First of all, I would like to express my gratitude towards THAPAR UNIVERSITY, for providing me a ... more First of all, I would like to express my gratitude towards THAPAR UNIVERSITY, for providing me a platform to do my thesis work at such an esteemed institute. I wish to express my deep gratitude to Mr. Parteek Bhatia, Assistant Professor, Computer Science and Engineering Department, Thapar University, Patiala for his valuable advice and guidance in carrying out my thesis. I would like to thank Dr. Maninder Singh, Head, Computer Science and Engineering Department, Thapar University, Patiala who has been a constant source of inspiration for me throughout this work. I am also thankful to all the staff members of the Department for their full cooperation and help. I am Thankful to my parents, brother and all my friends for their blessing and moral support. Thanks for boosting me with their constant encouragement, support and confidence. Last but not the least, I am thankful to God for providing me with the strength and ability to complete my work.
Abstract- Morphological analysis is a very important task in Natural Language Processing. Develop... more Abstract- Morphological analysis is a very important task in Natural Language Processing. Developing a knowledge base of legal words and morphological rules is an important task in computational linguistics. The paper focuses on how in-depth morphological analysis helps in showing the morphological strength of Indian Languages. In the process of working towards morphological analysis for Hindi language, we have implemented an algorithm. We discuss here how the morph analyzer is helpful in the NLP tasks by describing the working of morph analyzer. Further, we discuss the consequences of algorithm implemented by which the coverage of existing morph analyzer is improved.
Nearly 161 million persons live with a disabling visual impairment, of whom 37 million are blind.... more Nearly 161 million persons live with a disabling visual impairment, of whom 37 million are blind. About 90 % of them live in developing countries of Africa, Asia, Latin America and the Pacific Regions. 9 out of 10 blind children in developing countries have no access to education. The system of embossed writing invented by Louis Braille gradually came to be accepted throughout the world as the fundamental form of written communication for blind individuals. This paper is concerned about the transliteration of English and Hindi text to Braille. Braille is a dotted pattern used by the blind people for reading and writing. In this paper we have discussed the possible ways to teach Blind people [11]..Braille has become the pre-eminent tactile alphabet. Its characters are six- dot cells, two wide by three tall. Any of
Abstract- This paper presents a review for automatic text summarization techniques. Text Summariz... more Abstract- This paper presents a review for automatic text summarization techniques. Text Summarization is extraction of required information from the huge documents by ignoring the irrelevant details with the help of distinct approaches and provides the summary in compact way which reduces the effort and time of user. Due to the huge availability of data in all the fields the management and study of the information become difficult. Due to this the interest in the research of new automatic summarization techniques has been increased. Different approaches are used to determine summary. Plain text and multilingual text summarization play very important role in summary generation. This paper provides the review of all the existing text summarization techniques. Keywords-Summary;UNL,NLization, UNLization;IAN; EUGENE;Multilingual.
Chapter Objectives ✓ To learn to install Weka and the R language ✓ To demonstrate the use of Weka... more Chapter Objectives ✓ To learn to install Weka and the R language ✓ To demonstrate the use of Weka software ✓ To experiment with Weka on the Iris dataset ✓ To introduce basics of R language ✓ To experiment with R on the Iris dataset About Weka In this book, all data mining algorithms are explained with Weka and R language. The learner can perform and apply these algorithms easily using these well-know data mining tool and language. Let's first discuss the Weka tool. Weka is an open-source software under the GNU General Public License System. It was developed by the Machine Learning Group, University of Waikato, New Zealand. Although named after a flightless New Zealand bird, ‘WEKA’ stands for Waikato Environment for Knowledge Analysis. The system is written using the object oriented language Java. Weka is data mining software and it is a set of machine learning algorithms that can be applied to a dataset directly, or called from your own Java code. Weka contains tools for data pr...
