E-commerce Webpage Recommendation Scheme Base on Semantic Mining and Neural Networks

In e-commerce websites, web mining web page recommendation technology has been widely used. However, recommendation solutions often cannot meet the actual application needs of online shopping users. To address this problem, this paper proposes an e-commerce web page recommendation solution that combines semantic web mining and BP neural networks. First, the web logs of user searches are processed, and 5 features are extracted: content priority, time consumption priority, online shopping users' explicit/implicit feedback on the website, recommendation semantics and input deviation amount. Then, these features are used as input features of the BP neural network to classify and identify the priority of the final output web page. Finally, the web pages are sorted according to priority and recommended to users. This project uses book sales webpages as samples for experiments. The results show that this solution can quickly and accurately identify the webpages required by users.

Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning

In clinical treatment, identifying potential adverse reactions of drugs can help assist doctors in making medication decisions. In response to the problems in previous studies that features are high-dimensional and sparse, independent prediction models need to be constructed for each adverse reaction of drugs, and the prediction accuracy is low, this paper develops an adverse drug reaction prediction model based on knowledge graph embedding and deep learning, which can predict experimental results. Unified prediction of adverse drug reactions covered. Knowledge graph embedding technology can fuse the associated information between drugs and alleviate the shortcomings of high-dimensional sparsity in feature matrices, and the efficient training capabilities of deep learning can improve the prediction accuracy of the model. This article builds an adverse drug reaction knowledge graph based on drug feature data; by analyzing the embedding effect of the knowledge graph under different embedding strategies, the best embedding strategy is selected to obtain sample vectors; and then a convolutional neural network model is constructed to predict adverse reactions. The results show that under the DistMult embedding model and 400-dimensional embedding strategy, the convolutional neural network model has the best prediction effect; the average accuracy, F_1 score, recall rate and area under the curve of repeated experiments are better than the methods reported in the literature. The obtained prediction model has good prediction accuracy and stability, and can provide an effective reference for later safe medication guidance.

Real-Time Pill Identification for the Visually Impaired Using Deep Learning

The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the development and implementation of a deep learning-based mobile application designed to assist blind and visually impaired individuals in real-time pill identification. Utilizing the YOLO framework, the application aims to accurately recognize and differentiate between various pill types through real-time image processing on mobile devices. The system incorporates Text-to- Speech (TTS) to provide immediate auditory feedback, enhancing usability and independence for visually impaired users. Our study evaluates the application's effectiveness in terms of detection accuracy and user experience, highlighting its potential to improve medication management and safety among the visually impaired community. Keywords-Deep Learning; YOLO Framework; Mobile Application; Visual Impairment; Pill Identification; Healthcare

Solve routing problems with a residual edge-graph attention neural network

For NP-hard combinatorial optimization problems, it is usually difficult to find high-quality solutions in polynomial time. The design of either an exact algorithm or an approximate algorithm for these problems often requires significantly specialized knowledge. Recently, deep learning methods provide new directions to solve such problems. In this paper, an end-to-end deep reinforcement learning framework is proposed to solve this type of combinatorial optimization problems. This framework can be applied to different problems with only slight changes of input (for example, for a traveling salesman problem (TSP), the input is the two-dimensional coordinates of nodes; while for a capacity-constrained vehicle routing problem (CVRP), the input is simply changed to three-dimensional vectors including the two-dimensional coordinates and the customer demands of nodes), masks and decoder context vectors. The proposed framework is aiming to improve the models in literacy in terms of the neural network model and the training algorithm. The solution quality of TSP and the CVRP up to 100 nodes are significantly improved via our framework. Specifically, the average optimality gap is reduced from 4.53\% (reported best \cite{R22}) to 3.67\% for TSP with 100 nodes and from 7.34\% (reported best \cite{R22}) to 6.68\% for CVRP with 100 nodes when using the greedy decoding strategy. Furthermore, our framework uses about 1/3$\sim$3/4 training samples compared with other existing learning methods while achieving better results. The results performed on randomly generated instances and the benchmark instances from TSPLIB and CVRPLIB confirm that our framework has a linear running time on the problem size (number of nodes) during the testing phase, and has a good generalization performance from random instance training to real-world instance testing.