Big Data Precision Marketing Approach under IoT Cloud Platform Information Mining

In this article, an in-depth study and analysis of the precision marketing approach are carried out by building an IoT cloud platform and then using the technology of big data information mining. The cloud platform uses the MySQL database combined with the MongoDB database to store the cloud platform data to ensure the correct storage of data as well as to improve the access speed of data. The storage method of IoT temporal data is optimized, and the way of storing data in time slots is used to improve the efficiency of reading large amounts of data. For the scalability of the IoT data storage system, a MongoDB database clustering scheme is designed to ensure the scalability of data storage and disaster recovery capability. The relevant theories of big data marketing are reviewed and analyzed; secondly, based on the relevant theories, combined with the author’s work experience and relevant information, a comprehensive analysis and research on the current situation of big data marketing are conducted, focusing on its macro-, micro-, and industry environment. The service model combines the types of user needs, encapsulates the resources obtained by the alliance through data mining for service products, and publishes and delivers them in the form of data products. From the perspective of the development of the telecommunications industry, in terms of technology, the telecommunications industry has seen the development trend of mobile replacing fixed networks and triple play. The development of emerging technologies represented by the Internet of Things and cloud computing has also led to technological changes in the telecommunications industry. Operators are facing new development opportunities and challenges. It also divides the service mode into self-service and consulting service mode according to the different degrees of users’ cognition and understanding of the service, as well as proposes standardized data mining service guarantee from two aspects: after-sales service and operation supervision. A customized data mining service is a kind of data mining service for users’ personalized needs. And the intelligent data mining service guarantee is proposed from two aspects of multicase experience integration and group intelligence. In the empirical research part, the big data alliance in Big Data Industry Alliance, which provides data mining service as the main business, is selected as the research object, and the data mining service model of the big data alliance proposed in this article is applied to the actual alliance to verify the scientific and rationality of the data mining service model and improve the data mining service model management system.

A novel similarity measure for mining missing links in long-path networks

Network information mining is the study of the network topology, which may answer a large number of application-based questions towards the structural evolution and the function of a real system. The question can be related to how the real system evolves or how individuals interact with each other in social networks. Although the evolution of the real system may seem to be found regularly, capturing patterns on the whole process of evolution is not trivial. Link prediction is one of the most important technologies in network information mining, which can help us understand the evolution mechanism of real-life network. Link prediction aims to uncover missing links or quantify the likelihood of the emergence of nonexistent links from known network structures. Currently, widely existing methods of link prediction almost focus on short-path networks that usually have a myriad of close triangular structures. However, these algorithms on highly sparse or long-path networks have poor performance. Here, we propose a new index that is associated with the principles of Structural Equivalence and Shortest Path Length (SESPL) to estimate the likelihood of link existence in long-path networks. Through 548 real networks test, we find that SESPL is more effective and efficient than other similarity-based predictors in long-path networks. Meanwhile, we also exploit the performance of SESPL predictor and of embedding-based approaches via machine learning techniques. The results show that the performance of SESPL can achieve a gain of 44.09% over GraphWave and 7.93% over Node2vec. Finally, according to the matrix of Maximal Information Coefficient (MIC) between all the similarity-based predictors, SESPL is a new independent feature in the space of traditional similarity features.

Ekstraksi Informasi Destinasi Wisata Populer Jawa Timur Menggunakan Depth-First Crawling

Travel Destinations are an inseparable part of human life today. As one of the provinces with a large area, East Java is one of the most visited areas for its tourism. Many people are competing in finding information related to these tourist destinations on the internet, one of which is the Tripadvisor application. Of the many tourist attractions, several tourist attractions have different attractions and experiences each time. Tourists have widely used the Tripadvisor application in determining the location where they will visit on their vacation activities. With various features ranging from reviews and recommendations for sharing photos, TripAdvisor is one of the best applications in the inventory of tourist attractions. Of the many tourist destinations, it is necessary to analyze and evaluate both tourist attractions that have many visitors with tourist attractions that are rarely visited by both local and foreign visitors. This goal, information mining (web mining), was carried out on the TripAdvisor application to obtain information on East Java Province's popular destinations. Crawling results on the TripAdvisor website, obtained various kinds of information such as names of tourist attractions, locations, visitor reviews, photos, and ratings of these tourist attractions. Spatial Analysis, a Tourist Sentiment Analyst on tourist objects, can then be carried out. It can also be developed into the recommendation system for the best tourist attractions in East Java Province

Medical Information Mining-Based Visual Artificial Intelligence Emergency Nursing Management System

This study aims to design a set of the visual artificial intelligence system based on medical information mining for hospital emergency care management. A visual artificial intelligence emergency first aid nursing management system is designed by analyzing the needs of the emergency first aid nursing management system. The results show that system personnel allocation, comparative management, record management, query management analysis, basic setup analysis, nursing management basis, and nonfunctional requirements all need to be optimized for the emergency first aid management system. In this study, the comparative management module, log management module, and the query management module are designed, and the emergency first aid management system of different APP terminal functions in different modules is described in detail. The nursing document query business is tested, and the corresponding time of query of nursing assessment sheet, nurse shift record, nurse record, and physical sign observation sheet is 375.50 ms, 351.48 ms, 336.36 ms, and 245.57 ms, respectively. It shows that the visual artificial intelligence emergency nursing management system based on medical information mining can provide convenience for clinical work to a large extent and has potential application value in hospital emergency nursing work.

A Reliable Low Power Multiplier Using Fixed Width Scalable Approximation

Recent Approximate computing is a change in perspective in energy-effective frameworks plan and activity, in light of the possibility that we are upsetting PC frameworks effectiveness by requesting a lot of precision from them. Curiously, enormous number of utilization areas, like DSP, insights, and AI. Surmised figuring is appropriate for proficient information handling and mistake strong applications, for example, sign and picture preparing, PC vision, AI, information mining and so forth Inexact registering circuits are considered as a promising answer for lessen the force utilization in inserted information preparing. This paper proposes a FPGA execution for a rough multiplier dependent on specific partial part-based truncation multiplier circuits. The presentation of the proposed multiplier is assessed by contrasting the force utilization, the precision of calculation, and the time delay with those of a rough multiplier dependent on definite calculation introduced. The estimated configuration acquired energy effective mode with satisfactory precision. When contrasted with ordinary direct truncation proposed model fundamentally impacts the presentation. Thusly, this novel energy proficient adjusting based inexact multiplier design outflanked another cutthroat model.