A Cost-Efficient Autonomous Air Defense System for National Security

Khan, Fazle Rabby; Muhabullah, Md.; Islam, Roksana; Khan, Mohammad Monirujjaman; Masud, Mehedi; Aljahdali, Sultan; Kaur, Avinash; Singh, Parminder

doi:10.1155/2021/9984453

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…Khan et al [28] introduced an end-to-end air-defense system capable of autonomously detecting and targeting drones. The proposed system consists of three phases.…”

Section: Related Workmentioning

confidence: 99%

Detection of Unauthorized Unmanned Aerial Vehicles Using YOLOv5 and Transfer Learning

et al. 2022

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Drones/unmanned aerial vehicles (UAVs) have recently grown in popularity due to their inexpensive cost and widespread commercial use. The increased use of drones raises the possibility that they may be employed in illicit activities such as drug smuggling and terrorism. Thus, drone monitoring and automated detection are critical for protecting restricted areas or special zones from illicit drone operations. One of the most challenging difficulties in drone detection in surveillance videos is the apparent likeness of drones against varied backdrops. This paper introduces an automated image-based drone-detection system that uses an enhanced deep-learning-based object-detection algorithm known as you only look once (YOLOv5) to defend restricted territories or special zones from unauthorized drone incursions. The transfer learning to pretrain the model is employed for improving performance due to an insufficient number of samples in our dataset. Furthermore, the model can recognize the detected object in the images and mark the object’s bounding box by joining the results across the region. The experiments show outstanding results for the loss value, drone location detection, precision and recall.

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“…Khan et al [28] introduced an end-to-end air-defense system capable of autonomously detecting and targeting drones. The proposed system consists of three phases.…”

Section: Related Workmentioning

confidence: 99%

Detection of Unauthorized Unmanned Aerial Vehicles Using YOLOv5 and Transfer Learning

et al. 2022

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“…Shows an overview of the general cloud computing model. The user enters his data into the cloud, and the employee sends a query and receives information from the cloud, where he can direct data inquiries to the server for example in the social security system, the cloud is supposed to be confidential and secure, so the data source is stored on the cloud side, to ensure the reliability of Privacy and security of the query [7][8][9][10][11][12][13]. A problem may occur when the cloud service loses its reliability.…”

Section: Figure 1 Big Data In Cloud Computing Modelmentioning

confidence: 99%

Cloud computing environments and management for big data security, and performance (CBDS) Model

Alghamdi¹,

AlZain²,

Masud³

2022

ijhs

Self Cite

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Big data is a technology that is growing at a tremendous speed in many sectors and fields, and this huge growth is also accompanied by the development of cloud computing, Cloud computing provides innovative organization in order to be able to meet the services you need to deal with big data in terms of infrastructure, where big data is processed and stored in the companies and establishments concerned, and unfortunately, still, It suffers from several problems and gaps that may threaten the security of this data In order to improve data security in cloud computing, provide secure access, and improve performance. This paper will discuss several concerns that may threaten data security, access, and cloud computing performance while managing big data, and that is by proposing a model(CBDS) Cloud for Big Data Security to develop performance in cloud management that meets several needs that will contribute to maintaining data integrity and confidentiality, providing secure access to data, and will greatly improve the performance of the cloud during data processing, storage, and recovery from the cloud at the user’s desire and in complete security.

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“…Animation and synthesis require an extensive collection of motions the animator uses to make high-quality animation or movies. Any application can relate human motion to any environment, including training military soldiers, firefighters and other personnel [2,10].…”

Section: Introductionmentioning

confidence: 99%

Action Recognition for Multiview Skeleton 3D Data Using NTURGB + D Dataset

Bhogal,

Devendran

2023

CSSE

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Human activity recognition is a recent area of research for researchers. Activity recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety, monitor indoor and outdoor activities, develop Tele immersion systems, or detect abnormal activity recognition. Three dimensions (3D) skeleton data is robust and somehow view-invariant. Due to this, it is one of the popular choices for human action recognition. This paper proposed using a transversal tree from 3D skeleton data to represent videos in a sequence. Further proposed two neural networks: convolutional neural network recurrent neural network_1 (CNN_RNN_1), used to find the optimal features and convolutional neural network recurrent neural network network_2 (CNN_RNN_2), used to classify actions. The deep neural network-based model proposed CNN_RNN_1 and CNN_RNN_2 that uses a convolutional neural network (CNN), Long short-term memory (LSTM) and Bidirectional Long shortterm memory (BiLSTM) layered. The system efficiently achieves the desired accuracy over state-of-the-art models, i.e., 88.89%. The performance of the proposed model compared with the existing state-of-the-art models. The NTURGB + D dataset uses for analyzing experimental results. It is one of the large benchmark datasets for human activity recognition. Moreover, the comparison results show that the proposed model outperformed the state-ofthe-art models.

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A Cost-Efficient Autonomous Air Defense System for National Security

Cited by 11 publications

References 24 publications

Detection of Unauthorized Unmanned Aerial Vehicles Using YOLOv5 and Transfer Learning

Detection of Unauthorized Unmanned Aerial Vehicles Using YOLOv5 and Transfer Learning

Cloud computing environments and management for big data security, and performance (CBDS) Model

Action Recognition for Multiview Skeleton 3D Data Using NTURGB + D Dataset

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