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2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)最新文献

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Propels in Compiler Construction for Versatile Figuring 面向通用计算的编译器结构中的推进
2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) Pub Date : 2017-02-01 DOI: 10.1109/ICRTCCM.2017.69
R. Kannadasan, A. Saxena, Vachan Annand, N. Prabakaran, A. Krishnamoorthy, K. Naresh
{"title":"Propels in Compiler Construction for Versatile Figuring","authors":"R. Kannadasan, A. Saxena, Vachan Annand, N. Prabakaran, A. Krishnamoorthy, K. Naresh","doi":"10.1109/ICRTCCM.2017.69","DOIUrl":"https://doi.org/10.1109/ICRTCCM.2017.69","url":null,"abstract":"This paper shows a compiler machine for adaptable figuring. Our technique assembles the flexibility and comfort in a way that grants to port the structure to different centers with an irrelevant effort. In light of a present arrangement stream, we endeavor to accomplish another tier of handiness in the way of exploring and dividing programs written in C on most hoisted able to be done delineation tier. We show that the examination on this level is more successful than on lower ones as a result of usage of more communicative fabricate of programming. The better examination comes to fruition merged with Static single assignment based estimation for data way creation might provoke upper game plan nature of the last structure setup.","PeriodicalId":134897,"journal":{"name":"2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122043812","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
Identification of Epileptic Seizure Using ReHo from Rs-fMRI 利用Rs-fMRI的ReHo识别癫痫发作
2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) Pub Date : 2017-02-01 DOI: 10.1109/ICRTCCM.2017.61
A. A. Gnanadas, S. Sathishbabu, M. Vijayakarthik
{"title":"Identification of Epileptic Seizure Using ReHo from Rs-fMRI","authors":"A. A. Gnanadas, S. Sathishbabu, M. Vijayakarthik","doi":"10.1109/ICRTCCM.2017.61","DOIUrl":"https://doi.org/10.1109/ICRTCCM.2017.61","url":null,"abstract":"Localization of the epileptic seizure sources is a very essential procedure that assists the surgeon in concentrating on the diseased area leaving its vicinity unaffected. Epilepsy is a condition in which the brains own neurons releases abnormal electrical pulses that inhibits the normal activities of other neurons, that needs surgery to be corrected. Hence to enhance the surgical intervention a suitable methodology must be formulated that ensures the spatial localization of the epileptic seizure. This paper studies about Regional homogeneity (ReHo) which is a powerful correlating method that could be employed in measuring the brain activity. Two sample t tests are followed to compare the pre-defined standard with the ReHo that provides clear detailed epileptic seizure localization. Amplitude of Low Frequency Fluctuations (ALFF) is also measured and the resultant is subjected to sample t test and they are compared with ReHo to measure the similarity.","PeriodicalId":134897,"journal":{"name":"2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128327401","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: 3
Survey on WSN Using Clustering 基于聚类的WSN研究综述
2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) Pub Date : 2017-02-01 DOI: 10.1109/ICRTCCM.2017.87
S. Dhiviya, A. Sariga, P. Sujatha
{"title":"Survey on WSN Using Clustering","authors":"S. Dhiviya, A. Sariga, P. Sujatha","doi":"10.1109/ICRTCCM.2017.87","DOIUrl":"https://doi.org/10.1109/ICRTCCM.2017.87","url":null,"abstract":"Clustering algorithm incurs significantenergy consumption for clusters and its ClusterHeads (CH) in Wireless Sensor Network(WSN). WSN brings a new attention over a real-timeintelligent system with its limitations and gainedmore research challenges. In last few years, WSNcan be used and seen in many real time applicationslike disaster management, pollution monitoring, temperature monitoring, traffic monitoring, transportmonitoring, healthcare monitoring, battlefieldsurveillance and border security surveillance. Inthese applications, plenty of sensor nodes are usedand they are deployed in the sensing field whichoften transmits information to Base Station (BS) automatically by means of energy (i.e. battery presentin the sensor). Clustering is one of the techniquesused in WSN to utilize the energy efficiently by thesensor node. Clustering is a process of grouping thesensor nodes into clusters and each cluster will havea cluster head. This paper provides the survey on adistinct clustering algorithm for WSN which isclassified based on distinct clustering attributes.","PeriodicalId":134897,"journal":{"name":"2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129449157","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: 15
An Efficient Feature Subset Selection for Improved Stability Using T-Statistic 基于t统计量的特征子集选择提高稳定性
2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) Pub Date : 2017-02-01 DOI: 10.1109/ICRTCCM.2017.39