Chapter Objectives ✓ To apply the K-means algorithm in Weka and R language ✓ To interpret the res... more Chapter Objectives ✓ To apply the K-means algorithm in Weka and R language ✓ To interpret the results of clustering ✓ To identify the optimum number of clusters ✓ To apply classification on un-labeled data by using clustering as an intermediate step Introduction As discussed earlier, if data is not labeled then we can analyze this data by performing a clustering analysis, where clustering refers to the task of grouping a set of objects into classes of similar objects. In this chapter, we will apply clustering on Fisher's Iris dataset. We will use clustering algorithms to group flower samples into clusters with similar flower dimensions. These clusters then become possible ways to group flowers samples into species. We will implement a simple k-means algorithm to cluster numerical attributes with the help of Weka and R. In the case of classification, we know the attributes and classes of instances. For example, the flower dimensions and classes were already known to us for the Iris dataset. Our goal was to predict the class of an unknown sample as shown in Figure 8.1. Earlier, we used the Weka J48 classification algorithm to build a decision tree on Fisher's Iris dataset using samples with known class, which helped in predicting the class of unknown samples. We used the flower's Sepal length and width, and the Petal length and width as the specific attributes for this. Based on flower dimensions and using this tree, we can identify an unknown Iris as one of three species, Setosa, Versicolor, and Virginica. In clustering, we know the attributes for the instances, but we don't know the classes. For example, we know the flower dimensions for samples of the Iris dataset but we don't know what classes exist as shown in Figure 8.2. Therefore, our goal is to group instances into clusters with similar attributes or dimensions and then identify the class. In this chapter, we will learn what happens if we don't know what classes the samples belong to, or even how many classes there are, or even what defines a class? Since, Fisher's Iris dataset is already labeled, we will first make this dataset unlabeled by removing the class attribute, i.e., the species column. Then, we will apply clustering algorithms to cluster this data on the basis of its input attributes, i.e., Sepal length, Sepal width, Petal length, and Petal width.
Chapter Objectives ✓ To understand the concept of machine learning and its applications. ✓ To und... more Chapter Objectives ✓ To understand the concept of machine learning and its applications. ✓ To understand what are supervised and unsupervised machine learning strategies. ✓ To understand the concept of regression and classification. ✓ To identify the strategy to be applied to a given problem. Introduction to Machine Learning Machine Learning (ML) has emerged as the most extensively used tool for web-sites to classify surfers and address them appropriately. When we surf the Net, we are exposed to machine learning algorithms multiple times a day, often without realizing it. Machine learning is used by search engines such as Google and Bing to rank web pages or to decide which advertisement to show to which user. It is used by social networks such as Facebook and Instagram to generate a custom feed for every user or to tag the user by the picture that was uploaded. It is also used by banks to detect whether an online transaction is genuine or fraudulent and by e-commerce websites such ...
... Thapar University, Patiala By: Rohan Sharma (80832020) Under the supervision of: Mr. Parteek ... more ... Thapar University, Patiala By: Rohan Sharma (80832020) Under the supervision of: Mr. Parteek Bhatia Senior Lecturer, CSED ... degree of this or any other university. ( Rohan Sharma ) This is to certify that the above statement made by the candidate is correct and true to the ...
Abstract: Machine Translation (MT) refers to the use of computers for the task of translating aut... more Abstract: Machine Translation (MT) refers to the use of computers for the task of translating automatically from one language to another. The differences between languages and especially the inherent ambiguity of language make MT a very difficult problem. ...
......................................................................................... ...iii ... more ......................................................................................... ...iii Table of
Chapter Objectives ✓ To apply the K-means algorithm in Weka and R language ✓ To interpret the res... more Chapter Objectives ✓ To apply the K-means algorithm in Weka and R language ✓ To interpret the results of clustering ✓ To identify the optimum number of clusters ✓ To apply classification on un-labeled data by using clustering as an intermediate step Introduction As discussed earlier, if data is not labeled then we can analyze this data by performing a clustering analysis, where clustering refers to the task of grouping a set of objects into classes of similar objects. In this chapter, we will apply clustering on Fisher's Iris dataset. We will use clustering algorithms to group flower samples into clusters with similar flower dimensions. These clusters then become possible ways to group flowers samples into species. We will implement a simple k-means algorithm to cluster numerical attributes with the help of Weka and R. In the case of classification, we know the attributes and classes of instances. For example, the flower dimensions and classes were already known to us for the Ir...
