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The accurate identification of circulating abnormal cells (CACs) in four-color fluorescence images is highly dependent on the fluorescence expression under each channel. Previous studies have utilized instance segmentation and target detection ...

Given the limited accuracy of a singular deep learning model in bearing fault diagnosis, this study seeks to investigate and validate the efficacy of a deep learning model fusion strategy. It also aims to enhance the performance of deep learning models ...

Ensemble learning is a widespread technique to improve the prediction performance of neural networks. However, it comes at the price of increased memory and inference time. In this work we propose a novel model fusion technique called Neuron ...

In the current context of sustainable development and environmental protection issues, enterprises are paying more and more attention to green technology innovation. For this purpose, we introduced a composite neural network model, including the Siamese ...

In this study, we present an innovative hybrid approach for traffic sign detection in autonomous driving, combining YOLOv8’s real-time detection capabilities with the Segment Anything Model (SAM), enhanced through Visual Prompt Tuning. This methodology ...

License Plate Recognition (LPR) plays a critical role in various applications, such as toll collection, parking management, and traffic law enforcement. Although LPR has witnessed significant advancements through the development of deep learning, ...

Facial expression is a powerful, natural and universal signal for human beings to convey their emotional states and intentions. In this paper, we propose a new spatial-temporal facial expression recognition network which outperforms many state-of-...

Online conversation system user preference prediction can improve online conversation system to enhance the quality of the conversation. This paper design an online conversation quality preference assessment model for this problem from the novel ...

In view of the following problems of the Vision Transformer (ViT) model: a large number of parameters, the lack of global modeling ability and sensitivity to data enhancement. Inspired by MobileViT, based on Convolutional Neural Networks (CNN) and ...

In today’s world, with the deepening of population aging, chronic diseases have become the main diseases which affecting human health. Diabetes is a common chronic disease. Its incidence rate is high and rising year by year. For patients with ...

Minimally invasive transcatheter cardiac interventions are being adopted rapidly to treat a range of cardiovascular diseases. Pre-operative imaging, e.g., computed tomography (CT), plays an important role in surgical planning and simulation of ...

In recent years, with the continuous improvement of teaching informatization, online teaching or online and offline hybrid teaching has become the new normal of teaching in some schools. However, the biggest problem in online teaching is that it is ...

Knowledge tracking is to model the student’s learning interaction records so that we can evaluate the student’s learning state with relative accuracy and can adjust the students’ learning schedule appropriately or formulate a more reasonable learning ...

Classification of the Functions of Urban Area is of great significance to urban construction and refined management. High-resolution remote sensing images are widely used in urban area functional classification, and urban area functions are closely ...

This paper presents the techniques used in our contribution to Emotion Recognition in the Wild 2017 video based sub-challenge. The purpose of the sub-challenge is to classify the six basic emotions (angry, sad, happy, surprise, fear and disgust) and ...

In this paper, we present a video-based emotion recognition system submitted to the EmotiW 2016 Challenge. The core module of this system is a hybrid network that combines recurrent neural network (RNN) and 3D convolutional networks (C3D) in a late-...

Opposing the pre-dominant turn-wise statistics of acoustic Low-Level-Descriptors followed by static classification we re-investigate dynamic modeling directly on the frame-level in speech-based emotion recognition. This seems beneficial, as it is well ...