R. Karthika
{"title":"An Efficient Feature Subset Selection for Improved Stability Using T-Statistic","authors":"R. Karthika","doi":"10.1109/ICRTCCM.2017.39","DOIUrl":"https://doi.org/10.1109/ICRTCCM.2017.39","url":null,"abstract":"Large amounts of data gets accumulated and stored in the databases in day to day life that are high dimensional in nature. The data mining task is used to excavate the useful information from the high dimensional data. To classify or cluster the high dimensional data, the dimensionality of the data needs to be reduced. Feature selection is used to select the features that are relevant to the analysis and discards the features that are not relevant as well as redundant. There are so many feature subset selection algorithms available. In this paper, we evaluate the stability of the subset of the features selected using a measure called T-Statistic and improve the prediction accuracy of the classifier using Booster.","PeriodicalId":134897,"journal":{"name":"2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130283041","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
Landcover Mapping and Change Detection of Geographical Area on the Map 地图上地理区域的土地覆盖制图与变化检测
2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) Pub Date : 2017-02-01 DOI: 10.1109/ICRTCCM.2017.63
K. Govinda, B. Balamurugan, L. Deborah
{"title":"Landcover Mapping and Change Detection of Geographical Area on the Map","authors":"K. Govinda, B. Balamurugan, L. Deborah","doi":"10.1109/ICRTCCM.2017.63","DOIUrl":"https://doi.org/10.1109/ICRTCCM.2017.63","url":null,"abstract":"This paper addresses a simple, easy to implement method for calculating the geographical area of a place on the map using the Shoelace Algorithm. The procedure basically involves placing the Longitudes and Latitudes of the polygon for which the area needs to be found into the technique and obtain its area.","PeriodicalId":134897,"journal":{"name":"2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116379645","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: 0
A Novel Ensemble Approach for Effective Intrusion Detection System 一种有效入侵检测系统的集成方法
2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) Pub Date : 2017-02-01 DOI: 10.1109/ICRTCCM.2017.27
M. Rajasekaran, A. Ayyasamy
{"title":"A Novel Ensemble Approach for Effective Intrusion Detection System","authors":"M. Rajasekaran, A. Ayyasamy","doi":"10.1109/ICRTCCM.2017.27","DOIUrl":"https://doi.org/10.1109/ICRTCCM.2017.27","url":null,"abstract":"Machine learning algorithms are useful for decision making on valuable datasets which are using in emerging fields such as networks, medical and e-governance. Ensemble classifier is a most useful approach which is the combination of classification algorithms for performing effective classification in machine learning. Even though, the selection of ensemble is becoming very difficult task for the specific dataset. For this purpose, we introduce a novel ensemble which is the combination of attribute selection algorithm, multiclass support vector machine and k-NN classifier. Moreover, we use an Incremental Particle Swarm Optimization (IPSO) for optimizing the proposed system performance in terms of improving the classification accuracy. For conducting various experiments, we have used the five random subsets from the standard KDD'99 dataset.","PeriodicalId":134897,"journal":{"name":"2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132953161","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: 7
A Survey on Efficient Extraction of Named Entities from New Domains Using Big Data Analytics 基于大数据分析的新领域命名实体高效提取研究
2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) Pub Date : 2017-02-01 DOI: 10.1109/ICRTCCM.2017.34
C. Saju, A. S. Shaja
{"title":"A Survey on Efficient Extraction of Named Entities from New Domains Using Big Data Analytics","authors":"C. Saju, A. S. Shaja","doi":"10.1109/ICRTCCM.2017.34","DOIUrl":"https://doi.org/10.1109/ICRTCCM.2017.34","url":null,"abstract":"Named Entity Recognition (NER) is an important step in text mining. This paper proposes a survey on extracting named entities from new domains using big data analytics. The survey shows the methods and challenges applied for the efficient extraction of named entities from various fields having large corpus of data such as banking, medical and social networks. Most of NER methods discussed below are normally based on supervised learning techniques which often require a large amount of training dataset to train a good classifier. However, many applications in Information Retrieval (IR) and Natural Language Processing (NLP) suffer high from the noisy and short nature of texts.","PeriodicalId":134897,"journal":{"name":"2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121863828","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: 13
Event Recognition and Classification in Sports Video 体育视频中的事件识别与分类
