AIMC Topic: Machine Learning

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Design and Implementation of a UAV-Based Airborne Computing Platform for Computer Vision and Machine Learning Applications.

Sensors (Basel, Switzerland)
Visual sensing of the environment is crucial for flying an unmanned aerial vehicle (UAV) and is a centerpiece of many related applications. The ability to run computer vision and machine learning algorithms onboard an unmanned aerial system (UAS) is ...

A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach.

International journal of environmental research and public health
Machine and deep learning techniques are two branches of artificial intelligence that have proven very efficient in solving advanced human problems. The automotive industry is currently using this technology to support drivers with advanced driver as...

PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning.

Journal of molecular biology
Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, ...

Attributes learning network for generalized zero-shot learning.

Neural networks : the official journal of the International Neural Network Society
In the absence of unseen training data, zero-shot learning algorithms utilize the semantic knowledge shared by the seen and unseen classes to establish the connection between the visual space and the semantic space, so as to realize the recognition o...

Breast cancer detection using artificial intelligence techniques: A systematic literature review.

Artificial intelligence in medicine
Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer Foundation, in 2020 alone, more than 276,000 ne...

Challenges in translational machine learning.

Human genetics
Machine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as "translational machine learning", joint efforts and strong communication between data scientists and cli...

Utilizing Conversational Artificial Intelligence, Voice, and Phonocardiography Analytics in Heart Failure Care.

Heart failure clinics
Conversational artificial intelligence involves the ability of computers, voice-enabled devices to interact intelligently with the user through voice. This can be leveraged in heart failure care delivery, benefiting the patients, providers, and payer...

Artificial Intelligence and Mechanical Circulatory Support.

Heart failure clinics
Advances in machine learning algorithms and computing power have fueled a rapid increase in artificial intelligence research in health care, including mechanical circulatory support. In this review, we highlight the needs for artificial intelligence ...

Machine Learning in Cardiovascular Imaging.

Heart failure clinics
The number of cardiovascular imaging studies is growing exponentially, and so is the demand to improve the efficacy of the imaging workflow. Over the past decade, studies have demonstrated that machine learning (ML) holds promise to revolutionize car...