Informatics Publishing Limited

M. Anand Kumar ¹, K. Appathurai ¹, P. Nagarajan ¹

Affiliations
1 Department of Computer Science, Karpagam University, Coimbatore, IN

Abstract

The Internet Control Message Protocol is classic example of a client server application. The ICMP server executes on all IP end system computers and all IP intermediate systems (routers). The protocol is used to report problems with delivery of IP datagram within an IP network. It can be used to report about errors when a particular End System is not responding, when an IP network is not reachable, when a node is overloaded, when an error occurs in the IP header nformation, etc. The protocol is also frequently used by Internet managers to verify the correct operations of End Systems and to check that routers are correctly routing packets to the ecified destination address. The ICMP is also used for troubleshooting the TCP/IP networks. In this paper we demonstrate how the troubleshooting can be done more effective with the use of ICMP. We also discuss about security flaws of ICMP and problems in Source Quench message of ICMP messages and suggestions to solve the problems.

Keywords

End System, Error Reporting, Network Security, ICMP, Query, Router, Source Quench, TCP/IP, Troubleshooting.

Full Text

     

J. Jyothi ¹, K. Manjusha ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641 112, Tamil Nadu, IN

Abstract

In this Information age, all sources of information like historic documents, books, manuscripts are digitized and are available all over the world through internet in the form of scanned copies. These scanned images contain valuable information which are available either in colour or black and white for pleasant viewing. Optical Character Recognition (OCR) technology provides facility to search for keywords in these digital copies. In this paper, new method in which building an OCR system for Telugu language script; mainly focussing on the character recognition module. Features extracted through Discrete Wavelet Transform (DWT), Projection Profile (PP) and Singular Value Decomposition (SVD) is evaluated using k-Nearest Neighbour (k-NN) and Support Vector Machine (SVM) classifiers. Most productive results are obtained from the DWT features with SVM classifiers.

Keywords

Discrete Wavelet Transform, K-nearest Neighbour, Optical Character Recognition, Singular Value Decomposition, Support Vector Machine, Telugu Character Recognition.

Full Text

   

N. Abinaya ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641 112, Tamil Nadu, IN

Abstract

In this paper, we present a new approach for Named Entity Recognition (NER) in Tamil language using Random Kitchen Sink algorithm. Named Entity recognition is the process of identification of Named Entities (NEs) from the text. It involves the identifying and classifying predefined categories such as person, location, organization etc. A lot of work has been done in the field of Named Entity Recognition for English language and Indian languages using various machine learning approaches. In this work, we implement the NER system for Tamil using Random Kitchen Sink algorithm which is a statistical and supervised approach. The NER system is also implemented using Support Vector Machine (SVM) and Conditional Random Field (CRF). The overall performance of the NER system was evaluated as 86.61% for RKS, 81.62% for SVM and 87.21% for CRF. Additional results have been taken in SVM and CRF by increasing the corpus size and the performance are evaluated as 86.06% and 87.20% respectively.

Keywords

Conditional Random Field (CRF), Named Entities (NEs), Named Entity Recognition (NER), Natural Language Processing (NLP), Random Kitchen Sink (RKS), Support Vector Machine (SVM)

Full Text

   

Shriya Se ¹, R. Vinayakumar ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore Amrita Vishwa Vidyapetham, Amrita University, Coimbatore – 641112, Tamil Nadu, IN

Abstract

Objective: This paper aims at classifying the Tamil movie reviews as positive and negative using supervised machine learning algorithms. Methods/Analysis: A novel machine learning approaches are needed for analyzing the Social media text where the data are increasing exponentially. Here, in this work, Machine learning algorithms such as SVM, Maxent classifier, Decision tree and Naive Bayes are used for classifying Tamil movie reviews into positive and negative. Features are also extracted from TamilSentiwordnet. Findings: The dataset for this work has been prepared. SVM algorithm performs well in classifying the Tamil movie reviews when compared with other machine learning algorithms. Both cross validation and accuracy of the algorithm shows that SVM performs well. Other than SVM, Decision tree perform well in classifying the Tamil reviews. Novelty/Improvement: SVM gives an accuracy of 75.9% for classifying Tamil movie reviews which is a good milestone in the research field of Tamil language.

Keywords

Machine Learning, Maxent Classifier, Sentimental Analysis, Support Vector Machine, Tamil Language, TamilSentiwordnet.

