Nawaraj Paudel | Tribhuvan University (original) (raw)

Papers by Nawaraj Paudel

Research paper thumbnail of Attention based Recurrent Neural Network for Nepali Text Summarization

Journal of Institute of Science and Technology

Automatic text summarization has been a challenging topic in natural language processing (NLP) as... more Automatic text summarization has been a challenging topic in natural language processing (NLP) as it demands preserving important information while summarizing the large text into a summary. Extractive and abstractive text summarization are widely investigated approaches for text summarization. In extractive summarization, the important sentence from the large text is extracted and combined to create a summary whereas abstractive summarization creates a summary that is more focused on meaning, rather than content. Therefore, abstractive summarization gained more attention from researchers in the recent past. However, text summarization is still an untouched topic in the Nepali language. To this end, we proposed an abstractive text summarization for Nepali text. Here, we, first, create a Nepali text dataset by scraping Nepali news from the online news portals. Second, we design a deep learning-based text summarization model based on an encoder-decoder recurrent neural network with at...

Research paper thumbnail of Comparative Analysis of K-Means and Enhanced K-Means Algorithms for Clustering

NUTA Journal

Clustering in data mining is a way of organizing a set of objects in such a way that the objects ... more Clustering in data mining is a way of organizing a set of objects in such a way that the objects in same bunch are more comparable and relevant to each other than to those objects in other bunches. In the modern information retrieval system, clustering algorithms are better if they result high quality clusters in efficient time. This study includes analysis of clustering algorithms k-means and enhanced k-means algorithm over the wholesale customers and wine data sets respectively. In this research, the enhanced k-means algorithm is found to be 5% faster for wholesale customers dataset for 4 clusters and 49%, 38% faster when the clusters size is increased to 8 and 13 respectively. The wholesale customers dataset when classified with 18 clusters the speedup was seen to be 29%. Similarly, in the case of wine dataset, the speed up is seen to be 10%, 30%, 49%, and 41% for 3, 8, 13 and 18 clusters respectively. Both of the algorithms are found very similar in terms of the clustering accur...

Research paper thumbnail of Mushroom Classification using Random Forest and REP Tree Classifiers

Nepal journal of mathematical sciences, Aug 31, 2022

Research paper thumbnail of A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification

Computational Intelligence and Neuroscience

COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to... more COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to this date. The reason for peoples’ death is not only linked to its infection but also to peoples’ mental states and sentiments triggered by the fear of the virus. People’s sentiments, which are predominantly available in the form of posts/tweets on social media, can be interpreted using two kinds of information: syntactical and semantic. Herein, we propose to analyze peoples’ sentiment using both kinds of information (syntactical and semantic) on the COVID-19-related twitter dataset available in the Nepali language. For this, we, first, use two widely used text representation methods: TF-IDF and FastText and then combine them to achieve the hybrid features to capture the highly discriminating features. Second, we implement nine widely used machine learning classifiers (Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, Decision Trees, Random Forest, Extreme Tr...

Research paper thumbnail of Examining Adoption of Mobile Game in Nepal with Extended TAM

International Journal of Business Information Systems

Research paper thumbnail of Nepali Text Document Classification Using Deep Neural Network

Tribhuvan University Journal

An automated text classification is a well-studied problem in text mining which generally demand... more An automated text classification is a well-studied problem in text mining which generally demands the automatic assignment of a label or class to a particular text documents on the basis of its content. To design a computer program that learns the model form training data to assign the specific label to unseen text document, many researchers has applied deep learning technologies. For Nepali language, this is first attempt to use deep learning especially Recurrent Neural Network (RNN) and compare its performance to traditional Multilayer Neural Network (MNN). In this study, the Nepali texts were collected from online News portals and their pre-processing and vectorization was done. Finally deep learning classification framework was designed and experimented for ten experiments: five for Recurrent Neural Network and five for Multilayer Neural Network. On comparing the result of the MNN and RNN, it can be concluded that RNN outperformed the MNN as the highest accuracy achieved by MNN...

