It is difficult to perform real time object detection in Uncrewed Aerial Vehicle(UAV) based video surveillance because of the dynamic movement of camera, scale, occlusion, variation of illumination and limited power availability on-board of the computer. Purpose: In this paper of researches, the author suggests the proposed UAV Spatial-Temporal Network(UAV-STNet), which is a hybrid model of spatio-temporal deep learning model that is expected to improve the accuracy of the detection, balance of time, and real-time performance. Approaches: techniques: The proposed framework incorporates U-Net representing the system of total encoder involving extraction of multiscale space features mode, Transformer attention module which will involve global contextual modeling and Long Short-Term Memory(LSTM) network which will involve learning the short-term dependency in sequential frames. The model has been lives with and examined against a personalised UAV video dataset using a trade mark assortment of generally utilised measures of detection, including precision, recollection, mAPat 0.5 and inference velocity (FPS). Findings: UAV-STNet gave 94.5% precision, 93.1% recall and 93.2% mAP @0.5 and also 45 FPS. Better accuracy-tolerance efficient state is observed in comparative comparison with SSD, YOLOv3 and faster R-CNN which are evidently small object in motion affected scenes. Conclusions: The spatial, contextual and temporal modelling applied as a single-stop end-to-end architecture shows a powerful and computationally efficient solution of the topic of detecting real-time UAV video items, that brings benefits in the stability and reliability of intelligent surveillance systems using aerial vehicles.
Deep learning techniques are used across a wide range of fields for several applications. In recent years, deep learning-based object detection from aerial or terrestrial photos has gained popularity as a study topic. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed. A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles andclassification probabilities for an image. In layman's terms, it is a technique for instantly identifying and rec
... Show MoreGoal of research is to investigate the impact of the use of effective learning model in the collection of the fourth grade students/Department of physics in the material educational methods and the development of critical thinking .to teach this goal has been formulated hypothesis cefereeten zero subsidiary of the second hypothesis .To investigate the research hypothesis were selected sample of fourth-grade students of the department of physics at the univers
... Show MoreThe consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learn- ing algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying
... Show MoreThe current study aims to apply the methods of evaluating investment decisions to extract the highest value and reduce the economic and environmental costs of the health sector according to the strategy.In order to achieve the objectives of the study, the researcher relied on the deductive approach in the theoretical aspect by collecting sources and previous studies. He also used the applied practical approach, relying on the data and reports of Amir almuminin Hospital for the period (2017-2031) for the purpose of evaluating investment decisions in the hospital. A set of conclusions, the most important of which is: The failure to apply
... Show MoreStealth marketing is considered as one of the contemporary issues that researchers have begun to explore as a current understanding. It is the marketing approach used by organizations to promote their products and services to the public in implicit and indirect manner. In this article, the concept of stealth marketing will be discussed throw its advantages and disadvantages. In addition, the different techniques of stealth marketing have been discussed including: viral marketing, celebrity marketing, brand pushers, bait-and-tease marketing, video games marketing, and marketing in music. Furthermore, a new technique of marketing entitled “Marketing through social responsibility” has been added and discussed according to the themes in the
... Show MoreCloud storage provides scalable and low cost resources featuring economies of scale based on cross-user architecture. As the amount of data outsourced grows explosively, data deduplication, a technique that eliminates data redundancy, becomes essential. The most important cloud service is data storage. In order to protect the privacy of data owner, data are stored in cloud in an encrypted form. However, encrypted data introduce new challenges for cloud data deduplication, which becomes crucial for data storage. Traditional deduplication schemes cannot work on encrypted data. Existing solutions of encrypted data deduplication suffer from security weakness. This paper proposes a combined compressive sensing and video deduplication to maximize
... Show MoreIn this modern Internet era and the transition to IPv6, routing protocols must adjust to assist this transformation. RIPng, EIGRPv6 and OSPFv3 are the dominant IPv6 IGRP (Interior Gateway Routing Protocols). Selecting the best routing protocol among the available is a critical task, which depends upon the network requirement and performance parameters of different real time applications. The primary motivation of this paper is to estimate the performance of these protocols in real time applications. The evaluation is based on a number of criteria including: network convergence duration, Http Page Response Time, DB Query Response Time, IPv6 traffic dropped, video packet delay variation and video packet end to end de
... Show MoreHepatitis B is an inflammation of the liver that caused by Hepatitis B virus (HBV) which is DNA virus that infects the human and some kinds of animals such as chimpanzees and birds. This disease considered as the major disease of mankind and a serious global public health problem. HBsAg, HBeAg, HBcAb, HBeAb and HBsAb are markers used to detect the presence and the stage of infection. The current study included (181) individuals from both sexes, (137) males and (44) females. By ratio 3.11: 1.The mean age of patients 2.4033 ± 0.83519 (range 18-73) years as follows < 20 (11.6%), 21–40 (47.5%), 41–60 (29.8%) and > 60 (11.0%) . These patients are 73 (40.4%) Blood donors from Central Blood Bank, 88 (48.6%) Chronic kidney failure at Ibn –
... Show MoreThis work concentrates on the effect of a flex-skin trailing edge flap on the aerodynamic characteristics of SD7037 airfoil at low Reynolds numbers, in the range of 2^10 to 5^10 using computational methods. The study used a range of angle of attack (AOA) associated with the take-off phase and different flap angles. The numerical model was set up in Siemens STAR-CCM+ package using the κ-ω shear stress transport turbulence model and the (γ-Reθ) transition model which ensured the approximate solution of Navier-Stokes equations. The verification of the computational solution was done by the comparison with the available experimental data of the plain flap, and it was discovered that the results matched pretty well at lower AoAs. Results in
... Show MoreAutism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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