Full Text

   

R. M. Rahul Venkatesh Kumar ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Abstract

Objective: This paper aims at predicting the cuisine based on the ingredients using tree boosting algorithm. Methods/ Analysis: Text mining is important tool for data mining in Ecommerce websites. Ecommerce business is growing with significant rate both in Business-to-Business (B2B) and Business to Customer (B2C) categories. The machine learning based models and prediction method are used in real world ecommerce data to increase the revenue and study customer behavior. Many online cooking and recipe sharing websites have ardent to evolution of recipe recommendation system. In this paper, we describe a scalable end to end tree boosting system algorithms to predict cuisine based on the ingredients and also explored different data analysis and explained about the dataset types and their performances. Novelty/ Improvement: An accuracy of about 80% is obtained for cuisine prediction using XG-Boosting algorithm.

Keywords

Data Analysis, Prediction, Random Forest, Text Analytics, XGBoost.

Full Text

   

B. Premjith ¹, S. Sachin Kumar ¹, R. Shyam ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN),Amrita School of Engineering, Amrita University, Amrita Vishwa Vidyapeetham, Amritanagar, Coimbatore – 641 112, Tamilnadu, IN

Abstract

Objectives: A framework involving Scansnap SV600 scanner and Google Optical character recognition (OCR) for creating parallel corpus which is a very essential component of Statistical Machine Translation (SMT). Methods and Analysis: Training a language model for a SMT system highly depends on the availability of a parallel corpus. An efficacious approach for collecting parallel sentences is the predominant step in an MT system. However, the creation of a parallel corpus requires extensive knowledge in both languages which is a time consuming process. Due to these limitations, making the documents digital becomes very difficult and which in turn affects the quality of machine translation systems. In this paper, we propose a faster and efficient way of generating English to Indian languages parallel corpus with less human involvement. With the help of a special type of scanner called Scansnap SV600 and Google OCR and a little linguistic knowledge, we can create a parallel corpus for any language pair, provided there should be paper documents with parallel sentences. Findings: It was possible to generate 40 parallel sentences in 1 hour time with this approach. Sophisticated morphological tools were used for changing the morphology of the text generated and thereby increase the size of the corpus. An additional benefit of this is to make ancient scriptures or other manuscripts in digital format which can then be referred by the coming generation to keep up the traditions of a nation or a society. Novelty: Time required for creating parallel corpus is reduced by incorporating Google OCR and book scanner.

Keywords

Google OCR, Machine Translation, Parallel Corpus, Statistical Machine Translation, Scansnap SV600 Scanner.

Full Text

   

S. G. Ajay ¹, M. Srikanth ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Abstract

Objective: Word embedding models were most predominantly used in many of the NLP tasks such as document classification, author identification, story understanding etc. In this paper we make a comparison of two Word embedding models for semantic similarity in Tamil language. Each of those two models has its own way of predicting relationship between words in a corpus. Method/Analysis: The term Word embedding in Natural Language Processing is a representation of words in terms of vectors. Word embedding is used as an unsupervised approach instead of traditional way of feature extraction. Word embedding models uses neural networks to generate numerical representation for the given words. In order to find the best model that captures semantic relationship between words, using a morphologically rich language like Tamil would be great. Tamil language is one of the oldest Dravidian languages and it is known for its morphological richness. In Tamil language it is possible to construct 10,000 words from a single ischolar_main word. Findings: Here we make comparison of Content based Word embedding and Context based Word embedding models respectively. We tried different feature vector sizes for the same word to comment on the accuracy of the models for semantic similarity. Novelty/Improvement: Analysing Word embedding models for morphologically rich language like Tamil helps us to classify the words better based on its semantics.

Keywords

CBOW, Content based Word Embedding, Context based Word Embedding, Morphology, Semantic and Syntactic, Skip Gram.

Full Text

   

Sooraj Sudhakaran ¹, Shimil Jose ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Abstract

Objective: This paper aims in providing an overview about a part of Natural Language Generation – Parallel sentence generation which involves the generation of the English sentence as well as its Malayalam translated version. Methods/Analysis: A template based sentence generator approach is followed here. A system is proposed which takes input from a manually created bilingual dictionary and fills the slots in the template for parallel sentence generation. Finding: Using the proposed method, we have generated a total of 25,208 parallel sentences. This can be used in bilingual Machine Translation dictionary. Application/Improvement: In the proposed case use only four templates but by increasing the number of templates and by updating the dictionary, we can increase the size of the parallel corpus that can be generated.

Keywords

Bilingual, English-Malayalam, Machine Translation, Parallel sentence, Templates.

