http://ir.bdu.edu.et/handle/123456789/10142 Sat, 13 Jan 2001 07:28:08 GMT 2001-01-13T07:28:08Z http://ir.bdu.edu.et/handle/123456789/16473 DESIGNING NEXT PHRASE PREDICTION MODEL FOR AMHARIC LANGUAGE USING DEEP LEARNING TECHNIQUES WELELA, AMESALU Text entry is an essential aspect of human-computer interaction and can be performed through a keyboard, which mostly contains English letters. Typing Amharic text on a computer system may pose challenges like decreased typing speed, spelling, and grammar errors. These challenges allow to introduce of text prediction that facilitates fast entry of text into computers and handheld devices. Previous studies about Amharic next-word prediction lacked syntactic agreement due to inaccurate part-of-speech tagging. Additionally, a single word did not capture the context of the sentences. This study aims to design next phrase prediction model using deep learning approaches. The dataset for the prediction model was collected from Amharic student textbooks, Amharic teacher's guidebooks, Amharic Grammar entitled የአማርኛ ሰዋሰው by Baye Yimam, and news from Amhara mass media. The collected Amharic sentences required preprocessing, part of speech tagged with a pre-trained model, and a rule-based chunk tagged for the model development. Two prediction models were designed using LSTM and Encoder-Decoder deep learning techniques to compare and select the optimum one. The prediction models are trained using 2176 simple declarative sentences with split ratios of 80%, 10%, 10%, and 70%, 15%, and 15% for training, validation, and, testing sets. The accuracy of proposed models achieved 68.8%, and 70.4% in Encoder Decoder and LSTM respectively on the former split ratio. The LSTM model performs better than the Encoder-decoder model with a split of 80%, 10%, and 10% for training, testing, and validation sets. The finding of this study has a valuable role, especially for non–native and dyslexia users in typing coherent sentences as well as capturing the context of sentences by considering sequences of words rather than individual words. This study was limited to declarative sentences and syntactic information, which leads future researchers to encompass other types of sentences with semantic meanings. Keywords: Phrase prediction, deep learning, Long Short Term Memory, Encoder- Decoder, sentence chunk Thu, 01 Feb 2024 00:00:00 GMT http://ir.bdu.edu.et/handle/123456789/16473 2024-02-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/16471 AUTHENTICATING HOLY PICTURES USING DEEP LEARNING Temesgen, Muche The classification of holy pictures, particularly within the Ethiopian Orthodox Tewahedo Church (EOTC), presents a significant challenge due to the diverse cultural influences and intricate historical backgrounds shaping these images. This research addresses the pressing need for an accurate and efficient classification framework capable of distinguishing between authentic and manipulated holy pictures. Previous methodologies have struggled to capture the nuanced variations in color, object types, positions, orientations, and border characteristics inherent in these images, leading to suboptimal classification accuracy. To bridge this gap, we propose a novel deep learning-based approach that leverages object detection techniques, color feature extraction, and advanced neural network architectures to enhance classification accuracy. Our experimental evaluation, conducted on a comprehensive dataset comprising 8208 holy pictures, demonstrates the efficacy of the proposed framework. By integrating YOLOv8 object detection with the XceptionV3 model and incorporating channel attention and color space features, we achieved significant improvements in classification accuracy. Specifically, our model attained an accuracy of 96.5%, a precision of 97.3%, a recall of 95.7%, and an F1-score of 96.4%. These results underscore the effectiveness of our approach in accurately classifying holy pictures and distinguishing between authentic and manipulated images. Keywords: channel attention, color space; YOLOv8; object detection Sat, 01 Jun 2024 00:00:00 GMT http://ir.bdu.edu.et/handle/123456789/16471 2024-06-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/16470 Hate Speech Detection for Facebook Amharic Text Data Using Machine Learning TEGEGNE, YEMATA Mon, 01 Jan 2024 00:00:00 GMT http://ir.bdu.edu.et/handle/123456789/16470 2024-01-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/16469 Develop a Prediction Model for the Educational Opportunity of Orphans and Vulnerable Children in Bahir Dar City Using Machine Learning Approach Selam, Simeneh Anagaw Mon, 10 Jun 2024 00:00:00 GMT http://ir.bdu.edu.et/handle/123456789/16469 2024-06-10T00:00:00Z