Applications of neural networks in defense projects and efforts: a mapping study
Keywords:
machine learning, neural networks, project management, artificial intelligence, systematic mapping studyAbstract
Abstract: Defense is one domain that is very broad and diverse in nature with an enormous range of complex projects and efforts. In this field, the application of cutting-edge technologies is one of the keys in coping with the complexity and it has been actively pursued and researched by many countries. Currently, artificial intelligence is one of the most promising technologies that is predicted to shape the future of complex military projects and efforts. The objective of this paper is to obtain a more accurate vision of how far the use of neural networks has penetrated into military and defense projects. This article provides an overview of machine learning and deep learning, which is essential for understanding why militaries around world might use such algorithms to automate a range of military tasks. Furthermore, a systematic mapping study was conducted, which confirmed that these algorithms are already extensively employed as important components of several defense use cases and applications, with emphasis on image-related artificial intelligence tasks, also known as machine/deep vision. We also show that modern variants of neural networks, such as convolutional neural networks and convolutional recurrent neural networks, are commonly found, representing further evidence of the military’s will to quickly embrace cutting-edge technological paradigms.
References
Ajithkumar, N., Aswathi, P., & Bhavani, R. R. (2017). Identification of an effective learning approach to landmine detection. In 2017 1st international conference on electronics, materials engineering and nano-technology (IEMENTech) (pp. 1–5). IEEE.
Barbier, R. (2020). The purpose and mission of the space force. Retrieved from https://www.american.edu/sis/centers/security-technology/the-purpose-and-mission-of-the-space-force.cfm. Accessed April 4, 2021.
Berczi, L.-P., & Barfoot, T. D. (2017). Looking high and low: Learning place-dependent Gaussian mixture height models for terrain assessment. In 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3918–3925). IEEE.
Bistron, M., & Piotrowski, Z. (2021). Artificial intelligence applications in military systems and their influence on sense of security of citizens. Electronics, 10(7), 871. https://doi.org/10.3390/electronics10070871.
Bucci, B. A., & Vipperman, J. S. (2007). Performance of artificial neural network-based classifiers to identify military impulse noise. Journal of the Acoustical Society of America, 122(3), 1602–1610. https://doi.org/10.1121/1.2756969.
Calderone, L. (2019). What is machine vision. Retrieved from https://www.roboticstomorrow.com/article/2019/12/what-is-machine-vision/14548. Accessed April 4, 2021.
Ćetković, J., et al. (2018). Assessment of the real estate market value in the European market by artificial neural networks application. Complexity 2018.
Chen, Z., & Gao, X. (2018). An improved algorithm for ship target detection in SAR images based on Faster R-CNN. In 2018 ninth international conference on intelligent control and information processing (ICICIP) (pp. 39–43). IEEE.
Chikuruwo, M. N. H., Maregedze, L., & Garikayi, T. (2016). Design of an automated vibration monitoring system for condition based maintenance of a lathe machine (Case study). In 2016 international conference on system reliability and science (ICSRS) (pp. 60–63). IEEE.
Cobb, S., & Lee, A. (2014). Malware is called malicious for a reason: The risks of weaponizing code. In 2014 6th international conference on cyber conflict (CyCon 2014) (pp. 71–84). IEEE.
Cozien, R. F. (2001). Distributed image processing for automatic target recognition. In Machine vision and three-dimensional imaging systems for inspection and metrology (pp. 21–30). International Society for Optics and Photonics.
Cozien, R. F., Rosenberger, C., Eyherabide, P., Rossettini, J., & Ceyrolle, A. (2000a). Multiagent systems and neural networks of a distributed architecture for target identification of air images. In H. Shi, P. C. Coffield, & D. Sinha (Eds.), Parallel and distributed methods for image processing IV (pp. 164–174). International Society for Optics and Photonics.
Cozien, R. F., Rosenberger, C., Eyherabide, P., Rossettini, J., & Ceyrolle, A. (2000b). Target detection and identification using neural networks and multi-agents systems. In Proceedings of the third international conference on information fusion (pp. MOD1/10–MOD11/17 vol. 11). IEEE.
CRS Congressional Research Service. (2014). Defense acquisitions how DOD acquires weapons systems and recent efforts to reform the process. Retrieved from https://fas.org/sgp/crs/natsec/RL34026.pdf. Accessed April 4, 2021.
Dao-Duc, C., Xiaohui, H., & Morère, O. (2015). Maritime vessel images classification using deep convolutional neural networks. In Proceedings of the sixth international symposium on information and communication technology (pp. 276–281). ACM.
De Melo, V. V., Sotto, L. F., Leonardo, M. M., & Faria, F. A. (2019). Automatic meta-feature engineering for CNN fusion in aerial scene classification task. IEEE Geoscience and Remote Sensing Letters, 17(9), 1652–1656. https://doi.org/10.1109/LGRS.2019.2950415.
Dli, M., et al. (2022). Predicting the Equipment Useful Lifetime Based on the Deep Neural Networks. Cyber-Physical Systems: Intelligent Models and Algorithms, Springer: 135-144.
Dufaux, F., & Ebrahimi, T. (2008). Scrambling for privacy protection in video surveillance systems. IEEE Transactions on Circuits and Systems for Video Technology, 18(8), 1168–1174. https://doi.org/10.1109/TCSVT.2008.928225.
Dunfied, J., Tarbouchi, M., & Labonte, G. (2004). Neural network based control of a four rotor helicopter. In 2004 IEEE international conference on industrial technology, 2004. IEEE ICIT'04. (pp. 1543–1548). IEEE.
Dvir, D., Ben-David, A., Sadeh, A., & Shenhar, A. J. (2006). Critical managerial factors affecting defense projects success: A comparison between neural network and regression analysis. Engineering Applications of Artificial Intelligence, 19(5), 535–543. https://doi.org/10.1016/j.engappai.2005.12.002.
Easttom, C. (2019). A methodological approach to weaponizing machine learning. In Proceedings of the 2019 international conference on artificial intelligence and advanced manufacturing (pp. 1–5). ACM.
Fu, J., Li, D., Wang, G., & Wu, D. (2013). MEMS inertial switch for power management of environmental vibration monitoring. In 2013 fourth international conference on digital manufacturing & automation (pp. 780–782). IEEE.
Gajewski, J., & Vališ, D. (2021). Verification of the technical equipment degradation method using a hybrid reinforcement learning trees–artificial neural network system. Tribology International, 153, 106618. https://doi.org/10.1016/j.triboint.2020.106618.
Ghosh, M. and A. Thirugnanam (2021). Introduction to Artificial Intelligence. Artificial Intelligence for Information Management: A Healthcare Perspective, Springer: 23-44.
Guo, Q., Teng, L., Qi, L., Ji, X., & Xiang, J. (2019). A novel radar signals sorting method-based trajectory features. IEEE Access, 7, 171235–171245. https://doi.org/10.1109/ACCESS.2019.2955819.
He, D., Yang, G., Li, H., Chan, S., Cheng, Y., & Guizani, N. (2020). An effective countermeasure against UAV swarm attack. IEEE Network, 35(1), 380–385. https://doi.org/10.1109/MNET.011.2000380.
He, Q., Peng, Y., Zhang, M.-x., & Hao, J.-g. (2011). A composable modeling framework base-on BOM in war simulation. In 2011 international conference on computer science and service system (CSSS) (pp. 1900–1904). IEEE.
Hill, R. R., Miller, J. O., & McIntyre, G. A. (2001). Applications of discrete event simulation modeling to military problems. In Proceeding of the 2001 winter simulation conference (Cat. No. 01CH37304) (pp. 780–788). IEEE.
Hu, X.-D., Wang, X.-Q., Meng, F.-J., Hua, X., Yan, Y.-J., Li, Y.-Y., Huang, J., & Jiang, X.-L. (2020). Gabor-CNN for object detection based on small samples. Defence Technology, 16(6), 1116–1129. https://doi.org/10.1016/j.dt.2019.12.002.
Jackowski, J., & Wantoch-Rekowski, R. (2005). Classification of wheeled military vehicles using neural networks. In 18th international conference on systems engineering (ICSEng'05) (pp. 212–217). IEEE.
Jiang, W., Wu, X., Wang, Y., Chen, B., Feng, W., & Jin, Y. (2021). Time–frequency-analysis-based blind modulation classification for multiple-antenna systems. Sensors, 21(1), 231. https://doi.org/10.3390/s21010231.
