hop question answering problem, a neural network method. To do this, we preprocess our dataset using tokenization, normalization, stop word removal, and, padding before feeding it to a deep learning model such as CNN, LSTM, and Bi-LSTM to create question type classification based on the given input. Because there is no multi-hop Question answering training dataset in Amharic, training data must be created manually, which is time-consuming and tedious. It is around 1500 questions and contexts associated with five classes. The class depicts as ((0) for factoid_date, (1) for factoid_person, (2) for factoid_location, and (3) for factoid_organization. Accuracy, precision, the F-measure, and the confusion matrix are performance metrics used to evaluate the model's overall efficiency when applied to the provided dataset. According to performance measurements, the maximum achievable accuracy rates for this study's LSTM, CNN, and Bi-LSTM were 96%, 96.38%, and 97.04%, respectively. The findings indicated that the suggested Bi LSTM outperformed the other two models in terms of Amharic multi-hop questions type classification.
ANALYSIS AND MODELING OF 5G NETWORK PERFORMANCE BASED ON RESPONSE TIME REDUCTION
(Hawassa University, 2023-11) MEKASHA MEKURIA
Despite the fact that 5G technology has the benefits of meeting all of the key requirements for a 5G system and understanding the secrets for attaining a reduced response time, which was the most dominating component in 5G, the globe had adequate bandwidth in earlier generations for daily usage. However, response time was not a major concern, but for today's applications such as VANET and ongoing online gaming, as well as for vertical industries accessibilities such as SDN (software-defined network), NFV (network function virtualization), URLLC (ultra-reliable low latency communication), backhaul connection, and control or location update information, response time is more crucial than output. To address the aforementioned challenges and gaps, the study have analyzed the numerologies to 5G NR(radio network) recognizing KPI for cellular system analysis based on human demands and technological efforts to fulfil purpose, and address the aforementioned challenges by using the 5G toolbox for techniques of simulating hidden 5G numerologies. The simulation results show that our proposed approach outperforms state-of-the-art techniques because it yields the highest probability in regarding the requirements from the access network in response time reduction. As a practical implication of the study, the researcher have realized that the adaptable subframe structure leads to a very low symbol duration, which enables low response time, as time critical applications increased, and that wider subcarrier spacing could be used for users to provide them with very low response time symbol duration. In the future work, the study planned to incorporate the channel modeling of the mmwave band was relatively complex; which does not have any perfect channel model, high capacity backhaul connectivity, for its challenging for the exponentially growing data demands of 5G and would be required more additional exploration in depth and spectrum and interference management due to the scarcity of the spectrum resources and interference issues, thus needs efficiently manage the 5G spectrum, hence should be to conduct comparative performance analysis.
BI-DIRECTIONAL NEURAL MACHINE TRANSLATION FOR
(Hawassa University, 2023-07-12) ANDU ALEMAYEHU
Machine translation is one of the Natural Language Processing applications, which enables the translation of text from one natural language to another. This study aimed to design and develop a bidirectional English-Sidaamu Afoo neural machine translation system, as the need system has become increasingly important due to the growing number of language users, it needs to increase its presence on the web, For effective communication and information sharing, translation of various official documents, news articles, and other written texts in both languages is necessary and last to need integrating the other high-level NLP tools, but no prior solution in this area. Recently, Neural Machine Translation has emerged as a promising approach to machine translation, delivering state-of-the-art translation quality. Unlike traditional machine translation methods, NMT uses a single neural network that can be continuously fine-tuned to improve translation performance. This study aimed to develop a bidirectional Sidaamu Afoo-English machine translation system using deep learning techniques, specifically LSTM and Transformer models. In an attempt to do this study, due to un availability of parallel data for machine translation, we opted to collect parallel data from a religious domain, specifically from Bible and Sidaamu Afoo conversation. After gathering the data, experiments were conducted using 15,000 parallel sentences from different domains. To determine the optimal model, the efficiency in terms of training time, memory usage, and BLEU score was evaluated. The results showed that the Transformer model yielded the best results, with a BLEU score of 0.413 for Sidaamu Afoo to English translation and 0.465 for English to Sidaamu Afoo translation. Future work to enhance the performance of the system could include further research and the addition of more clean data and larger corpus sizes
