Research on the Realization of Travel Recommendations for Different Users Through Deep Learning Under Global Information Management

This article is mainly to study the realization of travel recommendations for different users through deep learning under global information management. The personalized travel route recommendation is realized by establishing personalized travel dynamic interest (PTDR) algorithm and distributed lock manager (DLM) model. It is hoped that this model can provide more complete data information of tourist destinations on the basis of the past, and can also meet the needs of users. The innovation of this article is to compare and analyze with a large number of baseline algorithms, highlighting the superiority of this model in personalized travel recommendation. In addition, the model incorporates the topic factor features, geographic factor features, and user preference features to make the data more in line with user needs and improve the efficiency and applicability of the model. It is hoped that the plan proposed in this article can help users make choices of tourist destinations more conveniently.

Novel Machine Learning for Big Data Analytics in Intelligent Support Information Management Systems

Two-dimensional 1 arrays of bi-component structures made of cobalt and permalloy elliptical dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a self-aligned shadow deposition technique. Brillouin light scattering has been exploited to study the frequency dependence of thermally excited magnetic eigenmodes on the intensity of the external magnetic field, applied along the easy axis of the elements. Scientific information technology has been developed rapidly. Here, the purposes are to make people's lives more convenient and ensure information management and classification. The machine learning algorithm is improved to obtain the optimized Light Gradient Boosting Machine (LightGBM) algorithm. Then, an Android-based intelligent support information management system is designed based on LightGBM for the big data analysis and classification management of information in the intelligent support information management system. The system is designed with modules of employee registration and login, company announcement notice, attendance and attendance management, self-service, and daily tools with the company as the subject. Furthermore, the performance of the constructed information management system is analyzed through simulations. Results demonstrate that the training time of the optimized LightGBM algorithm can stabilize at about 100s, and the test time can stabilize at 0.68s. Besides, its accuracy rate can reach 89.24%, which is at least 3.6% higher than other machine learning algorithms. Moreover, the acceleration efficiency analysis of each algorithm suggests that the optimized LightGBM algorithm is suitable for processing large amounts of data; its acceleration effect is more apparent, and its acceleration ratio is higher than other algorithms. Hence, the constructed intelligent support information management system can reach a high accuracy while ensuring the error, with apparent acceleration effect. Therefore, this model can provide an experimental reference for information classification and management in various fields.

Cross-Modality Transfer Learning for Image-Text Information Management

In the past decades, information from all kinds of data has been on a rapid increase. With state-of-the-art performance, machine learning algorithms have been beneficial for information management. However, insufficient supervised training data is still an adversity in many real-world applications. Therefore, transfer learning (TF) was proposed to address this issue. This article studies a not well investigated but important TL problem termed cross-modality transfer learning (CMTL). This topic is closely related to distant domain transfer learning (DDTL) and negative transfer. In general, conventional TL disciplines assume that the source domain and the target domain are in the same modality. DDTL aims to make efficient transfers even when the domains or the tasks are entirely different. As an extension of DDTL, CMTL aims to make efficient transfers between two different data modalities, such as from image to text. As the main focus of this study, we aim to improve the performance of image classification by transferring knowledge from text data. Previously, a few CMTL algorithms were proposed to deal with image classification problems. However, most existing algorithms are very task specific, and they are unstable on convergence. There are four main contributions in this study. First, we propose a novel heterogeneous CMTL algorithm, which requires only a tiny set of unlabeled target data and labeled source data with associate text tags. Second, we introduce a latent semantic information extraction method to connect the information learned from the image data and the text data. Third, the proposed method can effectively handle the information transfer across different modalities (text-image). Fourth, we examined our algorithm on a public dataset, Office-31. It has achieved up to 5% higher classification accuracy than “non-transfer” algorithms and up to 9% higher than existing CMTL algorithms.

Advanced and Secure Laboratory Management System

Advanced & Secure Laboratory Information Management System, TRLIMS is the management system which has live tracking system for all the testing and research conducted at the laboratory. This system is developed to achieve diverse functionality for the disciplines such as mechanical, chemical, environmental, microbiology and non-destructive fields. The basic features of this application are that it can manage the data related to client, employees and testing results of the laboratory. Apart from that since the application is fully hosted on server which offers flexibility, providing future scope for more hardware and operating system configuration. This application provides very enhanced turn-around-time (TAT) for the material testing laboratory It aims to manage the employees, clients and associated testing data to improve the lab productivity. The application allows clients to track their improvement in sample testing from time to time, the data is updated on server by employees who perform tests at the premises. This paper could provide guidance to understanding the operation mechanism of Laboratory Information Management System.

Evaluation Index of School Sports Resources Based on Artificial Intelligence and Edge Computing

As the demand for education continues to increase, the relative lack of physical resources has become a bottleneck hindering the development of school physical education to a certain extent. This research mainly discusses the evaluation index system of school sports resources based on artificial intelligence and edge computing. Human resources, financial resources, and material resources in school sports resources are the three major resources in resource science. University sports stadium information publicity uses Internet technology to establish a sports information management platform and mobile Internet terminals to optimize university sports resources and stadium information management services. It uses artificial intelligence technology to improve venue information management. It establishes a comprehensive platform for venue management information, collects multidimensional information, provides information resources and accurate information push, and links venue information with public fitness needs. Using edge computing to realize nearby cloud processing of video data, reduce the phenomenon of black screen jams during live broadcast, improve data computing capabilities, and reduce users’ dependence on the performance of terminal devices, build a smart sports resource platform, combine artificial intelligence (AI) to create smart communities, smart venues, and realize intelligent operations such as event service operations and safety prevention and control in important event venues. During the live broadcast of the student sports league, the nearby cloud processing of video data is realized in the form of edge computing, which improves the data computing ability and reduces the performance dependence on the user terminal equipment itself. In the academic survey of college physical education teachers, undergraduates accounted for 26.99%, masters accounted for 60.3%, and doctoral degrees accounted for 12.8%. This research will help the reasonable allocation of school sports resources.