AIMC Topic: Neural Networks, Computer

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Rethinking arithmetic for deep neural networks.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
We consider efficiency in the implementation of deep neural networks. Hardware accelerators are gaining interest as machine learning becomes one of the drivers of high-performance computing. In these accelerators, the directed graph describing a neur...

Optimal memory-aware backpropagation of deep join networks.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Deep learning training memory needs can prevent the user from considering large models and large batch sizes. In this work, we propose to use techniques from memory-aware scheduling and automatic differentiation (AD) to execute a backpropagation grap...

Scalogram based prediction model for respiratory disorders using optimized convolutional neural networks.

Artificial intelligence in medicine
Auscultation of the lung is a conventional technique used for diagnosing chronic obstructive pulmonary diseases (COPDs) and lower respiratory infections and disorders in patients. In most of the earlier works, wavelet transforms or spectrograms have ...

Comprehensive electrocardiographic diagnosis based on deep learning.

Artificial intelligence in medicine
Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irre...

Melanoma recognition by a deep learning convolutional neural network-Performance in different melanoma subtypes and localisations.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Deep learning convolutional neural networks (CNNs) show great potential for melanoma diagnosis. Melanoma thickness at diagnosis amongĀ others depends on melanoma localisation and subtype (e.g. advanced thickness in acrolentiginous or nodul...

Can machine learning account for human visual object shape similarity judgments?

Vision research
We describe and analyze the performance of metric learning systems, including deep neural networks (DNNs), on a new dataset of human visual object shape similarity judgments of naturalistic, part-based objects known as "Fribbles". In contrast to prev...

Recognition of Negative Emotion using Long Short-Term Memory with Bio-Signal Feature Compression.

Sensors (Basel, Switzerland)
Negative emotion is one reason why stress causes negative feedback. Therefore, many studies are being done to recognize negative emotions. However, emotion is difficult to classify because it is subjective and difficult to quantify. Moreover, emotion...

A Machine Learning Approach to Growth Direction Finding for Automated Planting of Bulbous Plants.

Scientific reports
In agricultural robotics, a unique challenge exists in the automated planting of bulbous plants: the estimation of the bulb's growth direction. To date, no existing work addresses this challenge. Therefore, we propose the first robotic vision framewo...

Hybrid Eye-Tracking on a Smartphone with CNN Feature Extraction and an Infrared 3D Model.

Sensors (Basel, Switzerland)
This paper describes a low-cost, robust, and accurate remote eye-tracking system that uses an industrial prototype smartphone with integrated infrared illumination and camera. Numerous studies have demonstrated the beneficial use of eye-tracking in d...

Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion.

Sensors (Basel, Switzerland)
Aiming at the requirement of rapid recognition of the wearer's gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multi-source feature para...