2026 Tarek M. Taha: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	37	10493	10197	4393	4210	197	10043

Overview

Tarek M. Taha is affiliated with the University of Dayton in the United States. Their research contributions span several areas within computer science and engineering, particularly emphasizing electrical and electronic engineering as well as artificial intelligence.

Their recent publications cover diverse topics and have appeared in notable venues over the past few years. Some of the recent papers include:

Inception recurrent convolutional neural network for object recognition, 2021, Machine Vision and Applications
MitosisNet: End-to-End Mitotic Cell Detection by Multi-Task Learning, 2020, IEEE Access
Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology, 2022, Applied Sciences
Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN), 2022, Diagnostic Pathology
Detection and Classification of Drones Through Acoustic Features Using a Spike-Based Reservoir Computer for Low Power Applications, 2022, 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC)

Taha collaborates frequently with a number of researchers, including:

Chris Yakopcic (10 collaborations)
Md. Shahanur Alam (5 collaborations)
Md Zahangir Alom (4 collaborations)
Vijayan K. Asari (3 collaborations)
Anil V. Parwani (2 collaborations)

Their work is often published in venues such as:

Preprints.org
IEEE Access
2022 International Joint Conference on Neural Networks (IJCNN)
arXiv (Cornell University)
Machine Vision and Applications

The main fields they contribute to are:

Computer Science
Engineering

In terms of subfields of study, Taha's research involves:

Electrical and Electronic Engineering
Artificial Intelligence
Computer Vision and Pattern Recognition
Aerospace Engineering
Cellular and Molecular Neuroscience

The topics frequently covered in their work include:

Advanced Memory and Neural Computing
Ferroelectric and Negative Capacitance Devices
Advanced Neural Network Applications
AI in cancer detection
Neural Networks and Reservoir Computing
Digital Imaging for Blood Diseases
Indoor and Outdoor Localization Technologies

Best Publications

A State-of-the-Art Survey on Deep Learning Theory and Architectures

Zahangir Alom;Tarek M. Taha;Chris Yakopcic;Stefan Westberg

1623
Citations
Recurrent residual U-Net for medical image segmentation

Zahangir Alom;Chris Yakopcic;Mahmudul Hasan;Tarek M. Taha

1211
Citations
Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net)

Zahangir Alom;Chris Yakopcic;Tarek M. Taha;Vijayan K. Asari

857
Citations
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation.

Md. Zahangir Alom;Mahmudul Hasan;Chris Yakopcic;Tarek M. Taha

671
Citations
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.

Md. Zahangir Alom;Tarek M. Taha;Christopher Yakopcic;Stefan Westberg

555
Citations
A Memristor Device Model

C. Yakopcic;T. M. Taha;G. Subramanyam;R. E. Pino

367
Citations
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network

Zahangir Alom;Chris Yakopcic;Mst Shamima Nasrin;Tarek M Taha

335
Citations
Intrusion detection using deep belief networks

Md. Zahangir Alom;VenkataRamesh Bontupalli;Tarek M. Taha

273
Citations
Generalized Memristive Device SPICE Model and its Application in Circuit Design

Chris Yakopcic;Tarek M. Taha;Guru Subramanyam;Robinson E. Pino

256
Citations
Improved inception-residual convolutional neural network for object recognition

Zahangir Alom;Mahmudul Hasan;Chris Yakopcic;Tarek M. Taha

128
Citations
Memristor crossbar deep network implementation based on a Convolutional neural network

Chris Yakopcic;Zahangir Alom;Tarek M. Taha

127
Citations
FPGA Implementation of Izhikevich Spiking Neural Networks for Character Recognition

Kenneth L. Rice;Mohammad A. Bhuiyan;Tarek M. Taha;Christopher N. Vutsinas

126
Citations
Network intrusion detection for cyber security using unsupervised deep learning approaches

Zahangir Alom;Tarek M. Taha

122
Citations
Memristor SPICE model and crossbar simulation based on devices with nanosecond switching time

Chris Yakopcic;Tarek M. Taha;Guru Subramanyam;Robinson E. Pino

120
Citations
Enabling back propagation training of memristor crossbar neuromorphic processors

Raqibul Hasan;Tarek M. Taha

109
Citations
COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches

Alom Mz;Rahman Mms;Nasrin Ms;Taha Tm

107
Citations
On-chip training of memristor crossbar based multi-layer neural networks

Raqibul Hasan;Tarek M. Taha;Chris Yakopcic

106
Citations
Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks.

Zahangir Alom;Paheding Sidike;Mahmudul Hasan;Tarek M Taha

105
Citations
Extremely parallel memristor crossbar architecture for convolutional neural network implementation

Chris Yakopcic;Zahangir Alom;Tarek M. Taha

96
Citations
Acceleration of spiking neural networks in emerging multi-core and GPU architectures

Mohammad A. Bhuiyan;Vivek K. Pallipuram;Melissa C. Smith;Tarek Taha

87
Citations
Handwritten Bangla Digit Recognition Using Deep Learning.

Md. Zahangir Alom;Paheding Sidike;Tarek M. Taha;Vijayan K. Asari

87
Citations

Frequent Co-Authors

Vijayan K. Asari University of Dayton

Ajit K. Roy United States Air Force Research Laboratory

James D. Meindl Georgia Institute of Technology

Qinru Qiu Syracuse University

External Links

Personal website of Tarek M. Taha

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