Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts

2018 ◽  
Vol 153 ◽  
pp. 91-104 ◽  
Author(s):  
Ala’ M. Al-Zoubi ◽  
Hossam Faris ◽  
Ja’far Alqatawna ◽  
Mohammad A. Hassonah
Keyword(s):  
Social Networks ◽  
Support Vector Machines ◽  
Optimization Algorithm ◽  
Online Social Networks ◽  
Whale Optimization Algorithm ◽  
Support Vector ◽  
Whale Optimization ◽  
Vector Machines

Hyperparameter tuning of AdaBoost algorithm for social spammer identification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Krithiga R. ◽  
Ilavarasan E.
Keyword(s):  
Social Networks ◽  
Optimization Algorithm ◽  
Online Social Networks ◽  
Whale Optimization Algorithm ◽  
Ensemble Classifiers ◽  
Classification Problems ◽  
Data Set ◽  
Content Type ◽  
Adaboost Algorithm ◽  
Whale Optimization

Purpose The purpose of this paper is to enhance the performance of spammer identification problem in online social networks. Hyperparameter tuning has been performed by researchers in the past to enhance the performance of classifiers. The AdaBoost algorithm belongs to a class of ensemble classifiers and is widely applied in binary classification problems. A single algorithm may not yield accurate results. However, an ensemble of classifiers built from multiple models has been successfully applied to solve many classification tasks. The search space to find an optimal set of parametric values is vast and so enumerating all possible combinations is not feasible. Hence, a hybrid modified whale optimization algorithm for spam profile detection (MWOA-SPD) model is proposed to find optimal values for these parameters. Design/methodology/approach In this work, the hyperparameters of AdaBoost are fine-tuned to find its application to identify spammers in social networks. AdaBoost algorithm linearly combines several weak classifiers to produce a stronger one. The proposed MWOA-SPD model hybridizes the whale optimization algorithm and salp swarm algorithm. Findings The technique is applied to a manually constructed Twitter data set. It is compared with the existing optimization and hyperparameter tuning methods. The results indicate that the proposed method outperforms the existing techniques in terms of accuracy and computational efficiency. Originality/value The proposed method reduces the server load by excluding complex features retaining only the lightweight features. It aids in identifying the spammers at an earlier stage thereby offering users a propitious environment.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

Whale optimization algorithm fused with SVM to detect stress in EEG signals

2021 ◽  
Vol 15 (1) ◽  
pp. 87-97
Author(s):  
Richa Gupta ◽  
M. Afshar Alam ◽  
Parul Agarwal
Keyword(s):  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Support Vector ◽  
Eeg Signals ◽  
Hunting Behavior ◽  
Positron Emission ◽  
Whale Optimization ◽  
Signal Activity ◽  
Work Done

Identifying stress and its level has always been a challenging area for researchers. A lot of work is going on around the world on the same. An attempt has been made by the authors in this paper as they present a methodology for detecting stress in EEG signals. Electroencephalogram (EEG) is commonly used to acquire brain signal activity. Though there exist other techniques to extract the same like Functional magnetic resonance imaging (fMRI), positron emission tomography (PET) we have used EEG as it is economical. We have used an open-source dataset for EEG data. Various images are used as the target stressor for collecting EEG signals. After feature selection and extraction, a support vector machine (SVM) with a whale optimization algorithm (WOA) in its kernel function for classification is used. WOA is a bio-inspired meta-heuristic algorithm, based on the hunting behavior of humpback whales. Using this method, we had obtained 91% accuracy for detecting the stress. The paper also compared the previous work done in detecting stress with the work proposed in this paper.

scholarly journals Applying supervised learning algorithms on information derived from Social Network to enhance recommender systems

2021 ◽  
Author(s):  
Hesaneh Behzadfar
Keyword(s):  
Social Networks ◽  
Support Vector Machines ◽  
Learning Algorithms ◽  
Public Information ◽  
Pairwise Comparison ◽  
Support Vector ◽  
The Social ◽  
Vector Machines ◽  
Average Travel Time ◽  
Supervised Learning Algorithms

The aim of this research is to show how social networks can be used for marketing purposes. This is implemented with the assistance of learning algorithms. The method proposed in this research is based on the analysis of “Support Vector Machines”, which facilitates analysis of all information gathered from the social websites. It differs from other methods currently being used by social networking websites, which do not take advantage of classification. By using public information from social networks, a dataset was formed. It comprised of a thousand users and seven features. The examined features were location, age, gender, occupation, relationship status, and average travel time/year. In this research, the dataset will be examined twice: first using a regular SVM; and next by using “Weighted Feature Support Vector Machines”. For the latter, to assign weights, a method called “Pairwise Comparison” will be used to rank the importance of features.

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