International Journal of Swarm Intelligence (9 papers in press)
A comprehensive review on recent intelligent metaheuristic algorithms
by S. Rajalakshmi, S. Kanmani
Abstract: Metaheuristics is an interesting research area with significant advances in solving problems with optimisation. Substantial advancements in metaheuristic are being made, and various new algorithms are being developed every day. The analyses in this area will undoubtedly helpful for future improvements. This papers main objective is to conduct a literature review of some recent algorithms motivated by nature to compare their features. This paper reviews some recently published nature inspired algorithms such as squirrel search algorithm (SSA), improved squirrel search algorithm (ISSA), grey wolf optimiser (GWO) algorithm, random walk grey wolf optimiser (RW_GWO) algorithm, sailfish optimiser (SAO) algorithm, sandpiper optimisation algorithm (SOA), search and rescue operations (SRO) algorithm, slime mould optimisation (SMO) algorithm, grasshopper optimisation algorithm (GOA) and opposition based learning grasshopper optimisation algorithm (OBLGOA). This paper focuses on a brief introduction of these algorithms and key concepts involved in formulation of swarm intelligence. Finally, this work outlines the directions for conducting effective future research.
Keywords: metaheuristics; optimisation; swarm intelligence; improved metaheuristics.
Special Issue on: ICEODS-2019 Recent Advances in Engineering Optimisation and Data Science for Sustainable Future Development
Optimisation of EDM process parameters by application of genetic algorithm
by Sumit Sharma, Love Kishore Sharma, Dheeraj Joshi, Mohammad Israr, Ritesh Mathur, Deepak Sharma
Abstract: The EDM efficiency can be strengthen by suitably defining the critical factors and their values for obtaining desired responses. In present research work, response surface methodology (RSM) with face centred cubic (FCC) approach is implemented for correlating the response, i.e., material removal rate (MRR) with control factors, i.e., voltage, peak current, pulse on time. These factors provide maximum MRR of 230.6 gm/min. To obtain the optimal solution, genetic algorithm (GA) is coupled with the obtained mathematical model. The dielectric fluid used is kerosene oil. ANOVA and F-test are used to check for model validation. R2 and adjusted R2 which confirms the validity of proposed model. The work piece material used in research work is AISI 4140 steel.
Keywords: response surface methodology; RSM; face centred cubic; FCC; material removal rate; MRR; genetic algorithm; ANOVA.
Special Issue on: PCCDS-2020 Building Intelligent Applications Using Machine Learning
The web ad-click fraud detection approach for supporting to the online advertising system
by Pankaj Kumar Keserwani, Mahesh Chandra Govil, Emmanuel Shubhakar Pilli
Abstract: With the introduction of e-commercial activities, revenue is generated in several legal and illegal ways. Marketing of a product or service in an online environment is increasing day by day since most users are getting involved in an online environment for buying or selling items. As a result, attackers are getting more space for performing online attacks by different malicious activities. The advertisement (ad) fraud, which is increasing exponentially with each passing day, is one of them. Ad fraud causes defame for the online advertising system and low return on the advertisers investment (ROI). The paper reveals a methodology for detecting web ad-click frauds so that the ROI of an advertiser can be increased. The developed algorithm to detect the ad-click frauds in the proposed methodology can detect the web ad-click frauds. The results of the ad-click fraud algorithm are verified with the help of popular machine learning (ML) algorithms k-nearest neighbour (k-NN), random forest (RF), decision tree (DT), logistic regression (LR) and support vector machine (SVM) and achieved good accuracies.
Keywords: ad-click fraud; machine learning; methodology; algorithm; Amazon Web Service; AWS.
Hybrid ARIMA-deep belief network model using PSO for stock price prediction
by Shaikh Sahil Ahmed, Mahesh Kankar, Nagaraj Naik, Biju R. Mohan
Abstract: Forecast analysis is in very high demand in many fields for improving sales and operation planning in various industries and enterprises. So, accuracy is a significant factor in forecasting stock market prices. We already know there are existing deep learning models for stock market prediction such as gated recurrent unit (GRU), support vector machine (SVM), multilayer perceptron (MLP), etc. This paper enhanced the prediction of stock prices using series hybrid models over single deep learning models. The models we used are autoregressive integrated moving average (ARIMA), deep belief network (DBN), long short-term memory (LSTM), and performed analysis on hybrid models in comparison with single models. We have chosen a model as ARIMA, LSTM, and hybrid as ARIMA-DBN and ARIMA-LSTM. For finding the best fit parameter for ARIMA and DBN, the particle swarm optimisation (PSO) technique is used. We compared the various models based on performance errors like MSE, RMSE, MAPE, etc. As already existing ARIMA and LSTM is not good enough for forecasting
and so we worked over the ARIMA-DBN model to overcome the limitations of other models. After research, we found out that series hybrid ARIMA-DBN is effectively better than other single models for stock market prediction.
Keywords: deep learning; time series forecasting; autoregressive integrated moving average; ARIMA; linear and nonlinear models; particle swarm optimisation; PSO.
