Modern data science for analytical chemical data–A comprehensive review
E Szymańska - Analytica chimica acta, 2018 - Elsevier
Efficient and reliable analysis of chemical analytical data is a great challenge due to the
increase in data size, variety and velocity. New methodologies, approaches and methods …
increase in data size, variety and velocity. New methodologies, approaches and methods …
Multi-Class Imbalanced Data Handling with Concept Drift in Fog Computing: A Taxonomy, Review, and Future Directions
A network of actual physical objects or “IoT components” linked to the internet and equipped
with sensors, electronics, software, and network connectivity is known as the Internet of …
with sensors, electronics, software, and network connectivity is known as the Internet of …
Landslide susceptibility map** based on random forest and boosted regression tree models, and a comparison of their performance
S Park, J Kim - Applied Sciences, 2019 - mdpi.com
This study aims to analyze and compare landslide susceptibility at Woomyeon Mountain,
South Korea, based on the random forest (RF) model and the boosted regression tree (BRT) …
South Korea, based on the random forest (RF) model and the boosted regression tree (BRT) …
Evaluation of different machine learning models for predicting and map** the susceptibility of gully erosion
Gully erosion constitutes a serious problem for land degradation in a wide range of
environments. The main objective of this research was to compare the performance of seven …
environments. The main objective of this research was to compare the performance of seven …
An application of oversampling, undersampling, bagging and boosting in handling imbalanced datasets
Most classifiers work well when the class distribution in the response variable of the dataset
is well balanced. Problems arise when the dataset is imbalanced. This paper applied four …
is well balanced. Problems arise when the dataset is imbalanced. This paper applied four …
ADME properties evaluation in drug discovery: prediction of Caco-2 cell permeability using a combination of NSGA-II and boosting
The Caco-2 cell monolayer model is a popular surrogate in predicting the in vitro human
intestinal permeability of a drug due to its morphological and functional similarity with human …
intestinal permeability of a drug due to its morphological and functional similarity with human …
Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt
BA El-Haddad, AM Youssef, HR Pourghasemi… - Natural Hazards, 2021 - Springer
Floods represent catastrophic environmental hazards that have a significant impact on the
environment and human life and their activities. Environmental and water management in …
environment and human life and their activities. Environmental and water management in …
PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions
Background With the increasing development of biotechnology and informatics technology,
publicly available data in chemistry and biology are undergoing explosive growth. Such …
publicly available data in chemistry and biology are undergoing explosive growth. Such …
Boosted neural networks for improved short-term electric load forecasting
This paper presents an improved technique for short-term electric load forecasting making
use of boosted neural networks (BooNN). The BooNN consist of combining a set of artificial …
use of boosted neural networks (BooNN). The BooNN consist of combining a set of artificial …
A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection
In the age of sophisticated cyber threats, botnet detection remains a crucial yet complex
security challenge. Existing detection systems are continually outmaneuvered by the …
security challenge. Existing detection systems are continually outmaneuvered by the …