GET THE APP

Journal of Defense Management

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

Year wise published articles

33 33 4 5 9 14 20

Year wise citations received

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 2018 5
Important citations (315)

Brief, c. p. (2020). chinese perspectives on ai and future military capabilities.

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.

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.

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.

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.

Farr, c. (2019). malware analysis: the use of machine-based learning to detect malicious activity (doctoral dissertation, utica college).

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.

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.

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.

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.

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).

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.

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.

Sakenov, n., & tyler, b. j. (2019). survey of adaptive algorithms for intelligent agents.

Hajira tahir, chapter 3 - composite.

Soyluo?lu, b. (2021). modelling aircraft fighting maneuver dynamics using artificial intelligence algorithms.

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.

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.

Lee, j. (2020). why do we need industrial ai?. in industrial ai (pp. 5-32). springer, singapore.

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).

Relevant Topics

Top