AIMC Topic: Machine Learning

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Transport and Application Layer DDoS Attacks Detection to IoT Devices by Using Machine Learning and Deep Learning Models.

Sensors (Basel, Switzerland)
From smart homes to industrial environments, the IoT is an ally to easing daily activities, where some of them are critical. More and more devices are connected to and through the Internet, which, given the large amount of different manufacturers, ma...

Modern machine learning for tackling inverse problems in chemistry: molecular design to realization.

Chemical communications (Cambridge, England)
The discovery of new molecules and materials helps expand the horizons of novel and innovative real-life applications. In pursuit of finding molecules with desired properties, chemists have traditionally relied on experimentation and recently on comb...

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions.

Computational intelligence and neuroscience
Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, tra...

Endoscopy Artefact Detection by Deep Transfer Learning of Baseline Models.

Journal of digital imaging
To visualise the tumours inside the body on a screen, a long and thin tube is inserted with a light source and a camera at the tip to obtain video frames inside organs in endoscopy. However, multiple artefacts exist in these video frames that cause d...

Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning.

Digestive diseases and sciences
BACKGROUND: Inflammatory Bowel Diseases with its complexity and heterogeneity could benefit from the increased application of Artificial Intelligence in clinical management.

Formula Graph Self-Attention Network for Representation-Domain Independent Materials Discovery.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The success of machine learning (ML) in materials property prediction depends heavily on how the materials are represented for learning. Two dominant families of material descriptors exist, one that encodes crystal structure in the representation and...

Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors.

Proceedings of the National Academy of Sciences of the United States of America
Protein secondary structure discrimination is crucial for understanding their biological function. It is not generally possible to invert spectroscopic data to yield the structure. We present a machine learning protocol which uses two-dimensional UV ...

A Performance Improvement Strategy for Concrete Damage Detection Using Stacking Ensemble Learning of Multiple Semantic Segmentation Networks.

Sensors (Basel, Switzerland)
Semantic segmentation network-based methods can detect concrete damage at the pixel level. However, the performance of a single semantic segmentation network is often limited. To improve the concrete damage detection performance of a semantic segment...

Automated estimation of cancer cell deformability with machine learning and acoustic trapping.

Scientific reports
Cell deformability is a useful feature for diagnosing various diseases (e.g., the invasiveness of cancer cells). Existing methods commonly inflict pressure on cells and observe changes in cell areas, diameters, or thickness according to the degree of...

Machine learning-aided engineering of hydrolases for PET depolymerization.

Nature
Plastic waste poses an ecological challenge and enzymatic degradation offers one, potentially green and scalable, route for polyesters waste recycling. Poly(ethylene terephthalate) (PET) accounts for 12% of global solid waste, and a circular carbon e...