Political Non-verbal Discourse and Lying Detection Techniques for Psycholinguistic Purposes: A Diagnostic Perspective

Social Network is an emerging e-service for Content Sharing Sites (CSS). It is an emerging service which provides reliable communication. Some users over CSS affect user’s privacy on their personal contents, where some users keep on sending annoying comments and messages by taking advantage of the user’s inherent trust in their relationship network. Integration of multiple user’s privacy preferences is very difficult task, because privacy preferences may create conflict. The techniques to resolve conflicts are essentially required. Moreover, these methods need to consider how users would actually reach an agreement about a solution to the conflict in order to offer solutions acceptable by all of the concerned users. The first mechanism to resolve conflicts for multi-party privacy management in social media that is able to adapt to different situations by displaying the enterprises that users make to reach a result to the conflicts. Billions of items that are uploaded to social media are co-owned by multiple users. Only the user that uploads the item is allowed to set its privacy settings (i.e. who can access the item). This is a critical problem as users’ privacy preferences for co-owned items can conflict. Multi-party privacy management is therefore of crucial importance for users to appropriately reserve their privacy in social media.

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GNSS Spoofing Detection Techniques by Cellular Network Cross-check in Smartphones

Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019) ◽

10.33012/2019.17076 ◽

2019 ◽

Author(s):

Francesco Formaggio ◽

Silvia Ceccato ◽

Francesco Basana ◽

Nicola Laurenti ◽

Stefano Tomasin

Keyword(s):

Cellular Network ◽

Detection Techniques ◽

Spoofing Detection

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A Review on Heart Disease Detection Techniques

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0200 ◽

2017 ◽

Vol 7 (7) ◽

pp. 395

Author(s):

. Anika ◽

Navpreet Kaur

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Review Paper ◽

Vascular Diseases ◽

Disease Detection ◽

Automated Classification ◽

Feature Extraction Method ◽

Detection Techniques ◽

Cardio Vascular ◽

Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

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FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v8i3.3399 ◽

2009 ◽

Vol 8 (3) ◽

pp. 887-897

Author(s):

Vishal Paika ◽

Er. Pankaj Bhambri

Keyword(s):

Neural Networks ◽

Fuzzy Inference ◽

Facial Features ◽

Facial Appearance ◽

Feature Points ◽

Inference System ◽

Detection Techniques ◽

The Face ◽

Hair Style ◽

Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

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