Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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2016 - 201912020 - 20221

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Subject: search.filters.subject.Using Deep Learning

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    Characterisation of Mental Health Conditions in Social Media Using Deep Learning Techniques
    (Springer, Cham, 2022-09-19) Sharma, T; Panchendrarajan, R; Saxena, A
    Social media has become the easiest and most popular choice for online users to share their views, thoughts, opinions, and emotions with their friends and followers. The shared content is very helpful in making valuable conclusions about an individual’s personality. Nowadays, researchers across the world are collecting social media data for understanding the mental health of social media users. Mental illness is a big concern in today’s society, and in no time, it can turn into suicidal thoughts if efficacious methods are not taken into account. Early detection of such mental health conditions provides a potential way for effective social intervention. This has opened up opportunities to the research community to automatically determine various mental health conditions, such as anxiety, depression, and near-suicidal thoughts from user-generated content. The deep learning techniques have been extensively used in this research area to better understand the mental health of users on social networking platforms. In this chapter, we conduct a comprehensive review of the research works that use deep learning techniques to identify various mental health conditions from social media data. We discuss methods to detect different types of mental health conditions, such as anxiety, depression, stress, suicide, and anorexia. We also provide the details of the datasets used in these studies. The chapter is concluded with promising future directions.
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    Intruder detection using deep learning and association rule mining
    (IEEE, 2016-12-08) Thilina, A; Attanayake, S; Samarakoon, S; Nawodya, D; Rupasinghe, L; Pathirage, N; Edirisinghe, T; Krishnadeva, K
    With the upsurge of internet popularity, nowadays there are millions of online transactions that are being processed per minute thus increasing the possibilities of intruder attacks over the recent times. There have been various intruder detection techniques such as using traditional machine learning based algorithms. These algorithms were widely used to identify and prevent intruder activities in the recent past. Furthermore, multilayer neural networks[5] were also used in this regard to perform the detection. Hence multi-layer neural networks inherit fundamental drawbacks due to its inability to perform training due the problems such as overfitting, etc. In contrast, deep learning algorithms were introduced to overcome these issues effectively. We propose a novel framework to perform intruder detection and analysis using deep learning nets and association rule mining. We utilize a recurrent network to predict intruder activities and FP-Growth to perform the analysis. Our results show the effectiveness of our framework in detail.