Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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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 Spectral Indices based Land Cover Classification using Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2024) Payani, C.A.; Gupta, C.; Anand Kumar, M.In this paper, we use Landsat 8 and 9 satellite data to predict the land area that is suitable for agriculture and farming. Early identification and deriving insights from areas and their land properties will give us the scope for better utilization of the area. To achieve this, We used 2 manually created datasets using google earth engine. Even though the main motive is to predict the productive cropland using the created dataset. classification task we intended is to identify the type of region in the given area of land as Water, Barren land, cropland, forests, and urban areas. Deep feed forward neural network and 1D CNN models are used for this classification. The DFNN model consistently outperformed the 1D CNN across all datasets, showing superior classification accuracy and overall performance. On both the KAPLCU, MLCU, and Hybrid datasets, DFNN demonstrated better precision, recall, and F1 scores, confirming its effectiveness in classifying land regions based on satellite imagery. Future work could involve exploring land cover classification using government datasets and developing labeled repositories from unlabeled satellite images to further expand research potential in this domain. © 2024 IEEE.