Hybridizing Artificial Neural Networks Through Feature Selection Based Supervised Weight Initialization and Traditional Machine Learning Algorithms for Improved Colon Cancer Prediction-Reference-Cited by-同舟云学术

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Hybridizing Artificial Neural Networks Through Feature Selection Based Supervised Weight Initialization and Traditional Machine Learning Algorithms for Improved Colon Cancer Prediction

Published:2024 Issue: Volume:12 Page:97099-97114
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Sajjad Ahmed Nadeem Malik¹^ORCID,Hammad Waseem Muhammad¹^ORCID,Aziz Wajid¹^ORCID,Habib Usman²^ORCID,Masood Anum³^ORCID,Attique Khan Muhammad⁴^ORCID

Affiliation:

1. Department of Computer Science and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan

2. Software Engineering Department, FAST School of Computing, National University of Computer and Emerging Sciences, Islamabad, Pakistan

3. Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway

4. Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10580883.pdf?arnumber=10580883

Reference78 articles.

1. Patient safety in primary and outpatient health care

2. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020

3. Clear cell renal cell carcinoma ontogeny and mechanisms of lethality

4. Artificial intelligence role in modern science: Aims, merits, risks and its applications;Verma;Artif. Intell.,2023

5. The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

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