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Posts

Future Blog Post

less than 1 minute read

Published: January 01, 2199

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published: August 14, 2015

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published: August 14, 2014

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published: August 14, 2013

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published: August 14, 2012

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

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Weakly Supervised Learning Technique for Solving Partial Differential Equations; Case Study of 1-D Reaction-Diffusion Equation

Published in High-Performance Computing and Big Data Analysis: Second International Congress, TopHPC 2019, 2019

This paper introduces a new method that utilizes weak supervision and deep learning to solve partial differential equations (PDEs) using only boundary and initial conditions, making it suitable for unknown PDEs without labeled data. The approach is evaluated by solving the Reaction-Diffusion equation, demonstrating high consistency with the finite difference method and highlighting the effectiveness of weakly supervised learning in solving various types of differential equations.

Recommended citation: Zakeri, Behzad, et al. "Weakly supervised learning technique for solving partial differential equations; case study of 1-d reaction-diffusion equation." High-Performance Computing and Big Data Analysis: Second International Congress, TopHPC 2019, Tehran, Iran, April 23–25, 2019, Revised Selected Papers 2. Springer International Publishing, 2019. https://link.springer.com/chapter/10.1007/978-3-030-33495-6_28

Real-time Travel Time Estimation Using Matrix Factorization

Published in arXiv preprint, 2019

This paper addresses the estimation of travel times for various road segments and time intervals using GPS data and Matrix Factorization techniques. By aggregating GPS data into a matrix and applying the Alternating Least Squares (ALS) method with regularization, the proposed approach effectively solves the sparsity problem and accurately estimates travel times. Evaluation results using real data from a large online taxi dispatching system in Iran demonstrate the strength and effectiveness of the proposed method.

Recommended citation: Badrestani E, Bahrak B, Elahi A, Faramarzi A, Golshanrad P, Monsefi AK, Mahini H, Zirak A. Real-time travel time estimation using matrix factorization. arXiv preprint arXiv:1912.00455. 2019 Dec 1. https://arxiv.org/abs/1912.00455

Solving The Reaction-Diffusion Equation Based On Analytical Methods And Deep Learning Algorithm; The Case Study Of Sulfate Attack To Concrete

Published in arXiv preprint, 2019

In this study, a deep neural network was trained to predict the solution of the reaction-diffusion equation with varying coefficients, utilizing numerical and analytical solutions. Dimensional analysis technique was employed to reduce learning time and identify similar equation solutions. The results show that deep learning successfully estimated the solution of the reaction-diffusion equation with a constant coefficient, highlighting its accuracy in solving partial differential equations.

Recommended citation: Monsefi, Amin Karimi, and Rana Bakhtiyarzade. "Solving the Reaction-Diffusion equation based on analytical methods and deep learning algorithm; the Case study of sulfate attack to concrete." arXiv preprint arXiv:1912.05452 (2019). https://arxiv.org/abs/1912.05452

Performing Software Test Oracle Based on Deep Neural Network With Fuzzy Inference System

Published in High-Performance Computing and Big Data Analysis: Second International Congress, TopHPC 2019, 2019

This work introduces a novel software test Oracle based on deep learning and a fuzzy inference system, aimed at automating the testing process while minimizing time and cost. The Oracle maps the software output to a fuzzy space using Takagi-Sugeno-Kang fuzzy inference and trains a deep neural network. The performance of the Oracle is evaluated using different models, demonstrating its ability to accurately detect correct and false results.

Recommended citation: Monsefi, Amin Karimi, et al. "Performing software test oracle based on deep neural network with fuzzy inference system." High-Performance Computing and Big Data Analysis: Second International Congress, TopHPC 2019, Tehran, Iran, April 23–25, 2019, Revised Selected Papers 2. Springer International Publishing, 2019. https://link.springer.com/chapter/10.1007/978-3-030-33495-6_31

Deep Learning Prediction of Heat Propagation on 2-D Domain via Numerical Solution

Published in The 7th International Conference on Contemporary Issues in Data Science, 2020

In This work demonstrates the effectiveness of deep learning in solving the problem of two-dimensional heat transfer in an arbitrary domain. Using the finite volume method, the researchers trained a deep neural network on 100,000 cases to predict the heat transfer solution. The results show that the network achieved satisfactory precision compared to the commercial program ANSYS, indicating the potential of deep learning in accurately predicting physics-based heat transfer phenomena.