BI-DIRECTIONAL NEURAL MACHINE TRANSLATION FOR
(Hawassa Unversity, 2023-07-07) ANDU ALEMAYEHU
Machine translation is one of the Natural Language Processing applications, which enables the translation of text from one natural language to another. This study aimed to design and develop a bidirectional English-Sidaamu Afoo neural machine translation system, as the need system has become increasingly important due to the growing number of language users, it needs to increase its presence on the web, For effective communication and information sharing, translation of various official documents, news articles, and other written texts in both languages is necessary and last to need integrating the other high-level NLP tools, but no prior solution in this area. Recently, Neural Machine Translation has emerged as a promising approach to machine translation, delivering state-of-the-art translation quality. Unlike traditional machine translation methods, NMT uses a single neural network that can be continuously fine-tuned to improve translation performance. This study aimed to develop a bidirectional Sidaamu Afoo-English machine translation system using deep learning techniques, specifically LSTM and Transformer models. In an attempt to do this study, due to un availability of parallel data for machine translation, we opted to collect parallel data from a religious domain, specifically from Bible and Sidaamu Afoo conversation. After gathering the data, experiments were conducted using 15,000 parallel sentences from different domains. To determine the optimal model, the efficiency in terms of training time, memory usage, and BLEU score was evaluated. The results showed that the Transformer model yielded the best results, with a BLEU score of 0.413 for Sidaamu Afoo to English translation and 0.465 for English to Sidaamu Afoo translation. Future work to enhance the performance of the system could include further research and the addition of more clean data and larger corpus sizes.
Bi-Directional Sidaamu Afoo - Amharic Statistical Machine Translation
(Hawassa University, 2023-04-06) Kebebush Kamiso
Machine translation (MT) is the area of Natural Language Processing (NLP) that focuses on obtaining a target language text from a source language text using automatic techniques. It is a multidisciplinary field and the challenge has been approached from various points of view including linguistics and statistics. MT usually involves one or more approaches. Our preference for this study is to develop the bi directional Sidaamu Afoo - Amharic machine translation system, make use of a statistical machine translation (SMT) approach. To conduct the experiment, a parallel corpus was collected from all possible available sources. These include mostly the Old and New Testaments of the Holy Bible for both languages. We used the monolingual Contemporary Amharic Corpus and the Sidama Afoo corpus compiled by a research team in the Informatics Faculty of Hawassa University. Different preprocessing tasks such as tokenization, cleaning, and normalization have been done to make the corpus suitable for the system. To accomplish the objective of this thesis work, we conducted four experiments using word and morpheme-based translation units with SMT for Sidaamu Afoo - Amharic language pairs. The first two experiments focus on word-based SMT and the next two on morpheme-based translation using unsupervised morphological segmentation tool; Morfessor. For each experiment, we used 30,100 parallel sentences. Out of the total parallel sentences, we used 80% (24,100) of randomly selected parallel sentences for training, 10% (3,000) for tuning and another 10% (3,000) for testing. The basic tools used for accomplishing the machine translation are Moses for the translation process which is MGIZA ++ for word and morpheme alignment and KenLM for language modeling; Morfessor for morphological segmentation. For evaluation SacreBLEU package which are BLEU, ChrF and TER metrics. According to the experimental findings, the differences between Amharic to Sidaamu Afoo and Sidaamu Afoo to Amharic in the Word-based alignment translation were 6.2, 16, and 1.9 for BLUE, ChrF2, and TER, respectively. In the Morpheme-based alignment, the differences between Amharic to Sidaamu Afoo and Sidaamu Afoo to Amharic translation were 7.5, 20.4, and 5.1, for BLUE, ChrF2, and TER respectively. In conclusion, the results show that morpheme-based alignment performance is better than word based alignment, for Amharic to Sidaamu Afoo than Sidaamu Afoo to Amharic
CLASSIFICATION OF INJERA QUALITY USING CONVOLUTIONAL NEURAL NETWORK
(Hawassa University, 2023-09-08) MEBRHIT G/GEWERGS TEKLU
