Search Results for author: Parham Moradi

Found 6 papers, 4 papers with code

Neural Graph Collaborative Filtering Using Variational Inference

no code implementations20 Nov 2023 Narges Sadat Fazeli Dehkordi, Hadi Zare, Parham Moradi, Mahdi Jalili

The customization of recommended content to users holds significant importance in enhancing user experiences across a wide spectrum of applications such as e-commerce, music, and shopping.

Collaborative Filtering Recommendation Systems +1

Universal Feature Selection Tool (UniFeat): An Open-Source Tool for Dimensionality Reduction

1 code implementation30 Nov 2022 Sina Tabakhi, Parham Moradi

The Universal Feature Selection Tool (UniFeat) is an open-source tool developed entirely in Java for performing feature selection processes in various research areas.

Dimensionality Reduction feature selection

Self-Paced Multi-Label Learning with Diversity

2 code implementations8 Oct 2019 Seyed Amjad Seyedi, S. Siamak Ghodsi, Fardin Akhlaghian, Mahdi Jalili, Parham Moradi

The major challenge of learning from multi-label data has arisen from the overwhelming size of label space which makes this problem NP-hard.

Multi-Label Learning

Dynamic Graph-Based Label Propagation for Density Peaks Clustering

1 code implementation1 Jan 2019 Seyed Amjad Seyedi, Abdulrahman Lotfi, Parham Moradi, Nooruldeen Nasih Qader

The cut-off distance affects the local density values and is calculated in different ways depending on the size of the datasets, which can influence the quality of clustering.

Clustering Image Clustering

An Improved Density Peaks Method for Data Clustering

1 code implementation2 Jan 2017 Abdulrahman Lotfi, Seyed Amjad Seyedi, Parham Moradi

In the second step, a novel label propagation method is proposed to form clusters.

Clustering

Integration of graph clustering with ant colony optimization for feature selection

no code implementations4 Sep 2015 Parham Moradi, Mehrdad Rostami

In the second step, the features are divided into several clusters using a community detection algorithm and finally in the third step, a novel search strategy based on the ant colony optimization is developed to select the final subset of features.

Clustering Community Detection +3

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