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Clustering by Errors: A Self-Organized Multitask Learning Method for Acoustic Scene Classification.

Sensors (Basel, Switzerland)
Acoustic scene classification (ASC) tries to inference information about the environment using audio segments. The inter-class similarity is a significant issue in ASC as acoustic scenes with different labels may sound quite similar. In this paper, t...

Malicious Code Variant Identification Based on Multiscale Feature Fusion CNNs.

Computational intelligence and neuroscience
The increasing volume and types of malwares bring a great threat to network security. The malware binary detection with deep convolutional neural networks (CNNs) has been proved to be an effective method. However, the existing malware classification ...

Runoff forecasting model based on variational mode decomposition and artificial neural networks.

Mathematical biosciences and engineering : MBE
Accurate runoff forecasting plays a vital role in water resource management. Therefore, various forecasting models have been proposed in the literature. Among them, the decomposition-based models have proved their superiority in runoff series forecas...

A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System.

Sensors (Basel, Switzerland)
Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose ...

Detection and Characterization of Multiple Discontinuities in Cables with Time-Domain Reflectometry and Convolutional Neural Networks.

Sensors (Basel, Switzerland)
In this paper, a convolutional neural network for the detection and characterization of impedance discontinuity points in cables is presented. The neural network analyzes time-domain reflectometry signals and produces a set of estimated discontinuity...

DCACNet: Dual context aggregation and attention-guided cross deconvolution network for medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Segmentation is a key step in biomedical image analysis tasks. Recently, convolutional neural networks (CNNs) have been increasingly applied in the field of medical image processing; however, standard models still have some ...

Convolution-Based Encoding of Depth Images for Transfer Learning in RGB-D Scene Classification.

Sensors (Basel, Switzerland)
Classification of indoor environments is a challenging problem. The availability of low-cost depth sensors has opened up a new research area of using depth information in addition to color image (RGB) data for scene understanding. Transfer learning o...

Evolving Deep Architecture Generation with Residual Connections for Image Classification Using Particle Swarm Optimization.

Sensors (Basel, Switzerland)
Automated deep neural architecture generation has gained increasing attention. However, exiting studies either optimize important design choices, without taking advantage of modern strategies such as residual/dense connections, or they optimize resid...

Multi-layer information fusion based on graph convolutional network for knowledge-driven herb recommendation.

Neural networks : the official journal of the International Neural Network Society
Prescription of Traditional Chinese Medicine (TCM) is a precious treasure accumulated in the long-term development of TCM. Artificial intelligence (AI) technology is used to build herb recommendation models to deeply understand regularities in prescr...

Machine learning algorithm for feature space clustering of mixed data with missing information based on molecule similarity.

Journal of biomedical informatics
Clustering Algorithms have just fascinated significant devotion in machine learning applications owing to their great competence. Nevertheless, the existing algorithms quite have approximately disputes that need to be further deciphered. For example,...