INJERA is one of the most well-known Ethiopian foods. It is also a popular food all over the world because of its whole grain product and gluten-free nature. It is prepared from different grains like teff, maize, barley, etc. but teff is the most preferred grain and it contains many nutrients to have the advantage to our health. It can cause celiac disease and controls blood sugar levels. Nowadays, many enterprises are occupied with selling INJERA to hotels and individuals and also exporting to foreign countries. But, some of the enterprises sell INJERA by adulterating with foreign particles without the knowledge of consumers to gain their lone profit. Due to this, INJERA adulteration has become a serious problem now & this might be a health risk in the near future. This Adulterated INJERA is dangerous because it can be toxic and may affect one’s health. It could deprive nutrients crucial for proper growth and development. Classification of quality INJERA using the naked eyes and also through smelling, observing the appearance, and tasting is difficult due to their visual similarities. To address this problem, we proposed a deep learning algorithm for the classification of INJERA quality based on the INJERA images. To do so, the design science research methodology was followed. To conduct this study, a total of 2230 images were collected including 1115 pure Teff INJERA samples and 1115 mixed INJERA samples. After collecting the required images, we applied image pre processing such as image resizing, and image normalization on the image datasets before adding them to the model. In this study three different CNN models with different design options like the number of layers, stride size, kernel size, and padding and with and without dropout namely the CNN3L, CNN4L, and CNN5L, were trained to determine the quality of INJERA as pure or adulterated. All of the models were able to find a successful detection result after many experiments. The CNN3L model has 99.94 percent training and 99.11 percent testing accuracy, the CNN4L pre-trained model has 99.78 percent training and 99.55 percent testing accuracy, and the CNN5L has 99.89 percent training and 99.55 percent testing accuracy. The CNN3L model has a training loss of 0.72% and a testing loss of 2.80%, the CNN4L model has a training loss of 3.74% and a testing loss of 10.1%, and the CNN5L model has a training loss of 0.86 percent and a testing loss of 1.94 percent. The experimental result demonstrates that the CNN5L model is effective for the accurate recognition of INJERA images with higher accuracy (99.55 %) and less loss value (1.94 %) in this study
COLLABORATIVE APPROACH OF AGILE AND DEVOPS FOR CONTINUOUS DELIVERY OF QUALITY SOFTWARE
(Hawassa University, 2023-08) DESSALEGN MENGESHA
We are in the era of high demand for quality software in many organizations in order to achieve their organizational goals. Many organizations around the globe have shown great interest in the automation of their business processes. This in turn causes emerging and improvement of different software development methodologies and the way of service provision dramatically. Among those methodologies, Agile Software Development Methodologies and DevOps culture/tool have become more popular due to their capability on supporting rapid software development, continuous integration, and continuous delivery. Even though the two methodologies are complementary and have their own significant role in the software development lifecycle, using the two approaches independently will not bring development process improvement to the optimum level. Contextualizing the software development process enables the practitioners to improve their development process and for better productivity. The objective of this thesis work is to integrate the two approaches together with minor modifications to the DevOps team structure by extending the role of the DevOps team to the development environment. The research is conducted as experimental research and the evaluation was done by using two working projects, one using classical Agile as a control group and the other by integrated approach of Agile and DevOps as an experimental group. The number of changes accepted and developed and the number of deliveries in a specific period of time are used as measurement parameters. The experiment was done using students who joined Hawassa University Application Development Team for practical attachments. The findings of the experiment demonstrate that the experimental group project, which utilized agile methodologies in conjunction with DevOps practices, achieved superior outcomes compared to the control group project, which relied on the department's standard Agile/Scrum approach. This improvement was evident in metrics such as accepted changes and committed deliveries. Furthermore, the guideline applied to the experimental group project was refined and is included in this paper to serve as a valuable resource for future researchers and developers.