2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) Pub Date : 2017-02-01 DOI: 10.1109/ICRTCCM.2017.57
V. Ellappan, R. Rajasekaran
{"title":"Event Recognition and Classification in Sports Video","authors":"V. Ellappan, R. Rajasekaran","doi":"10.1109/ICRTCCM.2017.57","DOIUrl":"https://doi.org/10.1109/ICRTCCM.2017.57","url":null,"abstract":"Sports event recognition and classification is a challenging task due to the number of possible categories. On one hand, how to characterize legitimate event classification names and how to acquire preparing tests for these classes should be investigated, then again, it is non-inconsequential to accomplish acceptable order execution. To address these issues, a content mining pipeline is initially proposed to naturally find a gathering of video event classes. Part of-sound (POS) investigation is connected to games video titles and depictions, and State machine is utilized perceive the class. To enhance order execution, video content-based components are supplemented with scores from an arrangement of classifiers, which can be viewed as a sort of abnormal state highlights. Broad assessments exhibit the viability of the proposed event name mining method, and our component combination plan demonstrates empowering arrangement comes about.","PeriodicalId":134897,"journal":{"name":"2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130007680","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: 11
Achieving Privacy and Security Using QR Code by Means of Encryption Technique in ATM 利用QR码加密技术在ATM机中实现隐私与安全
2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) Pub Date : 2017-02-01 DOI: 10.1109/ICRTCCM.2017.36
V. Malathi, B. Balamurugan, S. Eshwar
{"title":"Achieving Privacy and Security Using QR Code by Means of Encryption Technique in ATM","authors":"V. Malathi, B. Balamurugan, S. Eshwar","doi":"10.1109/ICRTCCM.2017.36","DOIUrl":"https://doi.org/10.1109/ICRTCCM.2017.36","url":null,"abstract":"Smart Card has complications with validation and transmission process. Therefore, by using peeping attack, the secret code was stolen and secret filming while entering Personal Identification Number at the ATM machine. We intend to develop an authentication system to banks that protects the asset of user's. The data of a user is to be ensured that secure and isolated from the data leakage and other attacks Therefore, we propose a system, where ATM machine will have a QR code in which the information's are encrypted corresponding to the ATM machine and a mobile application in the customer's mobile which will decrypt the encoded QR information and sends the information to the server and user's details are displayed in the ATM machine and transaction can be done. Now, the user securely enters information to transfer money without risk of peeping attack in Automated Teller Machine by just scanning the QR code at the ATM by mobile application. Here, both the encryption and decryption technique are carried out by using Triple DES Algorithm (Data Encryption Standard).","PeriodicalId":134897,"journal":{"name":"2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127054767","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: 11
Secure Protocol for Subscriber Identity Module 订阅者身份模块的安全协议
2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) Pub Date : 2017-02-01 DOI: 10.1109/ICRTCCM.2017.74
Akshat Pradhan, K. Marimuthu, R. Niranchana, P. Vijayakumar
{"title":"Secure Protocol for Subscriber Identity Module","authors":"Akshat Pradhan, K. Marimuthu, R. Niranchana, P. Vijayakumar","doi":"10.1109/ICRTCCM.2017.74","DOIUrl":"https://doi.org/10.1109/ICRTCCM.2017.74","url":null,"abstract":"Subscriber Identity Module (SIM) is the backbone of modern mobile communication. SIM can be used to store a number of user sensitive information such as user contacts, SMS, banking information (some banking applications store user credentials on the SIM) etc. Unfortunately, the current SIM model has a major weakness. When the mobile device is lost, an adversary can simply steal a user's SIM and use it. He/she can then extract the user's sensitive information stored on the SIM. Moreover, The adversary can then pose as the user and communicate with the contacts stored on the SIM. This opens up the avenue to a large number of social engineering techniques. Additionally, if the user has provided his/her number as a recovery option for some accounts, the adversary can get access to them. The current methodology to deal with a stolen SIM is to contact your particular service provider and report a theft. The service provider then blocks the services on your SIM, but the adversary still has access to the data which is stored on the SIM. Therefore, a secure scheme is required to ensure that only legal users are able to access and utilize their SIM.","PeriodicalId":134897,"journal":{"name":"2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124210882","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: 3
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