Research paper thumbnail of Ta, Ti and Hf effects on Nb<sub>3</sub>Sn high-field performance: temperature-dependent dopant occupancy and failure of Kramer extrapolation

Superconductor Science and Technology, Nov 4, 2019

The increasing demand for improving the high-field (16-22 T) performance of Nb3Sn conductors requ... more The increasing demand for improving the high-field (16-22 T) performance of Nb3Sn conductors requires a better understanding of the properties of modern wires much closer to irreversibility field, HIrr. In this study we investigated the impact of Ta, Ti and Hf doping on the high-field pinning properties, the upper critical field, Hc2, and HIrr. We found that the pinning force curves of commercial Ti and Ta doped wires at different temperatures do not scale and that the Kramer extrapolation, typically used by magnet designers to estimate highfield critical current density and magnet operational margins from lower field data, is not reliable and significantly overestimates the actual HIrr. In contrast, new laboratory scale conductors made with Nb-Ta-Hf alloy have improved high-field Jc performance and, despite contributions by both grain boundary and point defect pinning mechanisms, have more predictable high-field behavior. Using Extended X-ray Absorption Fine Structure spectroscopy, EXAFS, we found that for the commercial Ta and Ti doped conductors, the Ta site occupancy in the A15 structure gradually changes with the heat treatment temperature whereas Ti is always located on the Nb site with clear consequences for Hc2. This work reveals the still limited understanding of what determines Hc2, HIrr and the high-field Jc performance of Nb3Sn and the complexity of optimizing these conductors so that they can reach their full potential for high-field applications.

Research paper thumbnail of Cost-Based Query Optimization in Centralized Relational Databases

Journal of Institute of Science and Technology

Query optimization is the most significant factor for any centralized relational database managem... more Query optimization is the most significant factor for any centralized relational database management system (RDBMS) that reduces the total execution time of a query. Query optimization is the process of executing a SQL (Structured Query Language) query in relational databases to determine the most efficient way to execute a given query by considering the possible query plans. The goal of query optimization is to optimize the given query for the sake of efficiency. Cost-based query optimization compares different strategies based on relative costs (amount of time that the query needs to run) and selects and executes one that minimizes the cost. The cost of a strategy is just an estimate based on how many estimated CPU and I/O resources that the query will use. In this paper, cost is considered by counting number of disk accesses for each query plan because disk access tends to be the dominant cost in query processing for centralized relational databases.

Research paper thumbnail of Attention based Recurrent Neural Network for Nepali Text Summarization

Journal of Institute of Science and Technology

Automatic text summarization has been a challenging topic in natural language processing (NLP) as... more Automatic text summarization has been a challenging topic in natural language processing (NLP) as it demands preserving important information while summarizing the large text into a summary. Extractive and abstractive text summarization are widely investigated approaches for text summarization. In extractive summarization, the important sentence from the large text is extracted and combined to create a summary whereas abstractive summarization creates a summary that is more focused on meaning, rather than content. Therefore, abstractive summarization gained more attention from researchers in the recent past. However, text summarization is still an untouched topic in the Nepali language. To this end, we proposed an abstractive text summarization for Nepali text. Here, we, first, create a Nepali text dataset by scraping Nepali news from the online news portals. Second, we design a deep learning-based text summarization model based on an encoder-decoder recurrent neural network with at...

Research paper thumbnail of Comparative Analysis of K-Means and Enhanced K-Means Algorithms for Clustering

NUTA Journal

Clustering in data mining is a way of organizing a set of objects in such a way that the objects ... more Clustering in data mining is a way of organizing a set of objects in such a way that the objects in same bunch are more comparable and relevant to each other than to those objects in other bunches. In the modern information retrieval system, clustering algorithms are better if they result high quality clusters in efficient time. This study includes analysis of clustering algorithms k-means and enhanced k-means algorithm over the wholesale customers and wine data sets respectively. In this research, the enhanced k-means algorithm is found to be 5% faster for wholesale customers dataset for 4 clusters and 49%, 38% faster when the clusters size is increased to 8 and 13 respectively. The wholesale customers dataset when classified with 18 clusters the speedup was seen to be 29%. Similarly, in the case of wine dataset, the speed up is seen to be 10%, 30%, 49%, and 41% for 3, 8, 13 and 18 clusters respectively. Both of the algorithms are found very similar in terms of the clustering accur...

