Launch of a new journal on applied mathematics: Editorial review on Vol. 1 No. 1

Ahmed Zeeshan

doi:10.59400/jam.v1i1.291

Launch of a new journal on applied mathematics: Editorial review on Vol. 1 No. 1

Ahmed Zeeshan Department of Maths & Stats, International Islamic University

Ariticle ID: 291

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DOI: https://doi.org/10.59400/jam.v1i1.291

Abstract

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References

[1] Chakraborty S, Singh P. Statistical analysis of a Tagore song based on Raga Kafi. Journal of Ap-pliedMath 2023; 1(1): 21–27. doi: 10.59400/jam.v1i1.85.

[2] Chakraborty S, Prasad A, Chakraborty A, Singh P. Impact of Hindustani ragas in stress manage-ment: A statistical study. Journal of AppliedMath 2023; 1(1): 48–54. doi: 10.59400/jam.v1i1.143.

[3] Huang BR, Sands T. Novel learning for control of nonlinear spacecraft dynamics. Journal of Ap-pliedMath 2023; 1(1): 1–20. doi: 10.59400/jam.v1i1.42.

[4] Ji P, Shi S. Machine learning in coal and gas out-burst prediction. Journal of AppliedMath 2023; 1(1): 28–29. doi: 10.59400/jam.v1i1.74.

[5] Shamaoon A, Ali Z, Maqbool Q. Conservation laws, exact solutions and nonlinear dispersion: A lie symmetry approach. Journal of AppliedMath 2023; 1(1): 55–63. doi: 10.59400/jam.v1i1.95.

[6] Bochkov AV. Towards a solution to the problem of safety management of structurally complex systems. Journal of AppliedMath 2023; 1(1): 30–44. doi: 10.59400/jam.v1i1.68.

Published

2023-07-05

How to Cite

Zeeshan, A. (2023). Launch of a new journal on applied mathematics: Editorial review on Vol. 1 No. 1. Journal of AppliedMath, 1(1), 291. https://doi.org/10.59400/jam.v1i1.291

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Issue

Vol. 1 No. 1 (2023)

Section

Editorial

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Editor-in-Chief

Assoc. Prof. Ahmad Zeeshan

International Islamic University, Pakistan

eISSN

2972-4805

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Bi-monthly

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Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

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Volume Arrangement

2024

2023

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Intrusion detection in information technology as well as operational technology networks is highly required in modern day systems due to the increased spate of cyber-attacks in both number and complexity. Anomaly-based intrusion detection systems which have the capacity to detect novel or zero-day attacks are highly employed in this regard. One important component of anomaly-based intrusion detection systems which ensures their behaviour is artificial intelligence in general and machine learning in particular. The burden in modern day cybersecurity research is to investigate and develop models that can outperform existing ones. This paper is aimed at developing a hybrid decision tree model using the stacking ensemble approach. Performances were measured on the basis of recall, precision, accuracy, F1-score, receiver operating characteristics and confusion matrices. The hybrid model presented a precision of 97%, accuracy of 81%, F1-score of 80% and AUC score of 0.96, respectively.

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