This paper describes an attempt to develop Punjabi WordNet by using expansion approach from Hindi... more This paper describes an attempt to develop Punjabi WordNet by using expansion approach from Hindi WordNet under Indradhanush WordNet Project. The origin, symbols, morphological and syntactic characteristics of Punjabi Language are presented in this paper. The lexical semantic relations used in Punjabi WordNet are elaborated. The need for synset categorization and the results of this categorization for Punjabi Language is also presented in this paper.
Quality of life of people in India is demeaning with the growing traffic congestion. Congestion l... more Quality of life of people in India is demeaning with the growing traffic congestion. Congestion leads to air pollution, decrease in accessibility and increased travel time. System and technology is growing even in microseconds. GPS is mounting its reputation in day to day life applications. With increasing use of smart phones and growing popularity of the GPS based applications, it has become feasible for the commuter to track the real time position of the vehicle and make better travelling decision. This paper aims to build the client server android based application for the efficient vehicle tracking.
28 Abstract— It was not possible in case of traditional database to store, analyze, visualize, an... more 28 Abstract— It was not possible in case of traditional database to store, analyze, visualize, and integrate spatial data in business and government applications. GIS systems store spatial and non-spatial data separately. This split data model has several drawbacks. Oracle spatial provides a completely open and standards-based architecture for the management of spatial data within a database management system. This research paper describes the process of developing a spatial application of Patiala city using Oracle spatial technologies. A simple graph is first created using the map of Patiala (obtained from Google Maps tool) and the coordinates of main locations of Patiala are figured out using this graph. A spatial database is then designed by using tables for colleges, roads, banks and Thapar University. MapViewer tool is used to integrate and visualize spatial data with maps. Oracle Map Builder tool is used to define the MapViewer mapping metadata that includes styles, themes and...
... 4.2.3 Insertion into Spatial Tables .....40 4.3 Working with Map Builder Tool: Defining Mappi... more ... 4.2.3 Insertion into Spatial Tables .....40 4.3 Working with Map Builder Tool: Defining MappingMetadata .....42 ... Table 4.5: Schema of table STBL_MISC.....36 ... Intergraph, and MapInfo. ...
Chapter Objectives ✓ To demonstrate the use of the decision tree ✓ To apply the decision tree on ... more Chapter Objectives ✓ To demonstrate the use of the decision tree ✓ To apply the decision tree on a sample dataset ✓ To implement a decision tree process using Weka and R Building a Decision Tree Classifier in Weka In this chapter, we will learn how Weka's decision tree feature helps to classify unknown samples of a dataset based on its attribute values. When Weka's decision tree is applied to an unknown sample, the decision tree classifies the sample into different classes such as Class A, Class B and Class C as shown in Figure 6.1. For example, if we want to predict the class of an unknown sample of a flower based on the length and width dimensions of its Sepal and Petal. The first step would be to measure Sepal length and width and Petal length and width of an unknown flower and compare these dimensions to the values of the samples in our dataset of known species. The decision tree algorithm of Weka will help in creating decision rules to predict the class of unknown flowe...
Abstract: Machine Translation is an important part of Natural Language Processing. It refers to u... more Abstract: Machine Translation is an important part of Natural Language Processing. It refers to using machine to convert one natural language to another. Statistical Machine Translation is a part of Machine Translation that strives to use machine learning paradigm towards translating text. Statistical Machine Translation consists of Language Model (LM), Translation Model (TM) and decoder. In this thesis, English to Hindi Statistical Machine Translation system has been developed. The development of Language Model, ...
