A Cross-Modal Image and Text Retrieval Method Based on Efficient Feature Extraction and Interactive Learning CAE

In view of the complexity of the multimodal environment and the existing shallow network structure that cannot achieve high-precision image and text retrieval, a cross-modal image and text retrieval method combining efficient feature extraction and interactive learning convolutional autoencoder (CAE) is proposed. First, the residual network convolution kernel is improved by incorporating two-dimensional principal component analysis (2DPCA) to extract image features and extracting text features through long short-term memory (LSTM) and word vectors to efficiently extract graphic features. Then, based on interactive learning CAE, cross-modal retrieval of images and text is realized. Among them, the image and text features are respectively input to the two input terminals of the dual-modal CAE, and the image-text relationship model is obtained through the interactive learning of the middle layer to realize the image-text retrieval. Finally, based on Flickr30K, MSCOCO, and Pascal VOC 2007 datasets, the proposed method is experimentally demonstrated. The results show that the proposed method can complete accurate image retrieval and text retrieval. Moreover, the mean average precision (MAP) has reached more than 0.3, the area of precision-recall rate (PR) curves are better than other comparison methods, and they are applicable.

A Graph Retrieval Architecture and System for Online Sign Language Dictionary- with an application to Taiwanese Sign Language

Emerging applications for an online sign language dictionary require that retrieval systems retrieve a target vocabulary through visual symbols. However, when people encounter an unknown vocabulary in sign language during communication, they require the online dictionary to retrieve the vocabulary with higher recall-rate and smaller-sized graph through a mobile device. Still, three situations show that the current online dictionary needs an extension. First, previous works lack of retrieving the target graph of a vocabulary through its complete visual symbol-portfolio. Secondly, they often respond a large number of possible images; however, their precisions and recall rates remain very low. Thirdly, previous works of sign language gloves can convert the visual symbols into the graphic features, but only part of the symbols, ignoring the symbols of expression and relative direction. Therefore, the aim of this study is, based on Taiwanese Sign Language, to design a new graph retrieval architecture for sign-language (GRAS), and to implement a new graph retrieval system for sign-language (GRSS) based on this architecture. Finally, we invite users to evaluate GRSS. The experimental results show that GRSS gets convincing performance. And, GRSS adopting RDF technology can improve the performance of GRSS without adopting RDF technology.

Exploiting User Friendship Networks for User Identification across Social Networks

Identifying offline entities corresponding to multiple virtual accounts of users across social networks is crucial for the development of related fields, such as user recommendation system, network security, and user behavior pattern analysis. The data generated by users on multiple social networks has similarities. Thus, the concept of symmetry can be used to analyze user-generated information for user identification. In this paper, we propose a friendship networks-based user identification across social networks algorithm (FNUI), which performs the similarity of multi-hop neighbor nodes of a user to characterize the information redundancy in the friend networks fully. Subsequently, a gradient descent algorithm is used to optimize the contribution of the user’s multi-hop nodes in the user identification process. Ultimately, user identification is achieved in conjunction with the Gale–Shapley matching algorithm. Experimental results show that compared with baselines, such as friend relationship-based user identification (FRUI) and friendship learning-based user identification (FBI): (1) The contribution of single-hop neighbor nodes in the user identification process is higher than other multi-hop neighbor nodes; (2) The redundancy of information contained in multi-hop neighbor nodes has a more significant impact on user identification; (3) The precision rate, recall rate, comprehensive evaluation index (F1), and area under curve (AUC) of user identification have been improved.

Prediction of Renal Damage in Children with Henoch-Schönlein Purpura Based on Machine Learning

Background This article is objected to explore the value of machine learning algorithm in predicting the risk of renal damage in children with Henoch-Schönlein Purpura, and to construct a predictive model of Henoch-Schönlein Purpura Nephritis in children and analyze the related risk factors of Henoch-Schönlein Purpura Nephritis in children. Methods Case data of 288 hospitalized children with Henoch-Schönlein Purpura from November 2018 to October 2021 were collected. The data included 42 indicators such as demographic characteristics, clinical symptoms and laboratory tests, etc. Univariate feature selection was used for feature extraction, and Logistic regression, support vector machine, decision tree and random forest algorithm were used respectively for classification prediction. Last, the performance of four algorithms are compared using accuracy rate and recall rate. Results The accuracy rate, recall rate and AUC of the established random forest model were 0.83, 0.86 and 0.91 respectively, which were higher than 0.74, 0.80 and 0.89 of the Logistic regression model; higher than 0.70, 0.80 and 0.89 of support vector machine model; higher than 0.74, 0.80 and 0.81 of the decision tree model. The top 10 important features provided by random forest model are Persistent purpura≥4weeks, Cr, Clinic time, ALB, WBC, TC, TG, Relapse, TG, Recurrent purpura and EB-DNA. Conclusion The model based on random forest algorithm has better performance in the prediction of children with allergic purpura renal damage, indicated by better classification accuracy, better classification effect and better generalization performance.

Research on Personalized Minority Tourist Route Recommendation Algorithm Based on Deep Learning

With the improvement of living standards, more and more people are pursuing personalized routes. This paper uses personalized mining of interest points of ethnic minority tourism demand groups, extracts customer data features in social networks, and constructs data features of interesting topic factors, geographic location factors, and user access frequency factors, using LDA topic models and matrix decomposition models to perform feature vectorization processing on user sign-in records and build deep learning recommendation model (DLM). Using this model to compare with the traditional recommendation model and the recommendation model of a single data feature module, the experimental results show the following: (1) The fitting error of DLM recommendation results is significantly reduced, and its recommendation accuracy rate is 50% higher than that of traditional recommendation algorithms. The experimental results show that the DLM constructed in this paper has good learning and training performance, and the recommendation effect is good. (2) In this method, the performance of the DLM is significantly higher than other POI recommendation methods in terms of the accuracy or recall rate of the recommendation algorithm. Among them, the accuracy rates of the top five, top ten, and top twenty recommended POIs are increased by 9.9%, 7.4%, and 7%, respectively, and the recall rate is increased by 4.2%, 7.5%, and 14.4%, respectively.

