Faculty Publications
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Publications by NITK Faculty
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Item Machine learning approach to manage adaptive push notifications for improving user experience(Association for Computing Machinery, 2020) Madhusoodanan, A.; Anand Kumar, M.; Fraser, K.; Yousuf, B.In this modern connected world mobile phone users receive a lot of notifications. Many of the notifications are useful but several cause unwanted distractions and stress. Managing notifications is a challenging task with the large influx of notifications users receive on a daily basis. This paper proposes a machine learning approach for notification management based upon the context of the user and his/her interactions with the mobile device. Since the proposed idea is to generate personalised notifications there is no ground truth data hence performance metrics such as accuracy cannot be used. The proposed solution measures the diversity score, the click through rate score and the enticement score. © 2020 ACM.Item Overlapping word removal is all you need: revisiting data imbalance in hope speech detection(Taylor and Francis Ltd., 2024) RamakrishnaIyer LekshmiAmmal, H.; Ravikiran, M.; Nisha, G.; Balamuralidhar, N.; Madhusoodanan, A.; Anand Kumar, A.K.; Chakravarthi, B.R.Hope speech detection is a new task for finding and highlighting positive comments or supporting content from user-generated social media comments. For this task, we have used a Shared Task multilingual dataset on Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) for three languages English, code-switched Tamil and Malayalam. In this paper, we present deep learning techniques using context-aware string embeddings for word representations and Recurrent Neural Network (RNN) and pooled document embeddings for text representation. We have evaluated and compared the three models for each language with different approaches. Our proposed methodology works fine and achieved higher performance than baselines. The highest weighted average F-scores of 0.93, 0.58, and 0.84 are obtained on the task organisers{'} final evaluation test set. The proposed models are outperforming the baselines by 3{\%}, 2{\%} and 11{\%} in absolute terms for English, Tamil and Malayalam respectively. © 2023 Informa UK Limited, trading as Taylor & Francis Group.