Jin, Z. (2019). SAR target recognition based on cholesky decomposition weighted kernel extreme learning machine. In Proceedings of the 2019 3rd international conference on advances in image processing (pp. 40–44). ACM.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415.
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Elseveir.
Kumar, E. V., Sundar, A. S., & Chaturvedi, S. K. (2011). Equipment degradation monitoring for sustained reliability. In 2011 proceedings-annual reliability and maintainability symposium (pp. 1–7). IEEE.
Kvasnica, P., & Kvasnica, I. (2013). Application of CUDA computing principles in automatic flight control simulation. In 2013 8th EUROSIM congress on modelling and simulation (pp. 538–543). IEEE.
Lange, K. (2020). Diving deep 65-plus years if nuclear-powered subs. Retrieved from https://www.defense.gov/Explore/Features/Story/Article/1736610/diving-deep-65-years-of-nuclear-powered-subs/. Accessed April 4, 2021.
Lewis, R. (2018). Autonomous intelligence for FMV ISR sensors with a human in the loop decision support system. In Proceedings of the 2nd international conference on vision, image and signal processing (pp. 1–6). ACM.
Li, B., Gao, P., Liang, S., & Chen, D. (2019). Intelligent flight control of combat aircraft based on autoencoder. In Proceedings of the 2019 4th international conference on robotics, control and automation (pp. 65–68). ACM.
Li, X., Sun, Q., Li, L., Liu, X., Liu, H., Jiao, L., & Liu, F. (2020). SSCV-GANs: Semi-supervised complex-valued GANs for PolSAR image classification. IEEE Access, 8, 146560–146576. https://doi.org/10.1109/ACCESS.2020.3004591.
Li, Y., Chen, R., Zhang, Y., & Li, H. (2020). A CNN-GCN framework for multi-label aerial image scene classification. In IGARSS 2020-2020 IEEE international geoscience and remote sensing symposium (pp. 1353–1356). IEEE.
Li, Y., Zhang, S., & Wang, W.-Q. (2020). A lightweight faster R-CNN for ship detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 19, 1–5. https://doi.org/10.1109/LGRS.2020.3038901.
Li, Z., You, Y., & Liu, F. (2020). Analysis on saliency estimation methods in high-resolution optical remote sensing imagery for multi-scale ship detection. IEEE Access, 8, 194485–194496. https://doi.org/10.1109/ACCESS.2020.3033469.
Luo, H., et al. (2018). Convolutional neural networks: Computer vision-based workforce activity assessment in construction. Automation in Construction 94: 282-289.
Marr, B. (2019). What is machine vision and how is it used in business today? Retrieved from https://www.forbes.com/sites/bernardmarr/2019/10/11/what-is-machine-vision-and-how-is-it-used-in-business-today/?sh=4be48e6d6939.
Minarik, V., & Kratky, M. (2019). Cybernetics fight against the UAV. In 2019 international conference on military technologies (ICMT) (pp. 1–4). IEEE.
Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
Park, H. O., Dibazar, A. A., & Berger, T. W. (2008). Protecting military perimeters from approaching human and vehicle using biologically realistic dynamic synapse neural network. In 2008 IEEE conference on technologies for homeland security (pp. 73–78). IEEE.
Pannu, A. (2015). Artificial intelligence and its application in different areas. Artificial Intelligence 4(10): 79-84.
Pellerin, C. (2016). Deputy secretary third offset strategy bolsters america's military deterrence. Retrieved from https://www.defense.gov/Explore/News/Article/Article/991434/deputy-secretary-third-offset-strategy-bolsters-americas-military-deterrence/. Accessed April 4, 2021.
Pei, S., et al. (2022). Neural Network Pruning by Recurrent Weights for Finance Market. ACM Transactions on Internet Technology (TOIT) 22(3): 1-23.
Peng, C., Yongli, W., Boyi, Y., Yuanyuan, H., Jiazhong, L., & Qiao, P. (2020). Cyber security situational awareness jointly utilizing ball K-means and RBF neural networks. In 2020 17th international computer conference on wavelet active media technology and information processing (ICCWAMTIP) (pp. 261–265). IEEE.
Petticrew, M., & Roberts, H. (2008). Systematic reviews in the social sciences: A practical guide. John Wiley & Sons.
