2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)最新文献

Recapitulization of tweets using graph-based clustering 使用基于图的聚类对tweet进行重述

2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA) Pub Date : 2018-01-24 DOI: 10.1109/cscita.2017.8066532

Vivian Brian Lobo, N. Ansari

引用次数: 0

Performance comparison of Wi-Fi IEEE 802.11ac and Wi-Fi IEEE 802.11n Wi-Fi IEEE 802.11ac与Wi-Fi IEEE 802.11n性能比较

2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA) Pub Date : 2017-04-07 DOI: 10.1109/CSCITA.2017.8066560

T. Kaewkiriya

{"title":"Performance comparison of Wi-Fi IEEE 802.11ac and Wi-Fi IEEE 802.11n","authors":"T. Kaewkiriya","doi":"10.1109/CSCITA.2017.8066560","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066560","url":null,"abstract":"This paper's objective is to present the performance comparison between Wi-Fi IEEE 802.11ac and Wi-Fi IEEE 802.11n by measuring the throughput and steaming rate of big data streaming. The performance test is divided into four scenarios, 1) data streaming to one device, 2) data streaming with different distances, 3) data streaming to multiple devices, and 4) live broadcast a big data streaming. The results of evaluations are as follow. In the first scenario, maximum transfer rate of IEEE 802.11ac is higher but the average transfer rate depends on the video type. In second scenario, maximum transfer rate to the first and second device of IEEE 802.11ac is significantly greater than IEEE 802.11n but IEEE 802.11ac cannot reach the third device which located on the different floor, while IEEE 802.11n can reach the third device with maximum transfer rate of 942.8 kbps. The throughput of IEEE 802.11ac is more stable than IEEE 802.11n by compare the data from the first and second device. In third scenario, the maximum transfer rate of IEEE 802.11ac is higher than IEEE 802.11n and throughput of IEEE 802.11ac is more stable than IEEE 802.11n. In fourth scenario, the maximum transfer rate and average transfer rate of IEEE 802.11ac is higher than IEEE 802.11n.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129180872","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: 12

Predicting the popularity of instagram posts for a lifestyle magazine using deep learning 使用深度学习预测生活方式杂志instagram帖子的受欢迎程度

2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA) Pub Date : 2017-04-07 DOI: 10.1109/CSCITA.2017.8066548

S. De, Abhishek Maity, Vritti Goel, S. Shitole, A. Bhattacharya

引用次数: 29

Image quality assessment based outlier detection for face anti-spoofing 基于图像质量评估的人脸离群点检测

2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA) Pub Date : 2017-04-07 DOI: 10.1109/CSCITA.2017.8066527

K. Karthik, Balaji Rao Katika

{"title":"Image quality assessment based outlier detection for face anti-spoofing","authors":"K. Karthik, Balaji Rao Katika","doi":"10.1109/CSCITA.2017.8066527","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066527","url":null,"abstract":"Planar spoofing is a well researched problem, wherein a high quality planar photograph can be replayed in front of a still camera as a substitute for another individual's face. Most modern day face recognition systems can be fooled by this process, as the perceptual information contained in a photo-of-a-photo, is virtually the same as that of a natural photograph of an individual. Current solutions attempt to detect this form of planar-spoofing through an extrinsic training process wherein both planar samples as well as regular photos are included as separate training sets. To avoid this form of explicit discriminant model-learning, we propose a single class training procedure for establishing and quantifying the quality of natural photographs taken under different lighting conditions, in terms of their CONTRAST PROFILE. Once this distribution is learnt, a suitable threshold is set based on the mean and standard deviation to pick up outliers. In this paper, we show that with just single poses of subjects, it is possible to achieve a low Equal Error Rate (EER) of 21.56% on the CASIA dataset and a rate of 8.57% upon cross-validation with a trimmed and shortened version of the MSU dataset.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125712050","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

Energy efficient cluster head selection technique for homogeneous wireless sensor networks 同质无线传感器网络的高效簇头选择技术

2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA) Pub Date : 2017-04-07 DOI: 10.1109/CSCITA.2017.8066524

V. Vijay, M. Singh

引用次数: 8

Comparing HiveQL and MapReduce methods to process fact data in a data warehouse 比较HiveQL和MapReduce方法处理数据仓库中的事实数据

2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA) Pub Date : 2017-04-01 DOI: 10.1109/CSCITA.2017.8066553

