Search results for: GRUs.

Authors: Tarunima Chatterjee, Pinaki Pratim Acharjya

Abstract:

Advanced neural network-based human activity recognition (HAR) system integration with Internet of Things technology is progressing quickly. This technique, which has important implications in the fields of fitness, healthcare, and smart home environments, correctly detects and categorizes human actions from sensor data using sensors and deep learning algorithms. This work presents an approach that combines multi-head CNNs with an attention mechanism, producing a detection rate of 95.4%. Traditional HAR systems are generally imprecise and inefficient. Data collection, spectrogram image conversion, feature extraction, optimization, and classification are all steps in the procedure. With its deep learning foundation, this HAR system has enormous potential for real-time activity monitoring, especially in the healthcare industry, where it may enhance safety and offer insightful data on user behaviour.

Keywords: Deep learning, Human Activity Recognition, HAR, Internet of Things, IoT, Convolutional Neural Networks, CNNs, Long Short-Term Memory, LSTM, neural machine translation, NMT, Inertial Measurement Unit, IMU, Gated Recurrent Units, GRUs.

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Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos

Abstract:

This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.

Keywords: Metaphor detection, deep learning, representation learning, embeddings.

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