scholarly journals Automated Microfossil Identification and Segmentation Using a Deep Learning Approach

2019 ◽  
Author(s):  
L.E Carvalho ◽  
G. Fauth ◽  
S. Baecker Fauth ◽  
G. Krahl ◽  
A. C. Moreira ◽  
...  
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
Computational Analysis ◽  
Extraction Process ◽  
Oil Extraction ◽  
High Rate ◽  
Learning Approach ◽  
3D Images ◽  
Identification Process

AbstractThe applicability of computational analysis to paleontological images ranges from the study of the animals, plants and evolution of microorganisms to the simulation of the habitat of living beings of a given epoch. It also can be applied in several niches, such as oil exploration, where there are several factors to be analyzed in order to minimize the expenses related to the oil extraction process. One factor is the characterization of the environment to be explored. This analysis can occur in several ways: use of probes, extraction of samples for petrophysical components evaluation, the correlation with logs of other drilling wells and so on. In the samples extraction part the Computed Tomography (CT) is of importance because it preserves the sample and makes it available for several analyzes. Based on 3D images generated by CT, several analyzes and simulations can be performed and processes, currently performed manually and exhaustively, can be automated. In this work we propose and validate a method for fully automated microfossil identification and extraction. A pipeline is proposed that begins in the scanning process and ends in an identification process. For the identification a Deep Learning approach was developed, which resulted in a high rate of correct microfossil identification (98% of Intersection Over Union). The validation was performed both through an automated quantitative analysis based upon ground truths generated by specialists in the micropaleontology field and visual inspection by these specialists. We also present the first fully annotated MicroCT-acquired publicly available microfossils dataset.

scholarly journals Deep Learning Approach for Analysis and Characterization of COVID-19

2022 ◽  
Vol 70 (1) ◽  
pp. 451-468
Author(s):  
Indrajeet Kumar ◽  
Sultan S. Alshamrani ◽  
Abhishek Kumar ◽  
Jyoti Rawat ◽  
Kamred Udham Singh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach

scholarly journals Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning

2021 ◽  
Author(s):  
Wina Permana Sari ◽  
Hisyam Fahmi
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Extraction Process ◽  
Test Accuracy ◽  
Learning Approach ◽  
Forgery Detection ◽  
Error Level ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Level Analysis

Digital image modification or image forgery is easy to do today. The authenticity verification of an image become important to protect the image integrity so that the image is not being misused. Error Level Analysis (ELA) can be used to detect the modification in image by lowering the quality of image and comparing the error level. The use of deep learning approach is a state-of-the-art in solving cases of image data classification. This study wants to know the effect of adding ELA extraction process in the image forgery detection using deep learning approach. The Convolutional Neural Network (CNN), which is a deep learning method, is used as a method to do the image forgery detection. The impacts of applying different ELA compression levels, such as 10, 50, and 90 percent, were also compared in this study. According to the results, adopting the ELA feature increases validation accuracy by about 2.7% and give the better test accuracy. However, the use of ELA will slow down the processing time by about 5.6%.

scholarly journals Extraction and Characterization of Coffee Silverskin Oil and Its Valorization by Enzymatic Hydrolysis

2021 ◽  
Vol 4 (1) ◽  
pp. 17-23
Author(s):  
Danyelle A. Mota ◽  
Anna Paula R. Silva ◽  
Jefferson Cleriston B. Santos ◽  
Milson S. Barbosa ◽  
Lays C. Almeida ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Enzymatic Hydrolysis ◽  
Physicochemical Properties ◽  
Hydrolytic Activity ◽  
Extraction Process ◽  
Oil Extraction ◽  
Porcine Pancreatic Lipase ◽  
Industry Waste ◽  
Coffee Silverskin

This study investigated the coffee silverskin (CS) crude oil extraction process and characterization of physicochemical properties and enzymatic hydrolysis for fatty acids production. The soxhlet and ultrasonic extractions showed CS oil yield similar to 3.8% and 3.1%, respectively. CS oil extracted by soxhlet presented favorable physicochemical properties with the quality and was used as the feedstock for fatty acids production by enzymatic hydrolysis. The porcine pancreatic lipase showed hydrolytic activity of 1156 U.g-1 ± 13.4. Therefore, we verified the potential of application in biotransformation reactions of oils with biocatalyst with fatty acids production and valorization of coffee industry waste.  

A Deep Learning Approach for Hepatocellular Carcinoma Grading

2020 ◽  
pp. 353-371
Author(s):  
Vitoantonio Bevilacqua ◽  
Antonio Brunetti ◽  
Gianpaolo Francesco Trotta ◽  
Leonarda Carnimeo ◽  
Francescomaria Marino ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Needle Biopsy ◽  
Regions Of Interest ◽  
Learning Approach ◽  
Cad Systems ◽  
Segmentation Algorithms ◽  
Computer Aided ◽  
Identification And Characterization

Introduction and objective: Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in grading Hepatocellular carcinoma (HCC) by means of Computed Tomography (CT) images, thus avoiding medical invasive procedures such as biopsies. The identification and characterization of Regions of Interest (ROIs) containing lesions is an important phase allowing an easier classification in two classes of HCCs. Two steps are needed for the detection of lesioned ROIs: a liver isolation in each CT slice and a lesion segmentation. Materials and methods: Materials consist in abdominal CT hepatic lesion from 18 patients subjected to liver transplant, partial hepatectomy, or US-guided needle biopsy. Several approaches are implemented to segment the region of liver and, then, detect the lesion ROI. Results: A Deep Learning approach using Convolutional Neural Network is followed for HCC grading. The obtained good results confirm the robustness of the segmentation algorithms leading to a more accurate classification.

A review on bovine hydroxyapatite; extraction and characterization

2021 ◽  
Author(s):  
Ali Moradi ◽  
Majid Pakizeh ◽  
Toktam Ghassemi
Keyword(s):  
Bone Substitutes ◽  
Extraction Process ◽  
Extraction Methods ◽  
High Rate ◽  
Bovine Bone ◽  
Future Perspectives ◽  
Electron Microscopies ◽  
Natural Bone ◽  
Bovine Hydroxyapatite

Abstract High rate of bone grafting surgeries emphasizes the need for optimal bone substitutes. Biomaterials mimicking the interconnected porous structure of the original bone with osteoconductive and osteoinductive capabilities have long been considered. Hydroxyapatite (HA), as the main inorganic part of natural bone, has exhibited excellent regenerative properties in bone tissue engineering. This manuscript reviews the HA extraction methods from bovine bone, as one of the principal biosources. Essential points in the extraction process have also been highlighted. Characterization of the produced HA through gold standard methods such as XRD, FTIR, electron microscopies (SEM and TEM), mechanical/thermodynamic tests, and bioactivity analysis has been explained in detail. Finally, future perspectives for development of HA constructs are mentioned.

Automated visual inspection of fabric image using deep learning approach for defect detection

2021 ◽  
Author(s):  
Vyacheslav V. Voronin ◽  
Roman Sizyakin ◽  
Marina Zhdanova ◽  
Evgenii A. Semenishchev ◽  
Dmitry Bezuglov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Visual Inspection ◽  
Learning Approach ◽  
Automated Visual Inspection
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