Sep 14, 2020 · In this paper, we present alternative deep learning architectures that perform data fusion, more specifically, feature-level fusion in the context of human ...

Sep 16, 2020 · In this paper, we present alternative deep learning architectures that perform data fusion, more specifically, feature-level fusion in the.

This paper presents alternative deep learning architectures that perform data fusion, more specifically, feature-level fusion in the context of human ...

Oct 22, 2024 · Hence, the focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for ...

We propose a novel hybrid network architecture to recognize human activities through the use of wearable motion sensors and DL techniques.

At last, convolutional neural network (ConvNet) is used for activity classification. The proposed method is implemented on Large- scale Continuous Gesture ...

Common architectures include CNN, LSTM, and transformer models. For instance, in agricultural applications, 1D-CNNs might be used to process spectral data [99], ...

Furthermore, the design of a Long Short Term Memory (LSTM) architecture model is outlined for the application of human activity recognition. An accuracy of ...

Sep 9, 2019 · The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate.

In order to achieve an effective and responsive classification, a decision tree based on multisensor data-stream is applied fusing data coming from embedded.