COMPUTATIONAL MODEL FOR WOLAYITTA LANGUAGE SPELLING CHECKER
(Hawassa Inversity, 2020-08-10) RAHEL SHUME
Spelling checker systems are built, and researches are conducted worldwide to meet the needs of different languages. In Ethiopia, spelling checker researches were carried out for only Amharic language. These works have paved the way for researches on other languages like Wolayitta. A computational spelling checker is proposed and adopted for the Wolayitta language in this research. Word level spelling checker were built based on an edit distance algorithm and language model based open source tools and platforms. A dictionary database was constructed from 13,313 words from Wolayitta lexicon dictionary and 20,000 words from Wolayitta Bible. The database was then clustered into training and testing. The statistical model accept input from the user then check whether it is available in dictionary or not if it is available it will do nothing if not it gives suggestion. The testing was done in two phases where the first is error detection rate and the second evaluation is error correction. Tests were carried out on the input data from the user, and accuracy of 94.57% was achieved for spelling error detection, while accuracy of around 90.98 % was retrieved for the spelling suggestion model. The results were promising, and further researches can be entertained as per the recommendations made by the researcher
CONTEXT-BASED SPELL CHECKER FOR SIDAAMU AFOO
(Hawassa University, 2022-03-04) MEKONEN MOKE WAGARA
A spell checker is one of the applications of natural language processing that is used to detect and correct spelling errors in written text. Spelling errors that occur in the written text can be non-word errors or real-word errors. A non-word error is a misspelled word that is not found in the language and has no meaning whereas a real-word error, that is, the word is a valid word in the language but it does not fit contextually in the sentence. We designed and implemented a spell checker for Sidaamu Afoo that can detect and correct both non-word and real-word errors. Sidaamu Afoo is one of the languages spoken in the Sidaama region in the south-central part of Ethiopia. It is an official working language and is used as a medium of instruction in primary schools of the Sidaama national regional state in Ethiopia. To address the issue of spelling errors in the Sidaamu Afoo text, a spell checker is required. In this study, the dictionary look-up approach with a hashing algorithm is used to detect non-word errors, and the character-based encoder-decoder model is used to correct the non-word errors. The LSTM model with attention mechanism and edit distance is used to detect and correct the context based spelling error. To conduct the experiment, 55440 sentences were used, of which 90% were for training (i.e., 49,896) and 10% were for testing (i.e., 5544). According to the experimental results, for an isolated spell checker, dictionary lookup with hashing achieved an accuracy of 93.05%, a recall of correct words of 91.51%, and a precision of incorrect words of 72.37% for detection. The encoder decoder model achieved a recall of 91.76% for corrections. For a context-sensitive spell checker, the LSTM model with attention and edit distance achieved an accuracy of 88.8%, recall of the correct word of 86.18%, and precision of the incorrect word of 62.84% for detection. It achieved a recall of 74.28% for the correction. The results of the experiment show that the model used to detect and correct both non-word and real-word spelling errors in Sidaamu Afoo’s written text performed well. Finally, to improve the performance of the model, we recommend using additional data set and a state-of-the-art transformer model.
DATA CENTER VIRTUALIZATION FRAMEWORK IN ETHIOPIAN HIGHER
(Hawassa University, 2022-03-04) ABDALLAH HUSEN
Ethiopian higher learning institutions(HLI) data center runs physical data center in which a one application for one server architecture is exercised. Such architecture leads to under utilization and wastage of resources. Using virtualization technology enables lowering the size of the infrastructure, thus resulting in a huge savings on energy and other resources including management time. Cost reduction , energy efficiency and the reduction of a company’s carbon footprint are some of the significant benefits of using virtualization but virtualization technology is a long way from Plug-and-Play to unlock those benefits effectively, an information technology (IT) expert requires the appropriate set of model to be followed. In most cases, organizations have enough resources to move to virtualization without requiring additional budget. Certain researches have been conducted on virtualization especially client virtualization, application virtualization and network virtualization. But, little attention was given for server virtualization. The objective of this research is to explore the current traditional infrastructure practice and to propose data center virtualization framework using design science research methodology (DSRM). The goal is to look for option(s) that brings a preferred solution for University data centers that increases service availability and utilization of hardware. In this work a virtualization framework is proposed, and evaluated using the same services that are currently used in the university. Experiments are carried out to check and compare the resource utilization of both the physical machines and Virtual machines(VMs). By using both the experiment and the analysis result, well-matched virtualization framework was developed. The developed frameworks was tested on three services. Before virtualization each of these services were running in separate physical machine. In our experiment, by consolidating these services using virtualization, the study showed that it is possible to provide the same service using only a single server. The resource utilization of that single server increased as follows: Central processing system (CPU) usage increased from 4% to 17%, physical memory usage increased from 15% of 16GB to 62% of 16GB, and capacity of hard disk space in use was 67.4% up from 32%. The Universities data center will use the proposed well-matched framework to enable them increase their levels of utilization of the servers
