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Evolving Systems

An Interdisciplinary Journal for Advanced Science and Technology

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Evolving Systems - Top 10 Downloaded Articles 2023

We want to thank the authors of our most-downloaded articles in 2023, and highlight to our readers some of the most impactful research to have been published in the journal. We hope that you will find the articles interesting.

An efficient real-time stock prediction exploiting incremental learning and deep learning (this opens in a new tab)
by Tinku Singh, Riya Kalra, Suryanshi Mishra, Satakshi, Manish Kumar 

A survey on recent trends in deep learning for nucleus segmentation from histopathology images (this opens in a new tab)
by Anusua Basu, Pradip Senapati, Mainak Deb, Rebika Rai, Krishna Gopal Dhal

Generic image application using GANs (Generative Adversarial Networks): A Review (this opens in a new tab)
by S. P. Porkodi, V. Sarada, Vivek Maik, K. Gurushankar

AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19 (this opens in a new tab)
by Hadi Alhares, Jafar Tanha, Mohammad Ali Balafar

A novel normalization algorithm to facilitate pre-assessment of Covid-19 disease by improving accuracy of CNN and its FPGA implementation (this opens in a new tab)
by Sertaç Yaman, Barış Karakaya, Yavuz Erol

Chimp optimization algorithm in multilevel image thresholding and image clustering (this opens in a new tab)
by Zubayer Kabir Eisham et al.

Evolving fuzzy neural classifier that integrates uncertainty from human-expert feedback (this opens in a new tab)
by Paulo Vitor Campos Souza, Edwin Lughofer


Fuzzy PROMETHEE model for public transport mode choice analysis (this opens in a new tab)
by Laila Oubahman, Szabolcs Duleba

Tackling over-smoothing in multi-label image classification using graphical convolution neural network (this opens in a new tab)
by Vikas Chauhan, Aruna Tiwari, Boppudi Venkata, Vislavath Naik

Vaccination and isolation based control design of the COVID-19 pandemic based on adaptive neuro fuzzy inference system optimized with the genetic algorithm (this opens in a new tab)
by Zohreh Abbasi, Mohsen Shafieirad, Amir Hossein Amiri Mehra, Iman Zamani

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