First of all, I would like to express my gratitude towards THAPAR UNIVERSITY, for providing me a ... more First of all, I would like to express my gratitude towards THAPAR UNIVERSITY, for providing me a platform to do my thesis work at such an esteemed institute. I wish to express my deep gratitude to Mr. Parteek Bhatia, Assistant Professor, Computer Science and Engineering Department, Thapar University, Patiala for his valuable advice and guidance in carrying out my thesis. I would like to thank Dr. Maninder Singh, Head, Computer Science and Engineering Department, Thapar University, Patiala who has been a constant source of inspiration for me throughout this work. I am also thankful to all the staff members of the Department for their full cooperation and help. I am Thankful to my parents, brother and all my friends for their blessing and moral support. Thanks for boosting me with their constant encouragement, support and confidence. Last but not the least, I am thankful to God for providing me with the strength and ability to complete my work.
Abstract- Morphological analysis is a very important task in Natural Language Processing. Develop... more Abstract- Morphological analysis is a very important task in Natural Language Processing. Developing a knowledge base of legal words and morphological rules is an important task in computational linguistics. The paper focuses on how in-depth morphological analysis helps in showing the morphological strength of Indian Languages. In the process of working towards morphological analysis for Hindi language, we have implemented an algorithm. We discuss here how the morph analyzer is helpful in the NLP tasks by describing the working of morph analyzer. Further, we discuss the consequences of algorithm implemented by which the coverage of existing morph analyzer is improved.
Nearly 161 million persons live with a disabling visual impairment, of whom 37 million are blind.... more Nearly 161 million persons live with a disabling visual impairment, of whom 37 million are blind. About 90 % of them live in developing countries of Africa, Asia, Latin America and the Pacific Regions. 9 out of 10 blind children in developing countries have no access to education. The system of embossed writing invented by Louis Braille gradually came to be accepted throughout the world as the fundamental form of written communication for blind individuals. This paper is concerned about the transliteration of English and Hindi text to Braille. Braille is a dotted pattern used by the blind people for reading and writing. In this paper we have discussed the possible ways to teach Blind people [11]..Braille has become the pre-eminent tactile alphabet. Its characters are six- dot cells, two wide by three tall. Any of
Abstract- This paper presents a review for automatic text summarization techniques. Text Summariz... more Abstract- This paper presents a review for automatic text summarization techniques. Text Summarization is extraction of required information from the huge documents by ignoring the irrelevant details with the help of distinct approaches and provides the summary in compact way which reduces the effort and time of user. Due to the huge availability of data in all the fields the management and study of the information become difficult. Due to this the interest in the research of new automatic summarization techniques has been increased. Different approaches are used to determine summary. Plain text and multilingual text summarization play very important role in summary generation. This paper provides the review of all the existing text summarization techniques. Keywords-Summary;UNL,NLization, UNLization;IAN; EUGENE;Multilingual.
Chapter Objectives ✓ To learn to install Weka and the R language ✓ To demonstrate the use of Weka... more Chapter Objectives ✓ To learn to install Weka and the R language ✓ To demonstrate the use of Weka software ✓ To experiment with Weka on the Iris dataset ✓ To introduce basics of R language ✓ To experiment with R on the Iris dataset About Weka In this book, all data mining algorithms are explained with Weka and R language. The learner can perform and apply these algorithms easily using these well-know data mining tool and language. Let's first discuss the Weka tool. Weka is an open-source software under the GNU General Public License System. It was developed by the Machine Learning Group, University of Waikato, New Zealand. Although named after a flightless New Zealand bird, ‘WEKA’ stands for Waikato Environment for Knowledge Analysis. The system is written using the object oriented language Java. Weka is data mining software and it is a set of machine learning algorithms that can be applied to a dataset directly, or called from your own Java code. Weka contains tools for data pr...
