Search results for "LSTM"
showing 6 items of 16 documents
Cross-Subject Emotion Recognition Using Fused Entropy Features of EEG.
2022
Emotion recognition based on electroencephalography (EEG) has attracted high interest in fields such as health care, user experience evaluation, and human–computer interaction (HCI), as it plays an important role in human daily life. Although various approaches have been proposed to detect emotion states in previous studies, there is still a need to further study the dynamic changes of EEG in different emotions to detect emotion states accurately. Entropy-based features have been proved to be effective in mining the complexity information in EEG in many areas. However, different entropy features vary in revealing the implicit information of EEG. To improve system reliability, in this paper,…
Žestu atpazīšanas sistēmas realizācija Android lietotnē
2019
Darbā apskatīta ideja vienkāršot lietojumprogrammas palaišanu, bez meklēšanas ar žestu palīdzību. Darbā tiek apskatītas dažas žestu apatzīšanās metodes, īpašu uzmanību pievēršot dziļajiem neironu tīkliem. Lai žestu iemācītos no dažiem paraugiem ir realizēta datu papildināšana ar daudziem pārveidotiem žestu variantiem. Tika veikti eksperimenti labāko tīkla parametru atrašanai. Praktiski realizēta Android lietojumprogramma, kas ļauj atpazīt žestus, un spēj uz tālruņa, bez interneta lietošanas, apmācīt modeli uz jauniem, lietotāja definētiem žestiem. Darba mērķi ir: ● izveidot papildus mehānismu viedtālruņa vadībai; ● labāku neironu tīklu meklēšana apmācot atpazīt jaunus žestus.
Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs
2019
Abstract The T cell repertoire is composed of T cell receptors (TCR) selected by their cognate MHC-peptides and naive TCR that do not bind known peptides. While the task of distinguishing a peptide-binding TCR from a naive TCR unlikely to bind any peptide can be performed using sequence motifs, distinguishing between TCRs binding different peptides requires more advanced methods. Such a prediction is the key for using TCR repertoires as disease-specific biomarkers. We here used large scale TCR-peptide dictionaries with state-of-the-art natural language processing (NLP) methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific classifier to predict which TCR binds to which…
An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications.
2022
Traditional advertising techniques seek to govern the consumer’s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers’ actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and clas…
Neironu tīklu metodes kriptovalūtu vērtību prognozēšanai
2018
Kriptovalūtu tirgus nestabilitātes dēļ, pašam prognozēt, kā notiks vērtību svārstības ir gandrīz neiespējami. Tāpēc šajā darbā tiks apskatītas vairākas metodes, ar kuru palīdzību prognozēs dažādu kriptovalūtu vērtību tuvākajā nākotnē, izmantojot mākslīgos neironu tīklus. Darba mērķis ir salīdzināt dažādas metodes, kā analizēt kriptovalūtu datus un noteikt to vērtību tuvākajā nākotnē, balstoties gan uz dažādu kriptovalūtu vēsturisko informāciju, gan uz sociālā tīkla Twitter ierakstiem. Trenējot neironu tīklus, kuram tiek padoti sociālo tīklu dati apvienojumā ar kriptovalūtu informāciju, tika sasniegts pat 10%-15% pieaugums prognozēšanas precizitātei, salīdzinot ar parastu kriptovalūtu vēstur…
Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network
2023
Oxygen uptake (V˙O2) is an important metric in any exercise test including walking and running. It can be measured using portable spirometers or metabolic analyzers. Those devices are, however, not suitable for constant use by consumers due to their costs, difficulty of operation and their intervening in the physical integrity of their users. Therefore, it is important to develop approaches for the indirect estimation of V˙O2-based measurements of motion parameters, heart rate data and application-specific measurements from consumer-grade sensors. Typically, these approaches are based on linear regression models or neural networks. This study investigates how motion data contribute to V˙O2 …