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.

A combined effective time series model based on clustering and whale optimization algorithm for forecasting smart meters electricity consumption

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Medhat Abd el Azem El Sayed Rostum ◽  
Hassan Mohamed Mahmoud Moustafa ◽  
Ibrahim El Sayed Ziedan ◽  
Amr Ahmed Zamel
Keyword(s):  
Optimization Algorithm ◽  
Prediction Accuracy ◽  
Electricity Consumption ◽  
Whale Optimization Algorithm ◽  
Computational Time ◽  
Smart Meters ◽  
Content Type ◽  
Individual Level ◽  
Consumption Values ◽  
Whale Optimization

Purpose The current challenge for forecasting smart meters electricity consumption lies in the uncertainty and volatility of load profiles. Moreover, forecasting the electricity consumption for all the meters requires an enormous amount of time. Most papers tend to avoid such complexity by forecasting the electricity consumption at an aggregated level. This paper aims to forecast the electricity consumption for all smart meters at an individual level. This paper, for the first time, takes into account the computational time for training and forecasting the electricity consumption of all the meters. Design/methodology/approach A novel hybrid autoregressive-statistical equations idea model with the help of clustering and whale optimization algorithm (ARSEI-WOA) is proposed in this paper to forecast the electricity consumption of all the meters with best performance in terms of computational time and prediction accuracy. Findings The proposed model was tested using realistic Irish smart meters energy data and its performance was compared with nine regression methods including: autoregressive integrated moving average, partial least squares regression, conditional inference tree, M5 rule-based model, k-nearest neighbor, multilayer perceptron, RandomForest, RPART and support vector regression. Results have proved that ARSEI-WOA is an efficient model that is able to achieve an accurate prediction with low computational time. Originality/value This paper presents a new hybrid ARSEI model to perform smart meters load forecasting at an individual level instead of an aggregated one. With the help of clustering technique, similar meters are grouped into a few clusters from which reduce the computational time of the training and forecasting process. In addition, WOA improves the prediction accuracy of each meter by finding an optimal factor between the average electricity consumption values of each cluster and the electricity consumption values for each one of its meters.

Robust optimization of resource-constrained assembly line balancing problems with uncertain operation times

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wenrui Jin ◽  
Zhaoxu He ◽  
Qiong Wu
Keyword(s):  
Optimization Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Whale Optimization Algorithm ◽  
Line Balancing ◽  
Resource Constrained ◽  
Content Type ◽  
Whale Optimization ◽  
Minmax Regret ◽  
Assembly Line Balancing Problem

PurposeDue to the market trend of low-volume and high-variety, the manufacturing industry is paying close attention to improve the ability to hedge against variability. Therefore, in this paper the assembly line with limited resources is balanced in a robust way that has good performance under all possible scenarios. The proposed model allows decision makers to minimize a posteriori regret of the selected choice and hedge against the high cost caused by variability.Design/methodology/approachA generalized resource-constrained assembly line balancing problem (GRCALBP) with an interval data of task times is modeled and the objective is to find an assignment of tasks and resources to the workstations such that the maximum regret among all the possible scenarios is minimized. To properly solve the problem, the regret evaluation, an exact solution method and an enhanced meta-heuristic algorithm, Whale Optimization Algorithm, are proposed and analyzed. A problem-specific coding scheme and search mechanisms are incorporated.FindingsTheory analysis and computational experiments are conducted to evaluated the proposed methods and their superiority. Satisfactory results show that the constraint generation technique-based exact method can efficiently solve instances of moderate size to optimality, and the performance of WOA is enhanced due to the modified searching strategy.Originality/valueFor the first time a minmax regret model is considered in a resource-constrained assembly line balancing problem. The traditional Whale Optimization Algorithm is modified to overcome the inferior capability and applied in discrete and constrained assembly line balancing problems.

