International Conference on Advances in Computing, Communications and Informatics最新文献_第9页

Implementation of dictation system for Malayalam office document 马拉雅拉姆语办公文件听写系统的实现

International Conference on Advances in Computing, Communications and Informatics Pub Date : 2012-08-03 DOI: 10.1145/2345396.2345521

P. Devi, J. Stephen, G. S. Kurambath, R. Kumar

{"title":"Implementation of dictation system for Malayalam office document","authors":"P. Devi, J. Stephen, G. S. Kurambath, R. Kumar","doi":"10.1145/2345396.2345521","DOIUrl":"https://doi.org/10.1145/2345396.2345521","url":null,"abstract":"This paper describes the implementation of a dictation system for Malayalam office documents in OpenOffice Writer. Dictation system is built using state-of-the-art large vocabulary continuous speech recognition system for the Malayalam language. This system supports a vocabulary of 5000 most commonly used office domain words and is employed with a vocabulary updating facility to handle out-of-vocabulary words. The system is based on Hidden Markov Model (HMM), trained with huge (25 hours) amount of data. The training data is collected in room environment, ensuring the speaker variance and the phonetic richness. A hybrid model which integrates the rule based method with statistical method is used to handle the pronunciation variations for the creation of the pronunciation dictionary. The system is first of its kind which simplifies the tedious task of typing in Malayalam. Apart from dictating office documents with 75 ±5 % accuracy, the system is equipped with a facility of suggestion generation by which the user will be provided with alternate words for mis-recognized words. The system also supports some basic voice command operations for file operations like open, save, close etc. This system has an option to adapt to the user's voice which will improve the recognition accuracy by 2-5%. The system is successfully implemented in OpenOffice Writer and tested.","PeriodicalId":290400,"journal":{"name":"International Conference on Advances in Computing, Communications and Informatics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129499233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Web data mining trends and techniques Web数据挖掘的趋势和技术

International Conference on Advances in Computing, Communications and Informatics Pub Date : 2012-08-03 DOI: 10.1145/2345396.2345551

U. Patil, J. Patil

引用次数: 11

A novel algorithm for PAPR reduction in LTE system LTE系统中减少PAPR的新算法

International Conference on Advances in Computing, Communications and Informatics Pub Date : 2012-08-03 DOI: 10.1145/2345396.2345447

O. Omer, A. Abdelreheem

引用次数: 0

Identification of optimal cluster centroid of multi-variable functions for clustering concept-drift categorical data 聚类概念漂移分类数据的多变量函数最优聚类质心辨识

International Conference on Advances in Computing, Communications and Informatics Pub Date : 2012-08-03 DOI: 10.1145/2345396.2345417

K. Madhavi, A. Babu, A. A. Rao, S. Raju

{"title":"Identification of optimal cluster centroid of multi-variable functions for clustering concept-drift categorical data","authors":"K. Madhavi, A. Babu, A. A. Rao, S. Raju","doi":"10.1145/2345396.2345417","DOIUrl":"https://doi.org/10.1145/2345396.2345417","url":null,"abstract":"Identification of useful clusters in large datasets has attracted considerable interest in clustering process. Since data in the World Wide Web is increasing exponentially that affects on clustering accuracy and decision making, change in the concept between every cluster occurs named concept drift. This newly added time based data must be assigned/labeled into generated clusters at our hand. To say that the data labeling was performed well, the clusters must be efficient. Selecting initial cluster center (centroid) is the key factor that has high affection in generating effective clusters. The existing clustering methods selects centroid randomly. Different centroids results in different clusters. To avoid this random selection, we are proposing methods in selecting the centroid by analyzing the properties of data since the data with different properties exists in real world. Our previous work was concentrated in the identification centroid for the functions of single variable and two variable functions. This paper proposes methods in finding optimal cluster centroid for the multi-variable functions and then apply any existing clustering algorithm to generate clusters by using suitable distance measure.","PeriodicalId":290400,"journal":{"name":"International Conference on Advances in Computing, Communications and Informatics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133883421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Genetic algorithm based airlines booking terminal open/close decision system 基于遗传算法的航空公司订票终端开/关决策系统

International Conference on Advances in Computing, Communications and Informatics Pub Date : 2012-08-03 DOI: 10.1145/2345396.2345426

Aloysius George, B. R. Rajakumar, D. Binu

引用次数: 40

Control of computer process using image processing and computer vision for low-processing devices 利用图像处理和计算机视觉对低处理设备进行计算机过程控制

International Conference on Advances in Computing, Communications and Informatics Pub Date : 2012-08-03 DOI: 10.1145/2345396.2345583

S. Prasad, Abhay Prakash, S. K. Peddoju, D. Ghosh

引用次数: 4

A framework for preserving privacy in cloud computing with user service dependent identity 在用户服务依赖身份的云计算中保护隐私的框架

International Conference on Advances in Computing, Communications and Informatics Pub Date : 2012-08-03 DOI: 10.1145/2345396.2345419

