AIMC Topic: Algorithms

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Network Intrusion Detection Method Combining CNN and BiLSTM in Cloud Computing Environment.

Computational intelligence and neuroscience
A network intrusion detection method combining CNN and BiLSTM network is proposed. First, the KDD CUP 99 data set is preprocessed by using data extraction algorithm. The data set is transformed into image data set by data cleaning, data extraction, a...

Point Tracking Technology of Sports Image Sequence Marks Based on Fuzzy Clustering Algorithm.

Computational intelligence and neuroscience
Fuzzy clustering algorithms have received widespread attention in various fields. Point tracking technology has significant application importance in sports image data analysis. In order to solve the problem of limited tracking performance caused by ...

ResNet-50 for 12-Lead Electrocardiogram Automated Diagnosis.

Computational intelligence and neuroscience
Nowadays, the implementation of Artificial Intelligence (AI) in medical diagnosis has attracted major attention within both the academic literature and industrial sector. AI would include deep learning (DL) models, where these models have been achiev...

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance.

Computational intelligence and neuroscience
It can be challenging for doctors to identify eye disorders early enough using fundus pictures. Diagnosing ocular illnesses by hand is time-consuming, error-prone, and complicated. Therefore, an automated ocular disease detection system with computer...

Visualization and Analysis Model of Industrial Economy Status and Development Based on Knowledge Graph and Deep Neural Network.

Computational intelligence and neuroscience
This paper adopts knowledge mapping combined with a deep neural network algorithm to conduct in-depth research and analysis on the current situation and development of the industrial economy and designs a visual analysis model of economic development...

Image Quality Evaluation in Dual-Energy CT of the Chest, Abdomen, and Pelvis in Obese Patients With Deep Learning Image Reconstruction.

Journal of computer assisted tomography
OBJECTIVE: The aim of this study was to evaluate image quality in vascular and oncologic dual-energy computed tomography (CT) imaging studies performed with a deep learning (DL)-based image reconstruction algorithm in patients with body mass index of...

Deep learning algorithms for detection of diabetic macular edema in OCT images: A systematic review and meta-analysis.

European journal of ophthalmology
PURPOSE: Artificial intelligence (AI) can detect diabetic macular edema (DME) from optical coherence tomography (OCT) images. We aimed to evaluate the performance of deep learning neural networks in DME detection.

Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning.

NeuroImage
Parallel imaging is the most clinically used acceleration technique for magnetic resonance imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based parallel imaging reconstruction, sub-sampled k-space data are inter...

Deep-Learning Based Adaptive Ultrasound Imaging From Sub-Nyquist Channel Data.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Traditional beamforming of medical ultrasound images relies on sampling rates significantly higher than the actual Nyquist rate of the received signals. This results in large amounts of data to store and process, imposing hardware and software challe...

Mobile Robots for In-Process Monitoring of Aircraft Systems Assemblies.

Sensors (Basel, Switzerland)
Currently, systems installed on large-scale aerospace structures are manually equipped by trained operators. To improve current methods, an automated system that ensures quality control and process adherence could be used. This work presents a mobile...