DECISION FRAMEWORK FOR THE USAGE OF CLOUD TECHNOLOGY IN ETHIOPIA HIGHER EDUCATION INSTITUTIONS
(Hawassa Unversity, 2019-10-06) SELAM DESALGNE
The rapid technology advancements are always creating new opportunities and a new way of working. Cloud Technology is being popularizing across the world especially in academic institutions. It is not a new technology but rather a new delivery model for information and services using existing technologies. The paradigm has been recognized recently as key enabling efficient and effective technological services that will reshape the delivery and support of the educational services. This study is conducted on public Ethiopian Higher Educational Institutions to explore the critical determinants that influence the adoption of the Cloud Technology. Despite the fact that cloud computing offers great deal of opportunities, its adoption exacerbated with lack of standards and relative lack of general framework created dilemma for the institutions how to approach the cloud adoption. An exploratory study is carried out. This research work proposes TOETAD conceptual framework according to the Technology Organization Environment (TOE) model, Diffusion of Innovation (DOI) theory and Technology Acceptance Model (TAM) with added Decision Maker Context to the model. Adoption determinants for the technology will be examined through the lens of integrated model. The framework factors were identified by critically reviewing studies found in the literature together with factors from the industrial standards within the context of Ethiopia Higher Education Institutions. Data is collected by online questionnaire survey with IT managers, lectures, E-learning coordinators and Team Leaders from selected 17 Ethiopia Higher Educational Institutions with a total 103 respondent. On the other hand the proposed frame work is evaluated by an expert to validate the framework. The result also helps to encourage the Public Higher Educational Institutions in Ethiopia to understand the nature of the problem, increase their awareness about factors to be considered while adopting the cloud computing
DEEP LEARNING BASED FABA BEANS LEAF DISEASES DETECTION AND CLASSIFICATION
(Hawassa University, 2022-03-08) MARTHA MEZGEBU HAILU
Faba bean (Vicia Faba L.) is believed to be originated from the Near East and now days spread throughout the world. It’s one of the most domesticly legume in the world next to chickpea and pea. Ethiopia is the second leading producer of Faba beans next to China in the world. It shares 6.96% of world production and 40.5% with of Africa. Faba bean is grown primarily for its edible seeds that are used for human consumption. It also used for keeping human healthy and sustaining the productivity of the farming system through the fixation of nitrogen. However, most of the time it is affected by different diseases that result in reduction of quality and quantity of the Faba bean production. Those diseases are caused by fungus, virus, and bacteria. Usually Faba bean diseases appear on the leaf, flower, pods, seed, and stem a step by step and makes the crop out of usage. Mainly, leaf of Faba bean is more affected by diseases than other parts. It attacks both inside and outside of the leaves. Leaf plays an important role during the growing period of Faba bean. Without leaf there is no flower, without flower there is no pod, without pod there is no seed. Traditionally, farmers and experts detect and identify plant diseases by naked eyes. This method is inaccurate and expensive, because there are numerous diseases. Detection by using image processing techniques has been more accurate and fast. Therefore, we need to develop automatic deep learning based Faba bean leaf diseases detection and classification model. We designed Faba bean leaf disease model architecture using convolutional neural network for Faba bean leaf diseases detection and classification. CNN become accurate and precise method for the detection and classification of plant diseases. The study can be conducted in the plantation area of Faba bean in Oromia region, Arsi zone, D/Xijo Woreda, from the farmer plantation land particular reference to Bucho Silase kebele, Ethiopia, where the dataset has been collected. Leaves of healthy and infected crops are collected and labeled. Processing of image has been performed with pixel wise operations to enhance the image. It is followed with feature extraction the classification of patterns of captured leaves in order to identify Faba bean plant leaf diseases. Four classifier labels are used as ascochyta blight, chocolate spot- botrytis, rust, and Healthy leaf. The features extracted are fit into the neural network with the dataset was spilt into training set, validation set and testing set, 80%, 10%, and 10% respectively, with the batch size 32 and using Adam optimizer. Faba bean leaf diseases detection and classification model achieved the overall accuracy 99.58%
DESIGN OF A MORPHOLOGICAL GENERATOR AND ANALYZER FOR SIDAAMU AFOO VERBS USING FINITE-STATE TRANSDUCER
(Hawassa University, 2021-03-07) KITAW AYELE GESUMA
Sidaamu Afoo is an official language of the newly formed sidaama national state. It is one of the under-resourced languages of Ethiopia. There are very small attempts of study in the morphological perspective of the language. The morphological study is a low-level activity that paves the way for the development of high-level NLP applications. This study was interested in the design and implementation of a morphological generator and analyzer for the language verbs. The finite state transducer is used to design the verb morphological generator and analyzer system. Lexicon formalism is used for designing method and foma programming language as implementation toolkit. Forty-one distinct linguistic rules were used to form those variate word forms. The experimental result shows that out of 74,934 total generated words, 94.8% of words are correctly generated and analyzed by the system. Only 5.2% of words generated are invalid and wrongly analyzed. The factors of the problem are optional replacement rules and compiler ignorance of the glottal consonant of the language. Strict demarcation boundary for optional replacement rules and alternative techniques for glottal sound management left for future work.