Research paper thumbnail of Mushroom Classification using Random Forest and REP Tree Classifiers

Nepal journal of mathematical sciences, Aug 31, 2022

Research paper thumbnail of A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification

Computational Intelligence and Neuroscience

COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to... more COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to this date. The reason for peoples’ death is not only linked to its infection but also to peoples’ mental states and sentiments triggered by the fear of the virus. People’s sentiments, which are predominantly available in the form of posts/tweets on social media, can be interpreted using two kinds of information: syntactical and semantic. Herein, we propose to analyze peoples’ sentiment using both kinds of information (syntactical and semantic) on the COVID-19-related twitter dataset available in the Nepali language. For this, we, first, use two widely used text representation methods: TF-IDF and FastText and then combine them to achieve the hybrid features to capture the highly discriminating features. Second, we implement nine widely used machine learning classifiers (Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, Decision Trees, Random Forest, Extreme Tr...

Research paper thumbnail of Examining Adoption of Mobile Game in Nepal with Extended TAM

International Journal of Business Information Systems

Research paper thumbnail of Nepali Text Document Classification Using Deep Neural Network

Tribhuvan University Journal

An automated text classification is a well-studied problem in text mining which generally demand... more An automated text classification is a well-studied problem in text mining which generally demands the automatic assignment of a label or class to a particular text documents on the basis of its content. To design a computer program that learns the model form training data to assign the specific label to unseen text document, many researchers has applied deep learning technologies. For Nepali language, this is first attempt to use deep learning especially Recurrent Neural Network (RNN) and compare its performance to traditional Multilayer Neural Network (MNN). In this study, the Nepali texts were collected from online News portals and their pre-processing and vectorization was done. Finally deep learning classification framework was designed and experimented for ten experiments: five for Recurrent Neural Network and five for Multilayer Neural Network. On comparing the result of the MNN and RNN, it can be concluded that RNN outperformed the MNN as the highest accuracy achieved by MNN...

Research paper thumbnail of Ta, Ti and Hf effects on Nb<sub>3</sub>Sn high-field performance: temperature-dependent dopant occupancy and failure of Kramer extrapolation

Superconductor Science and Technology, Nov 4, 2019

The increasing demand for improving the high-field (16-22 T) performance of Nb3Sn conductors requ... more The increasing demand for improving the high-field (16-22 T) performance of Nb3Sn conductors requires a better understanding of the properties of modern wires much closer to irreversibility field, HIrr. In this study we investigated the impact of Ta, Ti and Hf doping on the high-field pinning properties, the upper critical field, Hc2, and HIrr. We found that the pinning force curves of commercial Ti and Ta doped wires at different temperatures do not scale and that the Kramer extrapolation, typically used by magnet designers to estimate highfield critical current density and magnet operational margins from lower field data, is not reliable and significantly overestimates the actual HIrr. In contrast, new laboratory scale conductors made with Nb-Ta-Hf alloy have improved high-field Jc performance and, despite contributions by both grain boundary and point defect pinning mechanisms, have more predictable high-field behavior. Using Extended X-ray Absorption Fine Structure spectroscopy, EXAFS, we found that for the commercial Ta and Ti doped conductors, the Ta site occupancy in the A15 structure gradually changes with the heat treatment temperature whereas Ti is always located on the Nb site with clear consequences for Hc2. This work reveals the still limited understanding of what determines Hc2, HIrr and the high-field Jc performance of Nb3Sn and the complexity of optimizing these conductors so that they can reach their full potential for high-field applications.

Research paper thumbnail of Cost-Based Query Optimization in Centralized Relational Databases

Journal of Institute of Science and Technology

Query optimization is the most significant factor for any centralized relational database managem... more Query optimization is the most significant factor for any centralized relational database management system (RDBMS) that reduces the total execution time of a query. Query optimization is the process of executing a SQL (Structured Query Language) query in relational databases to determine the most efficient way to execute a given query by considering the possible query plans. The goal of query optimization is to optimize the given query for the sake of efficiency. Cost-based query optimization compares different strategies based on relative costs (amount of time that the query needs to run) and selects and executes one that minimizes the cost. The cost of a strategy is just an estimate based on how many estimated CPU and I/O resources that the query will use. In this paper, cost is considered by counting number of disk accesses for each query plan because disk access tends to be the dominant cost in query processing for centralized relational databases.