Ibsa Jalata
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
Role not yet determined Is this you? Add your title
Formerly Arkansas Affiliated with University of Arkansas through 2024.
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Ibsa Jalata's research focuses on the application of machine learning, particularly deep learning and neural networks, to image processing and analysis. His work includes developing algorithms for tasks such as emotion recognition from crowd videos, movement analysis for neurological disorders, and image deblurring. He has investigated the use of graph convolutional neural networks for movement analysis and explored domain adaptation techniques for image deburring. Jalata's publications also address challenges in deep learning, such as data scarcity in magnetic resonance image reconstruction, and the development of lightweight deep learning models for face recognition on mobile devices. His research interests extend to accelerated magnetic resonance imaging using unsupervised deep learning architectures and material recognition via single photon vibrometers. Jalata has a recent publication in 2025 and has a total of 11 publications with 78 citations, holding an h-index of 5. He has collaborated with several researchers at the University of Arkansas at Fayetteville, including Ukash Nakarmi and Ky Luu.
Metrics
- h-index: 5
- Publications: 11
- Citations: 78
Selected Publications
-
Semi-Supervised Medical Image Segmentation using Puzzlemix Augmentation Technique (2024)
-
Cut-Puzzle mix: Scribble Guided Medical Image Segmentation without Segmentation Masks (2024)
-
Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction (2024)
-
When System Model Meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging (2023)
-
EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring (2022)
-
Non-volume preserving-based fusion to group-level emotion recognition on crowd videos (2022)
-
Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network (2021)
-
MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices (2019)
Collaboration Network
Top Collaborators
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
- Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices
- Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction
- When System Model Meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging
- Semi-Supervised Medical Image Segmentation using Puzzlemix Augmentation Technique
- Cut-Puzzle mix: Scribble Guided Medical Image Segmentation without Segmentation Masks
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
- MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
- MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
- MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices
- Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
- Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
- Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring
- Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction
Similar Researchers
Based on overlapping research topics