With the vigorous development of e-commerce, accurately modeling and predicting user behavior has become a key factor in improving business efficiency. Precisely understanding user behavior not only enables companies to provide personalized services but ...

Identifying and diagnosing key markers from WSI images is the key to accurate diagnosis and treatment of colon cancer (CC). However, how to extract marker-related features from huge scale WSI images is a challenge faced by AI models. In this study,...

With the development of artificial intelligence, electronic nose technology, which simulates human olfaction using sensors and pattern recognition techniques, is becoming increasingly mature and widely applied in various fields. Currently, the ...

Tag-aware recommender system leverages user-annotated historical data to enhance the understanding of user preferences and web service/item features, attracting widespread attention in academia and industry. However, most existing tag-aware ...

In response to the problems of high complexity, low utilization of contextual information, and low recognition efficiency in traditional English short text emotional polarity recognition, this article aims to improve it by combining attention mechanisms. ...

This paper introduces the ISSA-BiLSTM-TPA model to improve financial investment decision-making. Traditional deep learning models face limitations in handling the complexity and uncertainty of financial markets. Our approach incorporates attention ...

Group recommendation constitutes a burgeoning research focus in recommendation systems. Despite a multitude of approaches achieving satisfactory outcomes, they still fail to address two major challenges: 1) these methods confine themselves to ...

Biomarkers are critical in cancer diagnosis, prognosis, and treatment planning. However, this information is often buried in unstructured text form. In this paper, we make an analogy between Biomarker Information Extraction and Aspect-Based ...

As one of the downstream tasks of computer vision, Infrared-Visible Person Re-Identification(I-V ReID) lies in the imaging principle of infrared and visible images, leading to the modality gap between images. To reduce the difference, we propose an ...

Existing models still exhibit a deficiency in capturing more detailed contextual information when processing architectural images. This paper introduces a model for architectural image segmentation and retrieval based on an image segmentation network. ...

Most image inpainting algorithms have problems such as fuzzy images, texture distortion and semantic inaccuracy, and the image inpainting effect is limited when processing photos with large missing sections and resolution levels. The paper ...

• The model has improved the ability to extract and express multi-scale features.
• The method had included partial multi-scale channel attention and residual network.
• The backbone network structure can realize encoding and decoding ...

Biomedical named entity recognition (NER) is a crucial subtask in the field of information extraction within natural language processing (NLP). Its primary objective is to identify and classify entities in biomedical text, playing a pivotal role in ...

• A low-cost and highly flexible UAV is used to monitor FHB disease in wheat fields.
• The Illumination stretching algorithm CPS is used to tackle the issue of lost details cause by image overexposure.
• The image affine shift algorithm ...

Wheat plays a vital role in global food production, trade, and food security concerns. However, it is susceptible to Fusarium head blight (FHB), resulting in decreased yield and quality. With the aim of swiftly detecting FHB in fields to minimize ...

For crowded scenes in dense occlusion conditions, it was difficult to count the crowd accurately. A Transformer-based fusion attention mechanism crowd counting network was proposed. First, in order to adapt more efficiently to small-scale changes, the ...

Abstract

Human pose estimation is a pivotal task in computer vision, aiming to predict the spatial locations of key body joints within an image accurately. The challenge arises from the need to understand complex human poses, occlusions, and variations ...

Given that the current microgrid incorporates highly connected distributed energy sources, the conventional model control methods do not suffice to support complex and ever-changing operating scenarios. This paper proposes a deep learning-based energy ...

The rabbit breeding industry exhibits vast economic potential and growth opportunities. Nevertheless, the ineffective prediction of environmental conditions in rabbit houses often leads to the spread of infectious diseases, causing illness and death ...

With the rapid development of Ethereum, vast amounts of data are recorded on the blockchain through transactions, encompassing diverse and extensive textual information. While Long Short-Term Memory (LSTM) models have shown remarkable ...

Fingerprint recognition systems are highly dependent on the quality and accuracy of the fingerprint representation. Traditional fingerprint recognition algorithms often employ handcrafted features designed manually based on domain knowledge and ...

Object detection based on deep learning has achieved promising results on traditional datasets, but detecting objects in remote sensing imagery with diverse occlusions remains challenging. This is due to the fact that natural occlusions are common ...