Pojsomphong, N., Visoottiviseth, V., Sawangphol, W., Khurat, A., Kashihara, S., & Fall, D. (2020). Investigation of drone vulnerability and its countermeasure. In 2020 IEEE 10th symposium on computer applications & industrial electronics (ISCAIE) (pp. 251–255). IEEE.
Pokonieczny, K. (2018). Use of a multilayer perceptron to automate terrain assessment for the needs of the armed forces. ISPRS International Journal of Geo-Information, 7(11), 430. https://doi.org/10.3390/ijgi7110430.
Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., Shyu, M.-L., Chen, S.-C., & Iyengar, S. S. (2018). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys (CSUR), 51(5), 1–36. https://doi.org/10.1145/3234150.
Rajagopal, A., Joshi, G. P., Ramachandran, A., Subhalakshmi, R., Khari, M., Jha, S., Shankar, K., & You, J. (2020). A deep learning model based on multi-objective particle swarm optimization for scene classification in unmanned aerial vehicles. IEEE Access, 8, 135383–135393. https://doi.org/10.1109/ACCESS.2020.3011502.
Reddy, R. R., Mamatha, C., & Reddy, R. G. (2018). A review on machine learning trends, application and challenges in internet of things. In 2018 international conference on advances in computing, communications and informatics (ICACCI) (pp. 2389–2397). IEEE.
Rewari, H., Dixit, V., Batra, D., & Hema, N. (2018). Automated sign language interpreter. In 2018 eleventh international conference on contemporary computing (IC3) (pp. 1–5). IEEE.
Richards, R. A. (2002). Application of multiple artificial intelligence techniques for an aircraft carrier landing decision support tool. In 2002 IEEE world congress on computational intelligence. 2002 IEEE international conference on fuzzy systems. FUZZ-IEEE'02. proceedings (Cat. No. 02CH37291) (pp. 7–11). IEEE.
Sadeh, A., Dvir, D., & Shenhar, A. J. (2010). Defense vs. civilian projects: The effect of project type on performance. In PICMET 2010 technology management for global economic growth (pp. 1–10). IEEE.
Salehi, H. and R. Burgueño (2018). Emerging artificial intelligence methods in structural engineering. Engineering structures 171: 170-189.
Sagar, B., Niranjan, S., Kashyap, N., & Sachin, D. (2019). Providing cyber security using artificial intelligence–A survey. In 2019 3rd international conference on computing methodologies and communication (ICCMC) (pp. 717–720). IEEE.
Shailaja, K., Seetharamulu, B., & Jabbar, M. (2018). Machine learning in healthcare: A review. In 2018 second international conference on electronics, communication and aerospace technology (ICECA) (pp. 910–914). IEEE.
Shi, M., & Wang, H. (2020). Infrared dim and small target detection based on denoising autoencoder network. Mobile Networks and Applications, 25(4), 1469–1483. https://doi.org/10.1007/s11036-019-01377-6.
Shorten, D., Williamson, A., Srivastava, S., & Murray, J. C. (2018). Localisation of drone controllers from RF signals using a deep learning approach. In Proceedings of the international conference on pattern recognition and artificial intelligence (pp. 89–97). ACM.
Sohn, H., AnzaKu, E. T., De Neve, W., Ro, Y. M., & Plataniotis, K. N. (2009). Privacy protection in video surveillance systems using scalable video coding. In 2009 sixth IEEE international conference on advanced video and signal based surveillance (pp. 424–429). IEEE.
Somvanshi, M., Chavan, P., Tambade, S., & Shinde, S. (2016). A review of machine learning techniques using decision tree and support vector machine. In 2016 international conference on computing communication control and automation (ICCUBEA) (pp. 1–7). IEEE.
Stenning, B. E., & Barfoot, T. D. (2012). Path planning with variable-fidelity terrain assessment. Robotics and Autonomous Systems, 60(9), 1135–1148. https://doi.org/10.1109/IROS.2010.5652621.
Thomas, S. S., Gupta, S., & Subramanian, V. K. (2017). Smart surveillance based on video summarization. In 2017 IEEE region 10 symposium (TENSYMP) (pp. 1–5). IEEE.
Trifonov, R., Nakov, O., & Mladenov, V. (2018). Artificial intelligence in cyber threats intelligence. In 2018 international conference on intelligent and innovative computing applications (ICONIC) (pp. 1–4). IEEE.
