scholarly journals Improved Nearest Neighbor Propagation Algorithm Based on Internet of Things Technology in Financial Management Early Warning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaoyan Li
Keyword(s):  
Internet Of Things ◽  
Early Warning ◽  
Financial Management ◽  
Financial Risk ◽  
Nearest Neighbor ◽  
Market Competition ◽  
Warning System ◽  
Propagation Algorithm ◽  
Expert Analysis ◽  
Internet Of Things Technology

In recent years, the increasing degree of economic globalization has provided a broader platform for the development of enterprises, but it also made enterprises bear more and more pressure of market competition. This paper mainly studies the application of improved nearest neighbor propagation algorithm based on Internet of Things technology in financial management early warning. This paper selects the mixed unbalanced panel data of 40 companies from 2006 to 2008 as the overall research sample. After eliminating the outliers and the samples of companies without data for two consecutive years, 390 datasets of 30 companies are selected as the modeling samples. The selection of risk early warning indicators should follow the following six principles: comprehensiveness, importance, scientificity, objective quantification, comparability, and operability. The standard deviation of index data is calculated to compare the strength and improve the integrity and effectiveness of the value. In this paper, Delphi expert analysis method is used to invite experts who have certain research in this field to propose the corresponding independent evaluation index scheme. On the premise of taking the summary results as the reference, the index contents which are not representative and different from the actual requirements are deleted, so as to finally determine the index system of the risk assessment scheme. The data show that the final correct rate of the financial risk early warning model can reach 91% and the total number of judgments is 200, where 182 are correct and only 18 are wrong. The results show that the establishment of a good financial risk early warning system can help enterprises better find and deal with risks and makes enterprises develop healthily.

Early Warning System for SMEs as a Financial Risk Detector

2009 ◽  
pp. 220-238 ◽  
Author(s):  
Ali Serhan Koyuncugil
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Quantitative Data ◽  
Financial Risk ◽  
Warning System ◽  
Theoretical Background ◽  
Effect Level

This chapter introduces an early warning system for SMEs (SEWS) as a financial risk detector which is based on data mining. In this study, the objective is to compose a system in which qualitative and quantitative data about the requirements of enterprises are taken into consideration, during the development of an early warning system. Furthermore, during the formation of system; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of effect level of every factor. Using the system, SME managers could easily reach financial management, risk management knowledge without any prior knowledge and expertise. In other words, experts share their knowledge with the help of data mining based and automated EWS.

The Internet of Things Based the Framework of Gannan Yak Disease Early Warning System Design

2014 ◽  
Vol 926-930 ◽  
pp. 2276-2279
Author(s):  
Guo Jun Ma
Keyword(s):  
Internet Of Things ◽  
Information Management ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Management Information ◽  
Model Base ◽  
Base Management ◽  
Internet Of Things Technology ◽  
The Internet Of Things

In view of the Gannan Tibetan yak breeding situation, proposed the Gannan yak disease early warning system based on Internet of things technology, from the model base management, information management, assessment prediction of disease,disease alarm four aspects of the design of the system framework.

Design and Realization on the Financial Early-Warning Analysis System of Colleges and Universities Based on Data Mining Technology

2014 ◽  
Vol 623 ◽  
pp. 229-233 ◽  
Author(s):  
De Jiang Qi ◽  
Hai Yan Hu
Keyword(s):  
Data Mining ◽  
Early Warning ◽  
Financial Management ◽  
Financial Risk ◽  
Warning System ◽  
Colleges And Universities ◽  
Mining Technology ◽  
Id3 Algorithm ◽  
Financial Management System ◽  
Analysis System

In this thesis, in order to solve the student arrearage problems in colleges and universities, risk weight factor is introduced to improve ID3 algorithm through the research on data mining technology and the combination with financial management system of colleges and universities so that ID3 decision-making tree algorithm can classify based on the risk weights of all the factors of the financial data; the early warning system scheme on the student arrearage problems in colleges and universities is designed so as to predict the high-risk defaulting students dynamically and accurately and lay scientific foundations for avoiding financial risk in colleges and universities.

scholarly journals Prototyping of Flooding Early Warning System using Internet of Things Technology and Social Media

2018 ◽  
Vol 197 ◽  
pp. 16003
Author(s):  
Aris Haris Rismayana ◽  
Castaka Agus Sugianto ◽  
Ida Bagus Budiyanto
Keyword(s):  
Social Media ◽  
Internet Of Things ◽  
Water Level ◽  
Water Distribution ◽  
Warning System ◽  
Integrated System ◽  
Water Levels ◽  
Prototype System ◽  
The Status ◽  
Internet Of Things Technology

