AIMC Topic: Machine Learning

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Detection of Image Level Forgery with Various Constraints Using DFDC Full and Sample Datasets.

Sensors (Basel, Switzerland)
The emergence of advanced machine learning or deep learning techniques such as autoencoders and generative adversarial networks, can generate images known as deepfakes, which astonishingly resemble the realistic images. These deepfake images are hard...

DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.

Communications biology
The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a stochastic semi-supervised ML framework to develop DrugnomeAI, which estimates the druggability likelihood for every protein-coding gene in the human ex...

Machine learning-enabled nanosafety assessment of multi-metallic alloy nanoparticles modified TiO system.

NanoImpact
Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials is crucial for rapid environmental and health risk assessment. However, existing structure-toxicity correlation models for such nanomaterials are only based on ...

An Orchestration Platform that Puts Radiologists in the Driver's Seat of AI Innovation: a Methodological Approach.

Journal of digital imaging
Current AI-driven research in radiology requires resources and expertise that are often inaccessible to small and resource-limited labs. The clinicians who are able to participate in AI research are frequently well-funded, well-staffed, and either ha...

A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction.

Journal of environmental management
Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flo...

Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients.

Artificial intelligence in medicine
In recent years, machine learning methods have been rapidly adopted in the medical domain. However, current state-of-the-art medical mining methods usually produce opaque, black-box models. To address the lack of model transparency, substantial atten...

Machine-Learning Assisted Handwriting Recognition Using Graphene Oxide-Based Hydrogel.

ACS applied materials & interfaces
Machine-learning assisted handwriting recognition is crucial for development of next-generation biometric technologies. However, most of the currently reported handwriting recognition systems are lacking in flexible sensing and machine learning capab...

Application of Transformer Models to Landslide Susceptibility Mapping.

Sensors (Basel, Switzerland)
Landslide susceptibility mapping (LSM) is of great significance for the identification and prevention of geological hazards. LSM is based on convolutional neural networks (CNNs); CNNs use fixed convolutional kernels, focus more on local information a...

Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals.

Sensors (Basel, Switzerland)
The use of machine learning (ML) techniques in affective computing applications focuses on improving the user experience in emotion recognition. The collection of input data (e.g., physiological signals), together with expert annotations are part of ...

Analysis of the Security and Reliability of Cryptocurrency Systems Using Knowledge Discovery and Machine Learning Methods.

Sensors (Basel, Switzerland)
Cryptocurrency, often known as virtual or digital currency, is a safe platform and a key component of the blockchain that has recently attracted much interest. Utilizing blockchain technology, bitcoin transactions are recorded in blocks that provide ...