AIMC Topic: Algorithms

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Stability properties of neural networks with non-instantaneous impulses.

Mathematical biosciences and engineering : MBE
In this paper, we consider neural networks in the case when the neurons are subject to a certain impulsive state displacement at fixed moments and the duration of this displacement is not negligible small (these are known as non-instantaneous impulse...

A modified comprehensive learning particle swarm optimizer and its application in cylindricity error evaluation problem.

Mathematical biosciences and engineering : MBE
Particle swarm optimizer was proposed in 1995, and since then, it has become an extremely popular swarm intelligent algorithm with widespread applications. Many modified versions of it have been developed, in which, comprehensive learning particle sw...

Neuromorphic computation with spiking memristors: habituation, experimental instantiation of logic gates and a novel sequence-sensitive perceptron model.

Faraday discussions
Memristors have been compared to neurons and synapses, suggesting they would be good for neuromorphic computing. A change in voltage across a memristor causes a current spike which imparts a short-term memory to a memristor, allowing for through-time...

Computing of temporal information in spiking neural networks with ReRAM synapses.

Faraday discussions
Resistive switching random-access memory (ReRAM) is a two-terminal device based on ion migration to induce resistance switching between a high resistance state (HRS) and a low resistance state (LRS). ReRAM is considered one of the most promising tech...

A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: from mitigation to exploitation.

Faraday discussions
Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility, they are characterized by their computationally relevant physical properties, such as their state-d...

Artificial intelligence in medical imaging of the liver.

World journal of gastroenterology
Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristi...

Deep learning in omics: a survey and guideline.

Briefings in functional genomics
Omics, such as genomics, transcriptome and proteomics, has been affected by the era of big data. A huge amount of high dimensional and complex structured data has made it no longer applicable for conventional machine learning algorithms. Fortunately,...

Volumetric analysis of breast cancer tissues using machine learning and swept-source optical coherence tomography.

Applied optics
In breast cancer, 20%-30% of cases require a second surgery because of incomplete excision of malignant tissues. Therefore, to avoid the risk of recurrence, accurate detection of the cancer margin by the clinician or surgeons needs some assistance. I...

Classification of Background Parenchymal Uptake on Molecular Breast Imaging Using a Convolutional Neural Network.

JCO clinical cancer informatics
PURPOSE: Background parenchymal uptake (BPU), which describes the level of radiotracer uptake in normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor. Our objective was to develop and vali...