When the rainy season arrives, flooding is a common phenomenon. Almost every street, housing, village, river, even in the city center, wherever floods can occur. One effort to prevent the flooding is to create a floodgate on reservoirs or dams that are used to control the water distribution. The water level at this dam must be checked frequently to anticipate if the water level is at a dangerous level. The inspection of water levels will be very difficult if it must be conducted by humans who must be available in the field at any time. This research aims to create a prototype system that can replace the human role in monitoring the dam water level condition at any time by developing an integrated system between hardware and software using IoT (Internet of Things) technology approach and social media (twitter and telegram). The developed system consists of the height sensor (distance), microcontroller and wifi module, which is placed on the water gate. This system serves to measure the water level at any time and send data in real time to the server. The results of system testing performed shows that when the system is in normal circumstances, the system sends data to the server every minute, and updates the status of water level in twitter every 5 minutes. In case the water level has exceeded a predetermined limit, the system sends data to the server every 5 seconds and passes the warning message to all registered telegram contacts.

scholarly journals Sistem Peringatan Dini Banjir Secara Real-Time Berbasis Web Menggunakan Arduino dan Ethernet

2017 ◽  
Vol 1 (1) ◽  
pp. 1 ◽  
Author(s):  
Dedi Satria ◽  
Syaifuddin Yana ◽  
Rizal Munadi ◽  
Saumi Syahreza
Keyword(s):  
Information System ◽  
Internet Of Things ◽  
Real Time ◽  
Early Warning ◽  
Major Influence ◽  
Google Maps ◽  
Flood Monitoring ◽  
Arduino Uno ◽  
Internet Of Things Technology ◽  
Monitoring Information

a b s t r a c tThe development of flood early warning technology has grown rapidly. The technology has led to an increase in technology in terms of communication and information. Internet of Things technology (IoTs) has provided a major influence on the development of early warning information system. In this article a protipe-based flood monitoring information system of Google Maps have been designed by integrating Ultrasonic sensors as the height of the detector, the Arduino Uno as a processor, U-Blox GPS modules Neo 6 m GSM module and as the sender of data is the height of the water and the coordinates to the station of the system informais flood. The design of the prototype produces information flood elevations along with location based Google Maps interface.Keywords:Flood, Arduino, Internet of Things Technology (IoTs), Ethernet a b s t r a kPengembangan teknologi peringatan dini banjir telah tumbuh dengan cepat. Teknologi tersebut telah mengarah kepada peningkatan di segi teknologi komunikasi dan informasi. Teknologi Internet of Things (IoTs) telah memberikan pengaruh besar terhadap perkembangan sistem informasi peringatan dini. Didalam artikel ini sebuah protipe sistem informasi monitoring banjir berbasis Google Maps telah dirancang dengan mengintegrasikan sensor ultrasonik sebagai pendeteksi ketinggian, Arduino Uno sebagai pemroses, modul GPS U-Blox Neo 6m dan modul GSM sebagai pengirim data ketinggian air dan koordinat ke stasion sistem informais banjir. Perancangan prototipe menghasilkan informasi ketinggian banjir beserta lokasinya berbasis antarmuka Google Maps.Kata Kunci: Banjir, Arduino, Internet of Things Technology (IoTs), Ethernet

scholarly journals Establishment of a Financial Crisis Early Warning System for Domestic Listed Companies Based on Three Decision Tree Models

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Gang Wang ◽  
Keming Wang ◽  
Yingying Zhou ◽  
Xiaoyan Mo ◽  
Weilin Xiao
Keyword(s):  
Financial Crisis ◽  
Decision Tree ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Comprehensive Evaluation ◽  
Warning System ◽  
Predictive Ability ◽  
Tree Models ◽  
The Impact

The financial crisis is a realistic problem that the general enterprise must encounter in the process of financial management. Due to the impact of the COVID-19 and the Sino-US trade war, domestic companies with unsound financial conditions are at risk of shutdowns and bankruptcies. Therefore, it is urgently needed to study the financial warning of enterprises. In this study, three decision tree models are used to establish the financial crisis early warning system. These three decision tree models include C50, CART, and random forest decision trees. In addition, the ROC curve was used for comprehensive evaluation of the accuracy analysis of the model to confirm the predictive ability of each model. This result can provide reference for domestic financial departments and provide financial management basis for the investing public.

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