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6. A SAR ATR Using a New Convolutional Neural Network Framework

Author

Kumar, Bibek, Ranjan, Ranjeet Kumar, Husain, Arshad, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Sisodia, Dilip Singh, editor, Garg, Lalit, editor, Pachori, Ram Bilas, editor, and Tanveer, M., editor

Published
2023
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7. Dress Pattern Classification Using ResNet Based Convolutional Neural Networks

Author

Singh, Manpinder, Dalmia, Saiba, Ranjan, Ranjeet Kumar, Singh, Amit, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Garg, Lalit, editor, Sisodia, Dilip Singh, editor, Kesswani, Nishtha, editor, Vella, Joseph G, editor, Brigui, Imene, editor, Xuereb, Peter, editor, Misra, Sanjay, editor, and Singh, Deepak, editor

Published
2023
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10. Image Encryption Using Chaotic Maps: A Survey

Author

Sharma, Madhu, Ranjan, Ranjeet Kumar, Bharti, Vishal, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kumar, Rajesh, editor, Ahn, Chang Wook, editor, Sharma, Tarun K., editor, Verma, Om Prakash, editor, and Agarwal, Anand, editor

Published
2022
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14. Botnet Detection Using Machine Learning Algorithms

Author

Joshi, Chirag, Bharti, Vishal, Ranjan, Ranjeet Kumar, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Dave, Mayank, editor, Garg, Ritu, editor, Dua, Mohit, editor, and Hussien, Jemal, editor

Published
2021
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16. Analysis of Feature Selection Methods for P2P Botnet Detection

Author

Joshi, Chirag, Bharti, Vishal, Ranjan, Ranjeet Kumar, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Mayank, editor, Gupta, P. K., editor, Tyagi, Vipin, editor, Flusser, Jan, editor, Ören, Tuncer, editor, and Valentino, Gianluca, editor

Published
2020
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18. Integration of Goal Hierarchies Built Using Multiple OLAP Query Sessions

Author

Parimala, N., Ranjan, Ranjeet Kumar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Nayak, Janmenjoy, editor, Abraham, Ajith, editor, Krishna, B. Murali, editor, Chandra Sekhar, G. T., editor, and Das, Asit Kumar, editor

Published
2019
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21. Identifying Dissimilar OLAP Query Session for Building Goal Hierarchy

Author

Parimala, N., Ranjan, Ranjeet Kumar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Pattnaik, Prasant Kumar, editor, Rautaray, Siddharth Swarup, editor, Das, Himansu, editor, and Nayak, Janmenjoy, editor

Published
2018
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32. Appendix I.pdf

Author

BIBEK KUMAR, Husain, Arshad, and Ranjan, Ranjeet Kumar

Subjects
GeneralLiterature_INTRODUCTORYANDSURVEY
Abstract

This is appendix part of the Multi objective enhanced fruit fly optimization algorithm.

Published
2021
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36. Credit Card Fraud Detection under Extreme Imbalanced Data: A Comparative Study of Data-level Algorithms.

Author

Singh, Amit, Ranjan, Ranjeet Kumar, and Tiwari, Abhishek

Subjects
*CREDIT card fraud, *FRAUD investigation, *ARTIFICIAL intelligence, *SMART cards, *ALGORITHMS, *RANDOM forest algorithms, *MACHINE learning
Abstract

Credit card fraud is one of the biggest cybercrimes faced by users. Intelligent machine learning based fraudulent transaction detection systems are very effective in real-world scenarios. However, while designing these systems, machine learning approaches suffer from the problem of imbalanced data, i.e. imbalanced class distribution. Therefore, balancing the dataset becomes an imperative sub-task. Investigation of state-of-the-art approaches reveals that there is a need for a systematic study of class imbalance handling strategies to design an intelligent and capable system to detect the fraudulent transaction. This work aims to provide a comparative study of different class imbalance handling methods. To compare the effectiveness and efficiency of different class imbalance approaches in conjunction with state-of-the-art classification approaches, we have performed an extensive experimental study. We compared these methods on many performance indicators such as Precision, Recall, K-fold Cross-validation, AUC-ROC curve and execution time. In this study, we found that the Oversampling followed by Undersampling methods performs well for ensemble classification models such as AdaBoost, XGBoost and Random Forest. [ABSTRACT FROM AUTHOR]

Published
2022
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