IEEE-CSE Projects 2018

Aim: a new approach that uses the social media platform Twitter to quantify suicide warning signs for individuals and to detect posts containing suicide-related content. Proposed System: the automatic identification of sudden changes in a user’s online behavior. To detect such changes, combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for hange detection in data streams. Existing System: traditional detection methods rely heavily on manually annotated speech, which can limit their effectiveness due in part to the varying forms of suicide warning signs in at-risk individuals [6, 11, 12]. Objectives: 1.       using research from the field of psychology, we design and develop behavioral features to quantify the level of risk for an individual according to his online behavior on Twitter (speech, diurnal activities, size of social networ...

Aim: To automatically identify SNMD patients at the early stage according to their OSN data with a novel tensor model that efficiently integrate heterogeneous data from different OSNs. Existing System: The SNMD data from different OSNs may be incomplete due to the heterogeneity (drawback) . For example, the profiles of users may be empty due to the privacy issue, different functions on different OSNs (e.g., game, check-in, event), etc.  Proposed System : propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD) , that exploits features extracted from social network data to accurately identify potential cases of SNMDs. The paper also exploits multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy.   Advantage: propose a novel tensor-based approach to address the issues of using heterogeneous data and incorporate domain knowledge in SNMD detection. Objectives: ·   ...