Pub Date : 2016-04-08DOI: 10.1109/ICRTIT.2016.7569535
Ashish Nargundkar, Y. S. Rao
We devise a system for measuring influence of Twitter users, which we call InfluenceRank, based on certain features extracted from their Twitter profiles and tweets authored over the duration of two months. As in the real world, influence of a user on social media may be judged by the engagement they drive through the content they publish. For a tweet, engagement can be most obviously measured by the number of retweets (RTs), favourites and replies it gets. Our system comprises of a regression based machine learning approach with InfluenceRank as the predictor variable against the set of our proposed features. The regression model has shown reasonable accuracy despite being fit on limited data.
{"title":"InfluenceRank: A machine learning approach to measure influence of Twitter users","authors":"Ashish Nargundkar, Y. S. Rao","doi":"10.1109/ICRTIT.2016.7569535","DOIUrl":"https://doi.org/10.1109/ICRTIT.2016.7569535","url":null,"abstract":"We devise a system for measuring influence of Twitter users, which we call InfluenceRank, based on certain features extracted from their Twitter profiles and tweets authored over the duration of two months. As in the real world, influence of a user on social media may be judged by the engagement they drive through the content they publish. For a tweet, engagement can be most obviously measured by the number of retweets (RTs), favourites and replies it gets. Our system comprises of a regression based machine learning approach with InfluenceRank as the predictor variable against the set of our proposed features. The regression model has shown reasonable accuracy despite being fit on limited data.","PeriodicalId":351133,"journal":{"name":"2016 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122513540","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}
Pub Date : 2016-04-08DOI: 10.1109/ICRTIT.2016.7569549
Shahjahan Shaik, J. Popat, T. Kishore Kumar
Phase delay and phase noise are the serious problems in wireless communication, which may degrade the total system performance and brings instability. In satellite position controlling systems, accelerometer sends the position information to the radar through free space, and then forwards to control system on the ground. Received signal contains high frequency random noise along with satellite position information. By using the low pass filters like Butterworth, Chebechev, can remove the high frequency noise. These filters provide phase delays and may cause the change in total system phase margin, stability and accuracy of information present in the signal. This paper presents a Kalman filtering based approach to overcome the phase delay and stability problems which estimates the desired sensor signal by taking a noisy contaminated sensor signal as input.
{"title":"Kalman filter based phase delay reduction technique","authors":"Shahjahan Shaik, J. Popat, T. Kishore Kumar","doi":"10.1109/ICRTIT.2016.7569549","DOIUrl":"https://doi.org/10.1109/ICRTIT.2016.7569549","url":null,"abstract":"Phase delay and phase noise are the serious problems in wireless communication, which may degrade the total system performance and brings instability. In satellite position controlling systems, accelerometer sends the position information to the radar through free space, and then forwards to control system on the ground. Received signal contains high frequency random noise along with satellite position information. By using the low pass filters like Butterworth, Chebechev, can remove the high frequency noise. These filters provide phase delays and may cause the change in total system phase margin, stability and accuracy of information present in the signal. This paper presents a Kalman filtering based approach to overcome the phase delay and stability problems which estimates the desired sensor signal by taking a noisy contaminated sensor signal as input.","PeriodicalId":351133,"journal":{"name":"2016 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123769670","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}
Pub Date : 2016-04-08DOI: 10.1109/ICRTIT.2016.7569524
M. Ushanandhini, M. S. Tech, M. E. Rajesh, Mr M Rajakani
Scene classification is treated to be a philosophical and the powerful method for HSR (High spatial resolution) images. Many academicians and researchers paid their attention towards the agglomeration of numerous features. Initially, a survey on PTM (Probabilistic Topic Model) was done in a finest manner and they have concluded that, a single feature (i.e., a spectral feature) was not best suited for HSR images. The next investigation was performed on CAT-PTM and their basic theory behind this method was that, the words of visual dictionary are highly correlated. Due to these inadequacies, the above methods are not valid for HSR images. Thus, the paper proposes SAL-PTM (Semantic Allocation Level - Probabilistic Topic Model) method through which three features (i.e., Texture, Scale Invariant Feature Transform and Spectral) are extracted for better performance. The semantic description of low level descriptors is generated by means of K-means clustering. Finally, the features obtained from the latent semantic allocations are isolated by means of PTM and their performance was evaluated and compared by using LDA (Latent Dirichlet Allocation) and PLSA (Probabilistic Topic Model). A U.S geological survey dataset and UC Merced Dataset was tested on SAL-PTM (Semantic Allocation Level-Probabilistic Topic Model). In response to that, a precise outcome was obtained suggesting that our proposed SAL-PTM method was confined to prove its effectiveness.
