In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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A review on genetic algorithm: past, present, and future

The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

/pdf/a-novel-pca-firefly-based-xgboost-classification-model-for-47dnj3dnwq.pdf

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

https://cyberleninka.org/article/n/688210.pdf

A survey on rough set theory and its applications

Fuzzy set theory, rough set theory and soft set theory are all generic mathematical tools for dealing with uncertainties. There has been some progress concerning practical applications of these theories, especially, the use of these theories in decision making problems. In the present article, we review some decision making methods based on (fuzzy) soft sets, rough soft sets and soft rough sets. In particular, we provide several novel algorithms in decision making problems by combining these kinds of hybrid models. It may be served as a foundation for developing more complicated soft set models in decision making.

A survey of decision making methods based on certain hybrid soft set models

A huge repository of terabytes of data is generated each day from modern information systems and digital technolo-gies such as Internet of Things and cloud computing. Analysis of these massive data requires a lot of efforts at multiple levels to extract knowledge for decision making. Therefore, big data analysis is a current area of research and development. The basic objective of this paper is to explore the potential impact of big data challenges, open research issues, and various tools associated with it. As a result, this article provides a platform to explore big data at numerous stages. Additionally, it opens a new horizon for researchers to develop the solution, based on the challenges and open research issues.

/pdf/a-survey-on-big-data-analytics-challenges-open-research-1uqa26i8p7.pdf

A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools

In this modern era of computing, information technology revolution has brought drastic changes in the way data are collected for knowledge mining. The data thus collected are of value when it provides relevant knowledge pertaining to the interest of an organization. Therefore, the real challenge lies in converting high dimensional data into knowledge and to use it for the development of the organization. The data that is collected are generally released on internet for research purposes after hiding sensitive information in it and therefore, privacy preservation becomes the key factor for any organization to safeguard the internal data and also the sensitive information. Much research has been carried out in this regard and one of the earliest is the removal of identifiers. However, the chances of re-identification are very high. Techniques like k-anonymity and l-diversity helps in making the records unidentifiable in their own way, but, these techniques are not fully shielded against attacks. In order to overcome the drawbacks of these techniques, we have proposed improved versions of anonymization algorithms. The result analysis show that the proposed algorithms are better when compared to existing algorithms. Index Terms—K-anonymity, l-diversity, Data Publication, Anonymization, Privacy preservation, Generalization, Suppression, Datafly algorithm.

/pdf/improved-anonymization-algorithms-for-hiding-sensitive-3v6up7y1eq.pdf

Improved Anonymization Algorithms for Hiding Sensitive Information in Hybrid Information System

Internet made a big revolution in the real world and thus poses so many challenges to the researchers by generating an enormous amount of data. The data generated contains an enormous amount of unwanted information. Before processing with such a dataset, the important features present in the dataset must be retrieved. The feature selection process is important because the performance of a model built for the purpose of classification, prediction or clustering depends mainly on the number of relevant features present in the dataset. In this proposed work, the real coded genetic algorithm is used to find the important features by considering the fuzzy rough degree of dependency as its fitness function for finding out optimum features for agricultural dataset, iris dataset and Pima Indian diabetes dataset. The experimental results show that the proposed work produces relevant features by maintaining classification accuracy.

A Comparative Study of Genetic Algorithm and Neural Network Computing Techniques over Feature Selection

In recent days, due to the advancement in technology there are increasing numbers of threats to multimedia data which are floating around in the Internet especially in the form of image data. Many methods exist to provide security for digital images but transform domain based digital watermarking could be considered as a promising method. Many transformation techniques are used to insert watermark in cover data, but this chapter deals with watermarking approaches in Hadamard transform domain. In traditional watermarking approaches the scaling parameter is empirically considered for inserting watermark but to maintain the quality of underlying cover images it needs to be calculated based on the content of the cover images. In order to make the watermarking algorithm completely automated the embedding and scaling parameters are calculated using the content of cover images. Many methods are existing for calculating scaling parameter adaptively but this chapter discusses various approaches using computational intelligence to arrive at optimum value of scaling and embedding parameters.

Improving the Security of Digital Images in Hadamard Transform Domain Using Digital Watermarking

Rough Set on Two Universal Sets Based on Multigranulation

Manufacturing of goods rely on its design methodology and the process parameters. The parameters used in manufacturing process play an important role to build a quality product. Initially heuristic techniques are used for parameter selection. Many researchers conducted research to predict the radial overcut using neural networks. Besides, fuzzy neural network gains more popularity due to presence of fuzzy system and neural network. In this paper fuzzy graph recurrent neural network architecture is used for modelling and predicting the radial over cut for an electro discharge machining information system.

D. P. Acharjya

Papers

Improved Anonymization Algorithms for Hiding Sensitive Information in Hybrid Information System

A Comparative Study of Genetic Algorithm and Neural Network Computing Techniques over Feature Selection

Improving the Security of Digital Images in Hadamard Transform Domain Using Digital Watermarking

Rough Set on Two Universal Sets Based on Multigranulation

A Fuzzy Graph Recurrent Neural Network Approach for the Prediction of Radial Overcut in Electro Discharge Machining