首页|Studies from Ghulam Ishaq Khan Institute of Engineering Science & Technology Have Provided New Information about Machine Learning (Emotion Detection Using Convolutional Neural Network and Long Short-term Memory: a Deep Multimodal Framework)
Studies from Ghulam Ishaq Khan Institute of Engineering Science & Technology Have Provided New Information about Machine Learning (Emotion Detection Using Convolutional Neural Network and Long Short-term Memory: a Deep Multimodal Framework)
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By a News Reporter-Staff News Editor at Network Daily News - Data detailedon Machine Learning have been presented. According to news reporting out of Topi, Pakistan, by NewsRxeditors, research stated, “Emotion detection systems play a crucial role in enhancing human-computerinteraction. Existing systems predominantly rely on machine learning techniques.”Financial support for this research came from GIK Institute graduate program research fund.Our news journalists obtained a quote from the research from the Ghulam Ishaq Khan Institute of EngineeringScience & Technology, “This study introduces a novel emotion detection method that employsdeep learning techniques to identify five basic human emotions and the pleasure dimensions (valence) associatedwith these emotions, using text and keystroke dynamics. To facilitate this, we develop a non-acteddataset, DEKT-345 x 2, which includes text and keystroke features. The dataset is created by inducingemotions in participants under controlled conditions. Deep learning models are subsequently employed topredict a person’s affective state using textual content. Semantic analysis of the text data is achieved byemploying the global vector (Glove) representation of words. For both text and keystroke-based analysis,one-dimensional convolutional neural network (Conv1D), long short-term memory (LSTM), sandwichConv1D, and sandwich LSTM models are employed. The robustness of our proposed method is assessedusing the DEKT-345 x 2 dataset, which collects text and keystroke information from 69 participants.Through parameter tuning on training and validation data, we establish models that demonstrate superiorperformance compared to five related approaches and three machine learning classifiers.”
TopiPakistanAsiaConvolutional NetworkCyborgsEmerging TechnologiesMachine LearningNetworksNeural NetworksGhulam Ishaq Khan Institute of Engineering Science & Technology
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