Polycystic ovary syndrome (PCOS) disorder is identified by the presence of a number of follicles present in the ovary of female reproductive system. Ultrasound imaging of the ovary contains essential information about the size, number of follicles and its position. In real time, the detection of PCOS is a difficult task for radiologists due to the various sizes of follicles and is highly connected with blood vessels and tissues. This often results in error diagnosis. For preprocessing various standard filtering techniques are applied on ovary image. Based on the performance, appropriate filter is chosen to remove the noise from the image. This paper presents an effectual active contour with modified Otsu threshold value to automated discovery of follicles from the ultrasound images. The performances of the proposed method illustrate the betterments of the proposed approach over other techniques. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Gopalakrishnan T

Department of Database Systems

School of Computer Science and Engineering

Vellore Campus

Iyapparaja M

Department of Digital Communications

School of Information Technology and Engineering

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Multimedia Tools and Applications

2-Levels of clustering strategy to detect and locate copy-move forgery in digital images

Twitter, a social blogging site which became the tremendous topic in today’s environment, which made several organizations and public to develop their identity and overwhelming through this social website. But unfortunately, twitter facing great challenges due to spammers who break the reputation of the website from deliberate users to stop using it. Researchers have proposed many techniques to overcome the issues faced by the spammers. As far researchers find a new path so as the spammers develop new techniques to travel in that path. So far, many algorithms were proposed to detect the spammers and some extraction techniques have developed to increase the potential of detection rate. In this paper, the main focus is about feature extraction of our data with a hybrid approach of combining logistic regression with dimensional reduction technique using principal component analysis. Our dataset contains 17 million users’ tweets with 159 features included in it. Then we are going to extract particular features from it which would be helpful for the further process of increasing the classification accuracy. For the classification process, our work extended for the process of classification of data using some machine learning techniques. From the proposed work the detection rate could be increased by using particular features for the classification process. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Cluster Computing

Feature extraction using LR-PCA hybridization on twitter data and classification accuracy using machine learning algorithms

Twitter, the social network which evolving faster and regular usage by millions of people and who become addicted to it. So spam playing a major role for Twitter users to distract them and grab their attention over them. Spammers actually detailed like who send unwanted and irrelevant messages or websites and promote them to several users. To overcome the problem many researchers proposed some ideas using some machine learning algorithms to detect the spammers. In this research work, a new hybrid approach is proposed to detect the streaming of Twitter spam in a real-time using the combination of a Decision tree, Particle Swarm Optimization and Genetic algorithm. Twitter has given access to the researchers to get tweets from its Twitter-API for real-time streaming of tweet data which they can get direct access to public tweets. Here 600 million tweets are created by using URL based security tool and further some features are extracted for representation of tweets in real-time detection of spam. In addition, our research results are compared with other hybrid algorithms which a better detection rate is given by our proposed work. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Wireless Personal Communications

Detecting Streaming of Twitter Spam Using Hybrid Method

Innovation is developing step by step tremendously. As there are numerous social locales where information likes pictures, sound, video and so forth are shared by the client to each other. In concentrate to all exercises on social locales, there is need of protection to pictures. Because of this reason, I utilized Adaptive protection strategy forecast instrument to give security to the pictures. Issue identified with pictures is the huge issue in social locales like Facebook, twitter and so on. So here the part of a social thought, security to pictures, metadata and so on is produced. To conquer this issue we produced an answer which is 2 systems which understanding to a background marked by the pictures gives appropriated answer for them. Here we give an arrangement to the specific sort of pictures by characterizing them and in addition giving protection to pictures which are transferred agreement to a calculation that we utilized. Consequently as indicated by this arrangement expectation pictures take after a similar approach on up and coming pictures and give successful security to them. © Published under licence by IOP Publishing Ltd.

Fulltext

IOP Conference Series: Materials Science and Engineering

Security policy speculation of user uploaded images on content sharing sites

In today’s quandary, the effort required to prevent the disease occurrence should be more as compared to the methodologies followed for identifying it. In this paper list down the different number of diseases are affected in distinct regions are extracted and by fixing disease based coupling and cohesion range metric the severity level of each disease and its dependence level will be confirmed. By preparing the coupling and cohesion metrics, additional tests are performed to measure its updating as the value is ‘1’ then the disease severity level will be very high otherwise consider as low. © RJPT All right reserved.

Journal	Data powered by TypesetMultimedia Tools and Applications
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
ISSN	1380-7501
Open Access	0