2016 International Conference on Information Technology (ICIT)最新文献_第6页

Routing Protocol for Wireless Sensor Networks Using Swarm Intelligence-ACO with ECPSOA 基于群智能-蚁群算法的无线传感器网络路由协议

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.018

J. Kumar, S. Tripathi, R. Tiwari

{"title":"Routing Protocol for Wireless Sensor Networks Using Swarm Intelligence-ACO with ECPSOA","authors":"J. Kumar, S. Tripathi, R. Tiwari","doi":"10.1109/ICIT.2016.018","DOIUrl":"https://doi.org/10.1109/ICIT.2016.018","url":null,"abstract":"In the wireless sensor networks(WSNs) with static and dynamic nodes, the movement of nodes or failure of sensor nodes may lead to the breakage of the existing routes. End-to-end delay, power consumption, and communication cost are some of the most important metrics in a wireless sensor networks when routing from a source to a destination. Recent approaches using the swarm intelligence (SI) technique proved that the local interaction of several simple agents to meet a global goal has a significant impact on WSNs routing. In this paper, a proposed routing algorithm that has an ant colony optimisation (ACO) algorithm with an endocrine cooperative particle swarm optimisation algorithm (ECPSOA) is used to improve the various metrics in WSNs routing. The ACO algorithm uses mobile agents as ants to identify the most feasible and best path in a network. Additionally, the ACO algorithm helps to locate paths between two nodes in a network. In the ECPSOA, finds the best solution for a particle's position and velocity, which can enhance the capacity of global search and improve the speed of convergence and accuracy of the algorithm. This routing algorithm has an improved performance when compared with the simple ACO algorithm in terms of delay, power consumption, and communication cost. Simulate with the help of network simulator OMNNET++, and analysis the result.","PeriodicalId":220153,"journal":{"name":"2016 International Conference on Information Technology (ICIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128119636","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: 8

A Practical Approach for Term and Relationship Extraction for Automatic Ontology Creation from Agricultural Text 面向农业文本本体自动生成的术语和关系提取方法

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.056

Neha Kaushik, N. Chatterjee

{"title":"A Practical Approach for Term and Relationship Extraction for Automatic Ontology Creation from Agricultural Text","authors":"Neha Kaushik, N. Chatterjee","doi":"10.1109/ICIT.2016.056","DOIUrl":"https://doi.org/10.1109/ICIT.2016.056","url":null,"abstract":"Large amount of data is created and stored in electronic media. Agriculture is no exception. Large unprocessed text are available on the various Government and other websites. Despite of large volume and availability, this data is underutilized. This data should be converted to an effective form so as to facilitate better information dissemination. Ontology is an efficient medium to carry out this task. This paper presents a simple and practical approach for automatic term and relationship extraction. Term extraction scheme uses domain-specific patterns to identify seed terms in crops subdomain of agriculture. Subsequently, NLP techniques are used to expand the terms collection. Term extraction scheme performs ahead of Termine, software for term extraction. The relationship extraction scheme employs patterns, position vectors and WordNet similarity to identify four type of relations from the agricultural text pertaining to crops. Relationships extraction scheme is evaluated using 10-fold cross validation. It runs well with an average precision of 88% on training data and 87% on test data. The resulting ontology is quite encouraging for future work.","PeriodicalId":220153,"journal":{"name":"2016 International Conference on Information Technology (ICIT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127232920","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

AAMI Based ECG Heart-Beat Time-Series Clustering Using Unsupervised ELM and Decision Rule 基于非监督ELM和决策规则的AAMI心电心跳时间序列聚类

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.039

J. R. Annam, R. Bapi

{"title":"AAMI Based ECG Heart-Beat Time-Series Clustering Using Unsupervised ELM and Decision Rule","authors":"J. R. Annam, R. Bapi","doi":"10.1109/ICIT.2016.039","DOIUrl":"https://doi.org/10.1109/ICIT.2016.039","url":null,"abstract":"Early detection of cardiovascular diseases can prevent the premature deaths caused by abnormal heartbeat problems. Application of unsupervised classification by Extreme learning machine is addressed for ElectroCardiogram (ECG) heart-beat time series clustering by a hybrid of Extreme learning machine and Decision rule using full heart-beat time series by alignment of R-peaks of all beats is proposed in this work. PQRST Time series of heart-beats having converted into equal length series by alignment of R-peaks of all heart-beats based on R-peak of largest length PQRST series in the data and by padding zeroes to the smaller length series on either side, was used in this experimentation. The main objective of this paper is to identify the abnormalities in ECG heart beats based on AAMI Categorization. Because of the large patient specific characteristics in ECG heartbeat morphology across individuals, the supervised methods trained on some ECG dataset may decrease performance on other datasets. In addition, these techniques require a considerable amount of known and labelled heartbeats which are not feasible in long–term ECG monitoring. Experiments were conducted on ECG data of 44 patients obtained from MIT-BIH Arrhythmia database. Results were compared with existing methods such as weighted support vector machine (SVM), hierarchical SVM and weighted linear discriminant analysis (LDA). Comparative analysis confirms the viability and superiority of the proposed approach in terms of Total classification accuracy (TCA). Proposed system achieved Sensitivities of 98.13%, 82.25%, 76.49% and 52.20%, PPV of 98.13%, 64.46%, 95.47%, 46.54% for N, S, V, and F classes respectively and TCA of 95.75%.","PeriodicalId":220153,"journal":{"name":"2016 International Conference on Information Technology (ICIT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131264406","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: 5

Automated CAPTCHA Generation from Annotated Images Using Encoder Decoder Architecture 使用编码器解码器架构从注释图像自动生成CAPTCHA

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.022

Madhura Das, A. Naresh, A. Narang, Anantharaman Narayana, R. Jayashree

{"title":"Automated CAPTCHA Generation from Annotated Images Using Encoder Decoder Architecture","authors":"Madhura Das, A. Naresh, A. Narang, Anantharaman Narayana, R. Jayashree","doi":"10.1109/ICIT.2016.022","DOIUrl":"https://doi.org/10.1109/ICIT.2016.022","url":null,"abstract":"A CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a program that protects websites from bots by generating and grading assessments that humans can pass but current computer programs cannot. CAPTCHAs provide security from bots in applications such as preventing comment spam in blogs, protecting website registrations etc. Recent breakthroughs in Artificial Intelligence (AI) have led to development of systems which can crack current image based CAPTCHAs with over 90% accuracy. This necessitates the need for new types of image based CAPTCHA which would be plausible for a human to solve it, and, at the same time, pose a challenge for a bot to break the CAPTCHA. To this end, the paper proposes a novel type of image based CAPTCHA where the user is presented with a set of images and a multiple answer based question based on the contents of the image-set. The question generated is such that a human is able to answer the question easily, whereas a bot would have to delve into the intricacies of image recognition, natural language processing on the question and then perform a knowledge correlation with the options to crack the CAPTCHA which is a rather tedious task to achieve. The novelty in the CAPTCHA presented can be expressed in terms of the CAPTCHA type itself as well as the deep learning architecture employed to synthesize the CAPTCHA. The proposed CAPTCHA generation system uses an Encoder Decoder architecture whose basic building block is a Gated Recurrent Unit (GRU) - a type of Recurrent Neural Network (RNN). The proposed system also facilitates a dynamic CAPTCHA generation mechanism eliminating the need to store a mapping between the images and questions.","PeriodicalId":220153,"journal":{"name":"2016 International Conference on Information Technology (ICIT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121584853","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