Identifying buzz in social media: a hybrid approach using artificial bee colony and k-nearest neighbors for outlier detection

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Reema Aswani ◽  
S. P. Ghrera ◽  
Arpan Kumar Kar ◽  
Satish Chandra
Keyword(s):  
Social Media ◽  
Outlier Detection ◽  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Bee Colony

scholarly journals Prognostic of RUL based on Echo State Network Optimized by Artificial Bee Colony

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Edgar J. Amaya ◽  
Alberto J. Alvares
Keyword(s):  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Remaining Useful Life ◽  
Data Repository ◽  
Echo State Network ◽  
Health State ◽  
Future Health ◽  
Large Space ◽  
Abc Algorithm ◽  
Bee Colony

Prognostic is an engineering technique used to predict the future health state or behavior of an equipment or system. In this work, a data-driven hybrid approach for prognostic is presented. The approach based on Echo State Network (ESN) and Artificial Bee Colony (ABC) algorithm is used to predict machine’s Remaining Useful Life (RUL). ESN is a new paradigm that establishes a large space dynamic reservoir to replace the hidden layer of Recurrent Neural Network (RNN). Through the application of ESN is possible to overcome the shortcomings of complicated computing and difficulties in determining the network topology of traditional RNN. This approach describes the ABC algorithm as a tool to set the ESN with optimal parameters. Historical data collected from sensors are used to train and test the proposed hybrid approach in order to estimate the RUL. To evaluate the proposed approach, a case study was carried out using turbofan engine signals show that the proposed method can achieve a good collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). The experimental results using the engine data from NASA Ames Prognostics Data Repository RUL estimation precision. The performance of this model was compared using prognostic metrics with the approaches that use the same dataset. Therefore, the ESN-ABC approach is very promising in the field of prognostics of the RUL.

Artificial Bee Colony-Based Influence Maximization Approach for Social Coding Portal GitHub

2019 ◽  
Vol 10 (2) ◽  
pp. 34-47
Author(s):  
Anuja Arora ◽  
Riyu Bana ◽  
Habib Shah ◽  
Divakar Yadav
Keyword(s):  
Social Media ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Influence Maximization ◽  
Social Community ◽  
Social Bees ◽  
Bee Colony ◽  
Egocentric Network ◽  
Post Marketing ◽  
Community Connectivity

Influence maximization is the main source of virality of any social media post/marketing activity. In recent trends, influence maximization has moved towards analytic approach instead of just being a suggestive metaphor for various social media paradigm. In this article, ego-centric approach and a bio-inspired algorithm is applied on social coding community, Github, for influence maximization. First, developers' and projects' egocentric network-based studies are conducted to find out influential developer and project based on varying social media measures. Second, artificial bee colony (ABC) bio-inspired algorithm is used to select social bees (i.e., developers set and projects set in rapid convergence towards an optimal solution to achieve influence maximization). Algorithm result ensures the best solution in terms of social community connectivity path optimization in less run time while finding most influential social bees.

scholarly journals An artificial bee colony-based hybrid approach for waste collection problem with midway disposal pattern

2019 ◽  
Vol 76 ◽  
pp. 629-637 ◽  
Author(s):  
Qu Wei ◽  
Zhaoxia Guo ◽  
Hoong Chuin Lau ◽  
Zhenggang He
Keyword(s):  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Waste Collection ◽  
Bee Colony

K-NN Based Outlier Detection Technique on Intrusion Dataset

2017 ◽  
Vol 7 (1) ◽  
pp. 58-70 ◽  
Author(s):  
Santosh Kumar Sahu ◽  
Sanjay Kumar Jena ◽  
Manish Verma
Keyword(s):  
Outlier Detection ◽  
Time Complexity ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
The Other ◽  
Detection Technique ◽  
K Nearest Neighbors ◽  
Time Required ◽  
Local Outlier ◽  
Quick Sort

Outliers in the database are the objects that deviate from the rest of the dataset by some measure. The Nearest Neighbor Outlier Factor is considering to measure the degree of outlier-ness of the object in the dataset. Unlike the other methods like Local Outlier Factor, this approach shows the interest of a point from both neighbors and reverse neighbors, and after that, an object comes into consideration. We have observed that in GBBK algorithm that based on K-NN, used quick sort to find k nearest neighbors that take O (N log N) time. However, in proposed method, the time required for searching on K times which complete in O (KN) time to find k nearest neighbors (k < < log N). As a result, the proposed method improves the time complexity. The NSL-KDD and Fisher iris dataset is used, and experimental results compared with the GBBK method. The result is same in both the methods, but the proposed method takes less time for computation.

Sign in / Sign up

Export Citation Format

Share Document