Recommended citation: Zakeri, Behzad, Amin Karimi Monsefi, and Babak Darafarin. "Deep learning prediction of heat propagation on 2-d domain via numerical solution." Data Science: From Research to Application. Springer International Publishing, 2020. https://link.springer.com/chapter/10.1007/978-3-030-37309-2_13

Will There Be A Construction? Predicting Road Constructions Based On Heterogeneous Spatiotemporal Data

Published in Proceedings of the 30th International Conference on Advances in Geographic Information Systems. 2022, 2022

A computational approach for predicting future road construction projects by integrating and analyzing various types of spatiotemporal data. The approach utilizes a deep-neural-network-based model trained on a large dataset called “US-Constructions,” which includes 6.2 million road constructions with diverse attributes and road-network features. Experimental results demonstrate the effectiveness of the approach in accurately predicting future road constructions in several major cities in the United States.

Recommended citation: Monsefi, Amin Karimi, Sobhan Moosavi, and Rajiv Ramnath. "Will there be a construction? Predicting road constructions based on heterogeneous spatiotemporal data." Proceedings of the 30th International Conference on Advances in Geographic Information Systems. 2022. https://arxiv.org/abs/2209.06813

Smart And Collaborative Industrial IoT: A Federated Learning And Data Space Approach

Published in Journal of Digital Communications and Networks, 2023

The challenges faced in adopting Industry 4.0, particularly in the field of smart factories and production, due to issues such as lack of high-quality and diverse data, fragmented data across different silos, and concerns regarding privacy and security. To address these challenges, the article proposes a decentralized architecture utilizing multi-party technologies, privacy-enhancing techniques, and AI approaches to create a collaborative platform and federated data space. Experimental results demonstrate the potential benefits of this approach for multi-party applications and data sharing based on the FAIR principles.

Recommended citation: Farahani, Bahar, and Amin Karimi Monsefi. "Smart and collaborative industrial IoT: A federated learning and data space approach." Digital Communications and Networks 9.2 (2023): 436-447. https://www.sciencedirect.com/science/article/pii/S2352864823000354

Novel Physics-Based Machine-Learning Models for Indoor Air Quality Approximations

Published in 29TH ACM SIGKDD Conference On Knowledge Discovery And Data Mining, 2023

The study introduces six novel physics-based machine learning models designed to accurately estimate indoor pollutant concentrations using cost-effective sensors, leveraging domain knowledge through a combination of physics concepts, Gated Recurrent Units, and Decomposition techniques, showcasing their superiority in terms of computational efficiency and accuracy over transformer-based models using real-world office data.

Recommended citation: Mohammadshirazi A, Nadafian A, Monsefi AK, Rafiei MH, Ramnath R. Novel Physics-Based Machine-Learning Models for Indoor Air Quality Approximations. arXiv preprint arXiv:2308.01438. 2023 Aug 2. https://arxiv.org/pdf/2308.01438.pdf

CrashFormer: A Multimodal Architecture to Predict the Risk of Crash

Published in ACM SIGSPATIAL, 2023

We propose CrashFormer, a multi-modal architecture that utilizes comprehensive (but relatively easy to obtain) inputs such as the history of accidents, weather information, map images, and demographic information. The model predicts the future risk of accidents on a reasonably acceptable cadence (i.e., every six hours) for a geographical location of 5.161 square kilometers.

Recommended citation: Karimi Monsefi, Amin, et al. "CrashFormer: A Multimodal Architecture to Predict the Risk of Crash." Proceedings of the 1st ACM SIGSPATIAL International Workshop on Advances in Urban-AI. 2023. https://dl.acm.org/doi/pdf/10.1145/3615900.3628769

Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain

Published in SIGKDD, 2024

In this paper, we introduce a new neural network architecture, termed LoGoNet, with a tailored self-supervised learning (SSL) method to mitigate such challenges. LoGoNet integrates a novel feature extractor within a U-shaped architecture, leveraging Large Kernel Attention (LKA) and a dual encoding strategy to capture both long-range and short-range feature dependencies adeptly. This combination of strategies is especially beneficial in medical image segmentation, given the difficulty of learning intricate and often irregular body organ shapes. Complementarily, we propose an SSL method tailored for 3D images to compensate for the lack of large labeled datasets.

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Amin Karimi Monsefi

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