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Drug target interaction prediction using artificial intelligence
(Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2021)
The aim of this research is to develop a system for drug target interaction prediction using
artificial intelligence which involves development of both machine learning and deep learningbased systems. In this paper, we ...
Breast sentinel lymph node cancer detection from mammographic images based on quantum wavelet transform and an atrous pyramid convolutional neural network
(Hindawi Limited, 2022)
This study proposes an optimal approach to reduce noise in mammographic images and to identify salt-and-pepper, Gaussian, Poisson, and impact noises to determine the exact mass detection operation after these noise reductions. ...
Classification of brain tumors using MRI images based on convolutional neural network and supervised machine learning algorithms
(Institute of Electrical and Electronics Engineers Inc., 2022)
Brain tumor is abnormal cells that originate from cranial tissue and is considered one of the most destructive diseases, and lead to the cause of death, where the early diagnosis is crucial for accelerating the therapy of ...
Deep transfer learning methods for classification colorectal cancer based on histology images
(Institute of Electrical and Electronics Engineers Inc., 2022)
Deep transfer learning is one of the common techniques used to classify different types of cancer. The goal of this research is to focus on and adopt a fast, accurate, suitable, and reliable for classification of colorectal ...
Multi-objective deep learning framework for COVID-19 dataset problems
(2023)
Background: It has been reported that a deadly virus known as COVID-19 has arisen in China and has spread rapidly throughout the country. The globe was shattered, and a large number of people on the planet died. It quickly ...
Covid-19 X-ray image classification using SVM based on Local Binary Pattern
(IEEE, 2021)
Coronavirus usually transmits from the animal
to the human, but now, the virus transmission is between
persons. Therefore, scientists and researchers are trying to
develop several types of machine learning methods to ...
Uni-temporal Sentinel-2 imagery for wildfire detection using deep learning semantic segmentation models
(Taylor and Francis Ltd., 2023)
Wildfires are common disasters that have long-lasting climate effects and serious ecological, social, and economic effects due to climate change. Since Earth observation (EO) satellites were launched into space, remote ...
An expert system to predict eye disorder using deep convolutional neural network
(Academic Platform Journal of Engineering and Science, 2021)
Glaucoma according to the W.H.O is one of the major causes of blindness worldwide. Due to its complexity and silent nature early detection of this disease makes it hard to detect. There have been several techniques over ...
Evaluation of deep transfer learning methodologies on the COVID-19 radiographic chest images
(International Information and Engineering Technology Association, 2023)
In 2019, the world had been attacked with a severe situation by the new version of the SARSCOV- 2 virus, which is later called COVID-19. One can use artificial intelligence techniques to reduce time consumption and find ...
Multivariate CDS risk premium prediction with SOTA RNNs on MI[N]T countries
(Finance Research Letters, 2022)
In this study, CDS risk premiums of Mexico, Indonesia and Turkey were predicted by applying state-of-the-art forecasters in deep learning recurrent neural networks architectures which are the most recent ground-breaking ...
