AIMC Topic: Benchmarking

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Deep Learning-Based Adaptive Compression and Anomaly Detection for Smart B5G Use Cases Operation.

Sensors (Basel, Switzerland)
The evolution towards next-generation Beyond 5G (B5G) networks will require not only innovation in transport technologies but also the adoption of smarter, more efficient operations of the use cases that are foreseen to be the high consumers of netwo...

Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System.

Sensors (Basel, Switzerland)
Recently, with the massive growth of IoT devices, the attack surfaces have also intensified. Thus, cybersecurity has become a critical component to protect organizational boundaries. In networks, Intrusion Detection Systems (IDSs) are employed to rai...

Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification.

Sensors (Basel, Switzerland)
As an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks ...

A Benchmark Dataset of Endoscopic Images and Novel Deep Learning Method to Detect Intestinal Metaplasia and Gastritis Atrophy.

IEEE journal of biomedical and health informatics
Endoscopy has been routinely used to diagnose stomach diseases including intestinal metaplasia (IM) and gastritis atrophy (GA). Such routine examination usually demands highly skilled radiologists to focus on a single patient with substantial time, c...

miDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies.

Biomolecules
The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural...

Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark.

Sensors (Basel, Switzerland)
Human pose estimation has a variety of real-life applications, including human action recognition, AI-powered personal trainers, robotics, motion capture and augmented reality, gaming, and video surveillance. However, most current human pose estimati...

Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionals.

Journal of biomedical informatics
Publicly accessible benchmarks that allow for assessing and comparing model performances are important drivers of progress in artificial intelligence (AI). While recent advances in AI capabilities hold the potential to transform medical practice by a...

GC-MLP: Graph Convolution MLP for Point Cloud Analysis.

Sensors (Basel, Switzerland)
With the objective of addressing the problem of the fixed convolutional kernel of a standard convolution neural network and the isotropy of features making 3D point cloud data ineffective in feature learning, this paper proposes a point cloud process...

N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning.

Scientific data
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain. However, the current artificial intelligence systems meet difficulties in achieving this ability. Similar challenges also exist for b...

Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: Methodology review and benchmark.

Journal of biomedical informatics
Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medicine is about the causal effects. The average treatment effect has long been studied as a measure of ...