DESIGN SPACE EXPLORATION AND OPTIMIZATION OF MECHANICAL COMPONENTS USING MACHINE-LEARNING TECHNIQUES
(Hawassa University, 2023-11) AKLILU TEKLEMARIAM
In today's highly competitive market, rapid product development with minimal resource utilization is crucial. This requires thoroughly evaluating all available design solutions to identify the most efficient options. While various techniques exist to explore design spaces and achieve optimal results, continuous research is dedicated to discovering more effective approaches that can be adapted to diverse design challenges. This thesis examines how supervised machine-learning techniques can be used to explore and optimize mechanical component design problems. Specifically, it focuses on three mechanical component design problems: pressure vessel design, helical coil spring design, and belt pulley drive design. Four machine learning classification models (support vector machine, random forest, gaussian naïve Bayes, and neural network) are tested. We prepared three different dataset sizes for both binary and multiclass classification using simulation-based design of experiments to investigate how dataset size affects model performance. We used the Latin hypercube sampling method to effectively sample points from the available design space. Additionally, hyperparameter tuning was performed to improve the performance of the evaluated models. Based on our findings, the random forest and support vector machine models outperform the others. Specifically, the random forest model excels in all three design problems for binary and multiclass classifications across various dataset sizes, even with default parameters. However, the support vector machine and neural network model can surpass the random forest's performance when hyperparameters are fine-tuned. On the other hand, the Gaussian naïve Bayes model exhibits the lowest accuracy in all three design problems. Interestingly, regardless of dataset size, there are no significant variations in the classifiers' performance for both binary and multiclass classifications. This suggests that the classifiers' effectiveness relies more on the dataset's representation of the original distribution than its size. This implies that reducing the sampling budget is possible using a small number of data points that accurately represent the design space. This study shows how machine learning classifiers efficiently solve mechanical component design issues, particularly in exploring design spaces and finding optimal values.