Chapter Objectives ✓ To apply the K-means algorithm in Weka and R language ✓ To interpret the res... more Chapter Objectives ✓ To apply the K-means algorithm in Weka and R language ✓ To interpret the results of clustering ✓ To identify the optimum number of clusters ✓ To apply classification on un-labeled data by using clustering as an intermediate step Introduction As discussed earlier, if data is not labeled then we can analyze this data by performing a clustering analysis, where clustering refers to the task of grouping a set of objects into classes of similar objects. In this chapter, we will apply clustering on Fisher's Iris dataset. We will use clustering algorithms to group flower samples into clusters with similar flower dimensions. These clusters then become possible ways to group flowers samples into species. We will implement a simple k-means algorithm to cluster numerical attributes with the help of Weka and R. In the case of classification, we know the attributes and classes of instances. For example, the flower dimensions and classes were already known to us for the Iris dataset. Our goal was to predict the class of an unknown sample as shown in Figure 8.1. Earlier, we used the Weka J48 classification algorithm to build a decision tree on Fisher's Iris dataset using samples with known class, which helped in predicting the class of unknown samples. We used the flower's Sepal length and width, and the Petal length and width as the specific attributes for this. Based on flower dimensions and using this tree, we can identify an unknown Iris as one of three species, Setosa, Versicolor, and Virginica. In clustering, we know the attributes for the instances, but we don't know the classes. For example, we know the flower dimensions for samples of the Iris dataset but we don't know what classes exist as shown in Figure 8.2. Therefore, our goal is to group instances into clusters with similar attributes or dimensions and then identify the class. In this chapter, we will learn what happens if we don't know what classes the samples belong to, or even how many classes there are, or even what defines a class? Since, Fisher's Iris dataset is already labeled, we will first make this dataset unlabeled by removing the class attribute, i.e., the species column. Then, we will apply clustering algorithms to cluster this data on the basis of its input attributes, i.e., Sepal length, Sepal width, Petal length, and Petal width.
Chapter Objectives ✓ To understand the concept of machine learning and its applications. ✓ To und... more Chapter Objectives ✓ To understand the concept of machine learning and its applications. ✓ To understand what are supervised and unsupervised machine learning strategies. ✓ To understand the concept of regression and classification. ✓ To identify the strategy to be applied to a given problem. Introduction to Machine Learning Machine Learning (ML) has emerged as the most extensively used tool for web-sites to classify surfers and address them appropriately. When we surf the Net, we are exposed to machine learning algorithms multiple times a day, often without realizing it. Machine learning is used by search engines such as Google and Bing to rank web pages or to decide which advertisement to show to which user. It is used by social networks such as Facebook and Instagram to generate a custom feed for every user or to tag the user by the picture that was uploaded. It is also used by banks to detect whether an online transaction is genuine or fraudulent and by e-commerce websites such ...
... Thapar University, Patiala By: Rohan Sharma (80832020) Under the supervision of: Mr. Parteek ... more ... Thapar University, Patiala By: Rohan Sharma (80832020) Under the supervision of: Mr. Parteek Bhatia Senior Lecturer, CSED ... degree of this or any other university. ( Rohan Sharma ) This is to certify that the above statement made by the candidate is correct and true to the ...
Abstract: Machine Translation (MT) refers to the use of computers for the task of translating aut... more Abstract: Machine Translation (MT) refers to the use of computers for the task of translating automatically from one language to another. The differences between languages and especially the inherent ambiguity of language make MT a very difficult problem. ...
......................................................................................... ...iii ... more ......................................................................................... ...iii Table of
Chapter Objectives ✓ To apply the K-means algorithm in Weka and R language ✓ To interpret the res... more Chapter Objectives ✓ To apply the K-means algorithm in Weka and R language ✓ To interpret the results of clustering ✓ To identify the optimum number of clusters ✓ To apply classification on un-labeled data by using clustering as an intermediate step Introduction As discussed earlier, if data is not labeled then we can analyze this data by performing a clustering analysis, where clustering refers to the task of grouping a set of objects into classes of similar objects. In this chapter, we will apply clustering on Fisher's Iris dataset. We will use clustering algorithms to group flower samples into clusters with similar flower dimensions. These clusters then become possible ways to group flowers samples into species. We will implement a simple k-means algorithm to cluster numerical attributes with the help of Weka and R. In the case of classification, we know the attributes and classes of instances. For example, the flower dimensions and classes were already known to us for the Ir...