Tracy–Singh Product and Genetic Whale Optimization Algorithm for Retrievable Data Perturbation for Privacy Preserved Data Publishing in Cloud Computing

2019 ◽  
Vol 63 (2) ◽  
pp. 239-253
Author(s):  
Thanga Revathi S ◽  
N Ramaraj ◽  
S Chithra
Keyword(s):  
Cloud Computing ◽  
Optimization Algorithm ◽  
Performance Metrics ◽  
Whale Optimization Algorithm ◽  
Data Publishing ◽  
Secret Key ◽  
Data Perturbation ◽  
Data Set ◽  
Proposed Model ◽  
Whale Optimization

Abstract This paper proposes a retrievable data perturbation model for overcoming the challenges in cloud computing. Initially, genetic whale optimization algorithm (genetic WOA) is developed by integrating genetic algorithm (GA) and WOA for generating the optimized secret key. Then, the input data and the optimized secret key are given to the Tracy–Singh product-based model for transforming the original database into perturbed database. Finally, the perturbed database can be retrieved by the client, if and only if the client knows the secret key. The performance of the proposed model is analyzed using three databases, namely, chess, T10I4D100K and retail databases from the FIMI data set based on the performance metrics, privacy and utility. Also, the proposed model is compared with the existing methods, such as Retrievable General Additive Data Perturbation, GA and WOA, for the key values 128 and 256. For the key value 128, the proposed model has the better privacy and utility of 0.18 and 0.83 while using the chess database. For the key value 256, the proposed model has the better privacy and utility of 0.18 and 0.85, using retail database. From the analysis, it can be shown that the proposed model has better privacy and utility values than the existing models.

scholarly journals Research on Alarm Reduction of Intrusion Detection System Based on Clustering and Whale Optimization Algorithm

2021 ◽  
Vol 11 (23) ◽  
pp. 11200
Author(s):  
Leiting Wang ◽  
Lize Gu ◽  
Yifan Tang
Keyword(s):  
Intrusion Detection ◽  
Hierarchical Clustering ◽  
Optimization Algorithm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Whale Optimization Algorithm ◽  
Cluster Center ◽  
Data Set ◽  
Network Intrusion ◽  
Whale Optimization

With the frequent occurrence of network security events, the intrusion detection system will generate alarm and log records when monitoring the network environment in which a large number of log and alarm records are redundant, which brings great burden to the server storage and security personnel. How to reduce the redundant alarm records in network intrusion detection has always been the focus of researchers. In this paper, we propose a method using the whale optimization algorithm to deal with massive redundant alarms. Based on the alarm hierarchical clustering, we integrate the whale optimization algorithm into the process of generating alarm hierarchical clustering and optimizing the cluster center and put forward two versions of local hierarchical clustering and global hierarchical clustering, respectively. To verify the feasibility of the algorithm, we conducted experiments on the UNSW-NB15 data set; compared with the previous alarm clustering algorithms, the alarm clustering algorithm based on the whale optimization algorithm can generate higher quality clustering in a shorter time. The results show that the proposed algorithm can effectively reduce redundant alarms and reduce the load of IDS and staff.

Adaptive whale optimization for intelligent multi-constraints power quality improvement under deregulated environment

2019 ◽  
Vol 17 (3) ◽  
pp. 490-514
Author(s):  
Niharika Thakur ◽  
Y.K. Awasthi ◽  
Manisha Hooda ◽  
Anwar Shahzad Siddiqui
Keyword(s):  
Power Quality ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Quality Improvements ◽  
Content Type ◽  
Facts Devices ◽  
Whale Optimization ◽  
Quality Issues ◽  
Better Than ◽  
Bus System

