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Found 4 papers, 0 papers with code
SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations
We showed that for similar accuracy, the speed-up achieved for SOMTimeS and K-means was 1. 8x on average; however, rates varied between 1x and 18x depending on the dataset.
Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward
Easy access to audio-visual content on social media, combined with the availability of modern tools such as Tensorflow or Keras, open-source trained models, and economical computing infrastructure, and the rapid evolution of deep-learning (DL) methods, especially Generative Adversarial Networks (GAN), have made it possible to generate deepfakes to disseminate disinformation, revenge porn, financial frauds, hoaxes, and to disrupt government functioning.
A Benchmark Study on Time Series Clustering
This paper presents the first time series clustering benchmark utilizing all time series datasets currently available in the University of California Riverside (UCR) archive -- the state of the art repository of time series data.
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering
Additionally, using available meteorological data associated with storm events, we examine the characteristics of computational clusters of storm events in the study watersheds and identify the features driving the clustering approach.
