AIMC Topic: Neural Networks, Computer

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Effective Vehicle-Based Kangaroo Detection for Collision Warning Systems Using Region-Based Convolutional Networks.

Sensors (Basel, Switzerland)
Traffic collisions between kangaroos and motorists are on the rise on Australian roads. According to a recent report, it was estimated that there were more than 20,000 kangaroo vehicle collisions that occurred only during the year 2015 in Australia. ...

A Globally Generalized Emotion Recognition System Involving Different Physiological Signals.

Sensors (Basel, Switzerland)
Machine learning approaches for human emotion recognition have recently demonstrated high performance. However, only/mostly for subject-dependent approaches, in a variety of applications like advanced driver assisted systems, smart homes and medical ...

Predicting human protein function with multi-task deep neural networks.

PloS one
Machine learning methods for protein function prediction are urgently needed, especially now that a substantial fraction of known sequences remains unannotated despite the extensive use of functional assignments based on sequence similarity. One majo...

Machine learning "red dot": open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification.

Clinical radiology
AIM: To develop a machine learning-based model for the binary classification of chest radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting prioritisation.

Quantum-like behavior without quantum physics II. A quantum-like model of neural network dynamics.

Journal of biological physics
In earlier work, we laid out the foundation for explaining the quantum-like behavior of neural systems in the basic kinematic case of clusters of neuron-like units. Here we extend this approach to networks and begin developing a dynamical theory for ...

Development of Ligand-based Big Data Deep Neural Network Models for Virtual Screening of Large Compound Libraries.

Molecular informatics
High-performance ligand-based virtual screening (VS) models have been developed using various computational methods, including the deep neural network (DNN) method. There are high expectations for exploration of the advanced capabilities of DNN to im...

Fight Recognition in video using Hough Forests and 2D Convolutional Neural Network.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
While action recognition has become an important line of research in computer vision, the recognition of particular events such as aggressive behaviors, or fights, has been relatively less studied. These tasks may be extremely useful in several video...

A Cascaded Deep Convolutional Neural Network for Joint Segmentation and Genotype Prediction of Brainstem Gliomas.

IEEE transactions on bio-medical engineering
GOAL: Automatic segmentation of brainstem gliomas and prediction of genotype (H3 K27M) mutation status based on magnetic resonance (MR) images are crucial but challenging tasks for computer-aided diagnosis in neurosurgery. In this paper, we present a...

How I learned to stop worrying and love machine learning.

Clinics in dermatology
Artificial intelligence and its machine learning (ML) capabilities are very promising technologies for dermatology and other visually oriented fields due to their power in pattern recognition. Understandably, many physicians distrust replacing clinic...

Improving efficiency in convolutional neural networks with multilinear filters.

Neural networks : the official journal of the International Neural Network Society
The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of flo...