Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery
MK Tripathi, A Nath, TP Singh, AS Ethayathulla… - Molecular Diversity, 2021 - Springer
The accumulation of massive data in the plethora of Cheminformatics databases has made
the role of big data and artificial intelligence (AI) indispensable in drug design. This has …
the role of big data and artificial intelligence (AI) indispensable in drug design. This has …
Injury severity prediction of traffic crashes with ensemble machine learning techniques: A comparative study
A better understanding of injury severity risk factors is fundamental to improving crash
prediction and effective implementation of appropriate mitigation strategies. Traditional …
prediction and effective implementation of appropriate mitigation strategies. Traditional …
Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers
The construction industry is among the riskiest industries around the world. Hence, the
preliminary studies exploring the consequences of occupational accidents have received …
preliminary studies exploring the consequences of occupational accidents have received …
A machine learning method with filter-based feature selection for improved prediction of chronic kidney disease
SA Ebiaredoh-Mienye, TG Swart, E Esenogho… - Bioengineering, 2022 - mdpi.com
The high prevalence of chronic kidney disease (CKD) is a significant public health concern
globally. The condition has a high mortality rate, especially in developing countries. CKD …
globally. The condition has a high mortality rate, especially in developing countries. CKD …
Scenario-based automated data preprocessing to predict severity of construction accidents
Occupational accidents are common in the construction industry, therefore developing
prediction models to detect high severe accidents would be useful. However, existing …
prediction models to detect high severe accidents would be useful. However, existing …
Evaluating classifier performance with highly imbalanced big data
Using the wrong metrics to gauge classification of highly imbalanced Big Data may hide
important information in experimental results. However, we find that analysis of metrics for …
important information in experimental results. However, we find that analysis of metrics for …
Detecting web attacks using random undersampling and ensemble learners
Class imbalance is an important consideration for cybersecurity and machine learning. We
explore classification performance in detecting web attacks in the recent CSE-CIC-IDS2018 …
explore classification performance in detecting web attacks in the recent CSE-CIC-IDS2018 …
Comparative Studies on Resampling Techniques in Machine Learning and Deep Learning Models for Drug-Target Interaction Prediction
AK Azlim Khan, NH Ahamed Hassain Malim - Molecules, 2023 - mdpi.com
The prediction of drug-target interactions (DTIs) is a vital step in drug discovery. The success
of machine learning and deep learning methods in accurately predicting DTIs plays a huge …
of machine learning and deep learning methods in accurately predicting DTIs plays a huge …
Threshold optimization and random undersampling for imbalanced credit card data
Output thresholding is well-suited for addressing class imbalance, since the technique does
not increase dataset size, run the risk of discarding important instances, or modify an …
not increase dataset size, run the risk of discarding important instances, or modify an …
Prediction of construction accident outcomes based on an imbalanced dataset through integrated resampling techniques and machine learning methods
Purpose Central to the entire discipline of construction safety management is the concept of
construction accidents. Although distinctive progress has been made in safety management …
construction accidents. Although distinctive progress has been made in safety management …