Journal of Defense Management
Open Access

ISSN: 2167-0374

+44 1478 350008

Journal of Defense Management : Citations & Metrics Report

Articles published in Journal of Defense Management have been cited by esteemed scholars and scientists all around the world. Journal of Defense Management has got h-index 6, which means every article in Journal of Defense Management has got 6 average citations.

Following are the list of articles that have cited the articles published in Journal of Defense Management.

	2022	2021	2020	2019	2018	2017	2016
Total published articles	33	33	4	5	9	14	20
Conference proceedings	0	0	0	0	0	0	0
Citations received as per Google Scholar, other indexing platforms and portals	85	62	78	37	39	21	6

Journal total citations count	373
Journal impact factor	2.3
Journal 5 years impact factor	3.6
Journal cite score	4.3
Journal h-index	6
Journal h-index since 2019	5

Important citations (315)

Rudd-orthner, r. n., & mihaylova, l. (2020). repeatable determinism using non-random weight initialisations in smart city applications of deep learning. journal of reliable intelligent environments, 6(1), 31-49. View at Publisher View at Google Scholar View at Indexing
Flórez zuluaga, j. a., patiño carrasco, e., ortega pabón, j. d., gallego león, k., & quintero montoya, o. l. (2020). a data fusion system for simulating critical scenarios and decision-making. ciencia e ingenieria neogranadina, 30(1), 89-106. View at Publisher View at Google Scholar View at Indexing
Zhou, k., wei, r., zhang, q., & xu, z. (2020). learning system for air combat decision inspired by cognitive mechanisms of the brain. ieee access, 8, 8129-8144. View at Publisher View at Google Scholar View at Indexing
Fu, q., fan, c. l., song, y., & guo, x. k. (2020). alpha c2–an intelligent air defense commander independent of human decision-making. ieee access, 8, 87504-87516. View at Publisher View at Google Scholar View at Indexing
Sathyan, a., cohen, k., & ma, o. (2020). comparison between genetic fuzzy methodology and q-learning for collaborative control design. arxiv preprint arxiv:2008.12678. View at Publisher View at Google Scholar View at Indexing
Israelsen, b., ahmed, n., center, k., green, r., & bennett jr, w. (2018). adaptive simulation-based training of artificial-intelligence decision makers using bayesian optimization. journal of aerospace information systems, 15(2), 38-56. View at Publisher View at Google Scholar View at Indexing
Bauckhage, c., ojeda, c., schücker, j., sifa, r., & wrobel, s. (2018). informed machine learning through functional composition. in lwda (pp. 33-37). View at Publisher View at Google Scholar View at Indexing
Zhang, h., & huang, c. (2020). maneuver decision-making of deep learning for ucav thorough azimuth angles. ieee access, 8, 12976-12987. View at Publisher View at Google Scholar View at Indexing
Xu, x., duan, l., & li, m. (2019). strategic learning approach for deploying uav-provided wireless services. ieee transactions on mobile computing, 20(3), 1230-1241. View at Publisher View at Google Scholar View at Indexing
Lee, m., valisetty, r., breuer, a., kirk, k., panneton, b., & brown, s. (2018). current and future applications of machine learning for the us army. us army research laboratory aberdeen proving ground united states. View at Publisher View at Google Scholar View at Indexing
Rudd-orthner, r. n., & mihaylova, l. (2019, june). non-random weight initialisation in deep learning networks for repeatable determinism. in 2019 10th international conference on dependable systems, services and technologies (dessert) (pp. 223-230). ieee. View at Publisher View at Google Scholar View at Indexing
Sathyan, a., ma, o., & cohen, k. (2018). intelligent approach for collaborative space robot systems. in 2018 aiaa space and astronautics forum and exposition (p. 5119). View at Publisher View at Google Scholar View at Indexing
Dong, y., ai, j., & liu, j. (2019). guidance and control for own aircraft in the autonomous air combat: a historical review and future prospects. proceedings of the institution of mechanical engineers, part g: journal of aerospace engineering, 233(16), 5943-5991. View at Publisher View at Google Scholar View at Indexing
Zhou, y., tang, y., & zhao, x. (2019). a novel uncertainty management approach for air combat situation assessment based on improved belief entropy. entropy, 21(5), 495. View at Publisher View at Google Scholar View at Indexing
Koteluk, o., wartecki, a., mazurek, s., ko?odziejczak, i., & mackiewicz, a. (2021). how do machines learn? artificial intelligence as a new era in medicine. journal of personalized medicine, 11(1), 32. View at Publisher View at Google Scholar View at Indexing
Ma, x., xia, l., & zhao, q. (2018, november). air-combat strategy using deep q-learning. in 2018 chinese automation congress (cac) (pp. 3952-3957). ieee. View at Publisher View at Google Scholar View at Indexing
Sathyan, a., & ma, o. (2019). collaborative control of multiple robots using genetic fuzzy systems. robotica, 37(11), 1922-1936. decentralized controlntelligent systems View at Publisher View at Google Scholar View at Indexing
Wang, y., huang, c., & tang, c. (2016). research on unmanned combat aerial vehicle robust maneuvering decision under incomplete target information. advances in mechanical engineering, 8(10), 1687814016674384. View at Publisher View at Google Scholar View at Indexing
Bartneck, c., lütge, c., wagner, a., & welsh, s. (2021). an introduction to ethics in robotics and ai (p. 117). springer nature. View at Publisher View at Google Scholar View at Indexing
Leuenberger, g., & wiering, m. a. (2018). actor-critic reinforcement learning with neural networks in continuous games. in icaart (2) (pp. 53-60). View at Publisher View at Google Scholar View at Indexing
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