Neural Computing and Applications

Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the ...

The COVID-19 pandemic has devastated the entire globe since its first appearance at the end of 2019. Although vaccines are now in production, the number of contaminations remains high, thus increasing the number of specialized personnel that can ...

Non-contrast computed tomography (NCCT) of the brain is critical to patients with acute ischemic stroke who receive thrombolysis and thrombectomy. It can help identify reperfusion-related hemorrhage, edema which need intervention. It also can ...

The convolutional neural network showed considerable success in medical imaging with explainable AI for cancer detection and recognition. However, the irrelevant and large number of features increases the computational time and decreases the ...

In this paper, offer a new framework for skin disease image recognition using deep learning techniques and local descriptor encoding approaches. For the purpose of detecting melanoma early, skin lesions must be accurately classified. In this ...

In the area of medical imaging, one of the factors that can negatively influence the performance of prediction algorithms is the limited number of observations for each class within a labeled dataset. Usually, in order to increase the samples, a ...

Skin cancer, particularly melanoma, has remained a severe issue for many years due to its increasing incidences. The rising mortality rate associated with melanoma demands immediate attention at early stages to facilitate timely diagnosis and ...

Skin cancer, primarily resulting from the abnormal growth of skin cells, is among the most common cancer types. In recent decades, the incidence of skin cancer cases worldwide has risen significantly (one in every three newly diagnosed cancer ...

Accurate segmentation of cardiac anatomy is a prerequisite for the diagnosis of cardiovascular disease. However, due to differences in imaging modalities and imaging devices, known as domain shift, the segmentation performance of deep learning ...

Deep learning has improved the performance of speaker identification systems in recent years, but it has also presented significant challenges. Typically, data-driven modeling approaches based on DNNs rely on large-scale training data, but due to ...

Named entity recognition (NER) is a fundamental task for natural language processing, which aims to detect mentions of real-world entities from text and classifying them into predefined types. Recently, research on overlapped and discontinuous ...

Emotional controllers have been successfully pursued toward various control objectives in the past two decades, but there remain considerable challenges in exploiting their theoretical and cognitive aspects. This paper addresses these two ...

In the field of nanoscience, the scanning electron microscope (SEM) is widely employed to visualize the surface topography and composition of materials. In this study, we present a novel SEM image classification model called NFSDense201, which ...

The research on video isolated sign language recognition (SLR) algorithms has made leaping progress, but there are problems that need to be solved urgently in the field of SLR. On the one hand, traditional sign language acquisition equipment has ...

In the field of smart manufacturing, the data-driven scheduling approach has become an effective way to solve the smart shop floor scheduling problem with high complexity and dynamics. However, most existing approaches rely too heavily on manual ...

The purpose of multiple kernel clustering (MKC) is usually to generate an optimal kernel by fusing the information of multiple base kernels. Among the methods of generating the optimal kernel, a neighborhood kernel is usually used to enlarge the ...

In mathematical modeling and decision analysis, the multipolar uncertainty is prevalent and requires specialized approaches. The theory of m-polar fuzzy (mF, in short) set is a strong extension of the fuzzy set because of its feature for dealing ...${}_{}$${}_{}$${}_{}$${}_{}$${}_{\mathrm{}}$

In recent years, Massive Open Online Courses (MOOCs) have become the main online learning method for students all over the world, but their development has been affected by the high dropout rate for a long time. Therefore, dropout prediction is a ...

Graph neural networks (GNNs) have become one of the most popular research topics in both academia and industry communities for their strong ability in handling irregular graph data. However, large-scale datasets are posing great challenges for ...

The deep neural network (DNN) relies heavily on local solvers like stochastic gradient descent (SGD). However, these methods are sensitive to initial points and hyperparameters for their local property, which affects the stability of the ...

Removing rain streaks from rainy images can improve the accuracy of computer vision applications such as object detection. In order to make full use of the frequency domain analysis characteristics of wavelet and combine the advantages of ...

Robotic Navigation Aids (RNAs) assist visually impaired individuals in independent navigation. However, existing research overlooks diverse obstacles and assumes equal responsibility for collision avoidance among intelligent entities. To address ...

Well-honed CNN architectures trained with massive labeled images datasets are the state-of-the-art solution in many fields. In this paper, the weights of five commonly used pre-trained models are carefully analyzed for extracting their numerical ...$\mathrm{}$$\mathrm{}$

In the heating, ventilation and air conditioning (HVAC) system, the fault diagnosis of the air handling unit (AHU) is critical to ensure the proper operation of the whole system. The AHU system with complex feature variables is susceptible to ...

This study aims to assess the spatiotemporal performance of Machine Learning-based techniques for simulating streamflow on a continental scale using Long-Sort Term Memory (LSTM) models. The dataset employed is derived from the Model Parameter ...

In the era of Internet access, software is easily available on digital distribution platforms such as app stores. The distribution of software on these platforms makes user feedback more accessible and can be used from requirements engineering to ...

Infrared and visible image fusion aims to highlight the infrared target and preserve valuable texture details as much as possible. However, the infrared target needs to be more apparent in most image fusion methods. A large amount of infrared ...

Intrusion detection systems (IDSs) are commonly employed to mitigate network security threats in various fields, including federated learning applications within the Internet of Medical Things (IoMT). However, IDSs face challenges owing to the ...

Occluded person re-identification (Re-ID) task has been a long-standing challenge since occlusions inevitably lead to the deficiency of pedestrian information. Most existing methods tackle the challenge by employing auxiliary models, including ...