Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Performance enhancement of HEVC lossless mode using context-based angular and planar intra predictions(Springer, 2020) Kamath, S.; Aparna., P.; Antony, A.Lossless mode of High-Efficiency Video Coding (HEVC), the state-of-the-art video coding standard, can be used for distortion-free reconstruction of the input data for a wide variety of applications. HEVC relies on the usage of efficient intra prediction strategies to achieve superior compression than its predecessor H.264. A large amount of spatial redundancy exists in almost all video sequences due to coherence, smoothness and the inherent correlation within the neighboring pixels. In this paper, a context-based intra prediction scheme is proposed to minimize this local redundancy by identifying the edges and textures to appropriately modify the prediction strategy at the pixel level, without further increase in the computational complexity. The variability in the sum of absolute differences and local pixel intensity values are chosen to derive the context of the nearby region around the target pixel in the planar and angular intra prediction modes respectively. The experimental results validate the superiority of the proposed method over the HEVC anchor and other state-of-the-art techniques in the literature. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.Item Leveraging Text Analysis and Machine Learning Models for Suicide Ideation Detection in Social Media Posts(Springer, 2025) Lobo, S.R.; Kamath, S.The escalating global suicide rates underscore the urgency to leverage social media platforms for mental health discourse. However, discerning suicidal content amidst mental health discussions poses challenges, given the nuanced progression from mental health disorders to suicidal ideation. This study addresses this issue by analyzing text related to suicide ideation within social media posts. Utilizing a dataset featuring text labels for suicide and non-suicide sourced from the SuicideWatch and Depression subreddits on Kaggle, we employ common word and bigram analysis to uncover patterns and relationships. Additionally, emotion analysis is conducted to discern underlying emotional tones in the text corpus. Machine learning and deep learning models are deployed for suicide ideation classification. Support Vector Machines (SVM) coupled with Universal Sentence Encoder (USE) word embedding achieved the highest performance among machine learning models, yielding an accuracy of 94.8% and an F1-score of 0.948. Notably, the deep learning model MentalBERT outperformed others, attaining an accuracy of 97.7% and an F1-score of 0.98. These findings hold promise for assisting professionals in timely identification and provision of mental health support to individuals exhibiting suicidal tendencies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.