Full Text

   

M. Mohankumar ¹, M. Anand Kumar ²

Affiliations
1 Department of Computer Science, Karpagam University, Coimbatore - 641021, Tamil Nadu, IN
2 Department of Information Technology, Karpagam University, Coimbatore - 641021, Tamil Nadu, IN

Abstract

Objectives: The main objectives of this research work are to propose a new green based model for Software engineering with no impact in the environment. Analysis: In the beginning stages of ICT manufacturing companies and software developing companies are given more imperative for Hardware and software. They don't think about the sustainability of resource used for manufacturing of a product or development of an application from the statistical analysis it was identified that there is a need to minimize the energy utilization and relate to CO₂ emission of IT equipment. Result: The experimental results verified that proposed model out performed than the existing software engineering model with little impacts on the environmental considerations. Improvement/Application: Experiments revealed that the proposed Green Based SDLC technique is able to reduce the power consumption.

Keywords

Capability Maturity Model, Environment and Development, Green Power Indicator, Green Tracker, Non Function Requirements, Sustainable Software Development Life Cycle.

Full Text

   

M. Mohankumar ¹, M. Anand Kumar ²

Affiliations
1 Department of Computer Science, Karpagam University, Coimbatore – 641021, Tamil Nadu, IN
2 Department of Information Techonology, Karpagam University, Coimbatore – 641021, Tamil Nadu, IN

Abstract

Background/Objectives: This paper proposes to introduce a Green Software Database Design Model and to create the awareness to overcome the energy consumption issues while designing the database in the prevailing state of the world. Methods/Statistical Analysis: The main aspect of the work is to contemplate the energy consumption during the design phase of the database. Findings: This approach is concerned with the database designing using Green Computing Technique to reduce the power utilization pattern of server at different work load conditions. The energy consumption database design is subsequently designed to estimate the collision of software applications based on their source utilization. Improvement/Application: The work is validated on the side of the desktop and the server side. This experiment demonstrates the effectiveness of the database design that provides the relevant information about the energy utilization of software application design on the database in software engineering.

Keywords

Database Design, Energy Consumption, Green Software Engineering, Software Application, Software Engineering

Full Text

   

S. Reshmi ¹, M. Anand Kumar ²

Affiliations
1 Karpagam University, Coimbatore - 641021, Tamil Nadu, IN
2 Department of Information Technology, Karpagam University, Coimbatore - 641021, Tamil Nadu, IN

Abstract

Background/Objectives: The pros in using network virtualization for the users and the resources offers effectual, meticulous, and protected sharing of the networking resources. Methods/Statistical Analysis: In network there is a problem of accountability that any malicious router can drop packets that are supposed to impart packets instead of throwouts. To understand the packet dropping issues in detail this paper recognizes the foremost attacks and to tackle these attacks algorithms are initiated. Findings: A concise assessment on two major attacks are dealt in this article: black hole attack and gray hole attack. If there is any malevolent node in the network, the number of data packets is not reaching the destination, since the packets dive in middle path. To overcome these issues, we identify proposed mechanisms against the attacks and improve the network recital in terms of package globule degree. Applications/Improvements: Heuristics algorithm and obfuscation algorithm are the algorithms which help in exploring lost packets in network while transmitting to end users.

Keywords

Black Hole Attack, Gray Hole Attack, Heuristics Algorithm, Network Virtualization, Obfuscation Algorithm, Virtual Routing and Forwarding.

Full Text

   

S. N. Vinithra ¹, S. J. Arun Selvan ¹, M. Anand Kumar ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641112, Tamil Nadu, IN

Abstract

We present a methodology for naturally grouping the estimation of Twitter messages. Miniaturized scale websites are a testing new wellspring of data for information mining methods. The aim of this paper is to focus the careful feeling of the information from the microblogging site Twitter. Tweets regularly likewise contain URLs to different sites. Tweets additionally contain a certain measure of OOV (Out-Of-Vocabulary) words, for example, Hash tags, a labeling framework for points permitting Tweets in a comparative vein of discussion to be found. Other OOV words incorporate notice which is a system to direct a Tweet to one or more users. The KH coder tool gives a conventional precision result where the content is POS labeled and MySQL is utilized for putting away points of interest as a part of the database. The R tool is utilized to view the factual examination of information. Further, machine learning calculation has likewise been performed. A preprocessing and highlight choice system in blend with a Maximum Entropy, Naive Bayes and Decision Tree classifiers has been exhibited and sensible results has been delivered. Accuracy of the machine adapting methods for sentiment has been thought about and statistical representation of the classes has been depicted through KH Coder.

Keywords

Data Mining, Decision Tree, Maximum Entropy, Microblogging, Naive Bayes, Oov, Sentiment Classification.

Full Text