Research on Face Recognition Classification Based on Improved GoogleNet

Face recognition is a relatively mature technology, which has some applications in many aspects, and now there are many networks studying it, which has indeed brought a lot of convenience to mankind in all aspects. This paper proposes a new face recognition technology. First, a new GoogLeNet-M network is proposed, which improves network performance on the basis of streamlining the network. Secondly, regularization and migration learning methods are added to improve accuracy. The experimental results show that the GoogLeNet-M network with regularization using migration learning technology has the best performance, with a recall rate of 0.97 and an accuracy of 0.98. Finally, it is concluded that the performance of the GoogLeNet-M network is better than other networks on the dataset, and the migration learning method and regularization help to improve the network performance.

Information Processing Methods of Electronic Warfare Events Based on Communication Technology

Traditional electronic countermeasure incident intelligence processing has problems such as low accuracy and stability and long processing time. A method of electronic countermeasure incident intelligence processing based on communication technology is proposed. First, use the integrated digital signal receiver to identify various modulation methods in the complex signal environment to facilitate the processing and transmission of communication signals, then establish an electronic countermeasure intelligence processing framework with Esper as the core, and flow the situation to the processing conclusion through the PROTOBUF interactive format Redis cache. The data can realize the intelligent processing of electronic countermeasure incidents. The experimental results show that the method proposed in this paper increases the recall rate by 5 to 20% compared with other methods. This method has high accuracy and stability for electronic countermeasure incident intelligence processing and can effectively shorten the time for electronic countermeasure incident intelligence processing.

Knowledge Verification Method Based on Artificial Intelligence-based Knowledge Graph Construction

The knowledge graph connects real-world entities and concepts through their relationships, connects all different types of information to obtain a relationship network, and can analyze “relationship” issues. Creating a knowledge graph is a continuous process, and it needs to continuously learn new knowledge and update existing knowledge in the library as time and events change. However, since the accuracy of the updated new knowledge cannot be guaranteed, the new knowledge must be verified. This paper aims to study the knowledge verification method based on artificial intelligence-based knowledge graph construction. Based on the analysis of the knowledge graph construction process, the knowledge graph construction method and the knowledge verification method, knowledge verification is realized by constructing a probabilistic soft logic model. The experimental results show that the recall rate, F1 value, and AUC value of the candidate knowledge set are verified by the knowledge verification model proposed in this paper. Therefore, it can be inferred that the knowledge verification model proposed in this paper is effective.

Context-aware POI Recommendation using Neutrosophic Set for Mobile Edge Computing

With the rapid development of mobile communication technology, there is a growing demand for high-quality point of interest(POI) recommendation. The POIs visited by users only account for a very small proportion. Thus traditional POI recommendation method is vulnerable to data sparsity and lacks a clear and effective explanation for POI ranking result. The POI selection made by the user is influenced by various contextual attributes. The challenge lies in representing accurately and aggregating multiple contextual information efficiently. We transform the POI recommendation into a contextual multi-attribute decision problem based on the neutrosophic set (NS) which is suitable for representing fuzzy decision information. We establish a unified framework of contextual information. Firstly, we propose a contextual multi-attribute NS transformation model of POI, including the NS model for single-dimensional attributes and the NS model for multi-dimensional attributes. And then through the aggregation of multi attribute NS, the POI that best conforms to user's preferences is recommended. Finally, the experimental results based on the Yelp dataset show that the proposed strategy performs better than the typical POI recommendation method in NDCG, accuracy, and recall rate.

The Use of Intelligent Sensing Algorithm for Internet of Things and Hash Spatial Information Location Technology

This exploration is aimed at quickly obtaining the spatial position information of microseismic focal points and increasing the accuracy of microseismic rapid positioning, to take timely corresponding measures. A microseismic focal point location system completely different from the traditional microseismic location method is proposed. The search engine technology is introduced into the system, which can locate the microseismic focal point quickly and accurately. First, the propagation characteristics of microseismic signals in coal and rock layers are analyzed, and the focal position information is obtained. However, the collected microseismic signal of the coal mine contains noise, so it is denoised at first. Then, a waveform database is established for the denoised waveform data and focal point position. The structure and mathematical model of the location-sensitive hash (LSH) based on P stable distribution are introduced and improved, and the optimized algorithm multiprobe LSH is obtained. The microseismic location model is established according to the characteristics of microseismic data. The values of three parameters, hash table number, hash function family dimension, and interval size, are determined. The experimental data of the parameters of the search engine algorithm are analyzed. The results show that when the number of hash tables is 6, the dimension k of the hash function family is 14, and the interval size W is 8000, the retrieval time reaches a relatively small value, the recall rate reaches a large value, and the proportion of retrieved candidates is large; the parameters of the search engine algorithm of the measured coal mine microseismic data are analyzed. It is obtained that when the number of hash tables is 4, the dimension k of the hash function family is 6, and the interval size W is 500, the retrieval time reaches a relatively small value, the recall rate obtains a large value, and the proportion of retrieved candidates is large. The contents studied are of great significance to the evaluation of destructive mine earthquakes and impact risk.