Quality 4.0: a review of big data challenges in manufacturing

CA Escobar, ME McGovern… - Journal of Intelligent …, 2021‏ - Springer
Industrial big data and artificial intelligence are propelling a new era of manufacturing, smart
manufacturing. Although these driving technologies have the capacity to advance the state …

A comprehensive report on machine learning-based early detection of alzheimer's disease using multi-modal neuroimaging data

S Sharma, PK Mandal - ACM Computing Surveys (CSUR), 2022‏ - dl.acm.org
Alzheimer's Disease (AD) is a devastating neurodegenerative brain disorder with no cure.
An early identification helps patients with AD sustain a normal living. We have outlined …

Study the influence of normalization/transformation process on the accuracy of supervised classification

VNG Raju, KP Lakshmi, VM Jain… - … on Smart Systems …, 2020‏ - ieeexplore.ieee.org
Recent developments in analytical technologies helped in develo** applications for real-
time problems faced by industries. These applications are often found to consume more time …

Machine learning for materials scientists: an introductory guide toward best practices

AYT Wang, RJ Murdock, SK Kauwe… - Chemistry of …, 2020‏ - ACS Publications
This Methods/Protocols article is intended for materials scientists interested in performing
machine learning-centered research. We cover broad guidelines and best practices …

Prediction of EV charging behavior using machine learning

S Shahriar, AR Al-Ali, AH Osman, S Dhou… - Ieee …, 2021‏ - ieeexplore.ieee.org
As a key pillar of smart transportation in smart city applications, electric vehicles (EVs) are
becoming increasingly popular for their contribution in reducing greenhouse gas emissions …

[HTML][HTML] A machine learning, bias-free approach for predicting business success using Crunchbase data

K Żbikowski, P Antosiuk - Information Processing & Management, 2021‏ - Elsevier
Predicting the success of a business venture has always been a struggle for both
practitioners and researchers. However, thanks to companies that aggregate data about …

Sediment subduction in Hadean revealed by machine learning

J Jiang, X Zou, RN Mitchell, Y Zhang, Y Zhao… - Proceedings of the …, 2024‏ - pnas.org
Due to the scarcity of rock samples, the Hadean Era predating 4 billion years ago (Ga)
poses challenges in understanding geological processes like subaerial weathering and …

Survey of network intrusion detection methods from the perspective of the knowledge discovery in databases process

B Molina-Coronado, U Mori… - … on Network and …, 2020‏ - ieeexplore.ieee.org
The identification of network attacks which target information and communication systems
has been a focus of the research community for years. Network intrusion detection is a …

[HTML][HTML] A machine learning model to predict unconfined compressive strength of alkali-activated slag-based cemented paste backfill

CB Arachchilage, C Fan, J Zhao, G Huang… - Journal of Rock …, 2023‏ - Elsevier
The unconfined compressive strength (UCS) of alkali-activated slag (AAS)-based cemented
paste backfill (CPB) is influenced by multiple design parameters. However, the experimental …

A novel method for multivariant pneumonia classification based on hybrid CNN-PCA based feature extraction using extreme learning machine with CXR images

M Nahiduzzaman, MOF Goni, MS Anower… - IEEE …, 2021‏ - ieeexplore.ieee.org
In this era of COVID19, proper diagnosis and treatment of pneumonia are very important.
Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia …