Neural Computing and Applications

Real-world engineering design problems are widespread in various research disciplines in both industry and industry. Many optimization algorithms have been employed to address these kinds of problems. However, the algorithm’s performance ...

As an enabling technology for edge computing scenarios, indoor localization has a broad prospect in a variety of location-based applications, such as tracking, navigating, and monitoring in indoor environments. In order to improve the location ...$$$$

By combining multilayer perceptrons (MLPs) and radial basis function neural networks (RBF-NNs), an efficient multilayer RBF network is proposed in this work for regression problems. As an extension to the existing multilayer RBF network (RBF-MLP-I)...

Depicting an image in a specific style (e.g., positive, negative, humor, and romantic) is drawing emerging attention. In consideration of the inadequacy of diversity in the stylistic dataset, a larger factual corpus is typically introduced to ...

Images may be distorted to different degrees in the process of acquisition, transmission, and reconstruction, which is not conducive to the perception and recognition of the human eye. Therefore, it is necessary to reasonably quantify the image ...

The fractional neuro-evolution-based intelligent computing has substantial potential to solve fractional order systems represented with Lane–Emden equation arising in astrophysics including Newtonian self-gravitating, spherically symmetric and ...

Telescopic cranes are powerful lifting facilities employed in construction, transportation, manufacturing and other industries. Since the ground workforce cannot be aware of their surrounding environment during the current crane operations in busy ...

The present work investigates a manufacturing-inventory system with a single machine and multiple products, featuring returns on sales and backorders. In the proposed model, some imperfect items, including scrapped and defective items, are ...

This paper handles the issue of adaptive control and faults estimation of a class of T-S singular fractional order systems(SFOSs) with $H_{\infty }$ performance, where the fractional order belongs to (0, 1). Firstly, a novel observer for SFOSs is proposed, ...${}_{}$

This paper investigates the time-varying formation control (TVFC) problem of multi-agent systems (MASs) with general linear dynamics under fixed topology by utilizing event-triggered mechanisms. In order to achieve the TVFC problem, two kinds of ...

In this study, a novel 2D fully chaotic map (FULLMAP) derived through a multi-objective optimization strategy with artificial bee colony (ABC) algorithm is introduced for image encryption procedures (IMEPs). First, a model for FULLMAP with eighth ...

Recently, the proposed non-parametric Bayesian based techniques which aim to model short-length textual documents through the multinomial distribution on the bag-of-words (BOW), aka mixture model-based approach. Although existing model can ...

Those fault detection and diagnosis (FDD) models can identify various faulty signals in industrial processes by extracting features from process data with high nonlinearity and correlations. However, the diagnostic performance of those models ...

Multimodal medical image fusion has been attracting the attention of researchers in recent years because it supports doctors in enhancing clinical diagnosis. Improving the quality of fused images and keeping important information from the input ...

The Vehicle Routing Problem (VRP) is a class of well-known combinatorial optimization problems. The great interest in the VRP is due to its practical importance, as well as the difficulty in solving it. The Capacitated Vehicle Routing Problem (...

Periodic patterns are occurrences that occur regularly over a long period of time at a specific location. In recent years, mining periodic patterns have become a popular area of research. There are several difficulties to map and find the ...

In recent years, face recognition (FR) has made great achievements with the development of deep convolutional neural networks (CNNs). To obtain highly discriminative features, the existing approaches train CNNs equipped with well-designed loss ...

Learning an expressive representation from multi-view data is a crucial step in various real-world applications. In this paper, we propose a semi-supervised multi-view representation learning approach named Co-Embedding. Unlike conventional multi-...

We investigate visual-semantic representations by combining visual features and semantic attributes to form a compact subspace containing the most relevant properties of each domain. This subspace can better represent image features for ...

Malaria microscopy, microscopic examination of stained blood slides to detect parasite Plasmodium, is considered to be a gold standard for detecting life-threatening disease malaria. Detecting the plasmodium parasite requires a skilled examiner ...

This paper considers the stabilization of stochastic delayed networks with Markovian switching via aperiodically intermittent control (AIC). The concepts of average control ratio and average control period are proposed to characterize the ...

In this paper, the finite-time stability of a class of fractional-order bidirectional associative memory neural networks(FBAMNNs) with time delay is concerned. Based on the monotonicity of function, a new inequality is proved. For $0<\alpha <1$ and $1<\alpha <2$, ...

Convolutional neural networks based object detection usually assumes that training and test data have the same distribution, which, however, does not always hold in real-world applications. In autonomous vehicles, the driving scene (target domain) ...

Partitional clustering-based image segmentation is one of the most significant approaches. K-means is the conventional clustering techniques even though very sensitive to noise and easy convergences to local optima depending on the initial cluster ...

Recently, wide installations of photovoltaic (PV) systems have been achieved in the electrical power systems. However, fluctuated output power of the PV generation and/or fluctuated load demands represent critical factors for the operation of PV ...

Parkinson's disease is one of the most prevalent neurodegenerative sicknesses distinguished by motor function impairment. Parkinson's disease (PD) diagnosis is a complicated job that demands the evaluation of numerous non-motor and motor signs. ...

In our day-to-day social interactions, non-verbal cues such as facial emotions play a vital role. These cues assist people in understanding and inferring the hidden emotional state of the individuals. However, blind and visually impaired persons (...

Development of next-generation electronic devices calls for the discovery of quantum materials hosting novel electronic, magnetic, and topological properties. Traditional electronic structure methods require expensive computation time and memory ...

Solutions in geotechnics have been optimizing with the aid of machine learning methods. The aim of this paper is to apply different machine learning algorithms in order to achieve rock mass classification. It is demonstrated that RMR ...

Wind energy is a green source of electricity that is growing faster than other renewable energies. However, dependent mainly on wind speed, this source is characterized by the randomness and fluctuation that makes challenging optimal management. ...