This paper describes an attempt to develop Punjabi WordNet by using expansion approach from Hindi... more This paper describes an attempt to develop Punjabi WordNet by using expansion approach from Hindi WordNet under Indradhanush WordNet Project. The origin, symbols, morphological and syntactic characteristics of Punjabi Language are presented in this paper. The lexical semantic relations used in Punjabi WordNet are elaborated. The need for synset categorization and the results of this categorization for Punjabi Language is also presented in this paper.
Quality of life of people in India is demeaning with the growing traffic congestion. Congestion l... more Quality of life of people in India is demeaning with the growing traffic congestion. Congestion leads to air pollution, decrease in accessibility and increased travel time. System and technology is growing even in microseconds. GPS is mounting its reputation in day to day life applications. With increasing use of smart phones and growing popularity of the GPS based applications, it has become feasible for the commuter to track the real time position of the vehicle and make better travelling decision. This paper aims to build the client server android based application for the efficient vehicle tracking.
28 Abstract— It was not possible in case of traditional database to store, analyze, visualize, an... more 28 Abstract— It was not possible in case of traditional database to store, analyze, visualize, and integrate spatial data in business and government applications. GIS systems store spatial and non-spatial data separately. This split data model has several drawbacks. Oracle spatial provides a completely open and standards-based architecture for the management of spatial data within a database management system. This research paper describes the process of developing a spatial application of Patiala city using Oracle spatial technologies. A simple graph is first created using the map of Patiala (obtained from Google Maps tool) and the coordinates of main locations of Patiala are figured out using this graph. A spatial database is then designed by using tables for colleges, roads, banks and Thapar University. MapViewer tool is used to integrate and visualize spatial data with maps. Oracle Map Builder tool is used to define the MapViewer mapping metadata that includes styles, themes and...
... 4.2.3 Insertion into Spatial Tables .....40 4.3 Working with Map Builder Tool: Defining Mappi... more ... 4.2.3 Insertion into Spatial Tables .....40 4.3 Working with Map Builder Tool: Defining MappingMetadata .....42 ... Table 4.5: Schema of table STBL_MISC.....36 ... Intergraph, and MapInfo. ...
Chapter Objectives ✓ To demonstrate the use of the decision tree ✓ To apply the decision tree on ... more Chapter Objectives ✓ To demonstrate the use of the decision tree ✓ To apply the decision tree on a sample dataset ✓ To implement a decision tree process using Weka and R Building a Decision Tree Classifier in Weka In this chapter, we will learn how Weka's decision tree feature helps to classify unknown samples of a dataset based on its attribute values. When Weka's decision tree is applied to an unknown sample, the decision tree classifies the sample into different classes such as Class A, Class B and Class C as shown in Figure 6.1. For example, if we want to predict the class of an unknown sample of a flower based on the length and width dimensions of its Sepal and Petal. The first step would be to measure Sepal length and width and Petal length and width of an unknown flower and compare these dimensions to the values of the samples in our dataset of known species. The decision tree algorithm of Weka will help in creating decision rules to predict the class of unknown flowe...
Abstract: Machine Translation is an important part of Natural Language Processing. It refers to u... more Abstract: Machine Translation is an important part of Natural Language Processing. It refers to using machine to convert one natural language to another. Statistical Machine Translation is a part of Machine Translation that strives to use machine learning paradigm towards translating text. Statistical Machine Translation consists of Language Model (LM), Translation Model (TM) and decoder. In this thesis, English to Hindi Statistical Machine Translation system has been developed. The development of Language Model, ...
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