Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Powiadomienia systemowe

Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Analysis of recent airborne gravity and magnetic data for the interpretation of basement structures underneath the south–western Benue trough using source edge detector filters

Abdullahi Mukaila, Kumar Raj, Idi Bello Yusuf, Singh Upendra Kumar, Abba Adamu Usman

Acta Geophysica

2023

Vol. 71, no. 4

1595--1606

We present the detail basement and trends of geological structures associated with the Mesozoic-Cenozoic volcanism in the south–western region of the Nigerian Benue trough using recent gravity and magnetic anomalies of the region. The analysis aimed at recognizing and mapping the basement structure that controlled the distribution and source host of hydrocarbon and other economic mineral resources in the region. The structural recognition and mapping is done on the basis of the utilization of the Tilt Angle (TA) and Total Horizontal Derivative of the Tilt Angle (THDTA) of gravity and magnetic data. From these techniques, we have been able to identify and mapped out those edges of anomalous sources due to the gravity and magnetic data that are in association with the basement geological structures of the area. Based on the mapped structural trends, it is observed that the basement structures derived from both the gravity and magnetic anomalies correlated well with the zones of volcanic rocks around Gboko and area between Lefin and Oturkpo. The two locations are sitting over gravity and magnetic highs suggesting high density and susceptibility material below the subsurface. The Euler deconvolution method suggested depths between 1 and 5 km from both gravity and magnetic data. Deeper basement of anomalous sources are suggested between 3 and 5 km. The 1 km depth interprets the regions of basement highs or corresponding to intrusive zones.

A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images

Joshi Rakesh Chandra, Yadav Saumya, Pathak Vinay Kumar, Malhotra Hardeep Singh, Khokhar Harsh Vardhan Singh, Parihar Anit, Kohli Neera, Himanshu D., Garg Ravindra K., Bhatt Madan Lal Brahma, Kumar Raj, Singh Naresh Pal, Sardana Vijay, Burget Radim, Alippi Cesare, Travieso-Gonzalez Carlos M., Dutta Malay Kishore

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 1

239--254

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.