Nothing Special   »   [go: up one dir, main page]

Ravichandran et al., 2023 - Google Patents

Enhanced CNN Architecture with Comprehensive Performance Metrics for Emotion Recognition

Ravichandran et al., 2023

Document ID
924708042325089799
Author
Ravichandran M
Bharathi P
Publication year
Publication venue
International Conference on Computing and Information Technology

External Links

Snippet

Engaging gameplay in video games provides a wide array of advantages, encompassing entertainment and personal growth. Immersive gaming experiences often stem from games that adapt to the player's emotional state. In the proposed study, participants engage in …
Continue reading at link.springer.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01Input arrangements or combined input and output arrangements for interaction between user and computer
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/01Social networking

Similar Documents

Publication Publication Date Title
Gumaei et al. A hybrid deep learning model for human activity recognition using multimodal body sensing data
US10261947B2 (en) Determining a cause of inaccuracy in predicted affective response
US20190102706A1 (en) Affective response based recommendations
Behoora et al. Machine learning classification of design team members' body language patterns for real time emotional state detection
US9665832B2 (en) Estimating affective response to a token instance utilizing a predicted affective response to its background
US8898091B2 (en) Computing situation-dependent affective response baseline levels utilizing a database storing affective responses
Anwar et al. A game player expertise level classification system using electroencephalography (EEG)
Pan et al. Video-based engagement estimation of game streamers: An interpretable multimodal neural network approach
US20200143286A1 (en) Affective Response-based User Authentication
Gonçalves et al. Assessing users’ emotion at interaction time: a multimodal approach with multiple sensors
Cuperman et al. An end-to-end deep learning pipeline for football activity recognition based on wearable acceleration sensors
JP2022505836A (en) Empathic computing systems and methods for improved human interaction with digital content experiences
Thiam et al. A temporal dependency based multi-modal active learning approach for audiovisual event detection
Moosavi et al. Early mental stress detection using q-learning embedded starling murmuration optimiser-based deep learning model
Ismail et al. A systematic review of emotion recognition using cardio-based signals
Ravichandran et al. Enhanced CNN Architecture with Comprehensive Performance Metrics for Emotion Recognition
Baldassarri et al. Wearables and machine learning for improving runners’ motivation from an affective perspective
Rida et al. From motion to emotion prediction: A hidden biometrics approach
Yadav et al. Automated Identification and Classification of Autism Spectrum Disorder using Behavioural and Visual Patterns in Children
Jacob et al. Affect sensing from smartphones through touch and motion contexts
Praveen et al. An In-depth Exploration of ResNet-50 for Complex Emotion Recognition to Unraveling Emotional States
Sharifara et al. A robot-based cognitive assessment model based on visual working memory and attention level
Qi et al. Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition
Diraco et al. Behavioral Change Prediction from Physiological Signals Using Deep Learned Features
Ravichandran et al. with Comprehensive Performance Metrics