no code implementations • 20 Feb 2024 • Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli
For this purpose, we have proposed the binary multi-objective coordinate search (MOCS) algorithm to solve large-scale feature selection problems.
no code implementations • 20 Feb 2024 • Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli
In this regard, we define feature selection as a multi-objective binary optimization task with the objectives of maximizing classification accuracy and minimizing the number of selected features.
no code implementations • 20 Feb 2024 • Ehsan Rokhsatyazdi, Shahryar Rahnamayan, Sevil Zanjani Miyandoab, Azam Asilian Bidgoli, H. R. Tizhoosh
Finding the optimal values for weights of ANNs is a large-scale optimization problem.
no code implementations • 7 Aug 2023 • Milad Sikaroudi, Maryam Hosseini, Shahryar Rahnamayan, H. R. Tizhoosh
This enables us to derive invariant features from training images without relying on training labels, thereby covering different abstraction levels.
no code implementations • 15 Apr 2023 • Pooria Mazaheri, Azam Asilian Bidgoli, Shahryar Rahnamayan, H. R. Tizhoosh
By forcing the model to learn the ranking of matched outputs, the representation learning is customized toward image search instead of learning a class label.
no code implementations • 2 Mar 2023 • Azam Asilian Bidgoli, Shahryar Rahnamayan, Taher Dehkharghanian, Abtin Riasatian, H. R. Tizhoosh
Coarse multi-objective feature selection uses the reduced search space strategy guided by the classification accuracy and the number of features.
1 code implementation • journal 2022 • Majid Seydgar, Shahryar Rahnamayan, Pedram Ghamisi, Azam Asilian Bidgoli
The generated pseudo labels of our proposed framework can be fed to various DNNs to improve their generalization capacity.
Ranked #1 on Semi-Supervised Image Classification on Salinas (using extra training data)
no code implementations • 15 May 2022 • Amir H Gandomi, Kalyanmoy Deb, Ronald C Averill, Shahryar Rahnamayan, Mohammad Nabi Omidvar
By using problem structure analysis technique and engineering expert knowledge, the $Fx$ method is used to enhance the steel frame design optimization process as a complex real-world problem.
no code implementations • 5 Apr 2022 • Milad Sikaroudi, Shahryar Rahnamayan, H. R. Tizhoosh
These variabilities are assumed to cause a domain shift in the images of different hospitals.
1 code implementation • IEEE Congress on Evolutionary Computation 2021 • Abdelrahman Elewah, Abeer A. Badawi, Haytham Khalil, Shahryar Rahnamayan, Khalid Elgazzar
This paper presents 3D-RadViz, a visualization method for high dimensional data using a three dimensional radial visualization technique.
no code implementations • 11 Jun 2021 • Shivam Kalra, Mohammed Adnan, Sobhan Hemati, Taher Dehkharghanian, Shahryar Rahnamayan, Hamid Tizhoosh
The feature extractor model is fine-tuned using hierarchical target labels of WSIs, i. e., anatomic site and primary diagnosis.
no code implementations • 8 Aug 2020 • Aditya Sriram, Shivam Kalra, Morteza Babaie, Brady Kieffer, Waddah Al Drobi, Shahryar Rahnamayan, Hany Kashani, Hamid. R. Tizhoosh
In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images.
no code implementations • 24 Jul 2020 • Kyle Robert Harrison, Azam Asilian Bidgoli, Shahryar Rahnamayan, Kalyanmoy Deb
In the context of optimization, visualization techniques can be useful for understanding the behaviour of optimization algorithms and can even provide a means to facilitate human interaction with an optimizer.
no code implementations • 29 Jun 2020 • Mostapha Kalami Heris, Shahryar Rahnamayan
One of the widely used models for studying economics of climate change is the Dynamic Integrated model of Climate and Economy (DICE), which has been developed by Professor William Nordhaus, one of the laureates of the 2018 Nobel Memorial Prize in Economic Sciences.
no code implementations • 7 Mar 2020 • Shahryar Rahnamayan, Seyed Jalaleddin Mousavirad
To the best our knowledge, there is no significant study to assess benchmark functions with various dimensions and landscape properties to investigate CD algorithm.
no code implementations • 27 Sep 2017 • Aditya Sriram, Shivam Kalra, H. R. Tizhoosh, Shahryar Rahnamayan
Autoencoders have been recently used for encoding medical images.
no code implementations • 8 Sep 2017 • Hojjat Salehinejad, Shahryar Rahnamayan, Hamid. R. Tizhoosh
Differential evolution (DE) algorithm with a small population size is called Micro-DE (MDE).
no code implementations • 22 May 2017 • Morteza Babaie, Shivam Kalra, Aditya Sriram, Christopher Mitcheltree, Shujin Zhu, Amin Khatami, Shahryar Rahnamayan, H. R. Tizhoosh
In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology.
no code implementations • 16 Sep 2016 • Shivam Kalra, Aditya Sriram, Shahryar Rahnamayan, H. R. Tizhoosh
In this paper, we introduce an approach to learn type-II opposites from the given inputs and their outputs using the artificial neural networks (ANNs).
no code implementations • 22 May 2016 • Fares Al-Qunaieer, Hamid. R. Tizhoosh, Shahryar Rahnamayan
This paper introduces a framework for the automated selection of the best resolution for image segmentation.
no code implementations • 16 Apr 2016 • Hamid. R. Tizhoosh, Shahryar Rahnamayan
A small number of equidistant projections, e. g., 4 or 8, is generally used to generate short barcodes.
no code implementations • 25 Dec 2015 • Hojjat Salehinejad, Shahryar Rahnamayan, Hamid. R. Tizhoosh
Furthermore, comprehensive comparative simulations and analysis on performance of the MDE algorithms over various mutation schemes, population sizes, problem types (i. e. uni-modal, multi-modal, and composite), problem dimensionalities, and mutation factor ranges are conducted by considering population diversity analysis for stagnation and trapping in local optimum situations.
no code implementations • 21 Apr 2015 • Hamid. R. Tizhoosh, Shahryar Rahnamayan
This, of course, is a very naive estimate of the actual or true (non-linear) opposite $\breve{x}_{II}$, which has been called type-II opposite in literature.