Neurocomputing

• Teaching-learning-based optimization (TLBO) is one of population-based heuristic stochastic swarm intelligent algorithm.
• In this paper, a comprehensive survey on the recent advances in TLBO is presented.
• Literature survey reveals ...

Teaching-learning-based optimization (TLBO) algorithm which imitates the teaching–learning process in a classroom, is one of population-based heuristic stochastic swarm intelligent algorithms. TLBO executes through similar iterative evolution ...

This paper studies the fixed-time stability of the discontinuous uncertain inertial neural networks (INNs) with distributed delay and time-varying delay. The conditions required by the theorem are less conservative, and fairly accurate settling ...

On a graph G = ( V , E ), we call S ⊂ V resolving if ∀ u , v ∈ V with u ≠ v, ∃ w ∈ V such that d ( u , w ) ≠ d ( v , w ) . The smallest possible cardinality of S is called the metric dimension, computing which is known to be NP-hard. Solving the ...

• Formulation of a self-supervised surrogate objective for learning an NP-hard problem
• Construction showing the surrogate loss’s minimum coincides with the optimal solution
• Extensive evaluation and significant improvements over ...

Contrastive learning has recently achieved remarkable success in many domains including graphs. However contrastive loss, especially for graphs, requires a large number of negative samples which is unscalable and computationally prohibitive with ...

This paper proposes a novel Neural Network Adaptive Observer (NNAO) for Nonlinear Systems with Partially and Completely Unknown Dynamics (NSPCUD), subject to variable sampled and delayed output. The method involves designing a neural network ...

• A neural network observer is proposed under variable sampled and delayed output.
• A new weight update law is designed to construct the neural network observer.
• The proposed observer is extended to more general nonlinear systems.

With the surge of machine learning in AI and data science, there remains an urgent need to not only compare the performance of different methods across diverse datasets but also to analyze machine learning behaviors with sensitivity using an ...

Inspired by the evolutionary process in nature, Differential Evolution (DE) has been widely concerned and used as a numerical global optimizer for decades of years, since its emerging in 1997. However, the performance of DE essentially depends on ...

• Recent works on Differential Evolution algorithm, especially from 2019 to 2023.
• Balance of the exploration and exploitation from different scales.
• Hybrid Differential Evolution from the algorithm level.
• Enhanced Differential ...

Most existing fair classifiers rely on sensitive attributes to achieve fairness. However, for many scenarios, we cannot obtain sensitive attributes due to privacy and legal issues. The lack of sensitive attributes challenges many existing fair ...

Aspect-based sentiment analysis (ABSA) is a fine-grained text classification task, and the cutting-edge ABSA models have achieved outstanding performance. Unfortunately, the robustness of these ABSA models is neglected. ABSA models must face ...

Affective computing is one of the most important research fields in modern human–computer interaction (HCI). The goal of affective computing is to study and develop the theories, methods, and systems that can recognize, explain, process, and ...

Zero-shot learning is a promising technique for diagnosing mechanical faults in complex and uncertain environments. However, when diagnosing mechanical faults across different severities using zero-shot learning, the impact of insensitive ...

• IFRN with EAM is proposed to hierarchically alleviate the effect of insensitive features.
• EAM can effectively eliminate insensitive features that the classifier cannot differentiate.
• IFRN performance is verified on the CWRU rolling ...

In various signal processing applications, such as audio signal recovery, the extraction of desired signals from a mixture of other signals is a crucial task. To achieve superior performance and efficiency in separator systems, extensive research ...

• Systemic review of the latest literature on BSS.
• Study in-depth knowledge about BSS and identify different parameters considered during the BSS process.
• Benchmark various AI-based BSS approaches.
• Analyze the state-of-the-art ...

Partial Label Learning (PLL) aims to induce a multi-class classifier to deal with the problem that each training instance is associated with a set of candidate labels, among which only one is valid but unknown. Feature selection, which choses ...

This paper studies the point-to-point consensus tracking problem for a class of nonlinear non-affine multi-agent systems, where the tracking target is a series of some given ideal output points rather than a complete ideal trajectory. For the ...

Clustering is an unsupervised classical data processing technique, in which Fuzzy K-Means is extensively researched in practical application owing to its efficiency. However, common outliers in real-world always lead to clustering degradation. In ...

• Provide a multi-perspective investigation of privacy-preserving federated learning.
• Deep analysis of advanced privacy-preserving federated learning mechanisms.
• Discussed the challenges of privacy-preserving federated learning.
• ...

Advances in the new generation of Internet of Things (IoT) technology are propelling the growth of intelligent industrial applications worldwide. Simultaneously, widespread adoption of artificial intelligence (AI) technologies, such as machine ...

For dialog generation models that introduce external knowledge, the key challenge lies in how to select the relevant knowledge. The existing common method is to directly use a retriever to fetch knowledge according to the prior distribution that ...

Stackelberg games allow players to access system information differently and take actions asynchronously. This paper introduces a robust adaptive dynamic programming-based method to solve the nonlinear two-player Stackelberg game subject to ...

• In our approach, a class of Stackelberg differential games with nonlinear dynamics is solved using the ADP method without knowing system drift dynamics. Notably, we take external disturbances into account, which contributes to the main ...

This paper addresses the formation consensus tracking control problem of uncertain dynamical multi-agent system with nonlinear dynamics based on a leader–follower configuration based on a undirected communication topology. The control design is ...

We consider physics-informed neural networks (PINNs) (Raissiet al., 2019) for forward physical problems. In order to find optimal PINNs configuration, we introduce a hyper-parameter optimization (HPO) procedure via Gaussian processes-based ...

This paper focuses on the control of spherical formation for multi-agent systems with double-integrator dynamics in three-dimensional space. To achieve the desired formation pattern on a spherical surface, N agents must be distributed evenly ...

Currently, weakly supervised semantic segmentation approaches adopt the Class Activation Map (CAM) to generate the initial attention maps from the standard classification backbone network, with only image-level class labels as training ...

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• A novel “destruction learning” method via self-supervised manner.
• The MDC module is with stronger sensitivity to the Mid-Level local parts.
• The LD Module explores the local feature details from the original images.

The task of human parsing aims to segment the human body into different semantic regions. Despite advancements in this field, there are still two issues with current works: boundary indistinction and parsing inconsistency. In this paper, we ...

Although learning a similarity graph with a 0–1 value is regarded as an important issue for clustering tasks, this topic has been scarcely reported in the literature since its NP-hard. Along these lines, this issue is addressed in the work by ...

In this paper, a novel continuous gradient-Zhang neuronet (GZN) model and three discrete GZN algorithms are proposed to solve time-variant overdetermined linear equation system (TVOLES) problems, and are developed on the basis of a gradient ...

Zero-shot learning provides models for targets for which instances are not available, commonly called unobserved targets. The availability of target side information becomes crucial in this context in order to properly induce models for these ...

• Provide a general-purpose zero-shot method for regression.
• Strategies for exploiting side information in the zero-shot regression task.
• Application to a real problem about air pollution prediction.