Computer Science

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

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Start date: 2023Subject: search.filters.subject.Deep Learning

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Now showing 1 - 5 of 5
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    ENSET DISEASE DETECTION AND CLASSIFICATION USING DEEP LEARNING TECHNIQUES
    (Hawassa University, 2024-12-10) : ENDASHAW NIGUSE ASTATEKE
    Ethiopians, especially those in Sidama and Central Ethiopia, are the main consumers of enset. Enset is thought to be a staple food source for 20 million people in Ethiopia. Ethiopians mostly employ enset plants as a staple food crop. For many Ethiopians, the plant's root and stems are a major source of energy due to their high fiber and carbohydrate content. Typically, stems are picked, cleaned, and fermented to produce kocho and bulla, which are food items that are similar to bread. This research investigates the application of deep learning techniques, specifically Convolutional Neural Networks (CNNs), for the detection and classification of diseases affecting Enset leaves and stems. By employing sophisticated image processing tools and methodologies, the study aims to improve the accuracy and efficiency of disease identification in Enset plants. Experimental findings underscore the effectiveness of the proposed CNN models in achieving notable accuracy rates in disease detection, showcasing the potential of deep learning in revolutionizing agricultural practices. The study not only emphasizes the importance of advanced image processing in agricultural contexts but also underscores the necessity for further research in crop disease detection to enhance agricultural sustainability and productivity. We collected from the Central Ethiopia Region (Wonago and Dilla) and the Sidama Region (Hawassa Zuria, Boricha, Yirgalem, and Aletawondo) to use a 5,000 image dataset. There are 1000 images in each class. A total of 700 training, 200 validation, and 100 testing images were chosen. We used pre-trained models, MobileNetV3Small and EfficientNetB7, to compare the results with the newly developed model. Bacterial wilt, Mosaic Virus, Bacterial leaf spot, Insect pest, and Healthy Leaves are the disease classes. The Nadam optimizer, 32 batch sizes, 65 epochs, and 0.001 learning rate are the selected hyperparameters. The model was stable at epoch 65 and has an accuracy rate of 99.30%. EfficientNetB7 and MobileNetV3Small, the pre trained models, have accuracy rates of 95.32% and 93.08%, respectively. The developed model's output has a high degree of accuracy in identifying and classifying diseases in Enset leaves
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    AMHARIC EXTRACTIVE TEXT SUMMARIZATION USING AmRoBERTa –BiLSTM MODEL
    (Hawassa University, 2024-04-14) 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
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    Multi-label Coffee Bean Classification Using Deep Learning
    (Hawassa University, 2024-04-12) Chernet Ewawey
    Coffee is a critical agricultural product and a significant economic commodity, particularly in Ethiopia, where it serves as a major export. Traditional methods of coffee bean assessment are labor-intensive and prone to human error, necessitating the development of automated, accurate solutions. This study addresses this problem by applying deep learning techniques, specifically Convolutional Neural Networks (CNNs), to classify coffee beans based on their quality and geographical origin. The primary objective of this research is to design and develop a robust CNN model capable of accurately classifying coffee beans using image data. To achieve this, we collected an extensive dataset of 3,965 coffee beans from the Ethiopian regions of Arsi, Yirgacheffe, Guji, and Sidama, photographing each bean from the front and back to obtain a total of 6,373 images. To enhance the dataset's balance, an additional 2,417 images were generated through augmentation, bringing the final dataset to 8,790 images. The methodology involved preprocessing these images, followed by training and evaluating multiple CNN architectures. Techniques employed include the use of both grayscale and RGB image data, Dropout layers to prevent over-fitting, and the Adam optimizer for efficient training. Our results indicate that the CNN model trained on RGB images achieved a peak accuracy of 99.66% in a specific experiment, while the average accuracy across all experiments was 99.06%. This highlights the significance of color information in enhancing feature extraction and model learning. The model's high accuracy and efficiency demonstrate its potential to automate and improve coffee quality assessment, offering a valuable tool for the coffee industry. By leveraging deep learning techniques, this study provides a scalable and precise solution for the classification of coffee beans, potentially transforming quality control processes and setting new standards in the coffee industry. The study's findings underscore the superiority of CNN-based models in handling complex image data, particularly when color features play a crucial role in classification tasks
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    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.
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    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.