DETECTION AND CLASSIFICATION OF INDIGENOUS ROCK MINERALS USING DEEP LEARNING TECHNIQUES
(Hawassa University, 2023-03-08) HADIA BERKA WAKO
Ethiopia is undoubtedly a place of riches, with a vast and diverse landmass that is rich in resources. However, less attention has been given in utilizing computing discipline like Artificial Intelligence to solve the current problems in the area of mineral mining in Ethiopia. GUJI Zone is one of Oromia 20 administrative zones blessed with different mineral resources. Despite the fact that mineral has lions share contribution to economy of Ethiopia, little work is done in modernizing the mining industry in Ethiopia especially in empowering small-scale Artisanal community. GUJI is one of the zones following outmoded techniques to identify minerals in mining industry. Rock mineral detection and classification employing conventional methods involves testing physical and chemical properties at both the micro- and macro-scale in the laboratory, which is expensive and time-consuming. Identifying tiny rock minerals and detecting its originality using traditional procedure and techniques takes too much time. Identification of minerals merely through visual observation is often erroneous. To address these problems, a deep learning approach for the classification and detection of Rock Minerals is proposed. The design- science research methodology is followed to achieve the objectives of the research. To conduct this study, 2000 images were collected from Guji’s zone and Mindat.org website. After collecting the images, image pre-processing techniques such as image resizing, image segmentation using roboflow, and image annotation are performed. Moreover, data augmentation is applied to balance the dataset and increase the number of images. This research work focuses on classifying and detecting fifteen types of rock minerals. Based on YOLOv7 deep learning model we have used 70% of the dataset to train the model and 30 % of the dataset to test the performance of the model. Finally, the developed model is evaluated using accuracy, precision, recall, and mAP with other models. Experimental result shows that the accuracy obtained from YOLOv7 is 76%mAP for large objects comparing to other models. Consequently, the pretrained weight of yolov7 achieved a 97.3% accuracy in classifying and detecting with other images
DEVELOPING A MORPHOLOGICAL ANALYZER AND GENERATOR FOR AWNGI VERB USING FINITE STATE TRANSDUCER
(Hawassa University, 2023-08-07) DAWIT KASSAHU ABEBE
Language is a powerful medium that coordinates day-to-day activities between an entity. The means of interaction between a computer and language structure is studied by natural language processing. Natural language processing has emerged as a means of increasing computers’ capability to understand natural languages. Awngi is one of the natural languages, grouped under the central Cushitic language family and spoken by more the 2.5 million people as native speakers settled in a different part of the Amhara regional state. It is a zona language that started as a medium of instruction in 1989 E.C. and uses the Geez script system. It is the one of under-resourced languages in Ethiopia. This morphological study is the first attempt that is used as a contribution to high-level NLP applications. The study is an implementation of the morphological analyzer and generation based on a finite-state transducer approach. The lexicon formalism is used to design the morphological analyzer and generation of the Awngi verb form because the Awngi language is morphologically very productive in terms of gender, person, number, and tenses. The system was developed using the foma programing language as an implementation toolkit. The foma programing language and the Ubuntu 22.04 Linux-based operating system are used for experimentation. For the experimental thirty-five distinct linguistic rules were developed to form those different word forms. The experimentshowsthat 76956 words were generated by the system out of this 93.6% of words are correctly generated and analyzed by the system and 6.4% of words were wrongly generated. The factors that the wrongly generated words were the problem of the epenthetic sound vowel እ/ɨ/ breaking up consonant clusters of words and the glottal approximant /h/, which is loaned from Amharic appears irregularly in the Awngi language word formation process. So, determining the correct phonological pattern of the glottal approximate /h/ and the alternative techniques for the epenthetic vowel እ/ɨ/ management is left for future work
DEVELOPING IMAGE-BASED ENSET PLANT DISEASE IDENTIFICATION USING CONVOLUTIONAL NEURAL NETWORK
(Hawassa University, 2020-11-07) UMER NURI MOHAMMED
Nowadays, decline in food plant productivity is a major problem causing food insecurity to which plant disease is one of the factors. Early identification and accurate diagnosis of the health status of food plants is hence critical to limit the spread of plant diseases and it should be in a technological manner rather than by the labor force. Traditional observation methods by farmers or domain experts is perhaps time-consuming, expensive and sometimes inaccurate. Based on the literature, the literature suggests that deep learning approaches are the most accurate models for the detection of plant disease. Convolutional Neural network (CNN) is one of the popular approaches that allows computational models that are composed of multiple processing layers to learn representations of image data with multiple levels of abstraction. These models have dramatically improved the state-of-the-art in visual object recognition and image classification that makes it a good way for enset plant disease classification problems. For this purpose, we used an appropriate CNN based model for identifying and classifying the three most critical diseases of enset plants: - enset bacterial wilt, enset Leaf spot, and Root mealybug diseases. Enset is one of a major source of food in the South, Central and Southwestern parts of Ethiopia. A total of 14,992 images are used for conducting experiments including augmented images with four different categories; three diseased and a healthy class obtained from the different agricultural sectors stationed at Hawassa and Worabe Ethiopia, these images are provided as input to the proposed model. Under the 10-fold cross-validation strategy, the experimental results show that the proposed model can effectively detect and classify four classes of enset plant diseases with the best classification accuracy of 99.53%, which is higher than compared to other classical deep learning models such as MobileNet and Inception v3 deep learning models