Mitigating the multicollinearity problem and its machine learning approach: a review

JYL Chan, SMH Leow, KT Bea, WK Cheng… - Mathematics, 2022 - mdpi.com
Technologies have driven big data collection across many fields, such as genomics and
business intelligence. This results in a significant increase in variables and data points …

The missing pieces of artificial intelligence in medicine

C Gilvary, N Madhukar, J Elkhader… - Trends in pharmacological …, 2019 - cell.com
Stakeholders across the entire healthcare chain are looking to incorporate artificial
intelligence (AI) into their decision-making process. From early-stage drug discovery to …

A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering

Y Li, H Zeng, M Zhang, B Wu, Y Zhao, X Yao… - International Journal of …, 2023 - Elsevier
Yield prediction is essential in food security, food trade, and field management. However,
due to the associated complex formation mechanisms of yield, accurate and timely yield …

Neuzz: Efficient fuzzing with neural program smoothing

D She, K Pei, D Epstein, J Yang… - 2019 IEEE Symposium …, 2019 - ieeexplore.ieee.org
Fuzzing has become the de facto standard technique for finding software vulnerabilities.
However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger …

A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy

J Yue, H Feng, G Yang, Z Li - Remote Sensing, 2018 - mdpi.com
Above-ground biomass (AGB) provides a vital link between solar energy consumption and
yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield …

[HTML][HTML] Estimation of above-ground biomass of winter wheat based on consumer-grade multi-spectral UAV

F Wang, M Yang, L Ma, T Zhang, W Qin, W Li, Y Zhang… - Remote Sensing, 2022 - mdpi.com
One of the problems of optical remote sensing of crop above-ground biomass (AGB) is that
vegetation indices (VIs) often saturate from the middle to late growth stages. This study …

Malware detection: a framework for reverse engineered android applications through machine learning algorithms

B Urooj, MA Shah, C Maple, MK Abbasi… - IEEE Access, 2022 - ieeexplore.ieee.org
Today, Android is one of the most used operating systems in smartphone technology. This is
the main reason, Android has become the favorite target for hackers and attackers …

A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones

HM Fenta, T Zewotir, EK Muluneh - BMC Medical Informatics and Decision …, 2021 - Springer
Background Undernutrition is the main cause of child death in develo** countries. This
paper aimed to explore the efficacy of machine learning (ML) approaches in predicting …

[HTML][HTML] Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain

M Chamberland, EP Raven, S Genc, K Duffy… - NeuroImage, 2019 - Elsevier
Various diffusion MRI (dMRI) measures have been proposed for characterising tissue
microstructure over the last 15 years. Despite the growing number of experiments using …

Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA

S Quiñones, A Goyal, ZU Ahmed - Scientific reports, 2021 - nature.com
Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across
spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk …