Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

Alhabib, Mustafa Haitham; Al-Dabagh, Mustafa Zuhaer Nayef; Almukhtar, Firas Husham; Hussein, Hussein Ibrahim

doi:10.24086/cuesj.v3n2y2019.pp80-84

Cited by 9 publications

(8 citation statements)

References 17 publications

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“…In the realm of the governmental problems, it's relied upon to make disarray for competitors and authorities with videos of things they didn't state to diminish their believability. Deep-fake videos can prompt genuine risks surprisingly fast and encourage various perils if not cautious [19]. In the past decade, facial detection has progressed considerably, mainly because of progress on deep learning and CNNs.…”

Section: Methodsmentioning

confidence: 99%

A robust facemask forgery detection system in video

Almukhtar¹

2022

PEN

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An in-depth fake video uses an Artificial Intelligent (AI), AI programming, and a Personal computer (PC) mix to create a deep fake video of the action. Deep-faking can also be used to represent images and sounds. We provide insights into our reviews in this document. We're showing our dataset to start. At this point, we present the subtleties and reproductively of exploratory settings to evaluate the discovered effects finally. It is no surprise to find deep fake videos, which only monitor a tiny section of the video (e.g., the target face appears quickly on the video; hence the time is limited). We remove our system's fixed duration's persistent effects as each video contributes to the preparation, approval, and testing sections to reflect this. The edge groups are isolated from each video successively (without outline skips). The entire pipeline is ready to be finished when the approval stage is ten years old. Convolutional Neural Network (CNN) was the best and most reliable of the classification systems. Fake videos typically use low-quality pictures to mask faults or insist that the general public regard camera defects as unexplainable phenomena. 'This is a common trope with Unidentified Flying Object (UFO) videos: ghostly orbs are lenses; snakes are compression artifacts on one's face. In this study, we have implemented a sophisticated, knowledgeable method to recognize false images. Our test results using various monitored videos have shown that we can reliably predict whether videos are monitored through with simple co-evolutionary Long Short-Term Memory (LSTM) structure.

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Section: Methodsmentioning

confidence: 99%

A robust facemask forgery detection system in video

Almukhtar¹

2022

PEN

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“…Such vulnerabilities lead to weakening the network's security and allowing the attackers to execute several security attacks. http://journals.cihanuniversity.edu.iq/index.php/cuesj CUESJ 2021, 5 (1): [20][21][22][23][24][25][26][27] There are also instances in which the attackers create their controllers as per their set of policies and protocols and trick the network elements through the same. The network elements trust the fake controller as the original and various attacks are executed.…”

Section: Attacks At Controller Layermentioning

confidence: 99%

“…Various tools are now available for the management of the APIs, the execution of the security controls, and the management of the security attacks associated with the SDN traffic. [23,24]…”

Section: Advanced Toolsmentioning

confidence: 99%

An Overview on SDN and NFV Security Orchestration in Cloud Network Environment

Aziz

Abdulqadder

2021

Cihan U Erbil SCI J

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Cloud networks are being used in most industries and applications in the current era. Software-defined networking has come up as an alternative tool and mechanism to follow and implement in a cloud networking environment in place of the traditional networking approaches. This paper includes the security aspects of computer networking concerning the cloud networking environment and software-defined networks. The security risks and vulnerabilities have been listed and described in this work, and the measures that may be adapted to detect, prevent, and control the same. The use of figures, diagrams, and codes has been done as applicable.

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“…In this article, we will discuss the current state of AI in the health sector in the United States and its future prospects (Al-Mukhtar, 2003) (William et al, 2022). The use of Artificial Intelligence (AI) in the health sector has seen significant growth in recent years, and the United States is at the forefront of this trend (Alhabib et al, 2019) (Firas & AL-Dabagh, 2017). AI has the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and increasing access to care.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Health Sector of USA

Pendy¹

2023

JISS

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The article describes how AI has already made a significant impact in the health sector in the US, with many promising future applications. In medical imaging, for example, AI algorithms have been developed that can detect and classify anomalies with high accuracy rates, potentially reducing the need for unnecessary biopsies or surgeries. Personalized medicine is another area where AI is being used to analyze large amounts of patient data and provide tailored treatments. AI is also being used to improve the accuracy and efficiency of electronic health records (EHRs) by automatically detecting errors and inconsistencies. In drug discovery, AI algorithms can analyze large data sets and predict which compounds are likely to be effective in treating a particular disease, potentially reducing the time and cost of drug development. In the future, AI is expected to play an even greater role in precision medicine, enabling clinicians to develop customized treatment plans based on a patient's individual genetic and medical history. AI is also being developed for use in virtual assistants, which can provide patients with personalized health advice and reminders. Clinical decision support systems are another area where AI is expected to have a significant impact, helping clinicians to make more informed decisions by analyzing patient data and providing recommendations based on the latest research and best practices. Overall, the article suggests that AI has the potential to transform healthcare by improving patient outcomes, reducing costs, and increasing access to care. However, the article also acknowledges that there are challenges to be addressed, such as ensuring the accuracy and fairness of AI algorithms and addressing privacy concerns.

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Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

Cited by 9 publications

References 17 publications

A robust facemask forgery detection system in video

A robust facemask forgery detection system in video

An Overview on SDN and NFV Security Orchestration in Cloud Network Environment

Artificial Intelligence in Health Sector of USA

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