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Characterization of Covid-19 infected pregnant women sera using laboratory indexes, vibrational spectroscopy, and machine learning classifications
(Elsevier B.V., 2022)
Herein, we show differences in blood serum of asymptomatic and symptomatic pregnant women infected with COVID-19 and correlate them with laboratory indexes, ATR FTIR and multivariate machine learning methods. We collected ...
Tuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological cases
(Elsevier, 2022)
The performance of a model in machine learning problems highly depends on the dataset and training algorithms. Choosing the right training algorithm can change the tale of a model. While some algorithms have a great ...
Abalone age prediction using machine learning
(Springer Science and Business Media Deutschland GmbH, 2022)
Abalone is a marine snail found in the cold coastal regions. Age is a vital characteristic that is used to determine its worth. Currently, the only viable solution to determine the age of abalone is through very detailed ...
Deep learning for liver disease prediction
(Springer Science and Business Media Deutschland GmbH, 2022)
Mining meaningful information from huge medical datasets is a key aspect of automated disease diagnosis. In recent years, liver disease has emerged as one of the commonly occurring diseases across the world. In this paper, ...
An interpretable decision-support systems for daily cryptocurrency trading
(Elsevier Ltd, 2022)
Cryptocurrencies, especially Bitcoin (BTC), have become an important commodity for both individual and corporate investors within the last decade. The limited supply, high volatility, and random price fluctuations have ...
Schoolchildren' depression and anxiety risk factors assessment and prediction: Machine learning techniques performance analysis
(JMIR, 2022)
Background: Depression and anxiety symptoms in early childhood have a major effect on children's
mental health growth and cognitive development. Studying the effect of mental health problems on
cognitive development has ...
Machine learning as a clinical decision support tool for patients with acromegaly
(SPRINGER, 2022)
Objective To develop machine learning (ML) models that predict postoperative remission, remission at last visit, and resistance to somatostatin receptor ligands (SRL) in patients with acromegaly and to determine the clinical ...
Deep learning-based framework for monitoring of debris-covered glacier from remotely sensed images
(Elsevier Science, 2022)
In recent years, deep learning (DL) methods have proven their efficiency for various computer vision (CV) tasks such as image
classification, natural language processing, and object detection. However, training a DL model ...
Super learner machine-learning algorithms for compressive strength prediction of high performance concrete
(John Wiley and Sons Inc, 2022)
Because the proportion between the compressive strength of high-performance concrete (HPC) and its composition is highly nonlinear, more advanced regression methods are demanded to obtain better results. Super learner ...
Schoolchildren’ depression and anxiety risk factors assessment and prediction: Machine learning techniques performance analysis
(JMIR, 2022)
Background: Depression and anxiety symptoms in early childhood have a major effect on children's
mental health growth and cognitive development. Studying the effect of mental health problems on
cognitive development has ...