Purpose Power quality issues highly affect the secure and economic operations of the power system. Although numerous methodologies are reported in the literature, flexible alternating current transmission system (FACTS) devices play a primary role. However, the FACTS devices require optimal location and sizing to perform the power quality enhancement effectively and in a cost efficient manner. This paper aims to attain the maximum power quality improvements in IEEE 30 and IEEE 57 test bus systems. Design/methodology/approach This paper contributes the adaptive whale optimization algorithm (AWOA) algorithm to solve the power quality issues under deregulated sector, which enhances available transfer capability, maintains voltage stability, minimizes loss and mitigates congestions. Findings Through the performance analysis, the convergence of the final fitness of AWOA algorithm is 5 per cent better than artificial bee colony (ABC), 3.79 per cent better than genetic algorithm (GA), 2,081 per cent better than particle swarm optimization (PSO) and fire fly (FF) and 2.56 per cent better than whale optimization algorithm (WOA) algorithms at 400 per cent load condition for IEEE 30 test bus system, and the fitness convergence of AWOA algorithm for IEEE 57 test bus system is 4.44, 4.86, 5.49, 7.52 and 9.66 per cent better than FF, ABC, WOA, PSO and GA, respectively. Originality/value This paper presents a technique for minimizing the power quality problems using AWOA algorithm. This is the first work to use WOA-based optimization for the power quality improvements.

A hybrid of WOA and mGWO algorithms for global optimization and analog circuit design automation

2019 ◽  
Vol 38 (1) ◽  
pp. 452-476 ◽  
Author(s):  
M.A. Mushahhid Majeed ◽  
Sreehari Rao Patri
Keyword(s):  
Circuit Design ◽  
Optimization Algorithm ◽  
Operational Amplifier ◽  
Analog Circuit ◽  
Whale Optimization Algorithm ◽  
Gray Wolf ◽  
Analog Circuit Design ◽  
Content Type ◽  
Aspect Ratios ◽  
Whale Optimization

PurposeThis paper aims to resolve the sizing issues of analog circuit design by using proposed metaheuristic optimization algorithm.Design/methodology/approachThe hybridization of whale optimization algorithm and modified gray wolf optimization (WOA-mGWO) algorithm is proposed, and the same is applied for the automated design of analog circuits.FindingsThe proposed hybrid WOA-mGWO algorithm demonstrates better performance in terms of convergence rates and average fitness of the function after testing it with 23 classical benchmark functions. Moreover, a rigorous performance evaluation is done with 20 independent runs using Wilcoxon rank-sum test.Practical implicationsFor evaluating the performance of the proposed algorithm, a conventional two-stage operational amplifier is considered. The aspect ratios calculated by simulating the algorithm in MATLAB are later used to design the operational amplifier in Cadence environment using 180nm CMOS standard process.Originality/valueThe hybrid WOA-mGWO algorithm is tailored to improve the exploration ability of the algorithm by combining the abilities of two metaheristic algorithms, i.e. whale optimization algorithm and modified gray wolf optimization algorithm. To build further credence and to prove its profound existence in the latest state of the art, a statistical study is also conducted over 20 independent runs, for the robustness of the proposed algorithm, resulting in best, mean and worst solutions for analog IC sizing problem. A comparison of the best solution with other significant sizing tools proving the efficiency of hybrid WOA-mGWO algorithm is also provided. Montecarlo simulation and corner analysis are also performed to validate the endurance of the design.

Multi-level framework for anomaly detection in social networking

2020 ◽  
Vol 38 (2) ◽  
pp. 350-366 ◽  
Author(s):  
Aditya Khamparia ◽  
Sagar Pande ◽  
Deepak Gupta ◽  
Ashish Khanna ◽  
Arun Kumar Sangaiah
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Online Social Networks ◽  
Design Methodology ◽  
Data Sets ◽  
Research Model ◽  
Multilevel System ◽  
Data Set ◽  
Content Type ◽  
Multi Level

Purpose The purpose of this paper is to propose a structured multilevel system that will distinguish the anomalies present in different online social networks (OSN). Design/methodology/approach Author first reviewed the related work, and then, the research model designed was explained. Furthermore, the details regarding Levels 1 and 2 were narrated. Findings By using the proposed technique, FScore obtained for Twitter and Facebook data set was 96.22 and 94.63, respectively. Research limitations/implications Four data sets were used for the experiment and the acquired outcomes demonstrate enhancement over the current existing frameworks. Originality/value This paper designed a multilevel framework that can be used to detect the anomalies present in the OSN.