S. M. Rahaman, M. Farhatullah

{"title":"A framework for preserving privacy in cloud computing with user service dependent identity","authors":"S. M. Rahaman, M. Farhatullah","doi":"10.1145/2345396.2345419","DOIUrl":"https://doi.org/10.1145/2345396.2345419","url":null,"abstract":"The widespread focus on the Cloud Computing has necessitated the corresponding mechanisms to ensure privacy and security. Various attempts have been made in the past to safeguard the privacy of the individual or agency trying to utilize the services being provided by the cloud. The most challenging task is to provide services to the users while also preserving the privacy of the user's information. In this paper a model that incorporates a three-level architecture, Preserving cloud computing Privacy (PccP) model is proposed which aims to preserve privacy of information pertaining to cloud users. The Consumer Layer deals with all the aspects which relate to enabling the user of the cloud to access the cloud services being provided by the cloud service provider. The Network Interface Layer creates an appropriate mapping between the original IP addresses of the users with a modified IP address, and thereby ensuring the privacy of the IP address of the users. The Privacy Preserved Layer utilizes the functionality of the Unique User Cloud Identity Generator for which an algorithm is proposed in this paper to generate an unique User Service Dependent Identity (USID) with privacy check by establishing mapping among the existing user identity (ID), if any to ID's available in a pool of User ID's to enhance the privacy of sensitive user information.","PeriodicalId":290400,"journal":{"name":"International Conference on Advances in Computing, Communications and Informatics","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132110190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Clustering and classifying informative attributes using rough set theory 利用粗糙集理论对信息属性进行聚类和分类

International Conference on Advances in Computing, Communications and Informatics Pub Date : 2012-08-03 DOI: 10.1145/2345396.2345416

R. Nayak, Debahuti Mishra, Satyabrata Das, Kailash Shaw, Sashikala Mishra, Ramamani Tripathy

{"title":"Clustering and classifying informative attributes using rough set theory","authors":"R. Nayak, Debahuti Mishra, Satyabrata Das, Kailash Shaw, Sashikala Mishra, Ramamani Tripathy","doi":"10.1145/2345396.2345416","DOIUrl":"https://doi.org/10.1145/2345396.2345416","url":null,"abstract":"Clustering techniques are the unsupervised data mining applications and are important in data mining methods for exploring natural structure and identifying interesting patterns in original data, also it is proved to be helpful in finding coexpressed samples. In cluster analysis, generally the given dataset is partitioned into groups based on the given features such that the data objects in the same group are more similar to each other than the data objects in other groups. The objects are clustered or grouped based on the principle of maximizing intra-class similarity and minimizing interclass similarity. In this paper, the rough set theory (RST) has been used for attribute clustering. RST is a theory adopted to deal with rough and unsure knowledge, which analyzes the clusters and finds the data principles when previous knowledge is not available, providing a new method for data classification. With the continuous change in data objects we have to improve these relevant technologies over time, and we have to propose creative theory in response, meeting the demands of application, though there are many rough set methods. In this paper; after implementing the rough set based attribute clustering method on real life leukemia dataset, we classify them using some of the traditional classification techniques such as Multilayered Perceptron (MLP) based classifier, Naïve Bayesian (NB) classifier and Support Vector Machine (SVM). At the end, the same classification techniques are applied to classify the original leukemia dataset before application of rough set based attribute clustering. Finally the paper provides a comparative analysis among the traditional classifiers and the proposed corresponding rough set based classifiers. Among all, the proposed MLP classifier is found to be the better classifier than the others giving higher classification accuracy and it is proved to be efficient having lower error ratio.","PeriodicalId":290400,"journal":{"name":"International Conference on Advances in Computing, Communications and Informatics","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114653143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An efficient DoG based fingerprint enhancement scheme 一种高效的基于DoG的指纹增强方案

International Conference on Advances in Computing, Communications and Informatics Pub Date : 2012-08-03 DOI: 10.1145/2345396.2345486

S. Samantaray, Sambit Bakshi, P. K. Sa

引用次数: 4

Temporal characteristics of clustering in mobile ad hoc network 移动自组织网络中聚类的时间特征

International Conference on Advances in Computing, Communications and Informatics Pub Date : 2012-08-03 DOI: 10.1145/2345396.2345439

J. Singh, P. Dutta, A. Chakrabarti

{"title":"Temporal characteristics of clustering in mobile ad hoc network","authors":"J. Singh, P. Dutta, A. Chakrabarti","doi":"10.1145/2345396.2345439","DOIUrl":"https://doi.org/10.1145/2345396.2345439","url":null,"abstract":"Clustering partitions the ad hoc network into several groups of nodes to induce a hierarchical architecture. Each group of nodes is called a cluster and is managed by a manager called cluster-head. One of the popular technique to cluster ad hoc network is based on node weights. The node weights are assigned on the basis of certain node parameters like average numbers of neighbours, sum of distances of neighbours etc. In node weight based clustering, the cluster formation and maintenance are decided by the weights of neighbouring nodes. In this article, We explore the impact of different mobility pattern on the weight based clustering algorithms. We have simulated the network using four different mobility patterns: (i) Random Way Point, (ii) Restricted Random Way Point, (iii) Gauss Markov and (iv) Random Direction mobility. We have also tried to find out the effect of average speed of nodes on clustering the network under different mobility patterns. The weights of mobile nodes are represented as a time series and modelled by Autoregressive model of order p i. e. AR(p). The order p of the model is found to lye between 1 and 3. The fitted model is then used to make prediction about the node weights. The predicted node weights are close the actual node weights as indicated by the statistical analysis.","PeriodicalId":290400,"journal":{"name":"International Conference on Advances in Computing, Communications and Informatics","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128298407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1