AIMC Topic: Software

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Deep convolutional neural network-based skeletal classification of cephalometric image compared with automated-tracing software.

Scientific reports
This study aimed to investigate deep convolutional neural network- (DCNN-) based artificial intelligence (AI) model using cephalometric images for the classification of sagittal skeletal relationships and compare the performance of the newly develope...

A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data.

BioMed research international
By comparing the performance of various tree algorithms, we can determine which one is most useful for analyzing biomedical data. In artificial intelligence, decision trees are a classification model known for their visual aid in making decisions. WE...

cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model.

Artificial intelligence in medicine
Cancer is a Toxic health concern worldwide, it happens when cellular modifications cause the irregular growth and division of human cells. Several traditional approaches such as therapies and wet laboratory-based methods have been applied to treat ca...

Invariant transformers of Robinson and Foulds distance matrices for Convolutional Neural Network.

Journal of bioinformatics and computational biology
The evolutionary histories of genes are susceptible of differing greatly from each other which could be explained by evolutionary variations in horizontal gene transfers or biological recombinations. A phylogenetic tree would therefore represent the ...

Integrating Deep Learning-Based IoT and Fog Computing with Software-Defined Networking for Detecting Weapons in Video Surveillance Systems.

Sensors (Basel, Switzerland)
Due to the widespread proliferation of multimedia traffic resulting from Internet of Things (IoT) applications and the increased use of remote multimedia-based applications, as a consequence of COVID-19, there is an urgent need to develop intelligent...

Low-Latency In Situ Image Analytics With FPGA-Based Quantized Convolutional Neural Network.

IEEE transactions on neural networks and learning systems
Real-time in situ image analytics impose stringent latency requirements on intelligent neural network inference operations. While conventional software-based implementations on the graphic processing unit (GPU)-accelerated platforms are flexible and ...

The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks.

IEEE transactions on neural networks and learning systems
Spiking neural networks are the basis of versatile and power-efficient information processing in the brain. Although we currently lack a detailed understanding of how these networks compute, recently developed optimization techniques allow us to inst...

An efficient deep equilibrium model for medical image segmentation.

Computers in biology and medicine
In this paper, we propose an effective method that takes the advantages of classical methods and deep learning technology for medical image segmentation through modeling the neural network as a fixed point iteration seeking for system equilibrium by ...

Construction and Model Realization of Financial Intelligence System Based on Multisource Information Feature Mining.

Computational intelligence and neuroscience
Multisource information mining systems and related business intelligence technology are currently a hot topic of research. However, the current commercial applications and applications are not ideal in terms of application. Because there is still muc...

Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room.

Medical image analysis
The fine-grained localization of clinicians in the operating room (OR) is a key component to design the new generation of OR support systems. Computer vision models for person pixel-based segmentation and body-keypoints detection are needed to better...