Unveiling the Impact of Extreme Learning Machine in the Defence and Military Sector

• Published in: Defense science journal Vol. 74; no. 4; pp. 517 - 525
• Main Authors: Pande, Shubhangi, Rathore, Neeraj Kumar, Purohit, Anuradha
• Format: Journal Article
• Language: English
• Published: 01.07.2024
• ISSN: 0011-748X; 0976-464X
• DOI: 10.14429/dsj.74.19174

Among the most well-known machine learning algorithms, Extreme Learning Machine (ELM) has seen widespread use across a variety of fields, including the defence and military industries. For problems like sluggish technique and iteratively altering the hidden layer’s parameters to optimise the efficiency of the gradient descent approach, a cutting-edge machine learning algorithm known as the ELM has been developed. Depending on the specific objective and circumstance, the Extreme Learning Machine (ELM) may be more appropriate than Deep Neural Network (DNN) techniques. The models constructed in this manner perform quite well in generalisation. The following three goals are emphasised in its unconventional structure: 1) A great degree of accuracy in learning 2) less need for direct human involvement 3) an extremely rapid rate of learning, and moreover, it provides an optimal response for the whole world. As a result of its quick training, flexibility, and resilience, the Extreme Learning Machine (ELM) has several uses in this field, including target detection and tracking, image and signal processing, cybersecurity and intrusion detection, decision support systems, pattern recognition and classification, etc. According to our findings, the ELM approach was used with low training time and the testing accuracy is excellent. Also, this study presents the contribution of the revolutionary machine learning algorithm ELM to the defence and military sectors.