Prediction of Pakistani Honey Authenticity Through Machine Learning-Reference-Cited by-同舟云学术

Prediction of Pakistani Honey Authenticity Through Machine Learning

Published:2022 Issue: Volume:10 Page:87508-87521
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Fatima Noureen¹^ORCID,Mujtaba Ghulam¹^ORCID,Akhunzada Adnan²,Ali Khurshed³,Shaikh Zahid Hussain⁴,Rabia Baby⁵,Shahani Javed Ahmed¹

Affiliation:

1. Department of Computer Science, Center of Excellence for Robotics, Artificial Intelligence, and Block Chain, Sukkur IBA University, Sukkur, Pakistan

2. Faculty of Computing and Informatics, University Malaysia Sabah, Kota Kinabalu, Malaysia

3. Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan

4. Department of Mathematics, Sukkur IBA University, Sukkur, Pakistan

5. Department of Education, Sukkur IBA University, Sukkur, Pakistan

Funder

Sind Research Support Program (SRSP) initiated by Sindh Higher Education Commission

University Malaysia Sabah

The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09857885.pdf?arnumber=9857885

Reference79 articles.

1. Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data

2. Do we need hundreds of classifiers to solve real world classification problems?;fernández-delgado;J Mach Learn Res,2014

3. No free lunch theorems for optimization

4. Imbalanced fault identification via embedding-augmented Gaussian prototype network with meta-learning perspective

5. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Developing a machine learning prediction model for honey production;Mediterranean Agricultural Sciences;2024-08-02

2. Detection and Identification of Honey Pollens by YOLOv7: A Novel Framework toward Honey Authenticity;ACS Agricultural Science & Technology;2024-07-03

3. Role of Blockchain in Health Application using Blockchain Sharding;2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT);2023-07-06

4. Low-cost rapid workflow for honey adulteration detection by UV–Vis spectroscopy in combination with factorial design, response surface methodology and supervised machine learning classifiers;Bioresource Technology Reports;2023-02