AIMC Topic: Algorithms

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Clinical target segmentation using a novel deep neural network: double attention Res-U-Net.

Scientific reports
We introduced Double Attention Res-U-Net architecture to address medical image segmentation problem in different medical imaging system. Accurate medical image segmentation suffers from some challenges including, difficulty of different interest obje...

An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques.

Computational intelligence and neuroscience
Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as fea...

Intelligent Monitoring System Based on Noise-Assisted Multivariate Empirical Mode Decomposition Feature Extraction and Neural Networks.

Computational intelligence and neuroscience
Because of the nonlinearity and nonstationarity in the vibration signals of some rotating machinery, the analysis of these signals using conventional time- or frequency-domain methods has some drawbacks, and the results can be misleading. In this pap...

An Improved Bearing Fault Diagnosis Model of Variational Mode Decomposition Based on Linked Extension Neural Network.

Computational intelligence and neuroscience
In bearing fault diagnosis, due to the insufficient obtained supervised data and the inevitable noise contained in the vibration signals, the problem of clustering bearing fault diagnosis with imbalanced data containing noise is caused. Thanks to the...

Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was proposed, aiming at achieving the binary classification of task-state EEG signals before and after spati...

A Fault Diagnosis System for a Pipeline Robot Based on Sound Signal Recognition.

Sensors (Basel, Switzerland)
Timely and accurate identification of fault types at the early stage of minor faults is significant for cutting off fault evolution. In order to have a clear understanding of the pipeline robot's own situation in the pipeline, this paper proposes a f...

SNARER: new molecular descriptors for SNARE proteins classification.

BMC bioinformatics
BACKGROUND: SNARE proteins play an important role in different biological functions. This study aims to investigate the contribution of a new class of molecular descriptors (called SNARER) related to the chemical-physical properties of proteins in or...

Capturing surface complementarity in proteins using unsupervised learning and robust curvature measure.

Proteins
The structure of a protein plays a pivotal role in determining its function. Often, the protein surface's shape and curvature dictate its nature of interaction with other proteins and biomolecules. However, marked by corrugations and roughness, a pro...

Depression screening using a non-verbal self-association task: A machine-learning based pilot study.

Journal of affective disorders
BACKGROUND: Effective screening is important to combat the raising burden of depression and opens a critical time window for early intervention. Clinical use of non-verbal depression screening is nascent, yet a promising and viable candidate to suppl...

Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.

Pediatric radiology
Most artificial intelligence (AI) studies have focused primarily on adult imaging, with less attention to the unique aspects of pediatric imaging. The objectives of this study were to (1) identify all publicly available pediatric datasets and determi...