A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model

Recommendation systems have become an important solution to information search problems. This article proposes a neural matrix factorization recommendation system model based on the multimodal large language model called BoNMF. This model combines BoBERTa's powerful capabilities in natural language processing, ViT in computer in vision, and neural matrix decomposition technology. By capturing the potential characteristics of users and items, and after interacting with a low-dimensional matrix composed of user and item IDs, the neural network outputs the results. recommend. Cold start and ablation experimental results show that the BoNMF model exhibits excellent performance on large public data sets and significantly improves the accuracy of recommendations.

Research on Edge Detection of LiDAR Images Based on Artificial Intelligence Technology

With the widespread application of Light Detection and Ranging (LiDAR) technology in fields such as autonomous driving, robot navigation, and terrain mapping, the importance of edge detection in LiDAR images has become increasingly prominent. Traditional edge detection methods often face challenges in accuracy and computational complexity when processing LiDAR images. To address these issues, this study proposes an edge detection method for LiDAR images based on artificial intelligence technology. This paper first reviews the current state of research on LiDAR technology and image edge detection, introducing common edge detection algorithms and their applications in LiDAR image processing. Subsequently, a deep learning-based edge detection model is designed and implemented, optimizing the model training process through preprocessing and enhancement of the LiDAR image dataset. Experimental results indicate that the proposed method outperforms traditional methods in terms of detection accuracy and computational efficiency, showing significant practical application value. Finally, improvement strategies are proposed for the current method's shortcomings, and the improvements are validated through experiments.

Application of Natural Language Processing in Financial Risk Detection

This paper explores the application of Natural Language Processing (NLP) in financial risk detection. By constructing an NLP-based financial risk detection model, this study aims to identify and predict potential risks in financial documents and communications. First, the fundamental concepts of NLP and its theoretical foundation, including text mining methods, NLP model design principles, and machine learning algorithms, are introduced. Second, the process of text data preprocessing and feature extraction is described. Finally, the effectiveness and predictive performance of the model are validated through empirical research. The results show that the NLP-based financial risk detection model performs excellently in risk identification and prediction, providing effective risk management tools for financial institutions. This study offers valuable references for the field of financial risk management, utilizing advanced NLP techniques to improve the accuracy and efficiency of financial risk detection.

Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm

In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability. This study harnesses advanced neural network techniques, notably the Backpropagation (BP) neural network, to pioneer a novel model for preempting credit risk in commercial banks. The discourse initially scrutinizes conventional financial risk preemptive models, such as ARMA, ARCH, and Logistic regression models, critically analyzing their real-world applications. Subsequently, the exposition elaborates on the construction process of the BP neural network model, encompassing network architecture design, activation function selection, parameter initialization, and objective function construction. Through comparative analysis, the superiority of neural network models in preempting credit risk in commercial banks is elucidated. The experimental segment selects specific bank data, validating the model's predictive accuracy and practicality. Research findings evince that this model efficaciously enhances the foresight and precision of credit risk management.

Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics

With the development and widespread application of digital image processing technology, image splicing has become a common method of image manipulation, raising numerous security and legal issues. This paper introduces a new splicing image detection algorithm based on the statistical characteristics of natural images, aimed at improving the accuracy and efficiency of splicing image detection. By analyzing the limitations of traditional methods, we have developed a detection framework that integrates advanced statistical analysis techniques and machine learning methods. The algorithm has been validated using multiple public datasets, showing high accuracy in detecting spliced edges and locating tampered areas, as well as good robustness. Additionally, we explore the potential applications and challenges faced by the algorithm in real-world scenarios. This research not only provides an effective technological means for the field of image tampering detection but also offers new ideas and methods for future related research.

Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment

This study proposes a multi-modal fusion framework Multitrans based on the Transformer architecture and self-attention mechanism. This architecture combines the study of non-contrast computed tomography (NCCT) images and discharge diagnosis reports of patients undergoing stroke treatment, using a variety of methods based on Transformer architecture approach to predicting functional outcomes of stroke treatment. The results show that the performance of single-modal text classification is significantly better than single-modal image classification, but the effect of multi-modal combination is better than any single modality. Although the Transformer model only performs worse on imaging data, when combined with clinical meta-diagnostic information, both can learn better complementary information and make good contributions to accurately predicting stroke treatment effects..

Research on Detection of Floating Objects in River and Lake Based on AI Intelligent Image Recognition

With the rapid advancement of artificial intelligence technology, AI-enabled image recognition has emerged as a potent tool for addressing challenges in traditional environmental monitoring. This study focuses on the detection of floating objects in river and lake environments, exploring an innovative approach based on deep learning. By intricately analyzing the technical pathways for detecting static and dynamic features and considering the characteristics of river and lake debris, a comprehensive image acquisition and processing workflow has been developed. The study highlights the application and performance comparison of three mainstream deep learning models -SSD, Faster-RCNN, and YOLOv5- in debris identification. Additionally, a detection system for floating objects has been designed and implemented, encompassing both hardware platform construction and software framework development. Through rigorous experimental validation, the proposed system has demonstrated its ability to significantly enhance the accuracy and efficiency of debris detection, thus offering a new technological avenue for water quality monitoring in rivers and lakes

A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product

In recent years, there have been frequent incidents of foreign objects intruding into railway and Airport runways. These objects can include pedestrians, vehicles, animals, and debris. This paper introduces an improved YOLOv5 architecture incorporating FasterNet and attention mechanisms to enhance the detection of foreign objects on railways and Airport runways. This study proposes a new dataset, AARFOD (Aero and Rail Foreign Object Detection), which combines two public datasets for detecting foreign objects in aviation and railway systems. The dataset aims to improve the recognition capabilities of foreign object targets. Experimental results on this large dataset have demonstrated significant performance improvements of the proposed model over the baseline YOLOv5 model, reducing computational requirements. improved YOLO model shows a significant improvement in precision by 1.2%, recall rate by 1.0%, and mAP@.5 by 0.6%, while mAP@.5-.95 remained unchanged. The parameters were reduced by approximately 25.12%, and GFLOPs were reduced by about 10.63%. In the ablation experiment, it is found that the FasterNet module can significantly reduce the number of parameters of the model, and the reference of the attention mechanism can slow down the performance loss caused by lightweight.

Improved YOLOv5 Based on Attention Mechanism and FasterNet for Foreign Object Detection on Railway and Airway tracks

In recent years, there have been frequent incidents of foreign objects intruding into railway and Airport runways. These objects can include pedestrians, vehicles, animals, and debris. This paper introduces an improved YOLOv5 architecture incorporating FasterNet and attention mechanisms to enhance the detection of foreign objects on railways and Airport runways. This study proposes a new dataset, AARFOD (Aero and Rail Foreign Object Detection), which combines two public datasets for detecting foreign objects in aviation and railway systems.The dataset aims to improve the recognition capabilities of foreign object targets. Experimental results on this large dataset have demonstrated significant performance improvements of the proposed model over the baseline YOLOv5 model, reducing computational requirements.Improved YOLO model shows a significant improvement in precision by 1.2%, recall rate by 1.0%, and mAP@.5 by 0.6%, while mAP@.5-.95 remained unchanged. The parameters were reduced by approximately 25.12%, and GFLOPs were reduced by about 10.63%. In the ablation experiment, it is found that the FasterNet module can significantly reduce the number of parameters of the model, and the reference of the attention mechanism can slow down the performance loss caused by lightweight.