As an important task in the field of remote sensing (RS) image processing, RS change detection (CD) has made significant advances through use convolutional neural networks (CNNs). The transformer recently been introduced into CD due to its excellent global perception capabilities. Some works have attempted combine CNN and jointly harvest local-global features; however, these not paid much attention interaction between features extracted by both. Also, resulted resource consumption. In this...

Decoupling domain-variant information (DVI) from domain-invariant (DII) serves as a prominent strategy for mitigating domain shifts in the practical implementation of deep learning algorithms. However, medical settings, concerns surrounding data collection and privacy often restrict access to both training test data, hindering empirical decoupling by existing methods. To tackle this issue, we propose an Autonomous Information Filter-driven Source-free Domain Adaptation (AIF-SFDA) algorithm,...

Artificial bee colony (ABC) algorithm is one of the most recently proposed swarm intelligence algorithms for global numerical optimization. It performs well in cases; however, there still exist some problems it cannot solve very well. This paper presents a novel hybrid Hooke Jeeves ABC (HJABC) with intensification search based on pattern and ABC. The main purpose to demonstrate how standard can be improved by incorporating hybridization strategy. tested comprehensive set 3 6 complex...

Artificial bee colony (ABC) algorithm is one of the most recently proposed swarm intelligence algorithms for global optimization. It performs well in cases; however, there still exist some problems it cannot solve very well. This paper presents a novel hybrid Hooke Jeeves ABC (HJABC) with intensification search based on pattern and ABC. The main purpose to demonstrate how standard can be improved by incorporating hybridization strategy. tested 7 benchmark functions including wide range...

As an important task in the field of remote sensing image interpretation, change detection (CD) has been extensively studied by scholars recent years. Affected illumination and environment during bitemporal images' acquisition, there will be many pseudochanges, pseudochanges seriously affect effect CD. Based on this, we propose a CD model named HMCNet, which introduces multilayer perceptron (MLP) into convolutional neural network (CNN)-based to form MLP-CNN hybrid model. HMCNet both good...

During recent years, risk analysis has been introduced into infrastructure engineering, and greatly improved the design, construction, operation. In this paper, we study of dams in perspective clustering analysis. Fuzzy c-means (FCM) is widely used many fields since it simple fast. However result FCM technique sensitive to initialization centres easily trapped local optima. To improve performance FCM, an artificial bee colony algorithm (ABC) with proposed. By introducing ABC, shortcomings...

Quality degradation (QD) is common in the fundus images collected from clinical environment. Although diagnosis models based on convolutional neural networks (CNN) have been extensively used to interpret retinal images, their performances under QD not assessed. To understand effects of performance CNN-based model, a systematical study proposed this paper. In our study, controlled by independently or simultaneously importing quantified interferences (e.g., image blurring, artifacts, and light...

Ultrasound is widely used for image-guided therapy (IGT) in many surgical fields, thanks to its various advantages, such as portability, lack of radiation and real-time imaging. This article presents the first attempt utilize multiple deep learning algorithms distal humeral cartilage segmentation dynamic, volumetric ultrasound images employed minimally invasive surgery.The dataset, consisting 5,321 were collected from 12 healthy volunteers. These randomly split into training validation sets...

Abstract Automated Essay Scoring (AES) has gained increasing attention in recent years. In this paper, we propose a novel automated Chinese essay scoring model, called BLA (BERT and Bi-LSTM with Attention), by using neural network. The model uses BERT network to obtain the sentence vectors for an essay, then two layers extract vector. We also consider topic instruction as long its vector representation BERT. obtained is used information further help obtaining more effective Moreover, present...

Change detection (CD) is a particularly important task in the field of remote sensing image processing. It practical importance for people when making decisions about transitional situations on Earth's surface. The existing CD methods focus design feature extraction network, ignoring strategy fusion and attention enhancement extracted features, which will lead to problems incomplete boundary changed area missing small targets final output change map. To overcome above problems, we proposed...

Understanding the interaction of T-cell receptor (TCR) with major histocompatibility-peptide (MHC-peptide) complex is extremely important in human immunotherapy and vaccine development. However, due to limited available data, performance existing models for predicting receptors complexes still unsatisfactory. Deep learning have been applied prediction tasks various fields achieved better results compared other traditional models. In this study, we leverage gMLP model combined attention...

The morphology of the retinal vascular structure in fundus images is great importance for ocular disease diagnosis. However, due to poor image quality and domain shifts between datasets, vessel segmentation has long been regarded as a problematic machine-learning task. This work proposes novel algorithm High-frequency Guided Cascaded Network (HGC-Net) address above issues. In our algorithm, self-supervision mechanism designed improve generalizability robustness model. We apply Fourier...

Deep learning models often encounter challenges in making accurate inferences when there are domain shifts between the source and target data. This issue is particularly pronounced clinical settings due to scarcity of annotated data resulting from professional private nature medical Despite existence decent solutions, many them hindered limitations collection computational complexity. To tackle data-scarce scenarios, we propose a Random frequency filtering enabled Single-source Domain...

Accurate stratigraphic information is the basis of seismic data interpretation and reserve prediction. Based on this, this paper firstly establishes a grey model based entropy, uses particle swarm optimization technology neural network to analyze track influence each factor degree hazards, gets weight occurrence geologic hazards through by using matlab software, secondly, theory entropy determine evaluation system its grading standard, finally evaluates comprehensive fuzzy clustering model....

The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited may not generalize well to other unseen domains, resulting a domain shift issue. Consequently, generalization (DG) is developed boost the performance domains. However, DG setup requires multiple source which impedes efficient deployment algorithms clinical scenarios. To address this challenge and improve model's...