Tracks Matching Based on an Acoustic Spectrum Signature Detection and a Multi-Frame Fusion Algorithms

In the recent research work, the handwritten signature is a suitable field to detection of valid signature from different environment such online signature and offline signature. In early research work, a lot of unauthorized person put the signature and theft the data in illegal manner from organization or industries. So we have to need identify, the right person on the basis of various parameters that can be detected. In this paper, we have proposed two methods namely LDA and Neural Network for the offline signature from the scan signature image. For efficient research, we have focused the comparative analysis in terms of FRR, SSIM, MSE, and PSNR. These parameters are compared with the early work and the recent work. Our proposed work is more effective and provides the suitable result through our method which leads to existing work. Our method will help to find legal signature of authorized use for security and avoid illegal work.

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STRN-ALK, A novel in-frame fusion with response to alectinib

JTO Clinical and Research Reports ◽

10.1016/j.jtocrr.2020.100125 ◽

2020 ◽

pp. 100125

Author(s):

Misako Nagasaka ◽

Nagaratna Sarvadevabatla ◽

Shawn Iwata ◽

Yubin Ge ◽

Ammar Sukari ◽

...

Keyword(s):

Frame Fusion

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Enhancing nuclear quadrupole resonance (NQR) signature detection leveraging interference suppression algorithms

10.1117/12.920942 ◽

2012 ◽

Author(s):

James A. DeBardelaben ◽

Jeremy K. Miller ◽

Wilbur L. Myrick ◽

Joel B. Miller ◽

G. Charmaine Gilbreath ◽

...

Keyword(s):

Nuclear Quadrupole Resonance ◽

Interference Suppression ◽

Signature Detection ◽

Quadrupole Resonance ◽

Nuclear Quadrupole

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Ground Clutter Mitigation and Insect Signature Detection for Polarimetric C-Band Doppler Weather Radar

10.1109/ghtc53159.2021.9612449 ◽

2021 ◽

Author(s):

Fidele Maniraguha ◽

Anthony Vodacek ◽

Emmanuel Ndashimye ◽

Gerard Rushingabigwi

Keyword(s):

Weather Radar ◽

Doppler Weather Radar ◽

Ground Clutter ◽

Signature Detection ◽

Clutter Mitigation

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Supervised Learning for Misuse/Signature Detection

Data Mining and Machine Learning in Cybersecurity ◽

10.1201/b10867-4 ◽

2011 ◽

pp. 57-83

Keyword(s):

Supervised Learning ◽

Signature Detection

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Revealing and Sharing Malware Profile Using Malware Threat Intelligence Platform

ACMIT Proceedings ◽

10.33555/acmit.v6i1.100 ◽

2021 ◽

Vol 6 (1) ◽

pp. 72-82

Author(s):

Faiz Iman Djufri ◽

Charles Lim

Keyword(s):

Cyber Security ◽

Security Threats ◽

Cyber Intelligence ◽

Intelligence Sharing ◽

Signature Detection ◽

Threat Intelligence ◽

User Data ◽

Self Discovery ◽

New Generation ◽

Security Incidents

Cyber Security is an interchange between attackers and defenders, a non-static balancing force. The increasing trend of novel security threats and security incidents, which does not seem to be stopping, prompts the need to add another line of security defences. This is because the risk management and risk detection has become virtually impossible due to the limited access towards user data and the variations of modern threat taxonomies. The traditional strategy of self-discovery and signature detection which has a static nature is now obsolete in facing threats of the new generation with a dynamic nature; threats which are resilient, complex, and evasive. Therefore, this thesis discusses the use of MISP and The Triad Investigation approach to share the Indicator of Compromise on Cyber Intelligence Sharing Platform to be able to address the newt threats.

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