Cognitive Artificial Intelligence: Concept and Applications for Humankind

Sumari, Arwin Datumaya Wahyudi; Ahmad, Adang Suwandi

doi:10.5772/intechopen.72764

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…In general, during a transmission the scattered waves depend on the distance to the target, so to measure these waves we need to measure this distance. In the literature, the scattered waves received are processed by algorithms to detect the presence, the distance and the type of the target [36][37][38][39][40]. Among the target classification methods are the AALF, AALP and ABP method [24].…”

Section: The Methods Of Classification Of Radar Signals Is Presented Inmentioning

confidence: 99%

Artificial Intelligence Approach for Target Classification: A State of The Art

Abdellaoui¹,

Daghouj²,

Fattah³

et al. 2020

Adv. sci. technol. eng. syst. j.

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The classification of static or mobile objects, from a signal or an image containing information as to their structure or their form, constitutes a constant concern of specialists in the electronic field. The remarkable progress made in past years, particularly in the development of neural networks and artificial intelligence systems, has further accentuated this trend. The fields of application and potential uses of Artificial Intelligence are increasingly diverse: understanding of natural language, visual recognition, robotics, autonomous system, Machine learning, etc. This paper is a state of the art on the classification of radar signals. It focuses on the contribution of artificial intelligence to the latter without forgetting target tracking. This by evoking the different feature extractors, classifiers and the existing identification deep learning algorithms. We detail also the process allowing carrying out this classification.

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Section: The Methods Of Classification Of Radar Signals Is Presented Inmentioning

confidence: 99%

Artificial Intelligence Approach for Target Classification: A State of The Art

Abdellaoui¹,

Daghouj²,

Fattah³

et al. 2020

Adv. sci. technol. eng. syst. j.

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“…In this section, we present a new model that combines human data modeling with human data fusion modeling [4][5][6][7][8]. This model uses a special computational method of cognitive development and we will provide a detailed test model to explain its application.…”

Section: Emulating Human-like Inference and Knowledge Acquisitionmentioning

confidence: 99%

Unveiling the Neural Mirage in the Pursuit of Transcendent Intelligence

Sarsia,

Mishra,

Joshi

et al. 2023

RAiSE-2023

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“…Information fusion aims to have knowledge that is extracted from such fused information. Information fusion in our research was inspired by the mechanism carried out by the brain to generate knowledge from the information sensed and perceived by at least two sensory organs [42].…”

Section: Information Fusionmentioning

confidence: 99%

Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation

Sumari¹,

Adinandra²,

Syulistyo³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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A hostile or unfriendly aircraft will mostly fly at low-level altitude or hide behind natural obstacles to avoid Radar detection. One of the ways to detect and recognize while at the same time identifying such aircraft is to perform air observation from the ground. A technique called Visual Aircraft Recognition (VACR) has been practiced in training soldiers to recognize and find an incoming aircraft from a distance using binoculars. Remembering so many types of aircraft have their challenge. To ease the task, we have designed and developed an intelligent military aircraft recognition and identification system using the combination of Back Propagation Neural Networks (BPNN) and Information Fusion to speed up the recognition and identification. We use 13 aircraft features fused into five primary ones as the inputs to the BPNN for the recognition, while the identification uses Hamming Distance to the recognition results. With 155 data consisting of 85 military aircraft and helicopters and 70 civilian aircraft and helicopters and applying the 80:20 scheme for the training and test data, our system can obtain 95.33% and 87% accuracy at the training phase and the test phase. It also succeeds in recognizing and identifying a new military aircraft that is not in the dataset, while the Information Fusion can speed up the recognition and identification by up to 6 seconds. This impacts the acceleration of aircraft recognition and identification.

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Cognitive Artificial Intelligence: Concept and Applications for Humankind

Cited by 6 publications

References 20 publications

Artificial Intelligence Approach for Target Classification: A State of The Art

Artificial Intelligence Approach for Target Classification: A State of The Art

Unveiling the Neural Mirage in the Pursuit of Transcendent Intelligence

Intelligent Military Aircraft Recognition and Identification to Support Military Personnel on the Air Observation Operation

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