Vergun, D. (2020). DOD officials discuss framework for advancing directed energy weapons. Retrieved from https://www.defense.gov/Explore/News/Article/Article/2309408/dod-officials-discuss-framework-for-advancing-directed-energy-weapons/. Accessed April 4, 2021.
Wang, T., Cao, C., Zeng, X., Feng, Z., Shen, J., Li, W., Wang, B., Zhou, Y., & Yan, X. (2020). An aircraft object detection algorithm based on small samples in optical remote sensing image. Applied Sciences, 10(17), 5778. https://doi.org/10.3390/app10175778.
Wei, M., Shi, D., Sun, S., Wang, P., & Hu, L. (2019). Convolutional neural network based side-channel attacks with customized filters. In International conference on information and communications security (pp. 799–813). Springer.
Wiidy, C., & Cheng, J. (2018). Here's the hadware the world's top 25 militaries have in their arsenals. Retrieved from https://www.businessinsider.com/here-are-the-worlds-most-powerful-militaries-2018-2. Accessed April 4, 2021.
Williams, E., & Shaffer, A. (2015). The defense innovation initiative the importance of capability prototyping. Joint Force Quartely, 77(2), 34–43.
Xiaosong, L., Ting, J., Tian, L., & Zenghua, L. (2019). Research on weapon equipment acquisition benefit evaluation based on artificial neural network. In Proceedings of the 2019 10th international conference on E-business, management and economics (pp. 123–127). ACM.
Yan, S., & Xinhua, H. (2009). Study on methods of warfare complex system modeling. In 2009 International forum on computer science-technology and applications (pp. 118–121). IEEE.
Zakariya, A. M., & Jindal, R. (2019). Arabic sign language recognition system on smartphone. In 2019 10th international conference on computing, communication and networking technologies (ICCCNT) (pp. 1–5). IEEE.
Zeng, Q., Qiu, J., & Liu, G. (2008). Application of hidden semi-markov models based on wavelet correlation feature scale entropy in equipment degradation state recognition. In 2008 7th world congress on intelligent control and automation (pp. 269–273). IEEE.
Zheng, L. and H. He (2021). Share price prediction of aerospace relevant companies with recurrent neural networks based on pca. Expert Systems with Applications 183: 115384.
Zhang, C., Zhang, X., Shi, C., & Liu, W. (2016). Aircraft trajectory prediction based on genetic programming. In 2016 3rd international conference on information science and control engineering (ICISCE) (pp. 158–162). IEEE.
Zhang, H., Luo, C., Wang, Q., Kitchin, M., Parmley, A., Monge-Alvarez, J., & De-La-Higuera, P. C.-. (2018). A novel infrared video surveillance system using deep learning based techniques. Multimedia Tools and Applications, 77(20), 26657–26676. https://doi.org/10.1007/s11042-018-5883-y.
Zhang, Q., Lu, J., Liu, T., & Yang, Z. (2019). Radar target recognition based on complex HRRP using convolutional neural network. In Proceedings of the 2019 international symposium on signal processing systems (pp. 5–9). ACM.
Zhang, X., & Han, Y. (2013). Research on climb performance and 4D trajectory prediction of aircraft. In 2013 international conference on computer sciences and applications (pp. 390–393). IEEE.
Zhang, Y., Liao, J., Ran, M., Li, X., Wang, S., & Liu, L. (2020). ST-Xception: A depthwise separable convolution network for military sign language recognition. In 2020 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 3200–3205). IEEE.
Zhao, X., Wei, Y., & Cai, W. (2018). Study on infrared stealth performance evaluation based on convolution neural network. In Proceedings of the 3rd international conference on multimedia and image processing (pp. 62–67). ACM.
Zhou, Z., Chen, J., Shen, B., Xiong, Z., Shen, H., & Guo, F. (2016). A trajectory prediction method based on aircraft motion model and grey theory. In 2016 IEEE advanced information management, communicates, electronic and automation control conference (iMCEC) (pp. 1523–1527). IEEE.
Zou, R., & Liu, J. (2018). Target identification technique based on track filtering. In 2018 IEEE CSAA guidance, navigation and control conference (CGNCC) (pp. 1–6). IEEE.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Value Management Journal
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