Haince Denis Pen, Prajyoti Dsilva, Sweedle Mascarnes

{"title":"Comparing HiveQL and MapReduce methods to process fact data in a data warehouse","authors":"Haince Denis Pen, Prajyoti Dsilva, Sweedle Mascarnes","doi":"10.1109/CSCITA.2017.8066553","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066553","url":null,"abstract":"Today Big data is one of the most widely spoken about technology that is being explored throughout the world by technology enthusiasts and academic researchers. The reason for this is the enormous data generated every second of each day. Every webpage visited, every text message sent, every post on social networking websites, check-in information, mouse clicks etc. is logged. This data needs to be stored and retrieved efficiently, moreover the data is unstructured therefore the traditional methods of strong data fail. This data needs to be stored and retrieved efficiently There is a need of an efficient, scalable and robust architecture that needs stores enormous amounts of unstructured data, which can be queried as and when required. In this paper, we come up with a novel methodology to build a data warehouse over big data technologies while specifically addressing the issues of scalability and user performance. Our emphasis is on building a data pipeline which can be used as a reference for future research on the methodologies to build a data warehouse over big data technologies for either structured or unstructured data sources. We have demonstrated the processing of data for retrieving the facts from data warehouse using two techniques, namely HiveQL and MapReduce.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"310 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122774125","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

Compact wideband quarter-wave transformer using stepped impedance microstrip lines 采用阶梯阻抗微带线的紧凑宽带四分之一波变压器

2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA) Pub Date : 2017-04-01 DOI: 10.1109/CSCITA.2017.8066550

Kochar Inderkumar, Sandip G. Dhende, Swapnil P. Chilap

引用次数: 4

Music features based on Hu moments for genre classification 音乐特征根据胡时刻进行类型分类

2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA) Pub Date : 2017-04-01 DOI: 10.1109/CSCITA.2017.8066566

Renia Lopes, Santosh V. Chapaneri, Deepak Jayaswal

{"title":"Music features based on Hu moments for genre classification","authors":"Renia Lopes, Santosh V. Chapaneri, Deepak Jayaswal","doi":"10.1109/CSCITA.2017.8066566","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066566","url":null,"abstract":"Automated musical genre classification using machine learning techniques has gained popularity for research and development of powerful tools to organize music collections available on web. Mel cepstral co-efficients (MFCC's) have been successfully used in music genre classification but they do not reflect the correlation between the adjacent co-efficients of Mel filters of a frame neither the relation between adjacent co-efficients of Mel filters of neighboring frames. This leads to loss of useful features. In this work, Hu moment based features are extracted from the spectrogram to study impact of energy concentration in the spectrogram. Under different musical genres the difference in rhythm in genres drastically changes the texture of spectrogram image. This alters the energy concentration in spectrogram. Hu moments being invariant to translation, scaling as well as rotation can capture useful features from spectrogram that are not considered by the MFCC's. Since the spectral moments are computed locally, they can assess the intensity of energy concentration at certain frequencies in spectrogram and prove as distinct features in characterizing different genres of music. Hu moment based features along with conventional music features lead to an accuracy of 83.33% for classifying 5 genres.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133312422","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

Capacitated vehicle routing problem 有能力车辆路线问题

2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA) Pub Date : 2017-04-01 DOI: 10.1109/CSCITA.2017.8066555

Tejal Carwalo, Jerin Thankappan, Vandana A. Patil

引用次数: 0

LPC based low dimensional features for object classification 基于LPC的低维特征对象分类

2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA) Pub Date : 2017-04-01 DOI: 10.1109/CSCITA.2017.8066545

N. Hassan, K. Bijoy

{"title":"LPC based low dimensional features for object classification","authors":"N. Hassan, K. Bijoy","doi":"10.1109/CSCITA.2017.8066545","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066545","url":null,"abstract":"Object classification in both images and videos is an important task within the field of computer vision. The process of classifying objects into predefined and semantically meaningful categories using its features is called object classification. Many researchers are working in this area to improve the accuracy of classification and to reduce the dimension of features extracted which are used for classifying the objects. In this paper, we propose Linear Predictive Coding(LPC) based signal approximation on the Tensor features which reduces the dimension of the feature by removing the redundancies in the feature set, so that the accuracy is increased with less computation time. Deep Neural Network (DNN) which is a type of artificial intelligence that could solve complex perceptual problems as fast as human brain is used in this work to classify the objects in videos and images. In the proposed method we employ the combination of Scale Invariant Feature Transform (SIFT) and Tensor features of reduced dimensions. Simulation results illustrate that the proposed model produces more accurate results than many existing methods.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"296 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124242599","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