Neurocomputing

A simple and fast method, named ideal kernel tuning, is proposed to select the radial basis kernel spread of the support vector machine for classification. The spread is selected directly from data, without any training nor test, in ...

User-generated content based on customer opinions and experience has become a rich source of valuable information for enterprises. The purpose of aspect-based sentiment analysis is to predict the sentiment polarity of specific targets ...

We present a novel model for left ventricle endocardium segmentation from echocardiography video, which is of great significance in clinical practice and yet a challenging task due to (1) the severe speckle noise in echocardiography ...

Coronary artery disease is among one of the diseases human suffer most. Intravascular coronary arterial image analysis consists of denoising, segmentation, detection, and three-dimensional reconstruction, having a significant meaning ...

Video super-resolution converts low-resolution videos to sharp high-resolution ones. In order to make better use of temporal information in video super-resolution, we design inverse recurrent net and hybrid local fusion. We concatenate ...

By simulating the neural mechanism of cognition, NMLI (Neural Model with Lateral Interaction) was recently proposed for learning tasks. It can realize both supervised and unsupervised learning while achieving outstanding results on few-...

• The first contribution is the introduction of a novel data augmentation technique that creates augmented data by combining hidden features of a data sample ...

Environmental sound classification (ESC) is an increasingly relevant field of research in recent years but has high computational overhead in its classification of environmental sounds. Knowledge distillation (KD) is a prominent ...

For a globally recognized plant breeding organization, manually recorded field observation data is crucial for plant breeding decision making. However, certain phenotypic traits such as plant color, height, kernel counts, etc. can only ...

In this study, we propose a modified projection neural network (PNN) with fixed-time convergence to solve the nonlinear projection equations. Under the assumptions of Lipschitz continuity and strict monotonicity, the existence of the ...

With the increasing maturity of convolution neural network (CNN) technology, the image super-resolution reconstruction (SR) method based on CNN is booming and has achieved many remarkable results. Undoubtedly, SR has become the ...

• We observe that dominant noisy samples cause memorization of DNN.
• To mitigate ...

In general, deep neural network is vulnerable to noisy labels also known as erroneous labels. As a main solution to mitigate this problem, sample selection techniques have been actively studied. However, if the labels are dominantly ...

Over the last years, advances in deep learning have brought huge developments to the studying of super-resolution reconstruction. However, most super-resolution methods only deal with simply down-sampled sharp images, which may lose ...

As a powerful machine learning method, deep learning has attracted the attention of numerous researchers. While exploring a high-performance neural network model, the floating-point operations of a neural network model are also ...

This paper reviews an almost new method for the design of optimal decision making controllers named as Human-Behavior learning. This new paradigm is inspired by the complementary learning that different areas of the human brain have to ...

As deep neural networks are increasingly used in applications suited for low-power devices, a fundamental dilemma emerges: the trend is to develop models to use the increasing amount of data, resulting in memory-intensive models; ...

• Proposes composite rotation that is composed of inner and outer rotations of image.

Many real-life problem settings have classes of data with very few examples for training. Deep learning networks do not perform well for such few-shot classes. In order to perform well in this setting, the networks should learn to ...

• An LM algorithm which can significantly improve the performance is employed to generate a better input weight matrix of ESN.

An echo state network (ESN) is a special structure of a recurrent neural network in which the recurrent neurons are randomly connected. ESN models that have achieved high accuracy on time series prediction tasks can be utilized as time ...

Multi-objective immune algorithm (MOIA) is a heuristic algorithm based on artificial immune system model. Due to its characteristics of antibody clonal selection, automatic antigen recognition and immune memory in the immune system, ...

The advent of deep learning has brought great change to the community of computer science and also revitalized numerous fields where traditional machine learning methods failed to make breakthroughs. Benefitted from the development of ...

With the rapid development of society and the economy, autonomous driving techniques are widely applied in many areas, such as autonomous vehicles, autonomous drones, and robotics. As a dominating technique, deep learning has become ...

Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL ...

In this paper, an adaptive neighborhood mutation based memetic differential evolution is proposed for multimodal optimization. In the proposed method, an adaptive neighborhood mutation (ANM) strategy is devised to allow the individuals ...

We propose a new method of program learning in a Domain Specific Language (DSL) which is based on gradient descent with no direct search. The first component of our method is a probabilistic representation of the DSL variables. At each ...

Network traffic classification is an increasingly significant prerequisite for network management. An accurate traffic classifier can contribute to traffic engineering, traffic intrusion detection and user behavior analysis. Recently, ...

3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to ...

• A novel fractional order gradient descent method based on quadratic loss function is proposed.

This article introduces a novel fractional order gradient descent method for the quadratic loss function. Based on Riemann-Liouville definition, a more practical fractional order gradient descent method with variable initial value is ...

Object detection in Unmanned Aerial Vehicle (UAV) imagery has a wide variety of applications in both military and civilian fields. As UAV images are usually captured from flexible perspectives, with multiple altitudes, containing ...

Research on dendrites has been conducted for decades, providing valuable information for the development of dendritic computation. Creating an ideal neuron model is crucial for computer science and may also provide robust guidance for ...

Fractional calculus is an abstract idea exploring interpretations of differentiation having non-integer order. For a very long time, it was considered as a topic of mere theoretical interest. However, the introduction of several useful ...