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                <text>Firefly Algorithm andDeep Neural Network Approach forIntrusion Detection</text>
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                <text>Firefly algorithm; Hybridization; Intrusion detection; Machine learning; Optimization; Swarm intelligence</text>
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                <text>Metaheuristic optimization has grown in popularity as a way for solving complex issues that are difficult to solve using traditional methods. With fast growth of the available storage space and processing capabilities of the modern computers, the machine learning domain, that can be succinctly formulated as the process of enabling the computers to make successful forecasts based on the previous experiences, has recently been under spectacular growth. This paper presents intrusion detection approach by utilizing hybrid method between firefly algorithm and deep neural network. The basic firefly algorithm, as a frequently employed swarm intelligence method, has several known deficiencies, and to overcome them, an enhanced firefly algorithm was proposed and used in this manuscript. For experimental purposes, KDD Cup 99 and NSL-KDD datasets from Kaggle and UCL repositories were taken and comparison with other frameworks that have been validated for the same datasets was executed. Based on simulation data, proposed method was able to establish better values for accuracy, precision, recall, F-score, sensitivity and specificity metrics than other approaches.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Zivkovic M.; Bacanin N.; Arandjelovic J.; Strumberger I.; Venkatachalam K.</text>
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                <text>Lecture Notes in Electrical Engineering, Vol-925, pp. 1-12.</text>
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                <text>ISSN: 18761100; ISBN: 978-981194830-5</text>
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                <text>Zivkovic M., Singidunum University, Danijelova 32, Belgrade, 11000, Serbia; Bacanin N., Singidunum University, Danijelova 32, Belgrade, 11000, Serbia; Arandjelovic J., Singidunum University, Danijelova 32, Belgrade, 11000, Serbia; Strumberger I., Singidunum University, Danijelova 32, Belgrade, 11000, Serbia; Venkatachalam K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>Chaotic Binary Ant Lion Optimizer Approach for Feature Selection on Medical Datasets with COVID-19 Case Study</text>
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                <text>ant lion optimizer; chaotic initialization; feature selection; optimization; swarm intelligence</text>
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                <text>Binary version of the ant lion optimizer (ALO) are suggested and utilized in wrapper-mode to pick the best feature subset for classification. ALO is a recently developed bio-inspired optimization approach that mimics ant lion hunting behavior. Furthermore, ALO balances exploration and exploitation utilizing a unique operator to explore the space of solutions adaptively for the best solution. The difficulties of a plethora of noisy, irrelevant, and misleading features, as well as the capacity to deal with incorrect and inconsistent data in real-world subjects, provide rationale for feature selection to become one of the most important requirements. A difficult machine learning problem is to choose a subset of important characteristics from a vast number of features that characterize a dataset. Choosing the most informative markers and conducting a high-accuracy classification across the data may be a difficult process, especially if the data is complex. The feature selection task is usually expressed as a bio-objective optimization challenge, with the goal of enhancing the performance of the prediction model (data training fitting quality) while decreasing the number of features used. Various evaluation criteria are employed to determine the success of the suggested approach. The findings show that the suggested chaotic binary algorithm can explore the feature space for optimum feature set efficiently.   2022 IEEE.</text>
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                <text>Zivkovic M.; Bacanin N.; Rakic A.; Arandjelovic J.; Stanojlovic S.; Venkatachalam K.</text>
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                <text>Proceedings - International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022, pp. 581-588.</text>
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                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICAISS55157.2022.10011032" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICAISS55157.2022.10011032&lt;/a&gt;
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                <text>Zivkovic M., Singidunum University, Faculty of Informatics and Computing, Belgrade, Serbia; Bacanin N., Singidunum University, Faculty of Informatics and Computing, Belgrade, Serbia; Rakic A., Singidunum University, Faculty of Informatics and Computing, Belgrade, Serbia; Arandjelovic J., Singidunum University, Faculty of Informatics and Computing, Belgrade, Serbia; Stanojlovic S., Singidunum University, Faculty of Informatics and Computing, Belgrade, Serbia; Venkatachalam K., Faculty of Computer Science and Engineering, CHRIST(Deemed to Be University), Bangladore, India</text>
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                <text>Support Vector Machine Performance Improvements by Using Sine Cosine Algorithm</text>
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                <text>Machine learning; Parameter optimization; Radiusmargin error; Sine cosine algorithm; Support vector machine (SVM)</text>
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                <text>The optimization of parameters has a crucial influence on the solution efficacy and the accuracy of the support vector machine (SVM) in the machine learning domain. Some of the typical approaches for determining the parameters of the SVM consider the grid search approach (GS) and some of the representative swarm intelligence metaheuristics. On the other side, most of those SVM implementations take into the consideration only the margin, while ignoring the radius. In this paper, a novel radiusmargin SVM approach is implemented that incorporates the enhanced sine cosine algorithm (eSCA). The proposed eSCA-SVM method takes into the account both maximizing the margin and minimizing the radius. The eSCA has been used to optimize the penalty and RBF parameter in SVM. The proposed eSCA-SVM method has been evaluated against four binary UCI datasets and compared to seven other algorithms. The experimental results suggest that the proposed eSCA-SVM approach has superior performances in terms of the average classification accuracy than other methods included in the comparative analysis.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Zivkovic M.; Vukobrat N.; Chhabra A.; Rashid T.A.; Venkatachalam K.; Bacanin N.</text>
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                <text>Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 791-803.</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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                <text>ISSN: 23674512</text>
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                <text>Zivkovic M., Singidunum University, Danijelova 32, Belgrade, 11000, Serbia; Vukobrat N., Singidunum University, Danijelova 32, Belgrade, 11000, Serbia; Chhabra A., Guru Nanak Dev University, Amritsar, India; Rashid T.A., Computer Science and Engineering Department, University of Kurdistan Hewler, KRG, Erbil, Iraq; Venkatachalam K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Bacanin N., Singidunum University, Danijelova 32, Belgrade, 11000, Serbia</text>
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