Neurocomputing

This paper introduces Elegans-AI models, a class of neural networks that leverage the connectome topology of the Caenorhabditis elegans to design deep and reservoir architectures. Utilizing deep learning models inspired by the connectome, this ...

In this article, we propose a novel hierarchical framework to resolve within-visual-range (WVR) air-to-air combat under complex nonlinear 6 degrees-of-freedom (6-DOF) dynamics of the aircraft and missile. The decision process is constructed with ...

Beyond the in-the-lab environment, deep-learning-based facial expression recognition (FER) models that provide reliable performance on wild datasets are gradually becoming applied to the real world. However, the fact that neural networks are ...

• Imperceptible perturbations that can occur at test time in applications using facial expression recognition (FER).
• Create a landmark-based mask specialized for face-related tasks and use it for adversarial training.
• Gives the model ...

There has been a growing interest in solving consensus optimization problems in a multi-agent system. It is known that there is an exactness-speed dilemma for decentralized optimization for the naive gradient method, where either we have fast ...

This paper studies the consensus problem of a new multi-agent system via multi-task, multi-control mechanism and multi-consensus strategy. Firstly, two multi-task algorithms are proposed, which can reasonably select some agent nodes with better ...

Weakly supervised 3D object detection for autonomous driving primarily focuses on cars because of their distinct rectangle boundaries and abundant instances. However, detecting categories with ambiguous rectangle boundaries and fewer instances ...

A multi-class discriminant analysis via adaptive weighted scheme (MDAAWS) has been proposed for supervised learning, which can deal with the issue that some classes may be vanished in the subspace. However, the acquisition of labeled data is ...

The semantic segmentation of outdoor LiDAR point clouds is one of the gigantic fields in the large-scale driving scenario. However, the performances of the state-of-the-art methods are unsatisfactory caused by the intrinsic limitations of the ...

• V-SIAM enrichs voxel feature details and recalibrates the voxel features.
• MsVFE fuses multi-scale voxel information of the sparse points.
• Our methods achieve SOTA performances on Toronto3D and KITTI-360 datasets.

Action recognition is one of the challenging video understanding tasks in computer vision. Although there has been extensive research in the task of classifying coarse-grained actions, existing methods are still limited in differentiating actions ...

The integration of Deep Neural Networks (DNN) with Natural Language Processing (NLP) technologies has opened new avenues in financial market prediction, particularly through the utilization of textual information. This study represents a ...

The six papers included in this special issue represent a selection of extended contributions presented at the 17th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2022 held in Salamanca, Spain, September 6th-8th, 2022, ...

In this article, an accelerated Q-learning algorithm with evolving control is established to solve the optimal tracking control problem. First, an accelerated Q-learning scheme is constructed with an advanced Q-function. By utilizing the advanced ...

GNN-based social recommendation aims to use social network information to improve recommendation performance of traditional user–item interaction network (U–I network). However, in graph neural network information aggregation, both social ...

The eight papers included in this special issue represent a selection of extended contributions presented at the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022 held in Salamanca, ...

Few-shot class incremental learning illustrates the challenges of learning new concepts, where the learner can access only a small sample per concept. The standard incremental learning techniques cannot be applied directly because of the small ...

This paper addresses the sliding-mode surface-based adaptive optimal nonzero-sum games control problem for continuous-time nonlinear systems with input constraints. By constructing a value function associated with the sliding-mode surface, the ...

Neural Architecture Search (NAS) methods have been successfully applied to image tasks with excellent results. However, NAS methods are often complex and tend to converge to local minima as soon as generated architectures yield good results. This ...

In this study, vibration control is studied for the flexible satellite through wireless communication, and given the fact that the actuators are prone to unknown faults, the input signal event-triggering and time-varying actuator faults are ...

In recent years, exchangeable network structures that take datasets as input have been widely used to obtain representations of various datasets. Although they perform well, analyzing exchangeable network with a dataset as input is challenging. ...

In underwater environments, imaging devices suffer from water turbidity, attenuation of lights, scattering, and particles, leading to low quality, poor contrast, and biased color images. This has led to great challenges for underwater condition ...

• Enhancing underwater images by measuring the proportional and color degradations.
• Quantifying the proportional degradation via Inception-inspired multi-scale features.
• Recovering color information by adaptive channel-wise attention ...

The task of multi-class vessel segmentation on retinal images is the basis for the arteriovenous quantitative analysis, and plays an important role in the diagnosis and treatment of cerebrovascular diseases. Due to the intricate details and ...