Open Citations

AN INFORMATION TECHNOLOGY APPROACH TO PREDICT BREAST CANCER USING MACHINE LEARNING

Zamart Ramazanova, Yeldar Baiken, Bakhyt T. Matkarimov, Arshat Urazbayev, Askhat Myngbay, Bauyrzhan Aituov

Scientific Journal of Astana IT University · 2025

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Bayesian hierarchical modeling of mucosal immune responses and growth efficiency in young animals: Demonstrating the superiority of data-dependent empirical priors

Debashis Chatterjee, Prithwish Ghosh

PLoS ONE · 2025

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Comparative Analysis of Machine Learning and Deep Learning Algorithms for Breast Cancer Classification

Prasun Mazumder

2025

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Evaluation of memristor performance in neural networks using an AHaH framework

Xiang Xu, Gangquan Si, Minglin Xu, Yukaichen Yang, Chenhao Li

Neurocomputing · 2025

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Redshift‐Agnostic Machine Learning Classification: Unveiling Peak Performance in Galaxy, Star, and Quasar Classification (Using SDSS DR17 )

Debashis Chatterjee, Prithwish Ghosh

Astronomische Nachrichten · 2025

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Whisperers of whales wander: A directional statistical investigation of whales’ migration influenced by geomagnetic, ocean current, and celestial cues

Debashis Chatterjee, Prithwish Ghosh

Journal for Nature Conservation · 2025

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From Deep Learning Maze to Neural Network Waltz: Unveiling Peak Performance in Stellar Classification (Using SDSS DR17)

Debashis Chatterjee, Prithwish Ghosh

Research Square · 2024

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Breast Cancer Detection by Extracting and Selecting Features Using Machine Learning

Priyanka M. Tambat, Sohel A. Bhura, Salim Y. Amdani, Suresh S. Asole

International Journal on Recent and Innovation Trends in Computing and Communication · 2023

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Spectral Classification of Quasar Subject to Redshift: A Statistical Study

Prithwish Ghosh, Shinjon Chakraborty

2023

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Analysis of Different Machine Learning Techniques with PCA in the Diagnosis of Breast Cancer

Hüseyin YILMAZ, Fatma Kuncan

Journal of Engineering Technology and Applied Sciences · 2022

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Breast Cancer Wisconsin (Diagnostic) Prediction

Citing Works

AN INFORMATION TECHNOLOGY APPROACH TO PREDICT BREAST CANCER USING MACHINE LEARNING

Bayesian hierarchical modeling of mucosal immune responses and growth efficiency in young animals: Demonstrating the superiority of data-dependent empirical priors

Comparative Analysis of Machine Learning and Deep Learning Algorithms for Breast Cancer Classification

Evaluation of memristor performance in neural networks using an AHaH framework

Redshift‐Agnostic Machine Learning Classification: Unveiling Peak Performance in Galaxy, Star, and Quasar Classification (Using SDSS DR17 )

Whisperers of whales wander: A directional statistical investigation of whales’ migration influenced by geomagnetic, ocean current, and celestial cues

From Deep Learning Maze to Neural Network Waltz: Unveiling Peak Performance in Stellar Classification (Using SDSS DR17)

Breast Cancer Detection by Extracting and Selecting Features Using Machine Learning

Spectral Classification of Quasar Subject to Redshift: A Statistical Study

Analysis of Different Machine Learning Techniques with PCA in the Diagnosis of Breast Cancer

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