AIMC Topic: Deep Learning

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Deep learning and optimization enabled multi-objective for task scheduling in cloud computing.

Network (Bristol, England)
In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling utilizing the multi-objective optimization and deep learning (DL) model. Initially, the multi-objec...

Beyond the Conventional Structural MRI: Clinical Application of Deep Learning Image Reconstruction and Synthetic MRI of the Brain.

Investigative radiology
Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep lear...

SNN-BERT: Training-efficient Spiking Neural Networks for energy-efficient BERT.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNNs) are naturally suited to process sequence tasks such as NLP with low power, due to its brain-inspired spatio-temporal dynamics and spike-driven nature. Current SNNs employ "repeat coding" that re-enter all input tokens a...

A minimalistic approach to classifying Alzheimer's disease using simple and extremely small convolutional neural networks.

Journal of neuroscience methods
BACKGROUND: There is a broad interest in deploying deep learning-based classification algorithms to identify individuals with Alzheimer's disease (AD) from healthy controls (HC) based on neuroimaging data, such as T1-weighted Magnetic Resonance Imagi...

Image cropping for malaria parasite detection on heterogeneous data.

Journal of microbiological methods
Malaria is a deadly disease of significant concern for the international community. It is an infectious disease caused by a Plasmodium spp. parasite and transmitted by the bite of an infected female Anopheles mosquito. The parasite multiplies in the ...

NABNet: Deep Learning-Based IoT Alert System for Detection of Abnormal Neck Behavior.

Sensors (Basel, Switzerland)
The excessive use of electronic devices for prolonged periods has led to problems such as neck pain and pressure injury in sedentary people. If not detected and corrected early, these issues can cause serious risks to physical health. Detectors for g...

MASDF-Net: A Multi-Attention Codec Network with Selective and Dynamic Fusion for Skin Lesion Segmentation.

Sensors (Basel, Switzerland)
Automated segmentation algorithms for dermoscopic images serve as effective tools that assist dermatologists in clinical diagnosis. While existing deep learning-based skin lesion segmentation algorithms have achieved certain success, challenges remai...

Current limitations in predicting mRNA translation with deep learning models.

Genome biology
BACKGROUND: The design of nucleotide sequences with defined properties is a long-standing problem in bioengineering. An important application is protein expression, be it in the context of research or the production of mRNA vaccines. The rate of prot...

Benchmarking robustness of deep neural networks in semantic segmentation of fluorescence microscopy images.

BMC bioinformatics
BACKGROUND: Fluorescence microscopy (FM) is an important and widely adopted biological imaging technique. Segmentation is often the first step in quantitative analysis of FM images. Deep neural networks (DNNs) have become the state-of-the-art tools f...

Linking disease activity with optical coherence tomography angiography in neovascular age related macular degeneration using artificial intelligence.

Scientific reports
To investigate quantitative associations between AI-assessed disease activity and optical coherence tomography angiography (OCTA)-derived parameters in patients with neovascular age-related macular degeneration (nAMD) undergoing anti-VEGF therapy. OC...