Task scheduling on cloud computing based on sea lion optimization algorithm

2021 ◽  
Vol 17 (2) ◽  
pp. 99-116
Author(s):  
Raja Masadeh ◽  
Nesreen Alsharman ◽  
Ahmad Sharieh ◽  
Basel A. Mahafzah ◽  
Arafat Abdulrahman
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Sea Lion ◽  
Hunting Behavior ◽  
Content Type ◽  
Combinatorial Optimization Problems ◽  
Whale Optimization

Purpose Sea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions. Design/methodology/approach Cloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance. Findings The performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively. Originality/value To the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

Sentiment analysis on newspaper article reviews: contribution towards improved rider optimization-based hybrid classifier

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pandiaraj A. ◽  
Sundar C. ◽  
Pavalarajan S.
Keyword(s):  
Feature Extraction ◽  
Sentiment Analysis ◽  
Optimization Algorithm ◽  
Support Vector ◽  
Analysis Model ◽  
Data Set ◽  
Hybrid Classifier ◽  
Content Type ◽  
Whale Optimization ◽  
Newspaper Articles

Purpose Up to date development in sentiment analysis has resulted in a symbolic growth in the volume of study, especially on more subjective text types, namely, product or movie reviews. The key difference between these texts with news articles is that their target is defined and unique across the text. Hence, the reviews on newspaper articles can deal with three subtasks: correctly spotting the target, splitting the good and bad content from the reviews on the concerned target and evaluating different opinions provided in a detailed manner. On defining these tasks, this paper aims to implement a new sentiment analysis model for article reviews from the newspaper. Design/methodology/approach Here, tweets from various newspaper articles are taken and the sentiment analysis process is done with pre-processing, semantic word extraction, feature extraction and classification. Initially, the pre-processing phase is performed, in which different steps such as stop word removal, stemming, blank space removal are carried out and it results in producing the keywords that speak about positive, negative or neutral. Further, semantic words (similar) are extracted from the available dictionary by matching the keywords. Next, the feature extraction is done for the extracted keywords and semantic words using holoentropy to attain information statistics, which results in the attainment of maximum related information. Here, two categories of holoentropy features are extracted: joint holoentropy and cross holoentropy. These extracted features of entire keywords are finally subjected to a hybrid classifier, which merges the beneficial concepts of neural network (NN), and deep belief network (DBN). For improving the performance of sentiment classification, modification is done by inducing the idea of a modified rider optimization algorithm (ROA), so-called new steering updated ROA (NSU-ROA) into NN and DBN for weight update. Hence, the average of both improved classifiers will provide the classified sentiment as positive, negative or neutral from the reviews of newspaper articles effectively. Findings Three data sets were considered for experimentation. The results have shown that the developed NSU-ROA + DBN + NN attained high accuracy, which was 2.6% superior to particle swarm optimization, 3% superior to FireFly, 3.8% superior to grey wolf optimization, 5.5% superior to whale optimization algorithm and 3.2% superior to ROA-based DBN + NN from data set 1. The classification analysis has shown that the accuracy of the proposed NSU − DBN + NN was 3.4% enhanced than DBN + NN, 25% enhanced than DBN and 28.5% enhanced than NN and 32.3% enhanced than support vector machine from data set 2. Thus, the effective performance of the proposed NSU − ROA + DBN + NN on sentiment analysis of newspaper articles has been proved. Originality/value This paper adopts the latest optimization algorithm called the NSU-ROA to effectively recognize the sentiments of the newspapers with NN and DBN. This is the first work that uses NSU-ROA-based optimization for accurate identification of sentiments from newspaper articles.

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