AIMC Topic: Algorithms

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An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets.

Mathematical biosciences and engineering : MBE
The unprecedented rise in the number of COVID-19 cases has drawn global attention, as it has caused an adverse impact on the lives of people all over the world. As of December 31, 2021, more than 2, 86, 901, 222 people have been infected with COVID-1...

Application of artificial intelligence to imaging interpretations in the musculoskeletal area: Where are we? Where are we going?

Joint bone spine
The interest of researchers, clinicians and radiologists, in artificial intelligence (AI) continues to grow. Deep learning is a subset of machine learning, in which the computer algorithm itself can determine the optimal imaging features to answer a ...

Examining unsupervised ensemble learning using spectroscopy data of organic compounds.

Journal of computer-aided molecular design
One solution to the challenge of choosing an appropriate clustering algorithm is to combine different clusterings into a single consensus clustering result, known as cluster ensemble (CE). This ensemble learning strategy can provide more robust and s...

Intravascular Tracking of Micro-Agents Using Medical Ultrasound: Towards Clinical Applications.

IEEE transactions on bio-medical engineering
OBJECTIVE: This study demonstrates intravascular micro-agent visualization by utilizing robotic ultrasound-based tracking and visual servoing in clinically-relevant scenarios.

Generalisable machine learning models trained on heart rate variability data to predict mental fatigue.

Scientific reports
A prolonged period of cognitive performance often leads to mental fatigue, a psychobiological state that increases the risk of injury and accidents. Previous studies have trained machine learning algorithms on Heart Rate Variability (HRV) data to det...

SNAL: sensitive non-associative learning network configuration for the automatic driving strategy.

Scientific reports
Nowadays, there is a huge gap between autonomous vehicles and mankind in terms of the decision response against some dangerous scenarios, which would has stressed the potential users out and even made them nervous. To efficiently identify the possibl...

Deep reinforcement learning for optimal experimental design in biology.

PLoS computational biology
The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence-reinforcement learning-to the optima...

A Novel Computer-Vision Approach Assisted by 2D-Wavelet Transform and Locality Sensitive Discriminant Analysis for Concrete Crack Detection.

Sensors (Basel, Switzerland)
This study proposes FastCrackNet, a computationally efficient crack-detection approach. Instead of a computationally costly convolutional neural network (CNN), this technique uses an effective, fully connected network, which is coupled with a 2D-wave...

A New Mixed-Gas-Detection Method Based on a Support Vector Machine Optimized by a Sparrow Search Algorithm.

Sensors (Basel, Switzerland)
To solve the problem of the low recognition rate of mixed gases and consider the phenomenon of low prediction accuracy when traditional gas-concentration-prediction methods deal with nonlinear data, this paper proposes a mixed-gas identification and ...

Deep learning for hetero-homo conversion in channel-domain for phase aberration correction in ultrasound imaging.

Ultrasonics
Echo imaging in ultrasound computed tomography (USCT) using the synthetic aperture technique is performed with the assumption that the speed of sound is constant in the system. However, tissue heterogeneity causes a mismatch between the predicted arr...