Skip to content
Log in
Sponsored AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI

Author

Z. Alimbayeva

View on Semantic Scholar

4 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Oct 2026

ROI-SSIM guided framework for 3D knee MRI reconstruction under artifact degradation

Magnetic resonance imaging (MRI) reconstruction is highly sensitive to undersampling and acquisition artifacts. Although deep learning-based approaches have significantly improved reconstruction quality, they are typically optimized using global fidelity metrics that may not fully reflect reconstruction quality within...

A. Bekbolat, Almira Askhatova, Aibek S. Niyetkaliyev et al. · 0 citations
Open access Sep 2026

Ensemble-Based EMG-IMU Gesture Recognition for Real-Time Prosthetic Hand Control

Reliable control of upper-limb prostheses remains challenging because muscle signals can change between sessions and wearable EMG sensors may suffer from poor electrode contact. This study presents a real-time prosthetic hand control platform based on an OYMotion gForce Pro armband and a five-finger Dynamixel-actuated...

Darkhan Zholtayev, Temirlan Meiramkhanov, A. Ozhikenova et al. · 0 citations
Open access Oct 2026

MULTI-TASK DEEP LEARNING FOR KNEE MRI PATHOLOGY CLASSIFICATION

Magnetic resonance imaging (MRI) is the primary non-invasive modality for diagnosing internal knee derangement, but manual reading of multi-planar examinations is time-consuming and subject to inter-observer variability, particularly for anterior cruciate ligament (ACL) and meniscus tears. Most deep learning approaches...

A. Bekbolat, A. Razak, Z. Alimbayeva et al. · 0 citations
Open access Aug 2026

Development and Clinical Evaluation of a Wearable 12-Lead Electrocardiographic Platform with Automated ECG Analysis for Telemedicine Applications

Wearable electrocardiographic technologies have become increasingly important for continuous cardiac monitoring; however, most existing portable systems are limited by the number of recorded leads or provide only basic signal acquisition without advanced automated analysis. This study presents a third-generation wearab...

Z. Alimbayeva, C. Alimbayev, K. Ozhikenov et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.