Neural Computing and Applications

POI group recommendation is one of the hottest research topics in location-based social networks, which recommends the most agreeable places for a group of users. However, traditional POI group recommendation methods only generate a consensus ...

Prostate diseases often occur in men. For further clinical treatment and diagnosis, we need to do accurate segmentation on prostate. There are already many methods that concentrate on solving the problem of automatic prostate MR image ...

Spectral efficiency (SE) optimization in massive multiple input multiple output (MIMO) antenna cognitive systems is a challenge originated from the coexistence restrictions. Traditional power allocation can optimize the SE; however, involving deep ...${}_{\mathrm{}}$${}_{}$

Cooperation is an essential behavior in multi-agent systems. Existing mechanisms have two common drawbacks. The first drawback is that malicious agents are not taken into account. Due to the diverse roles in the evolution of cooperation, malicious ...

The widespread of rumors on social media, carrying unreal or even malicious information, brings negative effects on society and individuals, which makes the automatic detection of rumors become particularly important. Most of the previous studies ...

Automated medical image segmentation plays an important role in many clinical applications, which however is a very challenging task, due to complex background texture, lack of clear boundary and significant shape and texture variation between ...

In this paper, we address the problem of automatic clothing parsing in surveillance images using the information from user-generated tags, such as “jeans” and “T-shirt.” Although clothing parsing has achieved great success in the fashion domain, ...

To predict the mortality of patients with coronavirus disease 2019 (COVID-19). We collected clinical data of COVID-19 patients between January 18 and March 29 2020 in Wuhan, China . Gradient boosting decision tree (GBDT), logistic regression (LR) ...

With the explosive growth of online information, many recommendation methods have been proposed. This research direction is boosted with deep learning architectures, especially the recently proposed graph convolutional networks (GCNs). GCNs have ...

Recommender systems face longstanding challenges in gaining users’ trust due to the unreliable information caused by profile injection or human misbehavior. Traditional solutions to those challenges focus on leveraging users’ social relationships ...

Recommendation system, or recommender system, is widely used in online Web applications like e-commerce Web sites and movie review Web sites. Sequential recommender put more emphasis upon user’s short-term preference through exploiting information ...

Names make up a large portion of queries in search engines, while the name ambiguity problem brings negative effect to the service quality of search engines. In digital academic systems, this problem refers to a large number of publications ...

As industrial systems become more complex and monitoring sensors for everything from surveillance to our health become more ubiquitous, multivariate time series prediction is taking an important place in the smooth-running of our society. A ...

Taxi demand prediction is essential to build efficient traffic transportation systems for smart city. It helps to properly allocate vehicles, ease the traffic pressure and improve passengers’ experience. Traditional taxi demand prediction methods ...

Graph convolution networks (GCNs) have become one of the most popular deep neural network-based models in many real-world applications. GCNs can extract features take advantage of both graph structure and node attributes based on convolutional ...

Palmprint shows great potential in biometric-based security due to its advantages of great stability, easy collection, and high accuracy. However, with insufficient training samples, how to extract discriminative features applicable in various ...

Graph neural networks and convolutional architectures have proven to be pivotal in improving the prediction of molecular properties in drug discovery. However, this is fundamentally a low data problem that is incompatible with regular deep ...$\mathrm{}$$\mathrm{}$

Task-oriented multimodal dialogue systems have important application value and development prospects. Existing methods have made significant progress, but the following challenges still exist: (1) Most existing methods focus on improving the ...

Chaotic map is a kind of discrete chaotic system. The existing chaotic maps suffer from optimal parameters in terms of chaos measurements. In this study, a novel approach of optimization of parametric chaotic map (PCM) using triple objective ...

Sign languages are gesture-based languages used by the deaf community of the world. Every country has a different sign language and there are more than 200 sign languages in the world. American Sign Language (ASL), British Sign Language (BSL), and ...

Clustering is a technique for statistical data analysis and is widely used in many areas where class labels are not available. Major problems related to clustering algorithms are handling high-dimensional, imbalanced, and/or varying-density ...

This paper investigates the outlier-resistant variance-constrained $H_{\infty }$ state estimation problem for a class of discrete time-varying recurrent neural networks with randomly occurring deception attacks. The randomly occurring deception attacks are ...${}_{}$${}_{}$

Enhanced machining quality, including the appropriate surface roughness of the machined parts, is the focus of many industries. This paper proposes and implements transformer-based deep learning (DL) architecture for machining roughness ...

Concrete is a very flexible composite material that is extensively employed in the building industry. Steel slag is a waste material produced during steelmaking. It is formed during the separation of molten steel from impurities in steelmaking ...

In the recent past, deep convolutional neural network (DCNN) has been used in majority of state-of-the-art methods due to its remarkable performance in number of computer vision applications. However, DCNN are computationally expensive and ...$$$$$$

Operational parameters such as cutting speed and peripheral speed in diamond wire cutting operation greatly affect the efficiency of the machine. The cutting machine’s amperage draw measures how hard the machine must work to run, and it is an ...

The knapsack problem (KP) is a discrete combinatorial optimization problem that has different utilities in many fields. It is described as a non-polynomial time (NP) problem and has several applications in many fields. The differential evolution (...$\mathrm{}$

In this paper, a novel water cycle algorithm is presented by dynamically assigning sea and rivers and devising an enhanced position updating strategy. To effectively maintain the diversity of solutions and ensure the convergence of algorithm, an ...

3D object detection has been used in many fields, such as virtual reality, automatic driving and target tracking. 3D object detection methods usually use point clouds as input, but point clouds are disordered and rotationally invariant. To solve ...

This paper introduces a new metaheuristic algorithm called bacteria phototaxis optimizer (BPO). It is designed to solving optimization issues. Inspired by the bacteria phototaxis under the control of photosensory proteins in nature, and based on ...