Browsing by Subject "Preprocessing"
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Item Automatic Text Summarization System for Nepali Language Based on Sentence Extraction(Department of Computer Science and I.T., 2013) Lamichhane, RajendraAutomated text summarization is a generic problem in the Natural Language Processing (NLP) community. It has grabbed great attention recently as the amount of information increases throughout the world, online and offline. As the volume and availability of data increases, it causes redundancy and scatterness over the world. So, there is the need of effective and powerful tool to summarize text documents automatically. So far, many researches have been done for English and other European languages with high performance. However, Nepali language still suffers from the little attentions and researches in this field. In this dissertation, a method has been proposed, which lets us to summarize Nepali text documents automatically based on sentence extraction techniques. The various stages involved in this approach which are: text preprocessing, feature extraction, sentence scoring and ranking, and summary generation. The proposed system is tested with various datasets collected from different sources such as books, newspapers, article, reports, etc. Automated evaluation techniques are used to validate the proposed system against the manual summaries. The overall accuracy of the proposed system is achieved as 79:18% precision, 71:77% recall and 75:02% F-Score. Cosine similarity measure gives overall similarity of 91:16% between manual summary and system summary.Item A Comparative Study Of Rainfall Prediction Neural Network And Decision Tree(Department of Computer Science and Information Technology, 2016) Shahi, RameshWeather forecasting is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems around the world. In this paper, data mining technique was used for forecasting precipitation. This was carried out using Artificial Neural Network and Decision Tree algorithms and meteorological data collected between 2004 and 2008 from the city of Kathmandu, Nepal. A data model for the meteorological data was developed and this was used to train the two different algorithms. The performances of these algorithms were compared using standard performance metrics, and the algorithm which gave the best results used to generate classification rules for the mean weather variables. A predictive Neural Network model was also developed for the weather prediction program and the results compared with actual weather data for the predicted periods. In order to build a model for Artificial Neural Network and Decision Tree. Among the total data set , 80%,70 % and 75 % data set, were used for training and 20%, 30 % and 35 % were used for the Testing. Experimentation results show, feed-forward multi layer Perceptron based neural network classifier has lower error rate than Decision Tree. MLP classification system has the average system accuracy rate of 77.98%, system error rate of 22.02%, precision rate of 12.08%, and recall rate of 78.57%. Similarly, Decision Tree system has the average system accuracy rate of 73.74%, system error rate of 26.26%, and precision rate of 9.52% recall rate of 71.42%.Item Nepali OCR(I.O.E. Pulchowk Campus, 2023-05) Bhusal, Abish; Chhetri, Gopal Baidawar; Bhattarai, Kiran; Pandey, Manjeethandwritten Nepali texts. Our system’s architecture consists of a Convolutional Neural Network (CNN) for feature extraction and a Recurrent Neural Network (RNN) for sequence recognition. The training dataset consists of around 80,000 Nepali handwritten images, which are preprocessed and augmented to increase the system’s robustness. NepaliOCR can be used to convert printed or handwritten Nepali text into editable digital formats, making it useful for a range of applications such as document digitization, language learning, and natural language processing. This paper presents an overview of the NepaliOCR system, including its architecture, training methodology, and performance evaluation. The lack of a reliable tool for Nepali handwriting recognition motivated us to develop this system. The system is developed as a part of the Bachelor in Computer Engineering Major Project. Machine learning has been used in the system to overcome the limitations of traditional computer systems. Our attempt to develop a tool for Nepali Handwriting Recognition using Machine Learning is discussed in this report. With the recent advancement in machine learning, our system shows great potential for practical applications in the digitization of Nepali handwriting.