Artificial Intelligence
The article Aims to compare the application effect of different machine learning algorithms in the recognition of badminton stroke types, so as to better meet the needs of quantitative analysis of badminton skills and tactics, and provide a more reasonable basis for the layout of game strategies.
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4 Views
This dataset comprises high-resolution 3-axis accelerometer recordings collected from human participants performing distinct hand gestures, intended for training gesture-based assistive interfaces. Each participant’s raw motion signals are individually organized, enabling both user-specific and generalizable model development. The dataset includes time-series accelerometer data, along with a feature-augmented version containing extracted statistical and temporal descriptors such as RMS, Jerk, Entropy, and SMA.
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7 Views
This data provides a comprehensive collection of air quality and meteorological data from several large cities in India. With 1,410 records, it includes key characteristics like the Air Quality Index (AQI) according to both U.S. and China standards, temperature, atmospheric pressure, humidity, wind speed, wind direction, and timestamps. By combining pollution concentrations with weather variables, the dataset facilitates better insight into the spatial and temporal patterns of urban air quality.
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96 Views
Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder that impairs a person's ability to concentrate, manage impulses, and maintain attention. ADHD can have a wide range of repercussions, including academic and professional difficulties as well as relationship and emotional issues. Individuals with ADHD may also have handwriting impairments, such as poor fine motor coordination, legibility, and writing speed. These writing difficulties may be related to dysgraphia, a specific writing impairment that affects people with ADHD.
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82 Views
This dataset collection supports the research presented in the manuscript titled “Privacy-preserving and Verifiable Federated Learning for Biometric Data in Edge Computing” (submitted to IEEE Transactions on Knowledge and Data Engineering). It includes three curated biometric datasets—SigD, BIDMC, and TBME—that are used to evaluate the BPVFL framework’s performance in privacy-preserving and verifiable federated learning scenarios.
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22 Views
This dataset collection supports the research presented in the manuscript titled “Privacy-preserving and Verifiable Federated Learning for Biometric Data in Edge Computing” (submitted to IEEE Transactions on Knowledge and Data Engineering). It includes three curated biometric datasets—SigD, BIDMC, and TBME—that are used to evaluate the BPVFL framework’s performance in privacy-preserving and verifiable federated learning scenarios.
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This dataset collection supports the research presented in the manuscript titled “Privacy-preserving and Verifiable Federated Learning for Biometric Data in Edge Computing” (submitted to IEEE Transactions on Knowledge and Data Engineering). It includes three curated biometric datasets—SigD, BIDMC, and TBME—that are used to evaluate the BPVFL framework’s performance in privacy-preserving and verifiable federated learning scenarios.
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Fair Use for Academic Research: If you use this dataset, please cite the following paper to ensure proper attribution
M. A. Onsu, P. Lohan, B. Kantarci, A. Syed, M. Andrews, S. Kennedy, "Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring," 30th IEEE Symposium on Computers and Communications (ISCC), July 2025, Bologna, Italy.
Preprint available here: https://arxiv.org/pdf/2502.11304
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135 Views
Adverse driving conditions like darkness, rain, and fog present significant challenges to professional drivers as well as to computer vision algorithms in autonomous vehicles. One potential solution is to use an on-board system for real-time image translation, transforming weather-affected images into clear ones.
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8 Views
This dataset contains 60,000 annotated records modeling UAV-based and IoT sensor-driven agriculture environments. Each record includes UAV imaging data (NDVI, NDRE, RGB damage score), IoT sensor values (NPK, pH, moisture, temperature, humidity), semantic labels (NDI, PDI), and metadata for energy consumption, latency, and service migration. It is designed for validating Digital Twin frameworks, semantic communication models, and Federated Deep Reinforcement Learning (FDRL) in precision farming.
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96 Views