A robust approach for digital watermarking of satellite imagery dataset
by Arshad Husain, Aditya Dev Mishra, Sisir Kumar Jena
Abstract: Digital watermarking is nowadays an active research area for protecting multimedia content such as authentication and copyright safeguard. The major challenges faced in most watermarking schemes are related to non-functional requirements such as security, reliability, robustness against attack. In the present study, a singular value decomposition (SVD) and principal component-based image watermark embed-ding, and corresponding watermark extraction method have been proposed. The pro-posed watermarking embedding method also used the concept of particle swarm optimisation (PSO) to obtain an optimum value of the watermark-scaling factor. The pro-posed method is an assessment with a satellite image dataset [Resourcesat-2 with linear imaging and self scanning (LISS-III) sensor] of the Sangam region, Prayagraj city, Uttar Pradesh, India (25 20'19.7"N, 81 58'43.8"E). Performance analysis parameter values such as peak signal-to-noise ratio (PSNR), mean square error (MSE), and normalised correlation (NC) are calculated to test the robustness and perceptibility of the proposed method. The experimental results show that the PSNR, MSE, and NC values are 44.6562, 2.2257, and 0.9650 respectively at the tested dataset. The proposed method performs robustly at various types of attacks and different wavelets.
Keywords: watermarking; multimedia; singular value decomposition; SVD; principal component-based image.
Abusive language detection using customised BERT
by Burre Chandu, Kaza Phani Rohitha, Nampally Nihal, K. Hima Bindu
Abstract: Freedom of expression and speech is widely misutilised in social media today. Unfortunately, the content shared on these platforms contains abusive content which is in multiple forms. This humongous amount of text requires automated detection of abusive language. This is a challenging problem because of a lot of noise, large vocabulary, context-dependency, and multilingualism. Deep learning (DL) models are being used due to the inefficiency of regular expressions, blacklists, and machine learning approaches. Hence, we have used neural language models for extracting high-quality features from the text. This paper demonstrates the usage of the natural language processing (NLP) model, bidirectional encoder representations from transformers commonly called BERT to perform various classification techniques. We have presented the results of fine-tuning the BERT pre-trained model for abusive language detection. The empirical analysis of offensive language identification and toxic comment classification datasets showed that these architectures achieved better results than existing models.
Keywords: abusive language detection; offensive language detection; hate speech; language models; transformers; attention; BERT; natural language processing; NLP; fine-tuning; attention; LSTM.
Unsupervised word translation for English-Hindi with different retrieval techniques
by Umesh Pant, Shweta Chauhan, Pankaj Pant, Philemon Daniel
Abstract: Word translation or incorporation of bilingual dictionaries is an important capability that impacts many multilingual language processing tasks. For translation from one language to another language, we either relied on parallel data or bilingual dictionaries. In this paper, we address this problem and generate best cross-lingual word embedding for English-Hindi language pair. Here, we neither use an aligned document or sentence aligned corpus nor any bilingual dictionary. We are following assumption of intra lingual similarity distribution that for the most frequent word the distribution graph is similar between Hindi and English corpus and embeddings are isometric. These cross-lingual words embedding can be used for unsupervised neural machine translation and cross-lingual transfer learning. Different retrieval techniques nearest neighbour, inverted nearest neighbours retrieval, inverted softmax, and cross-lingual word scaling are performed are performed and compared for the bi-lingual embedding of English-Hindi, which is trained for unsupervised and semi-supervised ways by passing seed dictionary. Bi-lingual word embedding is tested on generated English-Hindi dictionary.
Keywords: machine translation; MT; word embedding; cross-lingual word embedding; nearest neighbour; unsupervised learning.
Special Issue on: Application of Swarm Intelligence to Engineering Systems
A coupled multi-linear regression and genetic algorithm-based modelling and optimisation of surface roughness in machining of brass
by Suhail Ahmed Manroo, Suhail Ganiny
Abstract: In this paper, the authors present a multi-linear regression-based approach for the modelling of surface roughness during the turning of a commercial brass alloy. Three regression models are developed by utilising the experimental data gathered following a full-factorial-based design-of-experiments (DoEs) methodology. While the conventional practice has been to develop regression models using the entire experimental datasets, we deviate from the same and employ only a subset of the
available data for the purpose, the remaining data being used for the model validation. The results obtained herein reveal that the second order regression
model is statistically better than the other two in predicting the surface
roughness for both the datasets. The global minimum surface roughness is determined by using the developed regression models in conjunction with the genetic algorithm-based single objective optimisation. The regression models serve as candidate objective functions for the genetic algorithm. The optimisation results reveal that the global minimum obtained using the second order regression model is in close agreement (accuracy 94%) with the experimentally obtained minimum surface roughness and thus reaffirms the effectiveness of the second order regression model in predicting the surface roughness of brass during turning operation.
Keywords: surface roughness; modelling; multi-linear regression; optimisation; genetic algorithm.
MPPT optimisation techniques and power electronics for renewable energy systems: wind and solar energy systems
by Abrar Ahmed Chhipa, Shripati Vyas, Vinod Kumar, R.R. Joshi
Abstract: This study proposes the challenges faced by the present renewable energy scenario and the contribution of power electronics and maximum power point tracking (MPPT) optimisation techniques. India is one of the leading energy harvester country in the world. Power electronics keep power system operation stable, harvest electric power from renewable energy sources (RESs), and reduce energy consumption. This study has focused on power electronics and MPPT optimisation techniques for wind energy and photovoltaic energy and points out some aspects related to configuration for integration, energy storage technologies, reliability, and grid connection. Apart from this, modern optimisation techniques for MPPT control using artificial intelligence like fuzzy control and neural network control are also presented in this study.
Keywords: energy optimisation; optimisation techniques; renewable energy; wind energy; solar energy; energy storage; maximum power point tracking; MPPT; wind mppt; solar mppt; power electronics; artificial intelligence; fuzzy logic; neural network.