{"title":"Classification of high spatial resolution images using semantic allocation level-probabilistic topic model","authors":"M. Ushanandhini, M. S. Tech, M. E. Rajesh, Mr M Rajakani","doi":"10.1109/ICRTIT.2016.7569524","DOIUrl":"https://doi.org/10.1109/ICRTIT.2016.7569524","url":null,"abstract":"Scene classification is treated to be a philosophical and the powerful method for HSR (High spatial resolution) images. Many academicians and researchers paid their attention towards the agglomeration of numerous features. Initially, a survey on PTM (Probabilistic Topic Model) was done in a finest manner and they have concluded that, a single feature (i.e., a spectral feature) was not best suited for HSR images. The next investigation was performed on CAT-PTM and their basic theory behind this method was that, the words of visual dictionary are highly correlated. Due to these inadequacies, the above methods are not valid for HSR images. Thus, the paper proposes SAL-PTM (Semantic Allocation Level - Probabilistic Topic Model) method through which three features (i.e., Texture, Scale Invariant Feature Transform and Spectral) are extracted for better performance. The semantic description of low level descriptors is generated by means of K-means clustering. Finally, the features obtained from the latent semantic allocations are isolated by means of PTM and their performance was evaluated and compared by using LDA (Latent Dirichlet Allocation) and PLSA (Probabilistic Topic Model). A U.S geological survey dataset and UC Merced Dataset was tested on SAL-PTM (Semantic Allocation Level-Probabilistic Topic Model). In response to that, a precise outcome was obtained suggesting that our proposed SAL-PTM method was confined to prove its effectiveness.","PeriodicalId":351133,"journal":{"name":"2016 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127048139","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}
Pub Date : 2016-04-01DOI: 10.1109/ICRTIT.2016.7569544
Geeta F. Nadlamani, S. Shaikh
Cloud storage is getting popular as it offers flexible service with appealing benefits of on-demand data outsourcing. Putting away files remotely instead of locally boasts an array of preferences for both home and professional clients. In this paper we are trying to secure all kind of private data into cloud by providing some security levels which includes encryption, digital signature and visual cryptography. The utilization of visual cryptography is investigated to safeguard the privacy of a picture captcha. This picture captcha is broken down into two shares also known as sheets that are put away in separate database servers one with client and one with application such that the first picture captcha can be uncovered only when both shares are accessible. The individual sheet pictures do not uncover the character of the first picture captcha. When the original picture captcha is uncovered to the client it can be utilized as the secret word. In private auditing scheme for regenerated coded file there exist remote checking method, which include users to stay online, which is not practical. So to endeavor this, third party auditor (TPA) is used to resolve problem of clients for being online all the time.
