CIS: The Crypto Intelligence System for automatic detection and localization of cryptographic functions in current malware

2012 ◽  
Author(s):  
Felix Matenaar ◽  
Andre Wichmann ◽  
Felix Leder ◽  
Elmar Gerhards-Padilla
Keyword(s):  
Automatic Detection ◽  
Intelligence System ◽  
Detection And Localization

scholarly journals Automatic Detection and Localization of Macular Edema

2020 ◽  
Vol 5 (9) ◽  
pp. 552-558
Author(s):  
S. Sumithra ◽  
K. R. Remya ◽  
Dr. M. N. Giri Prasad
Keyword(s):  
Macular Edema ◽  
Vision Loss ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
The People ◽  
Simple Linear Iterative Clustering ◽  
Detection And Localization

Diabetic retinopathy is an eye disease and causes vision loss to the people who are suffering longer from the diabetes. Exudates, bright and red lesions are identified in the diabetic retinal eye. Automatic detection and localization of macular edema is a challenging issue since exudates have non uniform illumination and are low contrasted. Proposed algorithm to detect macular edema encompasses Simple Linear Iterative Clustering, Fisher linear discriminant and Support vector machine classifer. Optic Disc extraction prior to exudates extraction is also introduced. Performance of the proposed detection algorithm is tested on easily available databases: Diaretdb1, Messidor and E_optha Ex. Proposed method shows an accuracy of 97.81%, specificity 98.65 and Sensitivity 82.71%.

Automatic detection and localization of croaker’s fish calls using beamforming

2018 ◽  
Vol 144 (3) ◽  
pp. 1692-1692
Author(s):  
Ikuo Matsuo ◽  
Kazuki Yamato ◽  
Ryuzo Takahashi ◽  
Tomohito Imaizumi ◽  
Tomonari Akamatsu
Keyword(s):  
Automatic Detection ◽  
Detection And Localization

AI-Based Automatic Segmentation of Craniomaxillofacial Anatomy From CBCT Scans for Automatic Detection of Pharyngeal Airway Evaluations in OSA patients

2021 ◽  
Author(s):  
Kaan Orhan ◽  
Mamat Shamshiev ◽  
Matvey Ezhov ◽  
Alexander Plaskin ◽  
Aida Kurbanova ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Fast Method ◽  
Intelligence System ◽  
Pharyngeal Airway ◽  
Significant Difference ◽  
Automatic Software ◽  
The Mean

Abstract This study aims to generate and also validate an automatic detection algorithm for pharyngeal airway on CBCT data using an AI system which will procure an easy, errorless and fast method. The second aim is to validate the newly developed artificial intelligence system in comparison to commercially available software for 3D CBCT evaluation. A Convolutional Neural Network based machine learning algorithm did the segmentation of the pharyngeal airways in OSA and non-OSA patients. Radiologists used a semi-automatic software to manually determine the airway and their measurements were compared with the AI. OSA patients were classified as minimal, mild, moderate and severe groups and the mean airway volumes were compared. Narrowest points (mm), airway areas (mm2) and airway volumes (cc) of both OSA and non-OSA patients were also compared. There was no statistically significant difference between the manual and Diagnocat measurements in all groups (p>0.05). According to the results of the Diagnocat and manual segmentation, a successful algorithm which can automatically segment the pharyngeal airway from was created which can be used for a swift and precise measurement of pharyngeal airway volume.

scholarly journals Automatic detection and localization of sister chromatid exchanges.

1976 ◽  
Vol 24 (1) ◽  
pp. 168-177 ◽  
Author(s):  
G W Zack ◽  
J A Spriet ◽  
S A Latt ◽  
G H Granlund ◽  
I T Young
Keyword(s):  
Computer Analysis ◽  
Sister Chromatid ◽  
Automatic Detection ◽  
Sister Chromatid Exchanges ◽  
Banding Patterns ◽  
Sister Chromatids ◽  
Manual Methods ◽  
Detection And Localization ◽  
Chromatid Exchanges

Sister chromatids of human metaphase chromsomes from cells which have replicated twice in medium containing 5-bromodeoxyuridine exhibit unequal fluorescence when stained with the dye 33258 Hoechst. Sister chromatid exchanges occurring in these chromosomes are apparent as interchanges of brightly and dully fluorescing chromatids. A technique for detecting such exchanges by computer analysis of chromsome images has been developed and found to campare favorably with manual methods. The exchanges have been localized in the context of quinacrine banding patterns.

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