Wahyu Rahmaniar

Assistant Professor (specially appointed)

Tokyo Institute of Technology

Japan

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Wahyu Rahmaniar is an Assistant Professor (specially appointed) at the Tokyo Institute of Technology, Japan. His research interests encompass artificial intelligence, medical imaging, anomaly detection, and deep learning applications. Recently published articles reflect his focus on practical AI applications, including federated learning in medical realms and innovative solutions for spinal curvature assessment.

Wahyu Rahmaniar

Research Interests

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Articles (17)

Multi-AD: cross-domain unsupervised anomaly detection for medical and industrial applications

Future of the Medical World: Collaborative Medical Imaging AI With Federated Learning

Practical Applications of Artificial Intelligence: A Sector-Wise Review of Challenges and Future Directions

ChatGPT for Software Development: Opportunities and Challenges

Deep learning and quantum algorithms approach to investigating the feasibility of wormholes: A review

Auto-CA: Automated Cobb Angle Measurement Based on Vertebrae Detection for Assessment of Spinal Curvature Deformity

Career Pathways in Artificial Intelligence and Computer Vision for Women in Industry and Academia: Turning the Tide [Career Advisor]

Pose Detection and Recurrent Neural Networks for Monitoring Littering Violations

A Hybrid Hand-Crafted and Deep Neural Spatio-Temporal EEG Features Clustering Framework for Precise Emotional Status Recognition

Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network

Sensor integration for real-time data acquisition in aerial surveillance

Touchless Head-Control (THC): Head Gesture Recognition for Cursor and Orientation Control

Wide Range Head Pose Estimation Using A Single RGB Camera for Intelligent Surveillance

Wide Range Head Pose Estimation Using a Single RGB Camera for Intelligent Surveillance

Development of Surface EMG Game Control Interface for Persons with Upper Limb Functional Impairments

Real-Time Human Detection Using Deep Learning on Embedded Platforms: A Review

Real-time bi-directional people counting using an RGB-D camera

Collaborators (2)

Minoru Sasaki

Kenji Suzuki

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