{"title":"Preserving privacy using TPA for cloud storage based on regenerating code","authors":"Geeta F. Nadlamani, S. Shaikh","doi":"10.1109/ICRTIT.2016.7569544","DOIUrl":"https://doi.org/10.1109/ICRTIT.2016.7569544","url":null,"abstract":"Cloud storage is getting popular as it offers flexible service with appealing benefits of on-demand data outsourcing. Putting away files remotely instead of locally boasts an array of preferences for both home and professional clients. In this paper we are trying to secure all kind of private data into cloud by providing some security levels which includes encryption, digital signature and visual cryptography. The utilization of visual cryptography is investigated to safeguard the privacy of a picture captcha. This picture captcha is broken down into two shares also known as sheets that are put away in separate database servers one with client and one with application such that the first picture captcha can be uncovered only when both shares are accessible. The individual sheet pictures do not uncover the character of the first picture captcha. When the original picture captcha is uncovered to the client it can be utilized as the secret word. In private auditing scheme for regenerated coded file there exist remote checking method, which include users to stay online, which is not practical. So to endeavor this, third party auditor (TPA) is used to resolve problem of clients for being online all the time.","PeriodicalId":351133,"journal":{"name":"2016 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129107625","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}
Pub Date : 2016-04-01DOI: 10.1109/ICRTIT.2016.7569598
S. Swathi, V. Bhanumathi
A novel triple band monopole antenna is proposed in this paper. By introducing an E-Shaped patch on the top of the substrate and an inverted L-Shape slit in the infinite ground plane, circular polarization is realized to minimize the losses. It can operate at the frequency of 2.4 GHz, 3.3 GHz and 5.2 GHz for WLAN a/g and WIMAX applications. The dimension of the proposed antenna is relatively small (40 × 45mm2) and it can operate over the frequency range of 2.10-2.85 GHz, 3.1-3.65 GHz and 4.95-5.80 GHz. The simulated results obtained from HFSS 13.0 software and measured results from Agilent network analyser tool shows that the designed antenna performs the triple band operation.
{"title":"Triple band monopole antenna for WLAN and WIMAX applications","authors":"S. Swathi, V. Bhanumathi","doi":"10.1109/ICRTIT.2016.7569598","DOIUrl":"https://doi.org/10.1109/ICRTIT.2016.7569598","url":null,"abstract":"A novel triple band monopole antenna is proposed in this paper. By introducing an E-Shaped patch on the top of the substrate and an inverted L-Shape slit in the infinite ground plane, circular polarization is realized to minimize the losses. It can operate at the frequency of 2.4 GHz, 3.3 GHz and 5.2 GHz for WLAN a/g and WIMAX applications. The dimension of the proposed antenna is relatively small (40 × 45mm2) and it can operate over the frequency range of 2.10-2.85 GHz, 3.1-3.65 GHz and 4.95-5.80 GHz. The simulated results obtained from HFSS 13.0 software and measured results from Agilent network analyser tool shows that the designed antenna performs the triple band operation.","PeriodicalId":351133,"journal":{"name":"2016 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122442435","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}
Pub Date : 2016-02-01DOI: 10.1109/ICICES.2016.7518862
Neenu Jose, R. Ramesh
In recent years, variety of techniques were developed in the field of SAR image despeckling, which avail to inhibit the Speckle in SAR Image. This paper proposes a patch ordering based SAR image despeckling approaches, which uses two transform domain filtering. The proposed approach consists of two stage filtering. In the first step i.e. coarse filtering, denoising is done by simultaneous Sparse Coding (SSC). The diminutive artifacts engendered by the coarse filtering can be removed by second stage of filtering i.e. refined filtering. In this step, filtered image is obtained by Wavelet Hard thresholding. Experimental results showed that the proposed system achieves good Structural similarity Index Measure (SSIM), Peak Signal to Noise Ratio (PSNR) values for despeckled images.
{"title":"Patch ordering based SAR image despeckling via SSC and wavelet thresholding","authors":"Neenu Jose, R. Ramesh","doi":"10.1109/ICICES.2016.7518862","DOIUrl":"https://doi.org/10.1109/ICICES.2016.7518862","url":null,"abstract":"In recent years, variety of techniques were developed in the field of SAR image despeckling, which avail to inhibit the Speckle in SAR Image. This paper proposes a patch ordering based SAR image despeckling approaches, which uses two transform domain filtering. The proposed approach consists of two stage filtering. In the first step i.e. coarse filtering, denoising is done by simultaneous Sparse Coding (SSC). The diminutive artifacts engendered by the coarse filtering can be removed by second stage of filtering i.e. refined filtering. In this step, filtered image is obtained by Wavelet Hard thresholding. Experimental results showed that the proposed system achieves good Structural similarity Index Measure (SSIM), Peak Signal to Noise Ratio (PSNR) values for despeckled images.","PeriodicalId":351133,"journal":{"name":"2016 International Conference on Recent Trends in Information Technology (ICRTIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125612233","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}