A Secure and Efficient kNN Classification Algorithm Using Encrypted Index Search and Yao’s Garbled Circuit over Encrypted Databases

2018 ◽  
pp. 21-38
Author(s):  
Hyeong-Jin Kim ◽  
Jae-Hwan Shin ◽  
Jae-Woo Chang
Keyword(s):  
Classification Algorithm ◽  
Knn Classification ◽  
Garbled Circuit ◽  
Index Search

scholarly journals Influencing Factors and Forecasting Statistics of Enterprise Market Sales Based on Big Data and Intelligent IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhen Guo ◽  
Tao Zou
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Classification Algorithm ◽  
Processing Method ◽  
Enterprise Management ◽  
Training Process ◽  
Data Processing Method ◽  
Knn Classification ◽  
Positive Effect

With the acceleration of economic development, enterprise management is facing more severe challenges. Big data analysis based on the intelligent Internet of Things (IoT) has a positive effect on the development of enterprise management and can make up for the shortcomings of enterprise management. In this paper, we develop a big data processing method based on intelligent IoT which can mine the factors that affect the company’s market sales from the collected data. Then, we propose a KNN classification algorithm based on overlapping k -means clustering. This algorithm adds a training process to the traditional KNN algorithm, which can accurately classify data and greatly improve the efficiency of the classification algorithm. Numerical analysis results prove the effectiveness of the proposed algorithm.

Parallel recognition of illegal Web pages based on improved KNN classification algorithm

2013 ◽  
Vol 33 (12) ◽  
pp. 3368-3371 ◽  
Author(s):  
Yabin XU ◽  
Zhuo LI ◽  
Junyi CHEN
Keyword(s):  
Classification Algorithm ◽  
Web Pages ◽  
Knn Classification

scholarly journals A Counseling System of Multiple Intelligence Theory Combined With kNN Classification Algorithm

2021 ◽  
Vol 3 (2) ◽  
pp. 10-30
Author(s):  
Hiếu Lê Ngọc ◽  
Thanh Luong Van
Keyword(s):  
Multiple Intelligences ◽  
Career Path ◽  
Multiple Choice ◽  
Classification Algorithm ◽  
Multiple Intelligence ◽  
The Self ◽  
Multiple Choice Questions ◽  
Knn Classifier ◽  
Knn Classification ◽  
Theory Of Multiple Intelligences

Choosing the right career is always a big issue, an important concern for everyone. To have a job, which is suitable for you, firstly you must look at yourself, called the self, and you should be aware of what the self is then you can promote the strength of your own self and avoid your weakness. To help discover more about yourself, during researching and studying, we come up with the idea that we would propose a career counseling system based on Howard Gardner's theory. The system uses the theory of multiple intelligences (Abenti & Daradoumis, 2020) which is combined with the K-nearest neighbors (KNN) (Tang, Ying; Tang, Ying; Hare, Ryan; Wang, Fei-Yue;, 2020) algorithm to assist people and to give out a suitable suggestion about career path for them. We use the results of the eight intelligences retrieved from the KNN classification algorithm to give users the consulting for their career paths. This system is built with a dataset based on 56 multiple-choice questions. These include 48 multiple choice questions based on Howard Gardner's theory of multiple intelligences (Bravo, Leonardo Emiro Contreras; Molano, Jose Ignacio Rodriguez; Trujillo, Edwin Rivas, 2020), (businessballs, 2017) and 8 multiple choice questions which are the labels of the classifier. We divided the dataset into 8 subsets corresponding with 8 Intelligences defined by Howard Gardner with the collected dataset. In each subset, we build the KNN classifier model using KNN classification algorithm. This processing of 8 subsets come out with the results accuracy for the 8 Intelligences: linguistic intelligence (80.95%), logical-mathematical intelligence (82.14%), musical intelligence (96.43%), bodily-kinesthetic intelligence (82.14%), spatial-visual intelligence (82.14%), interpersonal intelligence (89.29%), intrapersonal intelligence (88.1%), existential intelligence (78.57%). With the outcome of 8 models, we have tested with 5 students and compared them to their actual intelligences. The comparison results tell us about the valuable potential in career path of the proposed counselling system, the advantages of this combination between Multiple Intelligence and KNN classifier.

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