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)

Brief, c. p. (2020). chinese perspectives on ai and future military capabilities. View at Publisher View at Google Scholar View at Indexing
Dunlap, k., cohen, k., & hobbs, k. (2021, june). comparing the explainability and performance of reinforcement learning and genetic fuzzy systems for safe satellite docking. in north american fuzzy information processing society annual conference (pp. 116-129). springer, cham. View at Publisher View at Google Scholar View at Indexing
Deng, l., wu, j., shi, j., xia, j., liu, y., & yu, x. (2020, november). research on intelligent decision technology for multi-uavs prevention and control. in 2020 chinese automation congress (cac) (pp. 5362-5367). ieee. View at Publisher View at Google Scholar View at Indexing
Hu, d., yang, r., zuo, j., zhang, z., wu, j., & wang, y. (2021). application of deep reinforcement learning in maneuver planning of beyond-visual-range air combat. ieee access, 9, 32282-32297. View at Publisher View at Google Scholar View at Indexing
Eltabey, m. m., mawgoud, a. a., & abu-talleb, a. (2020, october). the autonomy evolution in unmanned aerial vehicle: theory, challenges and techniques. in international conference on advanced intelligent systems and informatics (pp. 527-536). springer, cham. View at Publisher View at Google Scholar View at Indexing
Farr, c. (2019). malware analysis: the use of machine-based learning to detect malicious activity (doctoral dissertation, utica college). View at Publisher View at Google Scholar View at Indexing
Meng, g., zhou, m., zhang, h., & sun, d. (2019, october). threat assessment for rotte based on cooperative tactical recognition. in 2019 ieee international conferences on ubiquitous computing & communications (iucc) and data science and computational intelligence (dsci) and smart computing, networking and services (smartcns) (pp. 490-494). ieee. View at Publisher View at Google Scholar View at Indexing
Gu, m., guo, x., & zhang, x. (2020, november). robot confrontation based on genetic fuzzy system guided deep deterministic policy gradient algorithm. in 2020 chinese automation congress (cac) (pp. 538-544). ieee. View at Publisher View at Google Scholar View at Indexing
Chao, l., & jiafan, h. (2020, august). an air combat simulation system for intelligent decision-making. in 2020 12th international conference on intelligent human-machine systems and cybernetics (ihmsc) (vol. 2, pp. 104-108). ieee. View at Publisher View at Google Scholar View at Indexing
Wang, w., liu, h., & lin, w. (2021). adaptive multi-agent control strategy in heterogeneous countermeasure environments. international journal of multimedia data engineering and management (ijmdem), 12(2), 31-56. View at Publisher View at Google Scholar View at Indexing
Jin, x., wang, x., & yu, y. (2020, september). a knowledge-based express model of operational plan containing uncertainties. in proceedings of the 2020 the 2nd world symposium on software engineering (pp. 252-257). View at Publisher View at Google Scholar View at Indexing
Li, q., jiang, w., liu, c., & he, j. (2020, august). the constructing method of hierarchical decision-making model in air combat. in 2020 12th international conference on intelligent human-machine systems and cybernetics (ihmsc) (vol. 2, pp. 122-125). ieee. View at Publisher View at Google Scholar View at Indexing
Shi, m., dong, x., han, l., li, q., & ren, z. (2021, july). battlefield situation deduction and maneuver decision using deep q-learning. in 2021 40th chinese control conference (ccc) (pp. 3651-3656). ieee. View at Publisher View at Google Scholar View at Indexing
Sakenov, n., & tyler, b. j. (2019). survey of adaptive algorithms for intelligent agents. View at Publisher View at Google Scholar View at Indexing
Hajira tahir, chapter 3 - composite. View at Publisher View at Google Scholar View at Indexing
Soyluo?lu, b. (2021). modelling aircraft fighting maneuver dynamics using artificial intelligence algorithms. View at Publisher View at Google Scholar View at Indexing
Viaña perez, j., scott, d., kumar, m., & cohen, k. (2020, october). dynamic genetic algorithm for optimizing movement of a six-limb creature. in dynamic systems and control conference (vol. 84287, p. v002t36a005). american society of mechanical engineers. View at Publisher View at Google Scholar View at Indexing
Sathyan, a., ma, j., & cohen, k. (2021). decentralized cooperative driving automation: a reinforcement learning framework using genetic fuzzy systems. transportmetrica b: transport dynamics, 9(1), 775-797. View at Publisher View at Google Scholar View at Indexing
Lee, j. (2020). why do we need industrial ai?. in industrial ai (pp. 5-32). springer, singapore. View at Publisher View at Google Scholar View at Indexing
Bisig, c., montejo, j. b., verbryke, m. r., sathyan, a., & ma, o. (2020). genetic fuzzy systems for decentralized, multi-uav cargo handling. in aiaa scitech 2020 forum (p. 1117). View at Publisher View at Google Scholar View at Indexing
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