José Luis Gómez-Sirvent

Orcid: 0000-0003-3153-2088

According to our database1, José Luis Gómez-Sirvent authored at least 11 papers between 2022 and 2024.

Collaborative distances:
  • Dijkstra number2 of six.
  • Erdős number3 of five.

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
Assessment of music performance anxiety in a virtual auditorium through the study of ambient lighting and audience distance.
Virtual Real., March, 2024

Building information modeling and affective occupancy evaluation: A scoping review.
J. Ambient Intell. Smart Environ., 2024

Binary Classification Methods for Movement Analysis from Functional Near-Infrared Spectroscopy Signals.
Proceedings of the Artificial Intelligence for Neuroscience and Emotional Systems, 2024

Improved Surface Defect Classification from a Simple Convolutional Neural Network by Image Preprocessing and Data Augmentation.
Proceedings of the Bioinspired Systems for Translational Applications: From Robotics to Social Engineering, 2024

2023
Training industrial engineers in Logistics 4.0.
Comput. Ind. Eng., October, 2023

Defect detection and classification on semiconductor wafers using two-stage geometric transformation-based data augmentation and SqueezeNet lightweight convolutional neural network.
Comput. Ind. Eng., September, 2023

Fine-Tuned SqueezeNet Lightweight Model for Classifying Surface Defects in Hot-Rolled Steel.
Proceedings of the Advances in Computational Intelligence, 2023

VRPrOE Toolbox for Virtual Pre-occupancy Evaluation: Proof of Concept on a BIM Model of a Conservatory Classroom.
Proceedings of the Ambient Intelligence - Software and Applications, 2023

2022
Geometric transformation-based data augmentation on defect classification of segmented images of semiconductor materials using a ResNet50 convolutional neural network.
Expert Syst. Appl., 2022

A deep residual neural network for semiconductor defect classification in imbalanced scanning electron microscope datasets.
Appl. Soft Comput., 2022

Detection of Unknown Defects in Semiconductor Materials from a Hybrid Deep and Machine Learning Approach.
Proceedings of the Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence, 2022


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