scholarly journals A Relevant Customer Identification Algorithm Based on the Internet Financial Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guo Yangyudongnanxin
Keyword(s):  
Social Relationship ◽  
Search Algorithm ◽  
Classification Model ◽  
The Internet ◽  
Identification Algorithm ◽  
Association Model ◽  
Intelligent Search ◽  
Average Sampling ◽  
Customer Classification ◽  
Customer Identification

In order to improve the intelligent search capabilities of Internet financial customers, this paper proposes a search algorithm for Internet financial data. The proposed algorithm calculates the customers corresponding to the two selected financial platforms based on the candidate customer set selected from the seed dataset and combined with the restored social relationship. Moreover, it also calculates the similarity of each field between the pairs. Furthermore, this article proposes an entity customer classification model based on logistic regression. Through the SNC model, threshold propagation, and random propagation, the model is transformed into an algorithm that identifies the associated customers, eliminates redundant customers, and realizes associated user identification. Experimental results verify that pruning increases the accuracy of identifying related customers by 8.44%. The average sampling accuracy of the entire customer association model is 79%, the lowest accuracy is 40%, and the highest is 1. From the sampling results, the overall recognition effect of the model reaches the expected goal.

P2P Traffic Identification Algorithm Based on Topology

2012 ◽  
Vol 487 ◽  
pp. 297-300
Author(s):  
Ru Xia Sun ◽  
Chun Yong Yin
Keyword(s):  
False Alarm ◽  
Detection Rate ◽  
The Internet ◽  
Identification Algorithm ◽  
Distributed Network ◽  
Network Behavior ◽  
High Detection Rate ◽  
Traffic Identification ◽  
Major Threat ◽  
High Detection

The botnet consists of some computers controlled by an attacker and has become a major threat to the internet and users. Because the p2p botnet is a distributed network, making the identification of p2p bots is very difficult. In response to this threat, we present a p2p identification algorithm based on topology. This method only depends on three network behavior features. Our approach has a high detection rate and an acceptable low false alarm rate.

scholarly journals CNN based Image Classification Model

2019 ◽  
Vol 8 (11S) ◽  
pp. 1106-1114
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Classification ◽  
Error Rates ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
The Internet ◽  
Network Algorithms ◽  
Media Companies ◽  
Automatic Tagging

Images are the fastest growing content, they contribute significantly to the amount of data generated on the internet every day. Image classification is a challenging problem that social media companies work on vigorously to enhance the user’s experience with the interface. The recent advances in the field of machine learning and computer vision enables personalized suggestions and automatic tagging of images. Convolutional neural network is a hot research topic these days in the field of machine learning. With the help of immensely dense labelled data available on the internet the networks can be trained to recognize the differentiating features among images under the same label. New neural network algorithms are developed frequently that outperform the state-of-art machine learning algorithms. Recent algorithms have managed to produce error rates as low as 3.1%. In this paper the architecture of important CNN algorithms that have gained attention are discussed, analyzed and compared and the concept of transfer learning is used to classify different breeds of dogs..

scholarly journals Internet search engines and ways to improve quality of inquiries fulfillment when searching for scientific information on the Internet

2016 ◽  
pp. 79-87
Author(s):  
Yury Smirnov
Keyword(s):  
Search Engines ◽  
Search Algorithm ◽  
Scientific Information ◽  
Information Organization ◽  
The Internet ◽  
Internet Search ◽  
Popular Method ◽  
Combined Use ◽  
Internet Information

Existing Internet search engines are analyzed. Tagging, with its advantages and drawbacks, is examined as a popular method of Internet information organization and classification. The author concludes that every search engine is unique for its search algorithm, and combined use of many is seen and the most efficient for users.

Multi-sensor multi-objective optimization deployment on complex terrain based on Pareto optimal theory

2019 ◽  
Vol 10 (04) ◽  
pp. 1950023
Author(s):  
Gongguo Xu ◽  
Xiusheng Duan ◽  
Ganlin Shan
Keyword(s):  
Complex Terrain ◽  
Search Algorithm ◽  
Optimization Theory ◽  
Convergence Time ◽  
Sensor Deployment ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Intelligent Search ◽  
Sensing Model

Multiple optimization objectives are often taken into account during the process of sensor deployment. Aiming at the problem of multi-sensor deployment in complex environment, a novel multi-sensor deployment method based on the multi-objective intelligent search algorithm is proposed. First, the complex terrain is modeled by the multi-attribute grid technology to reduce the computational complexity, and a truncation probability sensing model is presented. Two strategies, the local mutation operation and parameter adaptive operation, are introduced to improve the optimization ability of quantum particle swarm optimization (QPSO) algorithm, and then an improved multi-objective intelligent search algorithm based on QPSO is put forward to get the Pareto optimal front. Then, considering the multi-objective deployment requirements, a novel multi-sensor deployment method based on the multi-objective optimization theory is built. Simulation results show that the proposed method can effectively deal with the problem of multi-sensor deployment and provide more deployment schemes at once. Compared with the traditional algorithms, the Pareto optimal fronts achieved by the improved multi-objective search algorithm perform better on both convergence time and solution diversity aspects.

IoT with Cloud Based End to End Secured Disease Diagnosis Model using Light Weight Cryptography and Gradient Boosting Tree

2020 ◽  
Vol 13 ◽  
Author(s):  
K. Shankar
Keyword(s):  
Internet Of Things ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Gradient Boosting ◽  
The Internet ◽  
Pima Indians ◽  
Healthcare Applications ◽  
Diagnosis Model ◽  
Consistent Performance ◽  
The Internet Of Things

Background: With the evolution of the Internet of Things (IoT) technology and connected devices employed in the medicinal domain, the different characteristics of the online healthcare applications become advantageous. Aim: The objective of this paper is to present an IoT and cloud-based secured disease diagnosis model. At present, various e-healthcare applications with the use of the Internet of Things (IoT) offers diverse dimensions and services online. Method: In this paper, an efficient IoT and cloud-based secured classification model are proposed for disease diagnosis. It is used to avail efficient and secured services to the people globally over online healthcare applications. The presented model includes an effective gradient boosting tree (GBT) based data classification and lightweight cryptographic technique named rectangle. The presented GBT–R model offers a better diagnosis in a secure way. Results: It is validated using the Pima Indians diabetes data, and extensive simulation takes place to verify the consistent performance of the employed GBT-R model. Conclusion: The experimental outcome strongly suggested that the presented model shows maximum performance with an accuracy of 94.92.

Sign in / Sign up

Export Citation Format

Share Document