In this article we describe an application of Machine Learning (ML) and Linguistic Modeling to generate persian poems. In fact we teach machine by reading and learning persian poems to generate fake poems in the same style of the original poems. As two well known poets we used Hafez (1310-1390) and Saadi (1210-1292) poems. First we feed the machine with Hafez poems to generate fake poems with the same style and then we feed the machine with the both Hafez and Saadi poems to generate a new style poems which is combination of these two poets styles with emotional (Hafez) and rational (Saadi) elements. This idea of combination of different styles with ML opens new gates for extending the treasure of past literature of different cultures. Results show with enough memory, processing power and time it is possible to generate reasonable good poems.

Recommended citation: Moghadam, Mehdi Hosseini, and Bardia Panahbehagh. "Creating a New Persian Poet Based on Machine Learning." arXiv preprint arXiv:1810.06898 (2018). https://arxiv.org/pdf/1810.06898.pdf

An introduction to a new text classification and visualization for natural language processing using topological data analysis

Published in arxiv, 2019

Topological Data Analysis (TDA) is a novel new and fast growing field of data science providing a set of new topological and geometric tools to derive relevant features out of complex high-dimensional data. In this paper we apply two of best methods in topological data analysis, “Persistent Homology” and “Mapper”, in order to classify persian poems which has been composed by two of the best Iranian poets namely “Ferdowsi” and “Hafez”. This article has two main parts, in the first part we explain the mathematics behind these two methods which is easy to understand for general audience and in the second part we describe our models and the results of applying TDA tools to NLP.

Recommended citation: Elyasi, Naiereh, and Mehdi Hosseini Moghadam. "An introduction to a new text classification and visualization for natural language processing using topological data analysis." arXiv preprint arXiv:1906.01726 (2019). https://arxiv.org/pdf/1906.01726

Topological Data Analysis for Classification of DeepSat-4 Dataset

Published in 2020 10th International Symposium onTelecommunications (IST) IEEE, 2020

Topological Data Analysis (TDA) is a new emerging and fast growing field of data science providing a set of tools from algebra, topology, and geometry to extract features from data based on its topological and geometrical features. This paper combines available methods from topological data analysis including persistent homology, persistent entropy, and persistent diagrams to build a strong topological feature extractor model from the topological properties of an image. By feeding features extracted by the topological model to machine learning models, we perform the classification task on DeepSat (SAT-4) dataset.

Recommended citation: Moghadam, Mehdi Hosseini, and Mir Mohsen Pedram. "Topological Data Analysis for Classification of DeepSat-4 Dataset." In 2020 10th International Symposium onTelecommunications (IST), pp. 246-250. IEEE, 2020. https://ieeexplore.ieee.org/abstract/document/9345829/

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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Mehdi Hosseini Moghadam

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